VOL. 22, NO. 4, 1996
Positive and Negative
Symptoms: Is Their Change
Related?
by Pil Czobor and Jan Volavka
Abstract
The objective of this study was to
investigate the relationship between
change in positive and negative
symptoms over time during haloperidol treatment Subjects were 178
acutely exacerbating chronic schizophrenia patients participating in a
clinical trial of haloperidol. Positive
and negative symptoms were measured by the positive and negative
symptom clusters of the Brief Psychiatric Rating Scale and by the
Scales for the Assessment of Positive and Negative Symptoms. Hierarchical linear model analysis was
used to estimate symptom-change
trajectories of positive and negative
symptoms over time in individual
patients and to investigate the interrelationship between these trajectories. Results indicate that positive
and negative symptom-change trajectories over time were closely
associated. This result did not
depend on extrapyramidal side
effects, depression, or the baseline
balance of positive and negative
symptoms. Thus, our study suggests
that in acutely exacerbating schizophrenia patients, changes of positive and negative symptoms over
time are related during the initial
phase of the pharmacological treatment This relationship may not
extend to other treatment paradigms
or beyond the initial phase of the
treatment
Schizophrenia Bulletin, 22(4):
577-590,1996.
The relationship between positive
and negative symptoms has been a
topic of debate since the 19th century.
Two opposing theories were formulated. Reynolds' theory, based on an
excess or lack of the vital tonus, postulated that positive and negative
symptoms were independent of each
other (Reynolds 1858). In contrast,
Jackson's hierarchical evolutionary
model of the mental processes
posited that positive and negative
symptoms were different manifestations of the same underlying disease
mechanism and that they were therefore associated (Jackson 1889).
Conflicting views about association or independence of positive and
negative symptoms in schizophrenia
continue to prevail in the literature.
For example, in his two-syndrome
concept of schizophrenia, Crow considered positive and negative symptoms as two principal and independent entities with markedly different
prognostic, therapeutic, and pathogenetic correlates (Crow 1980,1985).
Andreasen, however, regarded positive and negative symptoms as
related constructs—the opposite ends
of a continuum (Andreasen and
Olsen 1982).
Because recent factor-analytic data
indicate that both positive and negative symptoms may be best described
in terms of multidimensional models
(Arndt et al. 1991; Lenzenweger et al.
1991; Peralta et al. 1992,1994; Peralta
and Cuesta 1995), it is possible that
positive and negative symptoms constitute higher-order constructs (each
consisting of several narrower entities). However, although considerable uncertainty exists about what
may constitute an "elementary"
dimension within each of the two
principal constructs, and further validation studies are clearly needed, the
basic "positive-negative distinction"
has been shown to be clinically significant and valid (Carpenter et al.
1985,1988; Kay et al. 1986; Meltzer
Reprint requests should be sent to
Dr. P. Czobor, Nathan S. Mine Institute for
Psychiatric Research, Orangeburg, NY
10962.
SCHIZOPHRENIA BULLETIN
578
et al. 1986; Kay and Opler 1987;
Andreasen 1989; Breier et al. 1994).
Empirical studies into the relationship between positive and negative
symptoms generally report a weak
association or an independence, but
they have relied almost exclusively on
the cross-sectional approach (Rosen et
al. 1984; Kay et al. 1988; Walker et al.
1988; McKenna et al. 1989; Mortimer
et al. 1990). This approach, however, is
limited because in schizophrenia
patients, positive and negative symptoms are not invariant; they change
over time as a function of the underlying illness. Thus, to gain a more complete insight, it is essential to examine
the relationship between positive and
negative symptoms in a longitudinal
design. The principal purpose of this
study was to investigate longitudinal
covariation between positive and negative symptoms during the initial
(acute) phase of haloperidol treatment.
Previous studies of the longitudinal relationship between positive and
negative symptoms during neuroleptic treatment yielded equivocal
results. Focusing on the maintenance
phase, Amdt et al.'s study (1995)
revealed essentially no relationship
over a long period of time (2 years).
Studies examining the acute phase,
with a time span of several weeks,
raised the possibility of short-term
association, but remained inconclusive. Taken together—from no relationship (Breier et al. 1987; Serban et
al. 1992) to strong positive association (van Kammen et al. 1987; Tandon et al. 1990,1993)—variable findings have been reported. However,
the results of these studies are hard
to interpret because of certain design
limitations.
First, earlier studies examining the
acute phase have been based on data
limited to two time points, an inadequate basis for studying longitudinal
covariation (Bryk and Weisberg 1977;
Rogosa et al. 1982; Rogosa and Willett 1985). As pointed out by Raudenbush and Chan (1993), this type of
design describes a patient's trajectory
over time with low reliability and
therefore can lead to inconsistent
results.
Second, change in positive and
negative symptoms was measured as
a score of the difference between two
time points. However, "the unsuitability of such scores has long been
discussed" (Cronbach and Furby
1970, p. 68). The scores are systematically related to any random error of
measurement and contain some variance that is entirely due to the baseline (Cronbach and Furby 1970;
Cohen and Cohen 1983). Whereas the
confounding effect of baseline on difference scores has been recognized,
no study accounted for the effect of
baseline positive and negative symptoms.
Third, previous studies were limited by the failure of traditional statistical methods to address the complexity and individual variability of
positive and negative symptoms
(e.g., positive and negative symptoms coexist to an individually varying degree; treatment interacts with
patient-specific characteristics to produce a considerable variation in
response across patients).
Until recently no unified data analytical approach was able to address
simultaneously the essential clinical
concerns; however, new developments in the statistical theory of hierarchical linear models (HLM) now
enable us to do this (Bryk and Raudenbush 1992). Because the HLM
methodology, as compared to traditional methods, is based on less
restrictive assumptions (Bryk and
Raudenbush 1987; McLean et al.
1991) and can efficiently exploit prior
clinical information on individual
variability, we expected that our
investigation would provide more
appropriate statistical inferences and
a new and better insight into the relationship of positive and negative
symptoms over time.
Methods
The data for this investigation were
collected in two clinical trials of
haloperidol. The two trials represented subsequent phases of a
research project, the principal objective of which was to examine relationships between haloperidol blood
levels and clinical effects. The first
trial (Study 1) was conducted
between 1986 and 1990. Its specific
purpose was to test a therapeutic
window relationship between clinical
effects and haloperidol plasma levels
in the range of 2-35 ng/mL. The core
results of this study have been published (Volavka et al. 1992); the "window hypothesis" was not supported
by the data.
Nevertheless, results of Study 1
pointed to the possibility that an
association between clinical effects
and plasma levels may exist in the
lower end of the plasma level range.
Accordingly, the specific purpose of
the second trial, Study 2 (1991-94),
was to investigate the relationship of
plasma levels and effects in a low
plasma level range. The plasma level
range (observed values) in this study
was between 0.6 and 16.4 ng/mL.
The second study has also been completed (Volavka et al. 1995).
The same key study personnel and
identical rating procedures were
used in both studies. Furthermore,
the two studies shared certain design
features. Study 1 will be described
first. Because of the similarities
between the two studies, the description of Study 2 is shorter and focuses
on the differences.
579
VOL. 22, NO. 4, 1996
Study 1.
Design and data collection.
Study 1 was a placebo-controlled,
double-blind, crossover treatment
study with fixed plasma levels of
haloperidol (Volavka et al. 1992). The
study included a single-blind placebo
washout period and two doubleblind treatment phases. Patients eligible for the trial were randomly
assigned a plasma level between 2
and 35 ng/mL. The actual average
plasma level after 3 weeks was 17.6
± 10.3 ng/mL. The intended duration of the washout phase was 1
week; however, this period could be
reduced if psychotic symptoms were
exacerbated.
After the washout phase (average
duration 6.7 ± 4.3 days), the patients
started the first double-blind treatment phase, which lasted 6 weeks.
Patients who failed to demonstrate a
dearcut clinical response, defined as
50 percent or more improvement on
the total score of the Brief Psychiatric
Rating Scale (BPRS; Guy 1976),
entered the double-blind treatment
period. Benztropine mesylate was
permitted in the treatment phase for
extrapyramidal side effects. Further
details of the study have been published elsewhere (Volavka et al.
1992).
BPRS ratings and assessments of
extrapyramidal side effects (Simpson-Angus Scale; Simpson and
Angus 1970) were performed (1) at
study entry, (2) at the end of the
placebo period ("baseline"), and (3)
at each week during the entire double-blind treatment phase. The ratings were based on joint interviews
with two trained raters. The scores
given by each of the two raters were
averaged.
To establish interrater reliability,
joint rating sessions including all
raters were conducted before the
study. Raters' performance was
tested by additional patient interviews; those who had an intradass
correlation coeffident (ICC; Bartko
and Carpenter 1976)—a measure of
interrater reliability—of at least 0.8
partidpated in the trial. A total of 12
raters formed 10 pairs of raters. In 76
percent of all ratings, patients were
rated by the same raters throughout
the study. When one of the raters in a
pair was absent, there was only one
rater or a third rater was used. For
continuing reliability and to prevent
rater drift, calibration meetings were
performed during the study period.
Interrater reliabilities were retested at
the end of the trial; ICCs ranged
between 0.74 and 0.95.
In addition to the BPRS, the Scale
for the Assessment of Negative
Symptoms (SANS; Andreasen 1982,
1984a) and the Scale for the Assessment of Positive Symptoms (SAPS;
Andreasen 1984b) were also applied
in a subset of patients. These ratings
were not done weekly, however, but
were performed at study entry, at
baseline (end of placebo period), and
at the endpoint of the first and the
second treatment periods.
Subjects. Patients induded in the
study (age range 18-65 years) had to
meet both the DSM-III (American
Psychiatric Assodation 1980) and the
Research Diagnostic Criteria (RDC;
Spitzer et al. 1978) for schizophrenia
or schizoaffective disorder and had
to be inpatients throughout the study.
Additional major indusion criteria
were (1) a suffident initial (i.e.,
"placebo endpoint") severity with a
score of 4 (moderately severe) or
more on at least two of following
BPRS items: unusual thought content, conceptual disorganization, suspidousness, or hallurinatory behavior; (2) lack of clearcut response to
placebo (based on the 50% cutoff, see
above); (3) no depot neuroleptic
treatment in the month preceding
study entry; (4) no other mental disorders or significant somatic disease;
and (5) informed written consent.
Study 2.
Design and data collection. The
two major differences between Study
1 and Study 2 were (1) in Study 2, the
first and the second double-blind
treatment phases lasted 3 weeks each
(instead of 6 weeks as in the first
study) and (2) patients were randomly assigned to two fixed plasma
levels (2 or 10 ng/mL). The average
duration of the single-blind placebo
washout period was 6.5 ±1.9 days.
The average observed plasma level
after 3 weeks was 6.1 ± 4.8 ng/mL.
Psychometric assessments were
based on rating procedures identical
to those used in Study 1. However,
not all of the ratings were done by
two raters in this study. Patients were
rated jointly by two raters at study
entry, at the endpoint of the placebo
period, and at the endpoint of the
first and second double-blind treatment phases. Intermediate ratings
were done by one of the two raters.
An average of the two raters' scores
was computed whenever two ratings
were available. A total of 21 trained
raters were used in this 4-year trial.
ICCs were computed for each of the
12 pairs of raters who.rated jointly at
least five times; interrater reliabilities
ranged between 0.87 and 0.97.
Subjects. Inclusion and exdusion
criteria were identical to those used
in Study 1.
Variables. Because the BPRS was
assessed weekly in all patients
throughout the entire study period,
the BPRS positive and negative
symptom dusters were used as the
primary measures of positive and
negative symptoms. To distinguish
these measures of positive and nega-
SCHIZOPHRENIA BULLETIN
580
tive symptoms from the SAPS and
the SANS, in the rest of this article,
the BPRS positive and negative
symptom dusters will be referred to
as POS and NEC
The BPRS positive symptom duster consisted of the following four
items: conceptual disorganization,
suspidousness, hallucinatory behavior, and unusual thought content
(Breier et al. 1987,1994; Tandon et al.
1993).
The NEG score was based on the
following four items: emotional withdrawal, motor retardation, blunted
affect, and disorientation (Guy 1976).
The average item score (i.e., the total
score divided by the number of
items) was calculated for both POS
and NEG.
Whereas disorientation constitutes
part of the traditional BPRS negative
symptom duster (Guy et al. 1975;
Guy 1976), this item has been
reported as a separate factor in some
studies (e.g., Dingemans et al. 1983).
To investigate whether the inclusion
of disorientation affects our findings
(see Ancillary Analyses section), we
created a "core" BPRS negative symptom measure (NEGCORE) by omitting
this item from the negative symptom
construct.
Furthermore, to explore whether
the disorganized and psychotic features of positive symptoms were
related differently to negative symptoms, we segregated conceptual disorganization—an important symptom
indexing the disorganized dimension—from the BPRS positive symptoms duster. The resulting two measures—the conceptual disorganization
(POS^jo) and the positive, psychosis
symptom construct (POS p^)—were
examined in ancillary analyses.
In addition to the BPRS positive
and negative symptom dusters, the
average item scores on both SAPS and
SANS summary scores were also used
as measures of positive and negative
symptoms, respectively. Because these
measures were not obtained weekly,
they were used only in ancillary
analyses.
Statistical Analyses.
Conceptual background. The
prindpal goal of the hierarchical linear modeling approach (Bryk and
Raudenbush 1987,1992; Gibbons et
al. 1988,1993; McLean et al. 1991) is
to estimate and test "growth" functions or trajectories that adequately
describe individual patterns of
change on some characteristics (e.g.,
symptoms) over time. Conceptually,
the variation in the studied characteristic is described at each of two levels
in the HLM.
At Level 1, the characteristic is
described as varying within subject
over time as a person-specific change
trajectory (plus an error). The trajectories that can be used to model
change processes vary from simple
linear functions to complex nonlinear
curves. Typically, models that can
describe real-life change processes
tend to be more complicated than the
simple linear function, because the
rate of change over time is usually
not constant.
At Level 2 in the HLM, the personlevel change trajectories are viewed
as systematically varying across subjects. The random-effect part of the
HLM provides information about
these systematic individual differences in change trajectories over
time. The fixed-effect part of the
HLM provides information about the
mean change trajectory for a group of
subjects. The mean population
change trajectory and the individual
variation around the mean change
trajectory as well are modeled at
Level 2. To this end, models can be
set up to account for the effect of
measured predictor variables and /or
unidentified person-sperific characteristics.
Analytical model. In our HLM, a
separate and individually varying
trajectory (random effect) was estimated for the positive and negative
symptoms. In addition to the random-effect component, a mean or
"population" symptom-change trajectory was also spedfied in the
model. Because we assumed that this
trajectory would be different for positive and negative symptoms, a separate mean trajectory was derived for
the positive and the negative symptoms (fixed effect). The prindpal features of our HLM and their brief
rationale are the following.
Individually varying intercepts at
baseline. Consistent with data
(Andreasen and Olsen 1982), we
assumed that in any given patient, a
person-spedfic mixture of positive
and negative symptoms may exist. To
describe this condition, two separate
baseline values or intercepts (one for
the positive and one for the negative
symptoms) were built into the HLM,
and these values were permitted to
vary individually and independently
of each other.
Nonlinear symptom change.
Data
from pharmacological trials in schizophrenia patients indicate that
change in symptom severity is not
occurring steadily over time with an
invariant slope. After the start of the
medication, there is a fast decrease in
symptom severity, but later a substantial deceleration of the improvement occurs (Petrie et al. 1990; Van
Putten et al. 1990; Harvey et al. 1991;
Marder et al. 1991). To describe this
nonlinear symptom-change pattern
over time, we used a logarithmic
metric for time, as in the Gibbons et
al. (1993) study.
Correlation between positive and negative symptom-change trajectories. In-
dividual positive and negative symp-
VOL22.NO. 4, 1996
torn-change trajectories were allowed
to correlate with each other across
subjects. The test of this correlation
constituted the principal interest in
our study.
Correlation between symptom severity
at baseline and subsequent symptom
change over time. Many reports show
that baseline values are related to
subsequent change in symptoms
over time. However, the reported
relationships may partly be due to
various statistical artifacts (Cohen
and Cohen 1983). The simple pretestposttest design of previous studies
did not allow the disentanglement of
a true predictive relationship from
trivial effects (e.g., regression to the
mean). However, the relationship
between baseline and change over
time can be tested in the context of
the HLM (Bryk and Raudenbush
1987). To do this, we estimated a correlation between the baseline values
and the symptom-change trajectories
in our HLM.
Data analyses. All principal analyses were based on the HLM described above. Repeated assessments
of positive and negative symptoms
over time served as the dependent
variable. Our principal analysis was
based on four assessments: baseline
and treatment weeks 1,2, and 3. The
3-week period from the treatment
phase was chosen because the first
treatment period lasted 3 weeks in
Study 2.
The independent factors of time
(repeated measurements over time)
and symptom (positive and negative)
served as within-subject effects. To
describe a nonlinear change over
time, time was used in a log-transformed metric (i.e., baseline = log 1,
first week = log 2, etc.). Each symptom-change trajectory in each person
was represented by two parameters:
the trajectory's initial or starting
value (intercept) and its slope. Fixed
581
and random effects of the symptom
on the change trajectory's intercept
and on its slope were examined in
the HLM. An unstructured covariance matrix was specified to estimate
all interactions among all model
effects. The random-effect interaction
between slopes of positive and negative symptom trajectories indicated
covariation of positive and negative
symptoms over time.
Because our data were obtained in
two separate studies, an indicator
variable for study was included in
the initial analyses as a random-effect
covariate in the HLM. However, this
variable was eliminated from the
final model because it did not provide a significant contribution.
For ancillary analyses, we first
examined whether our results could
be generalized to a longer period. To
explore this issue, we extended the
observation period from 3 to 6 weeks
in a subset of patients who had their
data available for this purpose.
Because the first treatment period
lasted 6 weeks in Study 1, the subset
of patients for these analyses
included all patients from that study.
From Study 2, data after 3 weeks (i.e.,
from the second treatment period)
were used only from those patients
who maintained their plasma level
assignment throughout the entire
study.
Second, we investigated whether
use of alternative positive and negative symptom measures would influence our findings. To this end, a separate analysis was performed for each
alternative BPRS measure: NEG,-^,
POSpgQ, and POSpj^. These analyses
were identical to those described for
principal analyses.
Furthermore, because the SAPS
and the SANS were assessed in both
studies, we performed an exploratory analysis using these rating
scales. The purpose of this analysis
was to investigate whether results
similar to those for the BPRS measures can be obtained with more specialized indices of positive and negative symptoms.
However, compared to the BPRS
data, the SAPS and SANS data had
poor time resolution: in both studies,
the assessments were done at baseline and at the endpoint of each treatment period. To obtain an estimate of
symptom-change trajectories over
time, we needed at least three measurements for each patient; hence,
patients with missing data were not
used. Thus, for this analysis, we used
only completers whose plasma level
assignment remained constant
throughout both treatment periods.
Third, we investigated the impact
of potentially important covariates
on our principal results. Because
a lack of association between the
change in positive and negative
symptoms during pharmacological
treatment was reported in a group of
patients with predominantly negative symptoms (Serban et al. 1992),
we examined whether our results
depended on the baseline balance of
positive and negative symptoms. To
this end, the positive-negative symptom ratio (defined as the baseline
positive symptom score divided by
the baseline negative symptom score)
was introduced as a random-effect
covariate in our principal HLM. Furthermore, we examined whether our
findings were influenced by haloperidol plasma levels, age, chronicity,
depressive symptoms (score on the
BPRS anxiety-depression factor), and
extrapyramidal side effects (total
score on the Simpson-Angus Scale
and the akinesia rating).
Finally, in addition to the analyses
described above, we calculated the
Pearson correlation coefficients
between the changes of positive and
negative symptoms over time.
SCHIZOPHRENIA BULLETIN
582
Because of the problems mentioned
in the introductory paragraphs (i.e.,
measurement errors, dependence on
baseline), these values were not used
for statistical inference in our study.
However, because earlier studies
used Pearson correlations, these values are provided for comparability.
tailed test: z = 1.53, p < 0.11). No statistically significant difference in clinical improvement between the two
samples was found (F = 1.28, df =
1,176, p < 0.26); the overall relative
change of the BPRS total score at
study endpoint was 26% ± 33% in
Study 1 and 31% ± 26% in Study 2.
Results
Descriptive Data. The interrater
reliabilities in our study sample, as
measured by the ICC, were 0.88, 0.85,
and 0.80 for the BPRS, SAPS, and
SANS, respectively. Table 2 presents
BPRS positive and negative symptom
data used for the principal analysis.
The available sample size and the
means and standard deviations for
both measures are displayed for
those patients who were in the study
at each time point. Data in table 2
indicate that our patients manifested
more initial psychopathology on positive than on negative symptoms.
This difference, although to a lesser
extent, persisted throughout the
entire study period. Consistent with
previous studies, the rate of improvement over time appeared to be nonlinear, being faster at the beginning
of treatment and substantially decelerating later.
Sample Characteristics. Patients
who entered the double-blind treatment phase and who had at least one
valid measurement in the treatment
period were selected for the analyses.
Based on this criterion, a total of 178
patients were available for the analyses. Study 1 and Study 2 yielded 124
and 54 patients, respectively. Data on
trait characteristics as well as on the
total of the BPRS are provided separately in table 1 for patients from
each of the two studies. Patients from
the two studies were compared on
measures displayed in table 1. The
two study populations were generally similar to each other; no statistically significant differences were
found. Patients from the second
study tended to have a history of
more hospitalizations (Wilcoxon two-
Table 1. Basic descriptive data on patients from the two studies
Sample1
Variable
Age
Duration of
illness (yrs)
Number of
hospitalizations
BPRS total score
Study 1 (n = 124)
Mean (SD)
2
Study 2 (n = 54) 3
Mean (SD)
34.2 (8.4)
33.3
(7.6)
12.1
(7.5)
14.5
(9.6)
7.1
51.6
(4.6)
(9.8)
14.6
51.4
(22.5)
(8.9)
Note.—BPRS =. Brief Psychiatric Rating Scale (Guy 1976); SD = standard deviation.
'No significant difference between the two samples.
Study 1—Male = 91, Female = 33.
'Study 2—Male - 43, Female = 11.
2
Data in table 2 reflect an attrition
rate of approximately 13 percent after
3 weeks. At the endpoint of the 3
weeks, both positive and negative
symptoms had improved significantly; based on the last available
assessment within the 3-week period,
the improvement (difference in original scale units) was 1.10 ± 1.01 points
(paired t = 14.3, p < 0.0001) and 0.35
± 0.87 points (paired t - 5.4, p <
0.0001) for the positive and the negative symptoms, respectively. Finally,
the Pearson correlation coefficient
between the raw difference scores of
positive and negative symptoms at
the endpoint was 0.37 (n = 178, p <
0.0001).
Principal Analysis. The F tests of
fixed effects indicated that the estimated mean symptom change trajectories for positive and negative
symptoms were significantly different. In particular, there was a significant main effect for the symptom (F =
2335.3, df = 2,1340, p < 0.00001) and
an interaction between symptom and
time (F = 98.6, df = 2,1340, p <
0.00001). The likelihood ratio test
(LRT) of the random-effect part of the
HLM indicated that positive and
negative symptom-change trajectories varied significantly across individual patients (LRT x^ = 577.7, df =
10, p < 0.00001). Table 3 displays the
fixed-effect and the random-effect
parameters yielded by the HLM
analysis.
The fixed-effect portion of table 3
shows that both fixed-effect parameters (i.e., mean initial value, mean
slope) were highly significant. On the
average, the predicted baseline value
for the positive symptoms was 1.33
raw scale points greater than for the
negative symptoms. Furthermore,
the predicted mean rate of change
(slope) over time was significantly
different from zero for both the posi-
583
VOL.22, NO. 4, 1996
Table 2. Raw data on BPRS positive and negative symptoms
Symptoms
Week
Baseline
First
Second
Third
n
178
177
162
155
Positive1
Mean (SD)
Negative2
Mean (SD)
4.04
3.13
3.00
2.92
2.64
2.36
2.37
2.32
(0.87)
(0.96)
(1.08)
(1.07)
(0.81)
(0.67)
(0.75)
(0.74)
Note.—BPRS - Brief Psychiatric Rating Scale (Guy 1976); SD = standard deviation.
'Positive symptom score was based on conceptual disorganization, susplciousness, hallucinatory
behavior, and unusual thought content
2
Negative symptom score was based on emotional withdrawal, motor retardation, blunted affect, and
disorientatlon.
Table 3. Hierarchical linear model parameter estimates and test
statistics over a 3-week period1 (n = 178)
Fixed effects
POS
Intercept
Slope
NEG
Intercept
Slope
Random effects
POS
Intercept
Slope
Intercept x slope
NEG
Intercept
Slope
Intercept x slope
POS x NEG
Intercepts
Slopes
POS intercept x NEG slope
NEG intercept x POS slope
Residual
Parameter
estimate
SE
t
P
3.931
-1.940
0.064
0.138
61.4
-14.0
0.0000
0.0000
2.605
-0.575
0.056
0.112
45.8
-5.1
0.0000
0.0000
SE
z
p
0.504
1.891
-0.145
0.079
0.313
0.131
6.4
5.1
-1.1
0.0000
0.0001
NS
0.352
0.784
-0.244
0.063
0.249
0.104
5.6
3.1
-2.4
0.0000
0.0017
0.0020
0.178
0.876
-0.237
-0.304
0.268
0.051
0.218
0.098
0.107
0.015
3.5
4.0
-2.4
-2.8
17.9
0.00O4
0.0001
0.0160
0.0050
0.0000
Variance
estimate
Note.—SE => standard error, NS = not significant
'POS and NEG » positive and negative symptom clusters, respectively (Brief Psychiatric Rating Scale;
Guy 1976).
tive and negative symptoms. However, the estimated mean slope was
more than three times as big for the
positive than for the negative symptoms. Based on the predicted mean
slopes shown in table 3, we can
expect that over a 3-week period of
treatment there would be a decrease
of 1.16 points in the positive symptoms and 0.35 points in the negative
symptoms. The mean symptomchange trajectories for positive and
negative symptoms are displayed in
figure 1.
The random-effect portion of table
3 describes variation and covariation
of positive and negative symptomchange trajectories across individual
patients. As the table reveals, all variance components were significantly
different from zero, with one exception: Covariance between baseline
and change of positive symptoms
over time did not reach significance.
Thus, with regard to the principal
question of the study, the variance
component estimates indicated that
slopes of positive and negative
symptom-change trajectories were
highly significantly associated with
each other across subjects.
To express the extent of covariation
of positive and negative symptoms
over time with the traditionally used
correlation measure, correlation coefficients among parameters of positive
and negative symptom-change trajectories were computed from the variance-component estimates depicted
in table 3.
Table 4 shows that positive and
negative symptom-change trajectories (i.e., the slope estimates) were
highly correlated (r = 0.72). Furthermore, a correlation coefficient of
medium size (r = 0.42) was found between the estimated initial values of
the positive and negative symptomchange trajectories. To illustrate correlation between positive and nega-
SCHIZOPHRENIA BULLETIN
584
Figure 1. Mean symptom-change trajectory for positive and
negative symptoms over time
baseline
week 2
week
week 3
TIME
Vertical bars depict the 95 percent confidence limits at each week. Hierarchical linear model (HLM)
analysis showed positive and negative symptom-change trajectories to be significantty different from
each other (p < 0.00001). Furthermore, as Indicated by the HLM slope estimates, change over time
was significant for both positive (p< 0.0001) and negative symptoms (p< 0.0017).
Table 4. Correlation matrix among hierarchical linear model
random effects over a 3-week period1 (n = 178)
POS
POS
Intercept
Slope
NEG
Intercept
Slope
NEG
Intercept
Slope
Intercept
Slope
1.00
—
-0.15
1.00
0.42
-0.37
-0.38
0.72
—
—
—
—
1.00
—
-0.46
1.00
'POS and NEG = positive and negative symptom dusters, respectively (Brief Psychiatric Rating Scale;
Guy 1976).
tive symptoms over time, the symptom-change slopes for positive and
negative symptoms in each individual patient are displayed in figure 2.
Ancillary Analyses.
Generalizability to longer tune
period. The HLM analysis based on
the 6-week time period yielded
results similar to those based on the
3-week period.
The F-test offixed-effectsin the
HLM showed that the mean symptom-change trajectories were significantly different for positive and negative symptoms. As was the case for
3 weeks, results for 6 weeks showed
a significant main effect for the
symptom (F = 2452.5, df = 2,1934, p <
0.00001) and for the interaction
between symptom and time (F =
121.6, df= 2,1934, p < 0.00001). Furthermore, the likelihood ratio test of
the random effects revealed a significant interindividual variation in
symptom-change trajectories over
time (LRT x2 = 1048.4, df =W,p <
0.00001).
Fixed- and random-effect parameters of the HLM for 6 weeks were
similar to those for 3 weeks. All random effects achieved significance,
with the exception of covariation
between the intercept (i.e., baseline
positive or negative symptom severity) and the change of positive symptoms over time (i.e., slope). The correlation matrix between the intercepts
and the slopes for positive and negative symptom-change trajectories is
shown in table 5. Similar to the data
for 3 weeks, the slopes of positive
and negative symptom-change trajectories were strongly associated (r =
0.82).
Alternative measures of positive
and negative symptoms. By using
alternative BPRS measures, we found
that the relationship between positive
and negative symptoms remained
essentially unchanged. Specifically,
after exclusion of the disorientation
item from the negative symptom
cluster, the correlation between positive and negative symptoms (POS
and NEG^HE) was 0.69. The correla-
tions between POS,^, and NEG and
between POS,^,^ and NEG were 0.80
and 0.69, respectively.
As compared to the BPRS positive
and negative symptom measures, the
SAPS and SANS data were available
in a subset of patients (n = 68). SAPS
and the SANS results were consistent
with those for POS and NEG.
The F-tests of fixed effects showed
VOL22.NO. 4, 1996
585
Figure 2. Relationship between change in positive and negative
symptoms over time
0
1
I"
flu
1-2
I"
3
(A
O
ft.
.4
-3
-2
0
-1
NEGATIVE SYMPTOMS
Hierarchical linear model analysis was used to investigate symptom change over bme in individual
patients; the values displayed were yielded by this analysis. Each depicted point represents a corresponding pair of symptom-change slopes, calculated for positive and negative symptoms, calculated
from the Bnef Psychiatric Rating Scale (Guy 1976), over time in each individual patient A negative
slope value means improvement [decrease of symptom severity). Because the logarithmic (base 10)
metnc for bme was used in the analyses, each slope value can be considered a decrease in symptom
severity (in absolute scale units) over a period of 10 weeks (i.e., one logarithmic unit). Estimated correlation between positive and negative symptom-change slopes was 0.72 [n = 178, p< 0.0001).
Table 5. Correlation matrix among hierarchical linear model
random effects over a 6-week period1 (n = 178)
POS
POS
Intercept
Slope
NEG
Intercept
Slope
NEG
Intercept
Slope
Intercept
Slope
1.00
—
-0.17
1.00
0.33
-0.21
-0.36
0.82
—
—
—
—
1.00
—
-0.37
1.00
'POS and NEG = positive and negative symptom dusters, respectively (Brief Psychiatric Rating Scale;
Guy 1976).
that the mean positive and negative
symptom-change trajectories, as
indexed by the SAPS and SANS,
were significantly different from each
other. Analogous to the BPRS mea-
sures, the analyses yielded a main
effect for the symptom (F = 4.13, df =
2,372, p < 0.043) and an interaction
between symptom and time (F = 23.1,
df= 2,372, p< 0.00001).
The LRT of the random-effect part
of the HLM revealed a significant
variation in the individual symptomchange trajectories (LRT X2 = 143.9,
df= 10, p < 0.00001). Fixed- and random-effect parameters of the HLM
are depicted in table 6. All fixedeffect parameters were significant, as
with POS and NEG. The randomeffects portion of table 6 reveals that
the variance component estimate for
the covariance of positive and negative symptom slopes did not achieve
statistical significance. However, to
investigate the effect size, the correlation coefficient between positive and
negative symptom slopes was computed. The correlation coefficient (r =
0.59) was dose to those for POS and
NEG, indicating that the lack of statistical significance may be primarily
attributable to the smaller number of
subjects available for this analysis.
The Pearson correlation coefficient
between the SAPS and SANS raw difference scores at endpoint was 0.29
(n = 68,p< 0.017). The baseline SAPS
and SANS scores were 3.03 ±1.28
and 2.66 ± 0.94,respectively.The difference scores at the study endpoint
were 2.81 ± 4.57 and 1.00 ± 4.19 for
the SAPS and SANS, respectively.
Effect of covariates. To investigate whether a patient-specific balance of positive and negative symptoms (measured by the BPRS) at
baseline had an effect on our principal results, the ratio of positive and
negative symptoms in each individual patient at baseline was introduced as a covariate in our principal
HLM. The results remained essentially unchanged. With regard to our
main interest, the correlation between positive and negative symptom changes over time was 0.72 (p <
0.00001). The correlation between the
estimated initial values for positive
and negative symptoms was 0.47
(p <0.00001).
SCHIZOPHRENIA BULLETIN
586
Table 6. SAPS and SANS: Hierarchical linear model parameter
estimates and test statistics (n = 68)
Fixed effects
SAPS
Intercept
Slope
SANS
Intercept
Slope
Random effect
Parameter
estimate
SE
0.361
-1.108
0.177
0.163
2.03
-6.79
0.0428
0.0000
2.662
-0.253
0.113
0.133
23.47
-1.91
0.0000
0.0569
4.2
2.4
Variance
estimate
SAPS
Intercept
1.250
Slope
0.846
Intercept x slope -0.148
SANS
Intercept
0.456
Slope
0.296
Intercept x slope . 0.046
SAPS x SANS
Intercepts
0.201
Slopes
0.294
SAPS intercept
x SANS slope
0.142
SANS intercept
x SAPS slope
-0.009
Residual
•
0.441
SE
0.294
0.340
0.256
-0.5
0.0000
0.0128
0.5638
0.161
0.233
0.149
2.8
1.2
0.3
0.0046
0.2035
0.7559
0.150
0.184
1.3
1.6
0.1799
0.1097
0.120
0.7
0.4781
0.156
0.060
0.0
7.3
0.9552
0.0000
Note.—SAPS » Scale for the Assessment of Positive Symptoms (Andreasen 1984b), SANS = Scale
for the Assessment of Negative Symptoms (Andreasen 1984a); SE - standard error.
In addition to the positive-negative
symptom ratio, we introduced haloperidol blood level, age, age at onset,
depression (BPRS anxiety-depression
factor), and side effets (total score on
the Simpson-Angus Scale and akinesia rating) as covaiiates in our principal model. None of these variables
changed the main results.
Discussion
The principal rinding of the study is
that individual positive and negative
symptom-change trajectories were
strongly associated over time. This
finding was present for the BPRS positive and negative symptom constructs
as well as for the SAPS and SANS.
Furthermore, within the period we
examined, this result did not depend
on the time interval or on the baseline
balance of positive and negative
symptoms in individual patients.
The finding that negative symptoms improved during haloperidol
treatment contradicts the concept
proposed by Crow in his two-syndrome theory of schizophrenia (Crow
1980,1985). According to this concept, positive symptoms would be a
clinical manifestation of hyperdopaminergia, and therefore neu-
roleptic responsive; in contrast to
positive symptoms, negative symptoms would be due to a structural
brain deficit, and therefore would be
immutable to neuroleptic treatment.
Whereas some studies reported a
lack of improvement of negative
symptoms during treatment with
typical neuroleptics (Johnstone et al.
1978), the majority of the available
data did not support Crow's concept
with regard to neuroleptic responsivity (Cole et al. 1966; Meltzer et al.
1986; Breier et al. 1987; Kay and
Singh 1989). Our study indicated that
a highly significant change is present
in negative symptoms during haloperidol treatment (although the effect
was substantially less for negative
symptoms than for positive symptoms).
The fact that positive and negative
symptoms not only shared their neuroleptic responsivity, but were also
related in their response may be
linked to haloperidol's pharmacological action. Due to its dopamine D 2
receptor antagonist properties,
haloperidol is known to elicit a blockade of dopamine neurotransmission,
and its most prominent action is
thought to occur in the mesolimbic
and striatal structures (Wolkin et al.
1989; Deutch et al. 1991).
This pharmacological action of
haloperidol on the mesolimbic structures is held responsible for its beneficial effects on positive symptoms
(Stevens 1973; Creese et al. 1976; Snyder 1976; Crow 1980; Pettegrew and
Minshew 1992). However, a decrease
of dopamine transmission in the
mesolimbic and striatal regions could
produce, through the mesocortical
feedback loop (Le Moal and Simon
1991), a net increase in dopamine
activity in the frontal areas; this
would be associated with improvement of negative symptoms (Levin
1984; Andreasen et al. 1986;
587
VOL22.NO. 4, 1996
Andreasen 1989; Liddle et aJ. 1989).
We must note, however, that no temporal linkage between establishment
of the dopamine receptor blockade
after the start of treatment and development of symptom remission could
be demonstrated. Thus, further
knowledge of the neurophysiological
basis of the antipsychoa'c action is
clearly needed to understand the
relationship between positive and
negative symptoms.
To understand this relationship, we
should keep in mind that negative
symptoms may be produced by a
variety of distinct mechanisms (Carpenter et al. 1985,1988). They can be
primary, linked to the core pathophysiology of schizophrenia, or they
can be secondary to various pathogenic factors. Commonly implicated
factors, beyond environmental causes
such as chronic institutionalization,
include neuroleptic side effects,
depression, and positive symptoms.
Because these factors can change considerably over time during the initial
phase of pharmacological treatment,
it is possible that in our study the
association between positive and
negative symptoms was confounded
by changes in secondary negative
symptoms.
Typical high-potency antipsychotics such as haloperidol have been
shown to induce extrapyramidal side
effects (Meltzer and Stahl 1976), and
these effects (e.g., akinesia) can substantially overlap with negative
symptoms (Sommers 1985). Furthermore, negative symptoms can overlap not only with side effects, but
also with depressive symptoms (Kulhara and Chadda 1987; Siris et al.
1988; Kulhara et al. 1989). These
symptoms, manifested by a considerable proportion of schizophrenia
patients (Elk et al. 1986; Barnes et al.
1989), have been shown to undergo
intricate changes during treatment.
Whereas they remit in certain
patients, in approximately a third
(McGlashan and Carpenter 1976;
Siris et al. 1981) of the patients, they
worsen with the improvement of
psychosis (e.g., in postpsychotic
depression).
Thus, side effect- and depressionrelated changes of negative symptoms do not appear to be directly
associated with changes in positive
symptoms. Depending on various
factors such as neuroleptic dosage,
and an individual's propensity for
side effects and depression, they may
go in the direction opposite to the
therapeutic effects generally reported
for the positive symptoms. Accordingly, the above factors would not be
expected to account for the present
findings; in our analyses, no change
in the association between positive
and negative symptoms was found
after changes in side effects and
depression were covaried out.
We used no specific instrument to
separate primary and secondary negative symptoms; thus, we cannot
directly examine the possibility that
the association between positive and
negative symptoms was attributable
to a change in secondary negative
symptoms that "follow" positive
symptoms. However, given the fact
that this relationship is manifested in
certain specific areas of behavior
(e.g., social withdrawal as a result of
paranoia), we think it is unlikely that
it would account for the dose association between the overall ratings of
positive and negative symptoms we
found in our study.
It is possible that the association
we report may not extend beyond the
initial phase of pharmacological
treatment. In particular, whereas earlier pharmacological studies with a
duration similar to ours (van Kammen et al. 1987; Tandon et al. 1993)
reported a strong association of posi-
tive and negative symptoms over
time, in a recent study spanning a
period of 2 years Arndt et al. (1995)
found a weak association (r = 0.31)
between negative and psychotic
symptoms and no relationship
between negative and disorganized
symptoms. However, Arndt's sample
was small (n = 39), and the weekly
symptom ratings were established
retrospectively for a relatively long
period (6 months). Although this
method was shown to be reliable
(Arndt et al. 1995), it may not be sufficiently sensitive to follow weekly
fluctuations of positive and negative
symptoms.
Our study used a two-dimensional
model (positive-negative) rather
than a three-dimensional one (psychotic-disorganized-negative).
Could this be responsible for the relationship we found? To explore this
question, we separated conceptual
disorganization, a core symptom of
the disorganized dimension, from the
BPRS positive symptoms duster and
re-ran the analyses using both measures: the conceptual disorganization
and the remaining positive (psychosis) symptom construct. We found
that both measures were related to
negative symptoms, and the extent of
this relationship was similar to what
we found for the single, broader cluster of positive symptoms. Therefore,
it does not appear likely that using
the two-dimensional model instead
of a three-dimensional one explains
our findings.
In conclusion, this study suggests
that changes in positive and negative
symptoms over time may be related
during the initial phase of the pharmacological treatment; however,
because we did not have spedfic
measures for primary and secondary
negative symptoms, an expanded
replication of our results using such
measures appears necessary. More-
SCHIZOPHRENIA BULLETIN
588
over, studies are needed to determine
whether our results would generalize
to atypical neuroleptics, to longer
periods of time, and to different populations.
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Acknowledgments
This work was supported by USPHS
grants MH-41772 and MH-12959
from the National Institute of Mental
Health. The authors are grateful to
Jean-Pierre Lindenmayer, M.D., for
his critical review of the manuscript.
The Authors
Pal Czobor, Ph.D., is Research Scientist, Nathan S. Kline Institute for Psychiatric Research, Orangeburg, NY,
and Assistant Professor, Department
of Psychiatry, New York University
School of Medicine, New York, NY;
Jan Volavka, M.D., Ph.D., is Chief,
Clinical Research Division, Nathan S.
Kline Institute for Psychiatric Research, Orangeburg, NY, and Professor of Psychiatry, Department of Psychiatry, New York University School
of Medicine, New York, NY.