Cheng-Hsin General Hospital
Study Designs and Applied
Statistics Methods in the
Clinical Research
振興醫院教學研究部
董道興 副研究員
2008.2.26
Cheng-Hsin General Hospital
Part I: Basic Study Designs
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研究設計的重要性
• 好的研究設計較能得到可信的研究結果
• 好的研究設計較能提供正確的因果關係
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研究設計之分類
• 觀察性研究
1.個案研究與描述性研究(Case-series or Descriptive)
2.橫斷研究(Cross-sectional studies)
3.世代研究(Cohort or Prospective studies)
4.病例對照研究(Case-control or Retrospective studies)
5.歷史性世代研究 (Historical cohort studies)
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• 實驗性研究(Experimental studies)
1. 控制性試驗(Controlled trials)
A. Parallel or concurrent controls
a. Randomized
b. Not randomized
B. Sequential controls
a. Self-controlled
b. Crossover
C. External controls (including historical)
2.無控制組
• 統合性研究(Meta-analysis)
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觀察性研究
• 個案研究
1. 針對某些特定病徵的病人進行描述
2. 無對照組(control group)
3. 無研究假設 (research hypotheses)
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• 橫斷研究(Cross-sectional studies)
有病
研究樣本
沒病
Time
研究時間
No direction of inquiry
Question: What is happening?
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橫斷研究之特點
• 目的
1.描述健康狀態
2.分析因子與事件之相關
• 研究設計
同時間收集因子與疾病等變項
• 資料收集方法
1.病歷紀錄
2.觀察
3.面訪及電訪
4. 問卷填寫
• 優點
1. 簡單易行 2. 資料收集範圍廣闊 3. 較具經濟性
4. 可短時間內得到結果 5. 較易獲得大量樣本
• 缺點
1.無法判定因果關係(causal relationship)
2.推論易流於表面
1991-1993
A community-based screening
regime for type 2 diabetes
(n=12,489)
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The Study Procedure
Screening
for type 2
diabetes
(1991-1993)
WHO (1999) criteria
1994-1998
Type 2 diabetics
(n=1123)
DR screening for Type 2
diabetics in 1999-2002
(n=971)
Screening
for diabetic
retinopathy
(1999-2002)
725 Type 2 diabetics
received DR screening
Economic
evaluation
of diabetic
retinopathy
screening
(2003)
Sight year
measurement
Utility
survey
Economic evaluation
1. Cost-effectiveness analysis
2. Cost-utility analysis
3 .Cost-benefit analysis
Migrated out or death
(n=152)
Loss to follow-up
(n=246)
Epidemiologic information
1.Prevalence of DR
2.Incidence of DR
3.Risk factors of DR
4.Natural history of DR
Willingness-to-pay
survey
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• 世代研究(Cohort study)
暴露組
(exposed)
有病
研究世代
沒病
Direction of inquiry
有病
Question: What will happen?
非暴露組
(unexposed)
研究起始點
沒病
Time
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世代研究之特點
• 目的
探討暴露組和非暴露組之間發生率(incidence)是否相同
• 研究設計
1.觀察世代可選擇一組或兩組
2.長期觀察研究世代之得病情形
• 優點
1.可計算發生率
2.可了解因果關係
3.可以調查稀有暴露世代的得病情形
4.可減少選樣性偏差的發生
5.可探討多重疾病(multiple diseases)
• 缺點
1.觀察時間過長 2.費用較高
3.失去追蹤導致結果產生偏差(bias)
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• 主要測量指標
有暴露
沒暴露
有病
沒病
a
c
b
d
a+c
b+d
a+b
c+d
a+b+c+d
相對危險性(relative risk, RR)
[a/(a+b)]/ [c/(c+d)]
相差危險性(attributable risk, AR)
[a/(a+b)]-[c/(c+d)]
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• 成果評值(Outcome assessment)
1.功能狀態(Functional status)
2.生活品質(Quality of life)
3.滿意度(Patient satisfaction)
4.存活時間(survival time)
5.經濟評估(Economic evaluation)
A.Cost-effectiveness analysis –CEA
B.Cost-utility analysis –CUA
C.Cost-benefit analysis –CBA
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• 病例對照研究(Case-control studies)
暴露(exposed)
病例組(cases)
未暴露(unexposed)
暴露(exposed)
對照組(controls)
未暴露(unexposed)
Time
研究起始時間
Direction of inquiry
Question: What happened?
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病例對照研究法之特徵
• 目的
探討病例組與對照組其暴露分布是否相同
• 研究設計
選擇一組有病者為病例組, 無病者為對照組, 比較病例組與
對照組間過去的暴露經驗
• 病例組與對照組之選擇
1.定義清楚所選擇的病例組個案
2.選擇具有代表性的對照組個案
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• 優點
1.迅速便宜
2.所須樣本數較小
3.適合於稀有疾病之研究
4.可快速得到結果
5.可以探討多重暴露對單一疾病的相關
• 缺點
1.不易得到過去完整的暴露經驗
2.不易選取合適的對照組
3.僅能用估計的方式得到發生率
4.時序性不易確定
5.可能產生選樣性偏差(selection bias)或
回憶性偏差(recall bias)
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病例組
對照組
暴露
未暴露
a
c
b
d
a+c
b+d
a+b
c+d
1.藉著比較case及control組的exposed與unexposed的比值
(ratio),即比較a/b和c/d就可知exposure和disease是否有關
2.若case組暴露率較control組高 (a/b>c/d),表示暴露和發病
有正相關
3.case 組的exposure odds=a/b
control 組的exposure odds=c/d
則odds ratio(OR)=(a/b)/(c/d)=ad/bc
如果OR值越高,表示暴露與發病越有正相關.
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• 歷史性世代研究(Historical cohort studies)
暴露組
(exposed)
研究世代
有病
沒病
有病
Direction of inquiry
對照組
(unexposed)
沒病
Time
研究起始時間
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種類
觀察性研究設計之比較
過去
橫斷研究
病例對照研究
現在
未來
Subjects selected,
data gathered on
exposure status and
outcome
Information
obtained on
past exposure
世代研究
Case chosen
with outcome
Controls chosen
without outcome
Subjects selected
and classified as
to exposure
歷史性世代研究
Subjects
classified as to
exposure from
existing records
Subjects identified
who were exposed
in the past
Outcome measured
outcome
measured
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實驗性研究
(Experimental studies)
• Experimental studies that involve humans are called
clinical trials
• Controlled trials are studies in which the experimental
drug or procedure is compared with another drug or
procedure, sometimes a placebo and sometimes the
previously accept treatment
• Uncontrolled trials are studies in which the
investigators’ experience with the experimental drug
or procedure is described, but the treatment is not
compared with another treatment
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• 有對照組的實驗研究
實驗組
有結果
研究樣本
無結果
對照組
有結果
無結果
Time
研究起始時間
介入(intervention)
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• 實驗組與對照組
1.接受實驗步驟的對象 所組成的團體稱為實驗組,未接受實
驗步驟者則稱為對照組
2.實驗組的介入因素包括:治療性、預防性、干預性、社區
性試驗對照組則使用安慰劑(placebo)
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• 隨機分派(Random Allocation)
1.研究對象簽了同意書之後,根據random allocation來分組,
通常採用隨機號碼表
2.Randomization=Random Allocation
指每一個研究對象被分到任一組的機率是一樣的
3.以隨機分派的方法來分配實驗組與對照組可以提高兩組
的可比較性(comparability),而避免自我選擇(selfselection)的誤差, 以建立資料分析結果的效度(validity)
4.隨機分派方式:
(1)簡單隨機分派(simple randomization)
(2)分區隨機分派(Block randomization)
(3)分層隨機分派(Stratified randomization)
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• 盲目程序( Blinding procedure)
1.在評估實驗結果時,必須考慮研究者與參與者的bias,為了避免bias,
最好的方法就是double blind
2.單盲程序(single blind):實驗者知道,被實驗者不知道被分到哪一組
雙盲程序(double blind):實驗者和被實驗者都不知道會分到哪一組
三盲程序(triple blind):實驗者、被實驗者和資料分析者都不知道
3.雙盲或三盲程序有時不易做到:
(1)副作用(side effect)
(2)有些trial是home VS hospital
(3)醫師必須知道patient是屬於哪一組,便於照顧病人的情況
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• Randomized clinical trials
Provided the strongest evidence for concluding causation
• Nonrandomized trials
Do nothing to prevent bias in patient assignment
• Trials with self-controls
1.Only one group in which patients are assessed before
and after the intervention
2.Hawthorne effect
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• 交叉試驗(Crossover study)
with outcome
experimental
subjects
experimental
subjects
subjects
meeting
entry criteria
with outcome
without outcome
without outcome
with outcome
with outcome
controls
controls
without outcome
without outcome
Time
onset of study intervention
washout period intervention
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• Trials with external controls
subjects
with outcome
without outcome
with outcome
Results from
previous study
without outcome
Time
onset of study intervention in subjects only
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類實驗研究 (Quasi-experimental study)
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統合性研究(Meta-analysis)
• Meta-analysis uses published information from other
studies and combines the results so as to permit an
overall conclusion
• Meta-analysis is similar to review articles, but additionally
includes a quantitative assessment and summary of the
findings
• Meta-analysis is especially appropriate when the studies
that have been reported have small numbers of subjects
or come to different conclusions
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實驗設計評估
• How to evaluating the study design the medical
literatures?
1.In a clinical trial
(1) How are subjects recruited?
(2) Are subjects randomly assigned to the study group?
(3) Is there a control group?
(4) Are appropriate therapies included?
(5) Is the study blind? Double blind?
(6) How is compliance evaluated?
(7) If some cases are censored, is a survival method
such as Kaplan-Meier or the Cox model used?
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2. In a cohort study
(1) How are subjects recruited?
(2) Are subjects randomly selected from an eligible pool?
(3) How rigorously are subjects followed? How many
dropouts does the study have and who are they?
(4) If some cases are censored, is a survival method such
as Kaplan-Meier or the Cox model used?
3. In a case-control study
(1) Are subjects randomly selected from an eligible pool?
(2) Is the control group a good one?
(3) Are records reviewed independently by more than one
person?
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4. In a cross-sectional study
(1) Are the questions unbiased?
(2) Are subjects randomly selected from an eligible?
(3) What is the response rate?
5. In a meta-analysis
(1) How is the literature search conducted?
(2) Are the criteria for inclusion and exclusion of studies
clearly stated?
(3) Is an effort made to reduce publication bias?
(4) Is there information on how many studies are need to
change the conclusion?
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Part II: Basic Statistics Methods
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Types of clinical data analysis
• Time-unrelated analysis
• Time-related analysis
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Time-unrelated analysis
• Categorical scale
1. Proportion Z test & 2 test
2. Logistic regression
• Interval scale
1. Independent T-test (Two-sample)
2. ANOVA
3. Linear Regression
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Time-related analysis
• Balanced repeated measurement
1.Categorical scale
(1) Generalized Estimation Equation (GEE)
(2) Mixed model
2.Interval scale
(1) Pair T-test
(2) Mixed model
• Survival analysis
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Survival analysis
• Non-parametric method
Kaplan-Meier (Life-Table) method
• Semi-parametric method
 Cox regression model
• Parametric method
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Two-Sample Problems
• Continuous variable
1.Independent T-test
2.Paired T- test
• Binary variable
1.Proportion Z Test or Chi-square test
2.McNemar’s Test
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Independent T-test & Paired T-test
Example 1 : Evaluation of effectiveness of
community rehabilitation care (n=30)
Pre
83
95
75
Post
77
80
70
62
59
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Statistical evaluation
Mean
Pre
78.20
(SD=14.31)
Post
70.26
(SD= 18.04)
Paired T
T=-3.09
P=0.0079
Independent T
T=-1.33
P=0.19
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Correlation & Repeated
measurement
Paired T test enhance statistical
efficiency by considering ...... the
correlation between Pre- and Post(the same individual)
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2 test & Proportion Z test
Example 2 :
• The study on the assessment of mental
health program
• Intervention: Experimental & Control
• Outcome : Favorable (F) & Unfavorable (U)
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Two Data Sets
1. Data 1 :
Test
Control
F
40
16
U
20
48
F
10
2
U
2
4
2. Data 2 :
Test
Control
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Statistical evaluation
• Data 1
2 =(O-E)2/E
 2(1) =21.79 P=0.001
Proportion test: Diff=P1-P2
=0.667-0.25=0.417
(95%CI: 0.26-0.58)
• The intervention group has a more significant
favorable response than the control group
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Fisher Exact Test
• Data 2:
2(1) =4.50 P=0.034 (right??)
Fisher’s Exact Test :
One-Tail P=0.057
Two-Tail P=0.107
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Correlated Data
(McNemar’s Test)
Example 3 :
A match study on the efficacy of attending
CRC screening with respect to health
promotion (matched by sex and age)
Outcome: whether to attend CRC screening
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General
2

test
Health Promotion
+
-
+
43
33
-
16
26
Attend
2(1)=3.70 P=0.055 (right??)
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McNemar’s test
Health Promotion
Yes
+
+
27
16 43
No
-
6
10
16
33
26
59
2(1) =4.54 P=0.033
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Measuring agreement between
two people
Example 4:
Two reviewers on mammography
Second
Yes
Yes
20
No
5
No
3
200
First
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Kappa statistics
Kappa =(Observed-Expected agreement)
/(1-expected agreement)
=(O-E)/(1-E)
Kappa=0.70 (95% CI: 0.50-0.89)
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How to interpret kappa?
Byrt (1996)
0.93-1.00 
0.81-0.92 
0.61-0.80 
0.41-0.60 
0.21-0.40 
0.01-0.20 
0.00

Excellent agreement
Very good agreement
Good agreement
Fair agreement
Slight agreement
Poor agreement
No agreement
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Mutivariate analysis
• Continuous variable
1. Linear regression
2. Mixed model (repeated measurement)
• Binary variable
1. Logistic regression
2. GEE model (repeated measurement)
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Linear regression
Example 5 :
Is the effect of diet and exercise program
on the SBP effective?
Independent variable: diet, exercise
Outcome: SBP
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Multiple Linear Regression
Y= a+b1*diet+b2*exer+b3*diet*exer +
(Interaction term)
b’s
SE
T
P-value
diet:
1.03
2.22
0.47
0.64
exer:
0.78
0.13
6.21
0.0001
diet*exer :
0.30
0.20
1.54
0.13
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Logistic regression
Example 6:
A study was performed on 53 patients
with prostate cancer to collect data on
several variables considered predictive of
nodal involvement
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Definitions of predictors
• Age
• Acid: level of serum acid phosphate in
King-Armstrong units (0.46-1.26)
• X-ray examination: (0=negative 1=positive)
• Grade: Pathological grade
(0=less serious 1=more serious)
• Size: size of tumor (0=small, 1=large)
• Nodalinv: Laprectomy results
(0=No involvement;1=Involvement)
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Model selection
X-ray, Size and Acid are
three significant factors for
predicting nodal involvement
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Model Prediction
expβx 
P
1  expβ x 
Odds Ratio (OR)=exp()
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Probability prediction of
nodal involvement
(risk classification)
• High-risk
Acid= 1.26 , X-ray with positive finding,
and large tumor = 0.96
• Low-risk
Acid=0.48 , X-ray with negative finding,
and small tumor=0.05
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Generalized Estimation Equation
(GEE method)
• Indications: The regression model for
correlated binary outcome
• Example 7
A clinical trial comparing two treatments
for a respiratory disease
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Definitions of variables
• Outcomes: Respiratory status
(0=poor 1=good)four visits
• The main variable: Treatment
(1=Active 0=placebo)
• Other explanatory variables:
center, age, sex, and baseline
respiratory status
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Characteristics of GEE model
Working correlation matrix
to accommodate correlation
between outcomes
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Survival Analysis
• Outcome variable: survival time
• Starting pointsendpoints : Survival time
• Censoring problem :
i.e Right censoring: study ends or lost to
follow-up
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Kaplan-Meier method
Example 8:
Prognosis for women with breast cancer:
A histochemical marker called Helix
pomatia agglutinin (HPA) is used to
assess whether tumor have already been
metastasized
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Definition of Variables
32 Patients
• Survival time: from treatment to death
• Censoring: 1=dead 0=censoring
• Prognostic factor: HPA (1=positive
0=negative)
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Cox regression model
• Proportional hazard model:
One of common multiple regression
models for survival analysis.
• No baseline hazard estimation
• h(t,x1,x2,…,xn)=h0(t)exp(b1x1+b2x2+…+bnxn)
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Importance of the Cox Model
• Provided the only valid method of predicting a
time-dependent outcome
• Producing survival curves that are adjusted
for confounding factors.
• The Cox model can be extended to the case
of multiple events for a subject
• Cox model could estimate RRexp()
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Parametric regression models
Accelerated failure time
(AFT) model
• To accommodates left censoring and interval
censoring
• Distribution
1.exponential distribution
2.weibull distribution
3.log-normal distribution
4.log-logistic distribution
5.Gamma distribution
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Other methods for multiple variables
• Discriminant analysis
It assumes that the independent variables follow a
multivariate normal distribution, so it must be used with
caution if some X variables are nominal
• Log-Linear analysis
All the variables, both independent and dependent, are
measured on a nominal scale
• Factor analysis
All variables are considered to be independent variables
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• Cluster analysis
The object is to determine a classification or taxonomic
scheme that accounts for variance among the subjects
• Multivariate analysis of variance (MANOVA)
Involved multiple dependent variables as well as multiple
independent variables
• Canonical correlation analysis
When both the independent variables and the outcomes
are numerical and the research question focuses on the
relationship between the set of independent & dependent
variables
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Summary of conceptual framework for
questions involving two variables
Independent
variable
Dependent
variable
nominal
nominal
binary
numerical
nominal
(more than two values)
numerical
Method
chi-square
t-test
numerical
one-way ANOVA
numerical
regression; correlation
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Summary of conceptual framework for questions
involving two or more independent variables
Independent
variable
Dependent
variable
nominal
nominal
nominal and numerical
nominal and numerical
dichotomous
nominal
(two or more values)
nominal
numerical
numerical
numerical
nominal and numerical
nominal with confounding
numerical only
censored
Method
Log-linear
Logistic regression
Discriminant analysis
ANOVA
Multiple regression
Cox regression
numerical
ANCOVA
nominal
Mantel-Haenszel
--Factor analysis & Cluster analysis
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Thank you for your attention!