Development and Psychopathology 16 ~2004!, 825–856
Copyright © 2004 Cambridge University Press
Printed in the United States of America
DOI: 10.10170S0954579404040039
Trajectories of dynamic predictors of
disorder: Their meanings and implications
KENNETH J. SHER, HEATHER J. GOTHAM, and AMY L. WATSON
University of Missouri–Columbia
Abstract
Developmental psychopathologists are increasingly focused on characterizing heterogeneity of trajectories of psychological disorders across the life course ~e.g., developmentally limited vs. chronic forms of disorder!. Although the
developmental significance of trajectories has been highlighted, there has been little attention to relations between
trajectories and their etiologically and clinically relevant time-varying covariates ~dynamic predictors!. Depending
upon the functional relation between a disorder and a dynamic predictor, we expect to see different trajectories of
dynamic predictors. Thus, we propose a taxonomy of trajectories of dynamic predictors of course of disorder and
provide an initial investigation into its validity. Using a mixed-gender, high-risk sample of young adults followed
over 7 years, we identified dynamic predictors that covary with the course of alcohol use disorder ~AUD!. Based on
a logically derived classification to facilitate interpretation of findings, three comparison groups were examined:
persons whose AUD “remitted” ~n 5 33!, those with a chronic AUD ~n 5 29!, and nondiagnosers ~n 5 274!. We
hypothesized seven patterns of dynamic prediction ~stable vulnerability indicators, course trackers, deterioration
markers, developmentally specific variables, developmental lag markers, course-referenced variables, and recovery
behaviors! and found evidence for five of them. The interpretation of markers of risk for development and course of
AUDs and their implications for prevention, early intervention and formal0self-change treatments are discussed.
In recent years, there has been increasing recognition that the modal or mean course of disorder in an age cohort reflects a mixture of
multiple courses or “trajectories” ~e.g., Achenbach, 1990; Bates, 2000!, and the mean trajectory itself may fail to reflect any individual in
Preparation of this article was supported by NIH Grants
R37AA07231 and R01AA013987 to Kenneth J. Sher and
P50 AA11998 to Andrew Heath from the National Institute on Alcohol Abuse and Alcoholism. Portions of this
research were presented at the annual meeting of the Research Society on Alcoholism, Denver, CO, June 2000;
the biennial meeting for the Society for Research on Adolescence, April 2000; and the Michigan Symposium on
Developmental Discontinuities, June 2002. We gratefully
acknowledge Laurie Chassin, Patrick Curran, Andrea Hussong, Kristina M. Jackson, Jenny Larkins, Terrie Moffitt,
and Susan O’Neill for providing numerous, helpful comments on an earlier version of this article.
Address correspondence and reprint requests to: Kenneth J. Sher, Department of Psychological Sciences, 200
South Seventh Street, University of Missouri–Columbia,
Columbia, MO 65211; E-mail: [email protected].
the population. Problems with focusing on the
group mean have been recognized by methodologists for many years ~e.g., Estes, 1956!, and
developmental psychopathologists ~e.g., Moffitt, 1993; Zucker, 1987, 1995! have stressed
the practical and theoretical importance of
distinguishing among courses of psychopathology. Although the emphasis on course would
appear to be a recent trend, more than 100
years ago Emil Kraepelin ~1899!, arguably the
most influential psychopathologist to date,
highlighted the importance of course in diagnosis ~especially in distinguishing what are
today called schizophrenia and bipolar disorder!. The “neoKraepelinians,” who were the
architects of the Diagnostic and Statistical
Manual of Mental Disorders, 3rd edition
~DSM-III; American Psychiatric Association
@APA#, 1980!, 3rd edition—Revised ~DSMIII-R; APA, 1987!, and 4th edition ~DSM-IV;
APA, 1994!, embraced the careful syndromal
description pioneered by Kraepelin and recog-
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825
826
K. J. Sher, H. J. Gotham, and A. L. Watson
Figure 1. The five types of trajectories reported in the literature for binge drinking.
nized the importance of longitudinal observations ~e.g., Spitzer, 1983!. However, the
dominant nosology has largely failed to implement variation in course into today’s formal
framework. Although the DSM-IV does list
“course specifiers” for some disorders, the
DSM-IV’s incorporation of course information is both limited and not systematic across
disorders.
Although quantitative methods such as cluster analysis have been used to study heterogeneity in course of various behaviors ~e.g., binge
drinking; Schulenberg, O’Malley, Bachman,
Wadsworth, & Johnston, 1996!, the development and diffusion of statistical methods and
software for conducting growth mixture models ~e.g., Jones, Nagin, & Roeder, 2001; Muthén,
2001; Muthén & Muthén, 2001; Nagin, 1999;
Nagin & Tremblay, 2001! has been accompanied by a relative explosion of empirical reports of variations in trajectories of different
types of psychological disorders, syndromes,
symptoms, and behaviors ~Bongers, Koot, van
der Ende, & Verhulst, 2003; Jackson, Sher, Cooper, & Wood, 2002; Jackson, Sher, & Wood,
2000b!. Although there has been, and contin-
ues to be, debate regarding the differential utility of person-centered, categorical approaches
using trajectories versus more traditional
variable-centered approaches ~e.g., Bates, 2000;
Horn, 2000; Muthén & Muthén, 2000!, many
developmental psychopathologists have embraced trajectory-based approaches because
they highlight individual differences in
development.
Despite the many reports of trajectorybased investigations in the past few years,
there has been little conceptual or theoretical
development regarding the relationship of covariates to these trajectories. Most typically,
investigators examine the relation between
fixed covariates ~e.g., demographic variables,
baseline individual differences! and trajectory ~e.g., Galambos, Barker, & Almeida,
2003; Soldz & Cui, 2002! to identify prospective risk and protective factors for different
trajectories. Consider the prototypic trajectories that can be used to describe binge drinking portrayed in Figure 1 ~Schulenberg,
Wadsworth, O’Malley, Bachman & Johnston,
1996!. ~Note that the labels in Figure 1 have
been changed from those used by Schulen-
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Dynamic predictors
berg, Wadsworth, et al., 1996, to make them
more consistent with the terminology used in
this article.! Most typically, comparisons,
across trajectory classes, of the mean of continuous variables ~or proportions of polytomous variables! measured at or before baseline
are used to characterize relations between trajectories and fixed covariates. That is, test
the null hypothesis mND 5 mLO 5 mFl 5
mRem 5 mChr . Additionally, some investigators have examined the relation between trajectories and time-varying covariates at
specific time points ~e.g., Tucker, Orlando, &
Ellickson, 2003! to characterize predictors or
consequences of these trajectories in a more
developmental context. That is, test the null
hypothesis that, say, m ND.T2 5 m LO.T2 5
mFl.T2 5 mRem.T2 5 mChr.T2 and so on for each
time point. Although these approaches have
much merit for characterizing the fixed and
time-varying correlates of trajectories, we argue below that deeper insights into the nature
of the relation between a disorder and its correlate can be found by examining the mean
trajectories of time-varying covariates associated with disorder. For example, comparing
mean slopes mND.slope 5 mLO.slope 5 mFl.slope 5
mRem.slope 5 mChr.slope. Such comparisons, focusing on dynamic changes in a time-varying
covariate, offer important clues as to the nature of the functional relationship between a
disorder and its covariates.
Alcohol Use Disorders (AUDs) in Young
Adulthood
To illustrate the heuristic value of mean trajectories of dynamic predictors, we use prospective data from our ongoing study of AUDs
~alcohol abuse and dependence! in young
adulthood. From a population-based, epidemiological perspective, young adulthood is
clearly the time of greatest alcohol use and
alcohol problems. The mean course of alcohol involvement in the United States can be
summarized as follows. Most individuals begin using alcohol at approximately age 15,
increase use until about age 21, and then dramatically decrease their use during the later
20s ~e.g., Chen & Kandel, 1995; Johnston,
827
O’Malley, & Bachman, 2000!. Paralleling
these age-related changes in consumption,
rates of AUDs peak during the 20s and decrease monotonically thereafter ~Grant, Harford, Dawson, Chou, Dufour, & Pickering,
1994!. For many young adults, these problematic drinking patterns represent a developmentally limited phenomenon ~e.g., Zucker, 1987!.
For others, this period represents the early
stage of a more protracted course of pathologic alcohol involvement. Although several
potent role transitions, such as marriage and
entry into the labor market, appear related to
“maturing out” of developmentally limited
heavy alcohol involvement ~Curran, Muthén,
& Harford, 1998; Mullahy & Sindelar, 1992;
Schulenberg, O’Malley, et al., 1996!, relatively little is known concerning individual
difference variables that distinguish those
whose early-onset AUDs show a remitting versus a chronic course. Research on “natural
recovery” ~from alcoholism without treatment! has identified several individual difference variables associated with resolution of
alcohol problems. Unfortunately, most of this
research has been based on middle-aged samples, retrospective reports, and individuals who
self-recognized a prior alcohol problem that
was subsequently overcome ~see Watson &
Sher, 1998!. The relevance of these findings
to the normative, developmental course of alcohol problems in the general population is
unclear. As the epidemiological literature on
maturing out in young adulthood is relatively
uninformative regarding individual differences and work on natural recovery often fails
to recognize the developmental context in
which most resolution of drinking problems
occurs, a complete understanding requires an
integration of both literatures.
A Taxonomy of Dynamic Predictors
We now consider more extensively the notion
of dynamic predictors, variables that covary with
AUDs over time ~are measured on multiple
occasions along with AUDs! and may predict
the occurrence and0or course of AUDs. We
consider seven patterns of dynamic predictors ~Table 1, Figure 2!, at times drawing on
Nuechterlein and Dawson’s ~1984; Nuechter-
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K. J. Sher, H. J. Gotham, and A. L. Watson
Table 1. Taxonomy of dynamic predictors
Stable vulnerability indicators
Course trackers
Deterioration markers
Developmentally specific variables
Developmental lag markers
Course referenced variables
Recovery behaviors
Remain consistently different in individuals with disorder from
those without disorder
Differentiate individuals with or without disorder and covary
with symptomatology over time
Change over time in a direction that indicates cumulative
adverse effects
Predict course only at certain ages
Reflect slowed or inhibited normative developmental changes
Represent different constructs at different stages in the course
of a disorder
Represent long term recovery strategies
lein, 1990! discussion of psychopathology
indicators.
Although we use the term dynamic predictors to describe variables that covary with
course, the term is somewhat of a misnomer
for the first pattern that we consider, what
Nuechterlein and Dawson ~1984! have termed
stable vulnerability indicators ~Figure 2!.
These variables remain at a consistently different level in individuals with psychopathology ~whether symptomatic or asymptomatic!
compared to normal controls. They may distinguish course but do not covary with course
~and are, thus, nondynamic! as they remain
stable even when an individual’s AUD remits. Personality traits might be an example
as they are thought to remain stable across
long periods of development ~Block, 1971;
Costa & McCrae, 1986!. In cases of a monotonic relation between a stable vulnerability
indicator and course of disorder ~i.e., no disorder, remitted course, chronic course!, a severity model is implied as it suggests that the
variable is indexing a severity-graded vulnerability process.
A second pattern of dynamic variables we
refer to as course trackers ~Nuechterlein and
Dawson’s mediating vulnerability factors; Figure 2!. These variables differentiate individuals with a disorder compared to normal
controls, but also covary with degree of symptomatology over time. They are associated with
both the occurrence and course of an AUD,
and are likely to decrease over time as an
AUD remits. ~At this point, we do not address whether the functional relation is causal
or consequential.! Examples might include alcohol use and alcohol-related problems ~Bates
& Labouvie, 1995! and anxiety disorders
~Kushner, Sher, & Erickson, 1999!. Although,
as noted, personality traits are generally hypothesized to be stable, several studies have
found that impulsivity and sensation seeking
changed in parallel with alcohol involvement
during young adulthood ~Bates & Labouvie,
1995; Raskin White, Bates, & Buyske, 2001!.
A third pattern, closely related to course
trackers, we refer to as deterioration markers
~Figure 2!. The basic notion is that the continued presence of a chronic disorder is associated with cumulative adverse effects. A
prototype would be decreased pulmonary function as a consequence of chronic tobacco dependence. That is, increasing chronicity of an
AUD is associated with increasing likelihood
of certain negative effects. Although lifetime
exposure gradients for some types of alcoholrelated medical consequences ~e.g., liver disease! have been well established ~National
Institute on Alcohol Abuse and Alcoholism,
2000!, it is unclear to what extent we might
expect to see such effects in other types of
variables. ~A concept closely related to, but
distinct from, our definition of a deterioration marker is that of a scar. According to
Zeiss and Lewinsohn @1988, p. 151#, a scar
pattern is defined “as a deficit observed during @a diagnostic episode# that remained after
the . . . episode ended.” We view a deterioration marker as representing a distinct process
that is cumulative, progressive, and not necessarily permanent.!
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Dynamic predictors
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Figure 2. The seven possible patterns of dynamic predictors of alcohol use disorders over time.
A fourth pattern of dynamic variables, that
we term developmentally specific variables, is
similar to what Rutter ~1996, p. 212! termed
“age-indexed variations in susceptibility to en-
vironmental hazards” ~Figure 2!. Such factors
initially appear to track course ~i.e., predict early
occurrence of a disorder!, but actually only have
prognostic significance at certain ages ~Rutter,
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1996!. For example, parents and peers might
be more influential on drinking in young adulthood than in later adulthood ~e.g., Curran, Stice,
& Chassin, 1997; Scheier & Botvin, 1997!. Role
transitions ~e.g., finishing education, developing intimate relationships! may be good predictors during young adulthood, but then fade in
significance in later years.
A fifth pattern we have labeled developmental lag markers ~Figure 2!. As opposed to developmentally specific predictors, which we see
as leading ~i.e., etiologic! predictors related to
onset or course, developmental lag markers are
viewed as trailing ~i.e., consequential! indicators that reflect slowed or inhibited normative
developmental changes. For example, in previous work we found a dramatic decrease in selfreported distress during the early years of
college ~Sher, Wood, & Gotham, 1996!. If pathological alcohol involvement delayed this normative decrease ~perhaps by inhibiting the
development of adaptive coping skills or intimate relations!, we might expect a more gradual decline or later inflection point in the
trajectory of decreasing distress. Thus, like
course trackers, these indicators initially track
course, but as development progresses with relatively little variance in the indicator as it becomes less developmentally sensitive, its course
tracking ability diminishes. More important, it
is a trailing indicator of course, albeit one with
a slow time constant. Finally, like developmentally specific predictors, we only expect to observe developmental lag markers at stages where
the marker is developmentally relevant.
We have termed a sixth pattern, coursereferenced variables ~Figure 2!. These indicators appear to represent different constructs at
different stages in the course of a disorder. Such
variables may or may not initially predict a
disorder’s occurrence, but similar to developmentally specific variables, they have prognostic significance only at certain times. For
example, early in one’s drinking history various “self-control” attempts might indicate effective skills for moderating intake ~remitting
course!, while in the context of alcohol dependence they might reflect futile attempts at overcoming “loss of control” or “inability to abstain”
~Connors, Collins, Dermen, & Koutsky, 1998;
Guydish & Greenfield, 1990; Werch, 1990!.
K. J. Sher, H. J. Gotham, and A. L. Watson
Therefore, in this example, we would see an initially high level of a variable that decreases with
recovery, but within a chronic course the same
variable emerges late. There are two major conceptual distinctions between the developmentally specific variables and course-referenced
variables. The first is that developmentally specific variables predict the occurrence or persistence of a disorder only at certain ages; whereas,
the predictability of course-referenced variables is moderated, not necessarily by age, but
by stage of disorder ~e.g., duration of problematic alcohol involvement or degree of dependence!. A second distinction is that the meaning
of the course-referenced variables ~i.e., the constructs they indicate! changes as a function of
stage of substance involvement; whereas, the
fundamental meaning of the developmentally
specific predictors remains constant over time
even if their psychopathological relevance
changes.
Finally, a seventh pattern represents recovery behaviors that are conceptually similar to
the course-referenced variables, but are likely
to represent long-term recovery strategies
~Figure 2!. As such, they may not relate to the
occurrence of a disorder but later may be prognostic. For example, at baseline, few people in
a young-adult sample are likely to attend
Alcoholic’s Anonymous ~AA! meetings. However, those who later develop an AUD and then
remit from it may increase their AA attendance
and maintain a higher level of attendance.
Some other types of dynamic predictors may
also be significant factors, but a paucity of
research makes it difficult to classify them.
For example, research about whether life events
affect alcohol involvement has yielded conflicting results ~Bates & Labouvie, 1997; Perreira & Sloan, 2001; Scheier, Botvin, & Miller,
1999; Tucker, Vuchinich, & Pukish, 1995!.
Current Study
The current study seeks to identify dynamic
predictors of AUDs from a range of variables
shown to influence the development and cessation of alcohol problems: alcohol involvement, motivational factors for drinking and not
drinking, comorbid substance abuse and psychopathology, personality variables, and social0
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Dynamic predictors
environmental factors including life events.1
We employ a sample of young men and women
with three types of courses: persons whose
AUD remits, those with a chronic AUD, and
those who fail to diagnose over the study period; to facilitate clear comparisons of conceptually relevant groups. In the analyses
presented, we do not attempt to resolve direction of effect or model third-variable influences ~using either fixed or time-varying
covariates! as we are most interested in identifying simple patterns of covariation, not
ultimate causes. Although it would be informative to consider additional courses ~e.g.,
“late” onset cases!, sample size considerations led us to focus on these three groups of
clear remitters, chronics, and nondiagnosers
in composing logical groupings.
Method
Participants
Baseline sample. At baseline, 489 participants were recruited from an initial screening
sample of 3156 ~80% of a total class of 3944!
first-time freshmen at a large, midwestern university during the academic year 1987–1988.
Participants were classified as family history
positive for alcoholism ~FH1; n 5 252! if they
reported biological paternal alcoholism on both
a version of the Short Michigan Alcoholism
Screening Test ~SMAST; Selzer, Vinokur, &
van Rooijen, 1975! adapted to measure paternal drinking problems ~F-SMAST; Crews &
Sher, 1992! and a section of the Family
History-Research Diagnostic Criteria interview ~Endicott, Andreason, & Spitzer, 1978!
that assessed paternal drinking habits. Participants were classified as family history negative ~FH2; n 5 237! if they reported an
absence of substance use disorders in all biological first- and second-degree relatives and
1. In analyses not reported here, we attempted to determine whether cognitive0neuropsychological variables
measured at baseline were significant predictors of occurrence and0or course of AUDs. We used 10 variables
drawn from four standard cognitive0neuropsychological
instruments administered at Year 1, but the analyses
failed to identify any significant baseline predictors.
831
an absence of antisocial personality disorder
in all biological first-degree relatives ~see Sher,
Walitzer, Wood, & Brent, 1991!. The sample
was primarily of European Ancestry ~94%! at
a mean age of 18.4 years.
Longitudinal sample. Participants were assessed at baseline ~Year 1! when they were
freshmen, at three subsequent yearly intervals
~Years 2, 3, and 4 corresponding to the sophomore, junior, and senior years of college for
many!, and again 3 years later at Year 7. The
3-year interval between the fourth and five
waves of assessment was designed to capture
the postcollege transition for those students
who were likely to attain an undergraduate degree. By Year 7, 93.7% ~n 5 458! were still
participating and 451 had participated at all
five waves. As described more extensively elsewhere ~Gotham, Sher, & Wood, 2003!, at Year
7, 72% of the sample had completed a baccalaureate ~BA0BS! degree, 35% were married,
and 11% were parents.
At each wave, individuals were assessed
for past-year DSM-III alcohol abuse and0or
dependence. Considering the presence or absence of a past-year AUD diagnosis at five
time points results in 32 possible temporal patterns of diagnosis ~2 5 !; 30 patterns of diagnoses were identified in our sample. Figure 3
provides a graphic representation of those patterns of diagnoses exhibited by participants
who diagnosed at least once. Each pattern is
according to the temporal sequencing of AUD
diagnosis ~“1”! and nondiagnosis ~“0”!. Thus,
a “10000” pattern represents individuals who
diagnosed at Year 1 but did not diagnose at
Years 2, 3, 4, and 7. Although a number of
patterns are of interest,2 analyses for the current study focused on 336 participants ~75 FH2
men, 73 FH1 men, 95 FH2 women, 93 FH1
2. Although 30 temporal patterns of 12-month AUD diagnoses were found in our overall sample, this study
combined several patterns into three categories of
chronics, remitters, and nondiagnosers. These patterns
are thought to best represent conceptually meaningful
patterns studied in the literature. A large number of
studies exist concerning individuals who have remitted from AUDs, especially in comparison to samples
of those with long-term AUDs and those who have
never had an AUD.
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Figure 3. The temporal patterns of past-year DSM-III alcohol use disorder ~AUD! diagnoses ~Dx! over 7 years for those who were diagnosed at least once during the study ~n 5 177!; 1 5 AUD Dx
and 0 5 no Dx, with serial position corresponding to the year of data collection ~e.g., 1, 2, 3, 4, or 7!.
Dynamic predictors
women!. Chronics ~n 5 29! were classified if
they met diagnostic criteria for an AUD at all
five waves, the “11111” pattern. Remitters ~n 5
33! were classified if they met diagnostic criteria for an AUD at baseline and at least one
other time during the first four years of the
study ~Years 2, 3, or 4!, did not meet criteria
for an AUD at Year 7, and did not report any
past-year DSM-III symptoms of alcohol abuse
or dependence at Year 7. Although there are
other patterns suggestive of remission, we felt
it important to use the preceding guidelines,
especially so as not to mix in what are likely
to be a conceptually separate group of “late
onset” diagnosers who remit prior to the end
of the study. Nondiagnosers ~n 5 274! did not
meet diagnostic criteria for an AUD at any of
the five waves. At Year 1, the average age of
the sample was 18.5, and the majority of the
sample was White ~94%!. By Year 7, 73% ~n 5
244! had obtained a BA and 34% ~n 5 115!
were married.
Measures
AUDs. At each wave, an AUD was diagnosed
if an individual met DSM-III criteria for pastyear alcohol abuse or dependence based on
the Diagnostic Interview Schedule, Version
III-A ~DIS-III-A; Years 1 and 2; Robins,
Helzer, Croughan, Williams, & Spitzer, 1985!
or the DIS-III—Revised ~Years 3, 4, and 7;
Robins, Helzer, Cottler, & Goldring, 1989!.
All diagnoses were made according to DSMIII criteria to maintain diagnostic continuity.3
3. Note that DSM-III ~APA, 1980!, DSM-III-R ~APA,
1987! and DSM-IV ~APA, 1994! diagnoses of alcohol
abuse and dependence differ. Most strikingly, the DSMIII alcohol dependence diagnosis is narrower than DSMIII-R or DSM-IV. It is based on only three symptoms,
and a pattern of pathological use or impairment in
social0occupational functioning. DSM-III-R alcohol dependence is based on nine symptoms, and either persistence of symptoms for at least 1 month or their
repeated occurrence. DSM-IV alcohol dependence is
based on seven symptoms ~three or more within 12
months! and impairment in functioning or responsibilities. We compared DSM-III and DSM-III-R diagnoses
at Year 7 ~DSM-IV was not yet available!. Of the 457
participants with complete interview data at Year 7, 41
met DSM-III-R criteria for alcohol dependence but did
not meet criteria for either DSM-III alcohol depen-
833
Trained, lay interviewers who were unaware
of subjects’ FH status administered the DIS
~see Sher et al., 1991, for interviewer training
and quality control procedures!.
Alcohol involvement. The following alcohol
involvement variables were created from items
asked on a self-report measure given at each
wave of assessment.
Heavy drinking occasions. A composite of
three variables was made for heavy drinking
occasions per week, based on number of times
participants were drunk, high on alcohol, and
drank five or more drinks in one sitting over
the past month ~a 5 .91!.
Alcohol-related consequences. A 14-item
scale ~a 5 .74! assessed the occurrence of
negative alcohol-related consequences in the
past year ~e.g., fighting while drinking, damaging property!.
Alcohol dependence symptoms. A 14-item
scale ~a 5 .80! assessed symptomatology of
alcohol dependence displayed during the past
year ~e.g., feeling guilty about drinking, having blackouts!.
Motivations for drinking.
Reasons for drinking. A 15-item scale
adapted from Cahalan, Cisin, and Crossley
~1969! asked participants to rank how much
they agreed with each statement ~e.g., “I drink
because it helps me relax”! with responses
ranging from 0 ~strongly disagree! to 3
~strongly agree!. The means of six subscales
were used: tension reduction ~2 items, a 5
.58!, mood modification ~3 items, a 5 .84!,
social conformity ~4 items, a 5 .76!, celebration ~2 items, a 5 .69!, sensation seeking ~2
items, a 5 .51!, and taste ~1 item!.
dence or abuse. Examination of variables that make up
the criteria indicates that although 39 of the 41 participants did not endorse any of the 3 DSM-III dependence symptoms, they did endorse at least three of the
nine DSM-III-R dependence symptoms. Researchers
should be careful in comparing studies that use different systems for diagnosis and in comparing different
systems of diagnoses in the same population over time.
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Alcohol expectancies. Forty-four items
measured positive expectations of alcohol’s effects. Responses ranged from 0 ~not at all ! to
4 ~a lot!. Based on previous confirmatory and
exploratory factor analyses ~Kushner, Sher,
Wood, & Wood, 1994!, four subscales were
formed using unit-weighted scoring: tension
reduction ~9 items, a 5 .89; e.g., “Drinking
makes me feel less tense or nervous”!, social
lubrication ~8 items, a 5 .88; e.g., “Drinking
makes me feel less shy”!, activity enhancement ~9 items, a 5 .85; e.g., “Drinking makes
many activities more enjoyable”!, and performance enhancement ~9 items, a 5 .81; e.g.,
“Drinking helps me have better ideas”!.
Drinking restraint.
Reasons for not drinking. Eleven reasons
for limiting one’s drinking were developed by
project staff in consultation with J. E. Donovan, including religious beliefs, peer group influence, alcohol’s negative consequences, and
loss of control worries ~a 5 .79!. Responses
ranged from 0 ~strongly agree! to 4 ~strongly
disagree!, and items were reverse scored for
analysis.
Drinking restraint strategies. Seven items
assessed frequency of restrained drinking and
use of strategies to limit drinking, including
limiting drinking to certain occasions and limiting the number of drinks ~a 5 .83!. Responses ranged from 0 ~never! to 4 ~always!.
Comorbid substance use disorders.
Tobacco dependence. Individuals were
coded as having tobacco dependence if they
met DSM-III criteria ~on the DIS!.
Drug abuse0dependence. Drug abuse0
dependence was coded if participants met
past-year DSM-III criteria ~on the DIS! for
abuse and 0or dependence on cannabis,
barbiturates, opioids, amphetamines, cocaine,
or hallucinogens.
Other comorbid psychopathology.
Anxiety disorders. Individuals were coded
as having an anxiety disorder if they met
K. J. Sher, H. J. Gotham, and A. L. Watson
past-year DSM-III diagnostic criteria 4 ~on the
DIS! for either social phobia, generalized
anxiety disorder, agoraphobia with panic attacks, and0or panic attacks in the absence of
agoraphobia.
Major depression. Individuals were coded
as having major depression if they met pastyear DSM-III diagnostic criteria ~on the DIS!
for major depression.
Psychological distress. The Brief Symptom Inventory ~BSI; Derogatis, 1993!, a short
form of the Symptom Checklist 90-R ~Derogatis & Cleary, 1977!, assesses how much one
has been bothered or distressed in the past week
by 53 psychological0physiological symptoms
~e.g., faintness, feeling inferior to others!. Responses ranged from 0 ~not at all ! to 4 ~extremely!. The current study used the General
Severity Index ~BSI-GSI!, calculated by dividing the sum of the response values by the
total number of responses ~a 5 .95!. Derogatis ~1993! reported a 2-week test–retest reliability coefficient of .90 for the BSI-GSI.
Antisociality. A dichotomous variable was
created from a DIS item asking participants
who endorsed at least three antisocial personality items whether they had committed any
of those acts in the past year. It was coded 1 if
individuals responded yes and 0 if they either
4. Several comments need to be made regarding the scoring of the anxiety disorders. For generalized anxiety
disorder, although DSM-III diagnostic criteria require
“worry for at least one month,” the DSM-III-R requirement is 6 months. Therefore, some participants diagnosed with generalized anxiety disorder in this study
would not have been diagnosed if DSM-III-R criteria
had been used. Because agoraphobia without panic attacks is a controversial diagnosis, we excluded from
the agoraphobia group those individuals who did not
report panic attacks. Finally, for panic attacks ~without agoraphobia!, whereas DSM-III-R requires four
panic attacks in a 4-week period or one panic attack
and at least 1 month of worry about panic, DSM-III
requires three panic attacks in a 3-week period. To
identify nonagoraphobic individuals who have experienced repeated panic attacks over the course of their
lives, we grouped those reporting either more than five
panic attacks over their lifetime or at least three panic
attacks in any 1-month period in the absence of agoraphobic avoidance.
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Dynamic predictors
835
had not committed any of those behaviors in
the past year or had endorsed fewer than three
symptoms at baseline.5
drunk ~0 5 strongly disapprove to 4 5 strongly
approve!, and how much friends drink ~0 5
they don’t drink to 4 5 more than six drinks!.
Personality. Although our baseline assessment contained extensive assessment of personality traits, only a few were assessed over
time and lend themselves to the type of analyses undertaken.
Religious involvement. Four items assessed
religious involvement: frequency of church attendance ~0 5 three or more times a week to
7 5 never, reverse scored!, importance of religion ~0 5 extremely important to 4 5 unimportant, reverse scored!, considering self to
be religious ~0 5 yes, definitely to 2 5 no,
reverse scored!, and abstaining from behaviors ~e.g., alcohol use! for religious reasons
~0 5 yes and 1 5 no, reverse scored!. ~The
items were examined separately.!
Self-Consciousness Scale. This 23-item
scale ~Fenigstein, Scheier, & Buss, 1975! measures private self-consciousness ~10 items, a 5
.71!, public self-consciousness ~7 items, a 5
.81!, and social anxiety ~6 items, a 5 .74!.
Pearlin Mastery Scale (PMS). This 7-item
scale ~Pearlin & Radabaugh, 1976! is a measure of generalized coping ~a 5 .75!. The
scale’s authors found moderate correlations
among social distress ~such as economic hardship!, anxiety, and drinking for the relief of
distress, particularly for people who have little sense of mastery and low self-esteem.
Rosenberg Self-Esteem (RSE). The 10-item
RSE ~Rosenberg, 1979! assesses attitudes such
as being satisfied with oneself and feeling useless or that one is a failure ~a 5 .89!.
MacAndrew Alcoholism Scale (MAC). This
49-item scale ~a 5 .41; MacAndrew, 1965!
assesses alcoholism tendencies in an indirect
manner and in archival studies has been found
to predict the later occurrence of alcoholism
~Hoffmann, Loper, & Kammeier, 1974!.
Social0environmental variables.
Peer involvement. A sum of 6 items adapted
from Jessor and Jessor ~1973, 1981! assessed
levels of peer involvement with alcohol and
peer support for drinking ~a 5 .89!. Examples
are friend’s approval of drinking and getting
5. The DIS questions regarding antisocial personality
symptoms were worded and scored to create an aggregate of the number of symptoms over time, and a yearly
count could not be discerned. Therefore, as a proxy for
past-year symptoms, we used a question that asked all
respondents who had endorsed at least three lifetime
symptoms whether they had occurred in the past year.
Recent life events. The past-year occurrence of life events was assessed via a 47item, modified version of the Life Events
Survey ~Sarason, Johnson, & Siegel, 1978!.
Several items not likely to occur in a college
student sample were deleted, and age-relevant
items were added. Five a priori subscales ~sum
of items endorsed! were used: interpersonal
~21 items, a 5 .68!, work 0school ~12 items,
a 5 .55!, health ~2 items, a 5 .12!, financial
~2 items, a 5 .35!, and legal ~2 items, a 5
.09!.
Procedure
Participants were contacted early in their first
year of college and asked to participate in a
research project assessing health behaviors during the college years. Throughout the follow-up
period, attempts were made to retain and track
all participants, regardless of whether they remained at the university. Participants completed the DIS and a questionnaire battery at
each year of the study. Although at Years 2–7
attempts were made to have all participants
complete the assessment in the laboratory,
some participants completed phone interviews and mail-in questionnaires ~e.g., 27% at
Year 7!. In a number of analyses comparing
the covariance structures of participants who
completed assessments in the laboratory with
those who completed assessments by phone
and mail, we failed to find any differences
suggesting effects of method variation.
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836
Results
Attrition analyses
Individuals who left the study by Year 7 were
considered attriters ~n 5 32!, including 1 who
died during the follow-up period, 1 who found
out that he0she was adopted, 1 who was unable
to be located, and 29 who refused participation.Anonsignificant chi square of attrition and
AUD at Year 1 ~ p 5 .17! suggests that individuals with baseline AUD diagnoses did not differentially drop out of the study after Year 1. To
determine whether attrition affected other variables ~i.e., individuals with more antisocial personality symptoms might be more likely to drop
out!, 62 t test ~continuous variables! or chisquare ~categorical variables! analyses were performed on an attrition variable ~attriters 5 1,
nonattriters50, n5457! and Year 1 study variables. Compared to nonattriters, attriters scored
significantly higher on alcohol dependence
symptoms ~d 5 .37!, alcohol-related consequences ~d 5 .43!, reasons for not drinking of
“because I sometimes become rude or obnoxious” ~d5.39!, and “because I’m afraid I might
become an alcoholic” ~d 5 .45!, and restrained
drinking items regarding frequency of cutting
back ~d 5 .37! and amount by which one cuts
back ~d 5 .38!. Attriters scored significantly
lower on the PMS ~d 5 .41! and RSE ~d 5 .46!.
Although attriters versus nonattriters differed
on a few variables, the lack of a significant relation between attrition and Year 1AUD and the
overall high retention rate ~over 90%! suggest
the sample is not overly biased by more alcoholinvolved individuals leaving the study.
Characterizing the course of alcohol
involvement in nondiagnosers, remitters,
and chronics
Table 2 shows the frequencies of AUD course
over time, that is chronic, remitting, nondiagnosing, and an “other” group that includes all
other patterns, by FH and gender. Because there
are no FH1 women who chronically diagnosed with an AUD, the interaction of FH and
gender was not modeled. Although not a focus
here, results of a categorical maximum likelihood analysis predicting these four courses of
K. J. Sher, H. J. Gotham, and A. L. Watson
Table 2. Frequencies of course of AUD
over time by FH and gender
(N 5 451)
FH1
Chronics
Remitters
Nondiagnosers
Other
FH2
Men
Women
Men
Women
18
8
47
36
3
15
75
30
8
6
61
33
0
4
91
16
AUDs ~all other analyses use three groups of
AUDs! by main effects of FH and gender,
showed significant effects of FH, x 2 ~3, N 5
451! 5 17.45, p , .001, and gender, x 2 ~3,
N 5 451! 5 29.53, p , .0001. Generally, FH
positive individuals were more likely to be in
the Chronic and Remitting groups, and men
were more likely to be in the Chronic and
“other” groups. Due to these main effects, gender and FH are controlled in all analyses.
Validity of a priori designation of course
As described above, our classes were logically derived for the purpose of providing relatively “pure” prototypes that would facilitate
meaningful comparisons among courses. However, it can be argued that although “rational,”
these courses are arbitrary and may not represent the underlying nature of the phenomenon
under investigation. To address this issue, we
compared our logically derived classes with
empirically derived classes to establish their
reasonableness with respect to the overall data
structure. We accomplished this using latent
class growth analysis ~LCGA; Muthén, 2001!,
a type of latent growth mixture modeling suitable for categorical indicator variables such
as dichotomous diagnoses. Using full information, maximum likelihood to exploit all available data, a series of LCGA models using
Mplus Version 2.13 ~Muthén & Muthén, 2001,
2002! were estimated. The base model included intercept and linear and quadratic slopes
and a four-class model demonstrating the best
fit for the data using multiple fit indices ~see
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Dynamic predictors
837
Figure 4. A latent class growth analysis of trajectories of 12-month DSM-III alcohol use disorders over
7 years; Dx, diagnosers.
Jackson & Sher, 2004, for more details on this
analysis!.
The results of this analysis are reported in
Figure 4. As can be seen, there is clear evidence for all three of the logical groups with
trajectories clearly associated with a chronic
class ~12%!, a remitting class ~10%!, and a
nondiagnosing class ~66%!. In addition to these
three classes, the LCGA revealed a fourth class
labeled in Figure 4 as a “later onset” course
~13%!. Examination of the diagnostic configurations associated with this later onset course
reveals that individuals with this course tended
to not diagnose at Year 1 but did tend to diagnose at least twice during later follow-up
assessments. Cross-tabulation of the 336 subjects who met a priori logical classification,
the LCGA and the logical classes showed
100% agreement with respect to both the nondiagnosing and chronic class. With respect to
the remitting class, 26 of 33 ~79%! of the
logically derived remitters were in the LCGA
remitting class and 7 of 33 ~21%! were in the
LCGA chronic class. This is because the
LCGA classified patterns of consistent diagnosis through the first 4 years of the study
and no diagnosis at Year 7 as chronic. Given
the strong concordance between the logically
derived classes and the LCGA and our scientific judgment that patterns characterized by
late remission were more appropriately considered “in remission” rather than chronic ~especially with the restriction of no past-year
symptoms and the fact that the years following college are characterized by high rates of
maturing out; Sher & Gotham, 1999!, we
elected to proceed with the a priori groupings. Although the LCGA results provide empirical justification for including a fourth group
for comparison purposes, we elected to focus
on the three logical groupings for multiple
reasons. Foremost among these is that at most
measurement occasions ~especially Years 3,
4, and 7!, approximately half of the members
of the later onset group are diagnosing and
the other half are not diagnosing, complicating interpretation. Also, in contrast to the three
logical courses that provide meaningful comparisons among each other at the beginning
and end of the observation period ~i.e., remitters and chronics are similar diagnostically at
Year 1; remitters and nondiagnosers are similar diagnostically at Year 7!, later onset
courses provide less clear-cut comparisons.
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838
Analytic strategy
Throughout the analyses, two contrasts represent our primary interests: Remitters versus
Chronics, and Diagnosers ~Remitters and
Chronics combined! versus Nondiagnosers. To
illustrate the validity ~e.g., Tucker et al., 2003!
of these three courses of AUDs over 7 years,
we conducted a doubly multivariate, repeatedmeasures analysis of variance ~ANOVA! with
main effects of FH, gender, and AUD course
predicting the alcohol involvement variables
~heavy drinking occasions, alcohol-related
consequences, alcohol dependence symptoms! at all five occasions. ~That is, a multivariate approach to repeated measures @e.g.,
O’Brien & Kaiser, 1985# employing multiple
dependent measures using SAS Institute’s
@1992# PROC GLM @e.g., see example 9; pp.
988–993#.! The overall multivariate effects
for each contrast were significant ~all ps ,
.0001!. Also, the multivariate interactions between time and each contrast were significant: Time 3 Chronics versus Remitters,
Wilk’s lambda, F ~12, 313! 5 2.09, p , .05;
Time 3 Diagnosers versus Nondiagnosers,
Wilk’s lambda, F ~12, 313! 5 6.86, p , .0001.
Probing these interactions further, for each
contrast there were significant interactions with
the linear effect for each dependent variable.
~There were also interactions between the Diagnosers vs. Nondiagnosers contrast and nonlinear polynomial effects on each dependent
variable.! Figure 5 shows, as expected, that
alcohol involvement variables are course trackers. Although Chronics began at the highest
levels of alcohol involvement and Remitters
were next highest, Remitters greatly decreased alcohol involvement to the approximate level of nondiagnosers by Year 7. ~At
Year 7, simple effect tests failed to find significant differences between Remitters and
Nondiagnosers, although the difference approached significance, p 5 .052, on alcoholrelated consequences.!
K. J. Sher, H. J. Gotham, and A. L. Watson
to course of alcohol involvement in young
adulthood. To reduce the chance of Type I
error, doubly multivariate repeated-measures
ANOVAs were conducted for variables assessed at all five waves, as a function of AUD
course, gender, and FH main effects. Measures of comorbid psychopathology ~categorical and continuous variables! and peer
involvement ~composite variable! were examined separately with repeated-measures
weighted least-squares analyses ~categorical
variables! or ANOVAs ~continuous variables!. As in the previous analyses, multivariate planned contrasts of Chronics versus
Remitters and Diagnosers versus Nondiagnosers were conducted. Main effects of gender and FH were modeled as covariates but
are not discussed further. For continuous dependent variables, polynomial contrasts assessing linear and nonlinear trends over time
were computed ~both overall and in interaction with the between-group contrasts of
interest!. Table 3 presents results of the doubly multivariate analyses for groups of variables with significant multivariate Time 3
Contrast effects, and Figures 6–11 selectively illustrate effects of particular interest
~adjusted for FH and gender effects!. Note
that because we focus on dynamic predictors
in these analyses, only effects associated with
the interaction between time and our betweengroup contrasts are noted and reported in
Table 3 because the presence of such an interaction is necessary to establish covariation
between a potential dynamic predictor and
course. Stable group differences ~i.e., main
effects of group but no Group 3 Time interaction! are important because they suggest
stable vulnerability processes and generalized time trends ~i.e., no interaction with
between-group contrasts! can indicate important normative developmental changes.
However, these types of effects are neither
highlighted nor typically discussed because
they are largely tangential to the focus of the
article.
Dynamic prediction of course of AUDs
over time
Motivations for drinking.
Our primary goal was to determine whether
changes in variables over time were related
Reasons for drinking. The multivariate
effects for each contrast were significant ~all
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Figure 5. The levels of three alcohol involvement variables over time by the course of alcohol use
disorders: heavy drinking occasions, alcohol-related consequences, and alcohol dependence symptoms.
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839
840
K. J. Sher, H. J. Gotham, and A. L. Watson
Table 3. Results of significant doubly multivariate, repeated measures regression
analyses predicting sets of study variables from gender, family history of alcoholism,
and course of AUD
Chronics vs. Remitters
F of Wilks’ L
Reasons for drinking
Time 3 Contrast ~df 5 24, 299!
Tension reduction
Mood modification
Social conformity
Taste
Celebration
Sensation seeking
Alcohol expectancies
Time 3 Contrast ~df 5 16, 309!
Tension reduction
Activity enhancement
Performance enhancement
Restrained drinking
Time 3 Contrast ~df 5 28, 292!
1.60*
F of Wilks’ L
Polynomial
Effect F
2.43***
Cubic: 7.07**
Linear: 4.39*
Linear: 8.98**
Quadratic: 4.22*
Linear: 6.70*
Linear: 3.91*
Linear: 12.36***
Linear: 4.67*
Linear: 6.27*
2.42**
1.98*
Cubic: 5.83*
Linear: 6.86**
Linear: 16.41****
Cubic: 6.46*
Linear: 6.61*
1.59*
How often try cut back
How many fewer drinks
Drink low alcohol beverages
Pace drinking
Religious involvement
Time 3 Contrast ~df 5 16, 304!
Importance of religion
Recent life events
Time 3 Contrast ~df 5 20, 306!
Work events
Legal events
Health events
Polynomial
Effect F
Diagnosers vs. Nondiagnosers
1.43
~ p 5 .08!
Linear: 7.43**
Quadratic: 7.93**
Cubic: 4.44*
Linear: 8.80**
Quadratic: 6.24*
Linear: 8.44**
0.82 ns
1.73*
Quadratic: 5.51*
0.77 ns
2.05**
Quadratic: 5.80*
Linear: 5.82*
Linear: 7.57**
* p , .05. ** p , .01. *** p , .001. **** p , .0001.
ps , .0001!. As shown in Table 3, the multivariate interactions between Time and each
contrast were significant. Figure 6 depicts the
significant interaction between time and the
Chronics versus Remitters contrast for Mood
Modification, Celebration, and Sensation Seeking motives. These variables appear to be
course trackers, because Chronics and Remitters began at the same or nearly the same level
but over time the motives of Remitters decreased more rapidly than Nondiagnosers.
There was also a significant interaction between Time and the Diagnosers versus Nondiagnosers contrast. Significant Contrast 3 Time
~linear! effects were found for all but the Tension Reduction and Social Conformity mo-
tives ~although a Contrast 3 Time @quadratic#
effect was found for the latter!.
Alcohol expectancies. The multivariate effects for each contrast were significant ~all ps ,
.0001! as were the multivariate Contrast 3
Time interactions ~see Table 3!. As seen in
Figure 7, the significant interactions between
time and each contrast on Performance Enhancement suggests developmentally specific
effects. Although at Year 1 ~ p , .002! and
Year 3 ~ p , .001! Chronics reported much
stronger expectancies than Remitters, by Years
4 and 7 the two groups were similar ~all ps .
.59!. Equally important, the mean difference
between Diagnosers and Nondiagnosers at
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Figure 6. The levels of three reasons for drinking over time by course of alcohol use disorders: Mood
Modification, Celebration, and Sensation Seeking.
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841
842
K. J. Sher, H. J. Gotham, and A. L. Watson
Figure 7. The level of Performance Enhancement alcohol expectancies over time by course of alcohol
use disorders.
Years 4 and 7 was much smaller than at baseline ~although they remained significant, p ,
.01!. There were also significant Time 3 Diagnosers versus Nondiagnosers interactions for
Tension Reduction and Activity Enhancement
expectancies ~see Table 3!. Inspection of the
means indicated that both scales appear to be
course trackers as chronic diagnosers maintained relatively high levels of expectancies,
whereas those who went on to remit showed
initially high levels that decreased over time
close to the levels of Nondiagnosers.
Drinking restraint.
Reasons for not drinking. Although the
multivariate effect for the chronics versus remitters contrast was marginally significant ~ p 5
.05! and was significant ~ p , .0001! for the
Diagnosers versus Nondiagnosers contrast, neither multivariate Time 3 Chronics versus Remitters interaction nor Time 3 Diagnosers
versus Nondiagnosers interaction was significant ~ p 5 .73 and .75, respectively!.
Restrained drinking. Only the multivariate
effect for the Diagnosers versus Nondiagnosers contrast was significant ~ p , .0001;
Chronics versus Remitters, p 5 .11!. The multivariate Time 3 Diagnosers versus Nondiag-
nosers interaction was marginally significant,
and the Time 3 Chronics versus Remitters interaction was significant ~see Table 3!. Figure 8 depicts items with significant effects for
the Time 3 Chronics versus Remitters interaction. The top panel shows that both Chronics and Remitters began with the same high
frequency of trying to cut back, but over time,
the Remitters decreased to almost the level of
the Nondiagnosers, and the Chronics remained
at the same high level.
Although frequency of trying to cut back fits
our definition of a course tracker variable, the
drinking restraint strategies of drinking low alcohol beverages and pacing drinking appear to
be course-referenced indicators ~middle and
lower panels of Figure 8!. At Year 1, Remitters
used drinking restraint strategies of drinking low
alcohol beverages and pacing drinking more often than Nondiagnosers ~ p , .05 and , .001,
respectively! but over time they decreased use
of those strategies; by Year 7 they were no longer different than Nondiagnosers ~all ps . .53!.
In contrast, at Year 1, Chronics were similar to
Nondiagnosers on both frequency of drinking
low alcohol beverages and frequency of pacing drinking ~all ps . .51! but increased the use
of those strategies over time and by Year 7
tended to use them more than Nondiagnosers
~ p , .06 and .05, respectively!.
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Figure 8. The levels of three restrained drinking items over time by the course of alcohol use disorders:
frequency of trying to cut back, frequency of drinking low alcohol beverages, and frequency of pacing
drinking.
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843
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K. J. Sher, H. J. Gotham, and A. L. Watson
Figure 9. The level of psychological distress as measured by the BSI-GSI.
Comorbid substance use disorders.
Tobacco dependence. The Chronics versus
Remitters contrast was not significant ~ p 5
.34! and did not interact significantly with time
~ p 5 .85!. Diagnosers and Nondiagnosers differed in prevalence of tobacco dependence
~ p , .0001!, with approximately three times
as many Diagnosers being tobacco dependent
~20–28%! than Nondiagnosers ~5–9%!. However, a follow-up analysis found the Time 3
Diagnosers versus Nondiagnosers interaction
to be nonsignificant ~ p 5 .68!. This pattern of
comorbidity suggests that smoking might be a
stable vulnerability marker.6
Drug abuse0dependence. The overall
Chronics versus Remitters contrast was not significant ~ p 5 .47! and did not interact significantly with Time ~ p 5 .76!. Diagnosers and
Nondiagnosers differed in the prevalence of
drug abuse0dependence ~ p , .0001!, and in
an ad hoc analysis there was a marginally significant Time 3 Diagnosers versus Nondiagnosers interaction, x 2 ~4, N 5 336! 5 8.92,
p 5 .06. At each year, 2% of the Nondiag6. We have conducted extensive prospective analyses of
comorbidity between tobacco dependence and AUDs
~Jackson et al., 2000a; Sher, Gotham, et al., 1996! and
readers interested in a more probing analysis of this
relation are referred to those articles.
nosers had a drug use disorder, whereas the
prevalence for Diagnosers was 25, 22, 26,
20, and 14% for Years 1, 2, 3, 4, and 7,
respectively ~adjusted for FH and gender!. This
marginal interaction appears to reflect the
joint influence of age-graded declines in drug
use disorders and a floor effect in the
Nondiagnosers.
Other comorbid psychopathology.
Anxiety disorders. The overall Chronics
versus Remitters contrast was not significant
~ p 5 .64! and did not interact significantly
with Time ~ p 5 .92!. Diagnosers and Nondiagnosers differed in the prevalence of anxiety
disorders ~ p , .0001!, with Diagnosers generally reporting more anxiety disorders than
Nondiagnosers. The Time 3 Diagnosers and
Nondiagnosers interaction was not significant
~ p 5 .73!. More extensive analysis of AUDs
and anxiety disorder comorbidity with this data
is reported in Kushner et al. ~1999!. Findings
from that study suggest that a different pattern
might emerge if one were to compare anxiety
with physical dependence on alcohol as opposed to the broad-band diagnosis of AUD employed in the current analyses.
Major depression. The overall Chronics versus Remitters contrast was not significant ~ p 5
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Dynamic predictors
845
Figure 10. The percentage of participants reporting past-year antisocial personality disorder symptoms
over time by the course of alcohol use disorders and scores on the MAC scale.
.71! and did not interact with Time ~ p 5 .94!.
Diagnosers and Nondiagnosers differed in
prevalence of major depression ~ p , .0001!,
and there was a significant Time 3 Diagnosers versus Nondiagnosis interaction, x 2 ~4,
N 5 336! 5 9.93, p , .05. At each year, approximately 2% of Nondiagnosers reported
major depression, whereas the prevalence for
AUD diagnosers was 10, 12, 11, 9, and 3% for
Years 1, 2, 3, 4, and 7, respectively ~adjusted
for FH and gender!. The pattern is very similar to that observed for drug use disorders except there are lower base rates for major
depression.
Psychological distress. The overall Chronics versus Remitters effect was marginally significant ~ p 5 .055!. The Time 3 Chronics
versus Remitters interaction was significant,
F ~4, 1292! 5 5.03, p , .01, with significant
nonlinear effects ~ p , .001!. Figure 9 shows
that compared to Remitters who evidenced a
steadier decline, Chronics showed a lag in that
their psychological distress dropped sharply
between Years 3 and 4. The overall Diagnosers versus Nondiagnosers effect was significant ~ p , .0001!. There was a significant
Time 3 Contrast interaction, F ~4, 1292! 5
5.03, p , .01, with significant linear ~ p ,
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846
K. J. Sher, H. J. Gotham, and A. L. Watson
Figure 11. The level of peer involvement in drinking over time by the course of alcohol use disorders.
.001! and nonlinear ~ p , .001! effects. The
pattern is one of developmentally graded decreases in distress that are relatively dramatic
in late adolescence and then asymptote. Against
this background are initially higher levels of
distress associated with the presence of an
AUD, that decrease in a near linear fashion for
those who remit and in a delayed but steep
decrease in those with chronic problems. The
pattern is consistent with the concept of a developmental lag indicator, although the increasing level of distress from Year 4 to 7 among
Chronics is suggestive of the beginning of a
deterioration process.
Antisociality. The top panel of Figure 10
shows the changing patterns of having at least
one past-year adult ASP symptom. The overall effect for Chronics versus Remitters was
not significant ~ p 5 .24!; however, this may
be due in part to collinearity between FH
and antisociality, as when the covariate of
FH was deleted, the Chronics versus Remitters contrast was significant ~ p 5 .03!. However, even with FH deleted from the analysis,
the Time 3 Chronics versus Remitters interaction was not significant ~ p 5 .78!. The overall effect for Diagnosers versus Nondiagnosers
was significant ~ p , .0001!, with more diagnosers reporting a past-year symptom than
Nondiagnosers. The Time 3 Diagnosers versus Nondiagnosers interaction was not significant ~ p 5 .17!. Overall, the pattern suggests
antisociality as a stable vulnerability indicator.
Personality. For the personality variables ~SelfConsciousness scale, PMS, RSE, and MAC!,
both multivariate contrasts were significant ~all
ps , .05!. However, perhaps due to their presumed trait-like nature, neither of the multivariate Time 3 Contrast interactions was
significant ~ p 5 .12 for Chronics versus Remitters, p 5 .95 for Diagnosers vs. Nondiagnosers! suggesting that some of these variables
can be viewed as stable vulnerability indicators.7 The bottom panel of Figure 10 shows
results of the MAC scale over time. Chronically diagnosing individuals begin and remain
at higher levels on this measure than their remitting and nondiagnosing counterparts.
7. In previous work from this project, Walitzer and Sher
~1996! found a significant relation between the RSE
and AUDs, including a significant effect for pattern of
AUD diagnosis. Women who were either early or earlyand-late AUD diagnosers had significantly lower selfesteem than men in the same diagnosis groups. This
highlights that some personality variables may show
variability, and that overgeneralizations should not be
made regarding the current findings.
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Dynamic predictors
Social0environmental variables.
Peer involvement. The multivariate effect
for Chronics and Remitters was significant
~ p , .05!, with a significant Time 3 Contrast
interaction, F ~4, 1296! 5 3.53, p , .05, and a
significant linear effect, F ~1, 324! 5 8.51,
p , .01. Figure 11 shows that this variable is
a course tracker as both Chronics and Remitters began with much higher levels of peer
involvement in drinking, but over time Remitters’ peer involvement decreased almost to the
level of Nondiagnosers, whereas Chronics’ peer
involvement appears to have remained the
same. The multivariate effect for Diagnosers
versus Nondiagnosers was significant ~ p ,
.0001!, with a significant Time 3 Contrast interaction, F ~4, 1296! 5 3.44, p , .05, and
significant linear ~ p , .01! and nonlinear ~ p ,
.05! effects.
Religious involvement. Neither the multivariate effect for Chronics versus Remitters
~ p 5 .30!, nor the Time 3 Contrast interaction
~ p 5 .67! was significant. The multivariate
effect for Diagnosers versus Nondiagnosers
was significant ~ p 5 .02!, as was the Time 3
Contrast interaction ~see Table 3!. The item
regarding the importance of religion in dayto-day life showed significant nonlinear effects over time, such that while the level of
importance of religion remained higher and
steady for the Nondiagnosers, for the Diagnosers, it began lower, increased at Year 3,
and then decreased at Years 4 and 7.
Recent life events. Neither the multivariate
effect for Chronics versus Remitters ~ p 5 .90!
nor the multivariate Time 3 Chronics versus
Remitters interaction ~ p 5 .75! was significant. Both the multivariate effect for Diagnosers versus Nondiagnosers ~ p , .01! and
the multivariate Time 3 Contrast interaction
were significant ~see Table 3!. There were significant linear ~decreasing! effects for legal
and health events, with Diagnosers generally
experiencing more legal and health events, and
a significant nonlinear effect for work events.
Whereas Nondiagnosers experienced steadily
declining numbers of work events, Diagnosers
~who reported more events than Nondiag-
847
nosers! experienced an increase in work events
from Year 1 to Year 2, and then decreasing
numbers of events through Year 7.
Summary
Based on these analyses, several variables can
be considered dynamic predictors of whether
one remits from previous AUDs or continues to
diagnose, including mood modification, celebration, and sensation-seeking reasons for
drinking, performance enhancement alcohol expectancies, restrained drinking items, general
psychological distress, adult antisociality, and
peer involvement. Evidence was found for five
of the seven predicted patterns of dynamic predictors. Neither recovery behaviors nor deterioration markers were unambiguously identified
in the set of variables examined.
Discussion
As described at the outset, different patterns of
mean trajectories of covariates could have
different implications for their functional relationship to AUDs. We hypothesized seven patterns of dynamic prediction ~stable vulnerability
indicators, course trackers, deterioration markers, developmentally specific variables, developmental lag indicators, course-referenced
variables, and recovery behaviors! and found
evidence for five of them within the current data
set. Below we discuss those variables that appear to fit our hypothesized taxonomy of dynamic predictors and the implications of these
findings for understanding the relationship between trajectories and time-varying covariates.
Evidence for stable vulnerability indicators
Variables thought to reflect or be closely linked
to personality0temperament ~e.g., antisocial
behavior! were most likely to behave as stable vulnerability markers. Because our analyses focused on variables that were assessed
over time, the data reported here are not particularly probing of stable vulnerability indicators as, per study design, we did not assess
these at each wave. For example, baseline
personality variables reflective of behavioral
undercontrol ~e.g., Novelty Seeking from the
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848
Tridimensional Personality Questionnaire
@TPQ#; Cloninger, 1987; Psychoticism from
the Eysenck Personality Questionnaire @EPQ#,
Eysenck & Eysenck, 1975! and negative affectivity ~e.g., Harm Avoidance on TPQ, Neuroticism on EPQ! that appear fairly stable in
our larger dataset ~Sher, Bartholow, & Wood,
2000! were not available over time in the current study. However, measures of behavioral
undercontrol that we did assess regularly ~e.g.,
past-year antisociality, MacAndrew scale! were
consistent with the concept of stable vulnerability indicators. Considerable evidence suggests that temperamental characteristics are
associated with risk of alcohol problems and
dependence ~Sher, Trull, Bartholow, & Vieth,
1999! and, in fact, might partially mediate
genetic risk ~Slutske, Heath, Dinwiddle, Madden, Bucholz, Dunne, Statham, & Martin,
1998; Slutske, Heath, Madden, Bucholz,
Statham, & Martin, 2002!. However, it is possible that severe alcohol dependence can affect the measurement of personality ~Barnes,
1983; Sher et al., 1999!. Aside from the effects of alcohol dependence on personality,
“stability” is relative as there appear to be
normative decreases in negative affectivity and
behavioral control from adolescence until
adulthood ~Costa & McCrae, 1994, 1997;
McGue, Bacon, & Lykken, 1993!, only moderate ~rank order! stability of personality in
adolescence and young adulthood ~Roberts
& DelVecchio, 2000!, and social roles can
moderate personality stability ~Caspi & Roberts, 1999!. Thus, although we believe that
personality traits can function as stable vulnerability indicators, it is possible that personality traits can also be dynamically related to
course. Moreover, these may be indicators of
high risk for development of AUDs, but may
not be easily modifiable.
Evidence for course trackers
Closely tracking course, measures of alcohol
involvement showed a monotonic relation with
the nondiagnosing–remitting–chronically diagnosing gradient at baseline, indicating, not
surprisingly, that initial severity is related to
chronicity. For several variables, however, both
Chronics and Remitters began at similarly high
K. J. Sher, H. J. Gotham, and A. L. Watson
levels, and then remitters showed greater decreases over time. This pattern was exemplified by Mood Modification, Celebration, and
Sensation Seeking reasons for drinking, the
restraint strategy of trying to cut back on drinking, and peer alcohol involvement. Given that
individuals who do not remit from their AUDs
fail to decline in these variables in comparison to those who do remit, these markers of
problematic AUDs might be useful in monitoring treatment progress, or course, more
generally.
More fine-grained analyses ~e.g., crosslagged autoregressive models! could help distinguish whether course trackers are leading
indicators ~and potentially causal! or trailing indicators ~and consequential!. With respect to
drinking motives ~Sher & Wood, 1997! and alcohol expectancies ~Sher, Wood, Wood, &
Raskin, 1996! reciprocal influences have been
shown, demonstrating that both directional influences can be present. The considerably large
literature on peer influence also suggests reciprocal processes ~i.e., individuals are influenced
by their peers’ drinking but also select peers
based, in part, on their drinking!. For example,
Curran et al. ~1997! reported bidirectional effects for target individual and peer alcohol use
from adolescence into early young adulthood.
This suggests that targeting such variables in
either prevention or treatment programs might
alter the course of AUDs in some young adults.
Evidence for developmentally specific
variables
For some variables, we found noticeably
stronger prediction of the chronic course of
AUDs at earlier but not later time points. Specifically, Performance Enhancement alcohol
expectancies were initially higher in those with
AUDs who went on to develop chronic patterns, but these expectancies decreased over
time and eventually became roughly similar
to Nondiagnosers and Remitters during their
early 20s. The early severe deviation of Chronics that then tends to normalize suggests that
some markers are developmentally limited risk
factors that only have prognostic significance
at certain ages ~Rutter, 1996!. Previous research noted that although performance en-
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Dynamic predictors
hancement expectancies increase and then
decrease during adolescence for most young
people, these expectancies did not decrease in
problem drinking adolescents ~Christiansen,
Goldman, & Brown, 1985!. In an elaboration
of this work, we found that Performance Enhancement expectancies remain elevated in
chronically diagnosing young adults only during the early college years, indicating that there
are critical developmental parameters of this
association. This raises cautions about generalizing findings regarding prognosis across developmental periods and strongly argues for
designs and statistical analyses that are attentive to participants’ ages and, relatedly, “hidden” cohorts in age-heterogeneous samples.
Evidence for developmental lag markers
There is a marked normative decrease in subjective distress over the college years ~Sher,
Wood, & Gotham, 1996!, but against this normative trend there are individual variations in
trajectories. For those characterized by persistent AUDs, the decrease in generalized distress ~as assessed by the BSI-GSI! is delayed
but ultimately achieved ~or at least, approximately achieved, for a period of time!. These
findings are consistent with the theory suggesting that heavy substance involvement may
cause cognitive and0or affective damage and
interferes with the development of social and
cognitive competencies that are associated with
optimal development ~Baumrind & Moselle,
1985; Newcomb & Bentler, 1988!. Similar
to developmentally specific predictors, the association between AUDs and developmental
lag indicators is presumed to be dependent
upon the ages studied, and important effects
could be obscured by studying individuals at
the wrong time of life or by studying ageheterogeneous cohorts and failing to resolve
age-dependent effects.
Evidence for course-referenced variables
On two variables assessing drinking restraint
~frequency of drinking low alcohol beverages,
frequency of pacing drinking!, Remitters appeared to have initially relatively high levels
that decreased over time ~by later years, the
849
Remitters’ levels on these variables dropped
close to the level of Nondiagnosers!. However, the same variables showed the opposite
pattern, that is, an increase over time, in individuals with a chronic course. This contrasting pattern suggests that the “meaning” of an
observed variable can change over the course
of alcohol abuse0dependence and observed patterns of association between a manifest disorder and a covariate can be conditional upon
the stage of one’s “drinking career.”
More specifically, for those with a remitting course, use of drinking restraints might
represent a successful moderation strategy that
results in an amelioration of alcohol-related
problems, and the decline in use of the strategy over time signifies recovery. However, the
later increased use of these strategies by individuals with chronic courses of AUDs suggests that they develop a more persistent
problem before trying a strategy that, at least
throughout the years studied, does not appear
to produce success. This pattern also suggests
that those who go on to remit from an earlyonset AUD have an early notion that they are
having difficulties with alcohol involvement.
One potential implication of these findings is
that self-change strategies that are effective
for individuals with “early stage” alcohol abuse
or dependence ~see Sanchez–Craig, 1984!
might be ineffective ~or potentially even harmful in those whose dependence has progressed!
and thus course might be a relevant “matching
variable” for evaluating the effectiveness of
differing treatment approaches ~Project Match
Research Group, 1997!.
Deterioration markers
We were not able to identify any variables that
that showed progressive deterioration as a function of a chronic course, although there was
some indication that psychological distress
might be starting to show such a trend towards the end of our observation period. The
absence of a deterioration effect is almost certainly due to the types of variables we assessed, the severity of disorder manifested in
our nonclinical sample, and possibly the relative youth of our sample; there is little doubt
that deterioration can and frequently does oc-
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850
cur. For example, given existing epidemiological data, it seems a certainty that we would
ultimately observe increased tissue deterioration ~e.g., impaired liver function! had we
tracked this variable in a sufficiently large,
chronic group of alcohol-dependent individuals for an extended period of time. Furthermore, when deterioration occurs, it is likely
that later remission can be associated with permanent disability ~e.g., Korsakoff ’s psychosis! or improvement ~e.g., most alcohol-related
neuropsychological impairment! with simple
passage of time, continued use of affected abilities, or drug therapy ~Goldman, 1987; Rainer,
Mucke, Chwatal, & Havelec, 1996!. Thus,
there is considerable value in studying individuals who show deterioration patterns to determine what types of deterioration are likely
to be relatively permanent ~e.g., “scarring”!
versus short lived.
Recovery behaviors
Our study found no evidence for ~courseindependent! recovery behaviors. ~Recall that
certain drinking restraint strategies appear to
be course-referenced markers of an early recovery process.! This is not surprising in that
the study was not designed to track these types
of variables, although many variables ~e.g.,
AA attendance, drinking self-efficacy! might
be expected to increase during the initial stages
of recovery and to continue to increase or be
maintained for some period of time. Identification of recovery behaviors represents a
potentially promising research strategy for
identifying those variables associated with natural recovery processes ~Russell, Peirce, Chan,
Wieczorek, Moscato, & Nochajski, 2001; Sobell, Cunningham, Sobell, & Toneatto, 1993;
Watson & Sher, 1998!. As discussed elsewhere, understanding natural recovery not only
provides us with a better understanding of the
course of AUDs in the general population but
also has the potential to form the basis of new
intervention strategies.
Beyond the current taxonomy
We deliberately limited our taxonomy of dynamic predictors to those that we hypoth-
K. J. Sher, H. J. Gotham, and A. L. Watson
esized would be observable in our data set.
Because our cohort was ascertained when participants were well into their period of risk
for AUDs, we did not extend our analysis to
possible patterns of dynamic prediction that
involve premorbid covariates that are leading
indicators of onset risk. In addition to stable
vulnerability indicators that, by definition, are
present premorbidly, we can envision at least
two types of premorbid dynamic prediction.
The first involves various environmental or
biological influences that accrue during development and promote the development of
disorder. These predictors would be dynamic
in the sense they are changing and relate to
the onset ~and possibly later course! of disorder. For example, continued exposures to environmental toxins ~e.g., lead!, psychosocial
stressors ~e.g., poverty!, or “bad models” for
behavior ~e.g., deviant peers! could presage
the onset of disorder and provide targets for
preventive intervention even prior to symptoms. The second involves subclinical manifestations of disorder itself. Such a pattern of
“subclinical emergence” might be especially
important for some disorders, not just because it heralds the onset of later, frank disorder, but because it could represent an
opportune time for intervention. That is, risk
is already tangibly manifest ~and thus false
positive are less likely to be a problem than
they would for latent risk as exemplified by
stable vulnerability indicators! but disorder
has not yet appeared. For example, early intervention with adolescents and young adults
who are exhibiting risky drinking patterns
but are not yet alcohol dependent appears to
represent a promising intervention strategy
for altering trajectories of pathological drinking ~Baer, Kivlahan, Blume, McKnight, &
Marlatt, 2001!. Similarly, in recent years,
schizophrenia researchers have devoted more
attention to early intervention during the “prodromal” period in efforts to prevent the emergence of frank psychosis and more severe
disability ~see Cornblatt, Heinssen, Cannon, &
Lencz, 2003!. Such proximal indicators of risk
may guide us more in conceptualizing the timing of intervention more than more distal indicators such as stable vulnerability indicators.
As multivariate, prospective research on eti-
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Dynamic predictors
ology and course of disorder continues to
progress, refinement of the type of taxonomy
discussed in this article is almost certain.
Limitations
Several notes regarding the sample need to
be made. The collegiate nature of the sample
presumably reflects a relatively high functioning group of young adults. However, results
from the Monitoring the Future study suggest
that although alcohol use in college students
compared to their nonstudent peers is quite
similar, college students’ drinking patterns
are actually more likely to be characterized
by bingeing ~Johnston et al., 2000!. In addition, many of our participants dropped out or
stopped out of college over the course of the
study, so that although all were college freshman at Year 1, they did not continue to be an
exclusively college student sample. In addition, because we oversampled those with a FH
of alcoholism, there is a relatively high proportion of affected individuals in the sample.
Our sample is almost exclusively White ~94%!.
Thus, generalizations to other groups of young
adults should be made cautiously; African
American, Hispanic, and Asian American
groups have different prevalences and age distributions of alcohol involvement and AUDs
~e.g., Herd, 1988; Higuchi, Parrish, Dufour,
Towle, & Harford, 1994; Wechsler, Dowdall,
Davenport, & Castillo, 1995!, and predictors
of onset and recovery may also differ among
groups.
Perhaps the most critical limitations concern the joint effects of a relatively “late beginning” and “early end” of our observation
period coupled with a relatively small sample. Consequently, we were not able to assess
dynamic predictors premorbidly in those who
diagnose early and we were not able to identify a sufficiently large group of subjects with
a late onset of AUD to track how dynamic
predictors might vary as a function of age
of symptom onset. A related limitation stems
from studying a single cohort and, consequently, difficulty in disentangling developmentally specific and course-referenced
predictors. Greater confidence for this distinc-
851
tion could be achieved by replicating coursespecific predictors in different age cohorts.
As noted earlier, the data analytic approach
we used for examining dynamic predictors does
not resolve direction of effect. Previous analyses from this project have found evidence for
reciprocal effects over time for alcohol expectancies ~Sher, Wood, Wood, & Raskin, 1996!,
reasons for drinking ~Raskin, Sher, & Wood,
1993!, tobacco dependence ~Jackson, Sher, &
Wood, 2000a; Sher, Gotham, Erickson, &
Wood, 1996!, and anxiety disorders ~Kushner
et al., 1999!, but some variables might be exclusively predictive or exclusively consequential. Also, more sophisticated analyses are
possible. For example, rather than focusing on
a few, clinically relevant, prototypic courses
~and thus excluding those with a nonprototypic course!, we could have attempted to categorize all courses using latent class growth
analysis, a multivariate technique that can be
used to identify latent classes of trajectories
of diagnosis ~Jackson et al., 2000a!. These latent trajectories ~of diagnosis! could then be
related to latent growth curves for each dynamic predictor considered. This approach has
its appeal and, indeed, we have embarked upon
a series of analyses to extend the conceptual
approach described here to a complete multivariate framework both in this and in other
data sets. Although there is a methodological
rationale for employing more sophisticated approaches, we find it less desirable for the type
of initial theory building we are attempting
here.
The absence of role transition variables also
deserves mention. Role transitions such as becoming married or gaining full-time employment are important influences on the course
of AUDs during young adulthood, and we have
examined those influences in other work ~e.g.,
Gotham, Sher, & Wood, 1997, 2003; Wood,
Sher, & McGowan, 2000!. However, in this
study the majority of the sample was single
and not in the workforce until between Years
4 and 7, and during this time period relatively
few participants transitioned in and out of different roles, limiting variability of these factors. With data from future follow-ups we may
be able to delineate dynamic changes in role
transitions.
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852
Implications
We believe the major implications of the
present study are conceptual; beyond the increasingly recognized fact that there is considerable variability in course of disorder ~i.e.,
trajectories; e.g., Schulenberg, O’Malley, et al.,
1996! there is even greater variability in the
trajectories of the covariates ~i.e., dynamic predictors!. More important, this variability offers clues to the functional relation of the
covariate to the underlying disorder and to the
developmental boundary conditions under
which it serves to operate. From our perspective, the identification of the types of patterns
considered here represents a critical first step
towards understanding the etiological or consequential role of a diagnosis-related variable.
Thus, consistent with earlier work ~Sher et al.,
1999!, variables related to behavioral undercontrol and impulsivity, if measured early ~e.g.,
high school, early college!, may be markers of
high risk for both the occurrence of an AUD
and a potential chronic course of AUDs over
time. However, some risk factors, such as alcohol expectancies related to performance enhancement, may only be markers of high risk
when measured early in the drinking career of
young adults. Other variables may be associated with better prognoses when measured
early in the course of disorder but poorer prognosis when measured later. Finally, the extent
of negative consequences of a disorder can
vary as a function of normative developmental trajectories. The clear message from these
findings is that AUDs are clearly embedded
within a strong developmental context.
Beyond the primary conceptual and methodological contribution, there are several more
applied implications. For example, preventive
K. J. Sher, H. J. Gotham, and A. L. Watson
interventions, which are becoming prevalent
at colleges and universities ~e.g., Baer et al.,
2001; Ziemelis, Bucknam, & Elfessi, 2002!,
could target factors identified as influential to
the recovery process. The current study lends
support to prevention and early intervention
programs aimed at changing young adult’s motivations for using alcohol and expectancies
regarding alcohol’s effects ~Darkes & Goldman, 1993, 1998!.
Another implication is that the effectiveness of formal treatments and self-change strategies ~Sobell & Sobell, 1998; Vaillant, 1995!
could be improved by an understanding of factors that promote or inhibit resolution of an
alcohol problem. Although the direction of effects of the dynamic predictors and course of
AUDs cannot be discerned from the current
analyses, this study provides evidence for the
utility of several variables to be monitored during treatment ~e.g., reasons for drinking, restraint strategies, psychological distress, peer
involvement!. It may also be that attempts to
change these variables, such as the emphasis
in some treatment programs on “changing playmates and playgrounds” or on addressing comorbid smoking and other substance use or
other disorders such as depression, should be
specific targets of treatment.
We cannot emphasize enough, however, that
we see our approach as only a first step toward more elaboration and pruning of taxonomies of dynamic predictors. Future efforts
will most certainly be informed by others
studying different problems and different stages
of the life course. It is our hope that attention
to the concept of trajectories of dynamic predictors will provide another methodological
tool for uncovering important relations between disorders and their correlates.
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