Transcript PowerPoint 프레젠테이션 - 고려대학교 구로병원

임상시험에서 통계의 역할에 대한 개괄적 이해
고려대학교 의료원 구로병원
IRB 위원 및 임상시험 연구자 교육
2011/3/10
고려대학교 의과대학
의학통계학교실 이준영
BIOSTATISTICS
Contents
1. Clinical Trial – Science or Ethics?
2. Clinical Questions & Outcomes
3. Statistical Principles
−
Controls / Randomization / Blinding
4. Types of Clinical Trials
5. Statistical Analyses
6. Data Set
7. Data Management
8. Sample size Calculation
9. Roles of Biostatistics
10. Conclusion
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
Biased comparison의 예
Meta-analysis
OR=1.36
RR=0.95
OR=1.17 (0.98-1.39) 1.84 (1.52-2.25)
Most likely explanation: biases in the recall of
dietary items due to study design
Boyd, et al. Br J Cancer, 1993;68:627-636
Most likely explanation: Differential
recall of past exposure
Nelemans, et al. J Clin Epidemiol, 1995;48:1331-1342
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
Biased comparison의 예
Meta-analysis
Exposure level:
M~H
High
L~H
SMR=33(22-47) for Anatomists, Pathologist. Protective!!
Most likely explanation: lower prevalence of smoking among this group
Blair, et al. Scand J work Environ Health, 1990;16:381-393
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Clinical Trial – Scientific is ethical
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Clinical research and clinical trials
 Clinical research
• A clinical study to evaluate clinical usefulness
– Need both pathophysiologic and pharmacological insight
– To verify a theory or hypothesis about efficacy of a medical
intervention
• Randomized Clinical Trial has become the paradigm
– A means for measuring expected therapeutic-related benefits
• Objective and scientific data의 생성
– From human subjects who receives an intervention of interest
• Objectivity
– Reproducibility of the results (재현성)
– Repeatability of the results (반복성)
– Transparency of the process (투명성)
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Institutional Review Boards(IRB)
 Required for each research institution
 Must review each new protocol for
• Merit and ethics
• Informed consent / document
 May provide limited scientific review
• Design
• Population studied
• Adequacy of sample size
 Must review protocol progress annually
 Responsible for monitoring patient safety
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Clinical Questions & Outcomes
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Clinical outcome
 Outcome
• A (untoward) clinical event, over a fixed period of time
• Occurrence would be verified for each patient
• Characteristics
1.
2.
3.
4.
5.
Well defined & stable
Ascertained in all subjects
Unbiased (objective)
Reproducible
Specificity to question
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Primary vs. Secondary Question
 Primary
• Most important, central question
• Ideally, only one
• Stated in advance
• Basis for design and sample size
 Secondary
• Related to primary
• Stated in advance
• Limited in number
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Statistical Principles
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Objectivity of clinical trial
 Accuracy의 보장
• Lack of systematic error
• Avoid bias
• Not by proper analysis but by proper designing
• 예: Controls, blinding, random process
 Precision의 보장
• Lack of random error
• Based on the size of the treatment groups
• By an appropriate statistical model and analysis
 Transparency의 보장
• The integrity of clinical trial data
• By a qualified data management (DM)
 Participants’ right and well-being의 보호
• Ethics
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BIOSTATISTICS
Systematic errors (Bias)
 Systematic error에 영향을 미치는 요인들
• Extraneous factors (외생요인)
– Attention of the physician
– Psychology of patient
– Adjustment life style
– Co-medication
• Prognostic factors (예후요인)
–
Risk profile of the patients
• Information on outcome (결과에 관한 사전 정보)
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Three Comparability Criteria
 Treatment comparison이 관심 therapeutic
effect에 관한 실제 차이를 반영하고 있는가?
• Comparability of extraneous effects
 Group들 간 prognosis는 비교 가능한가?
• Comparability of prognosis
 Group들 간 outcome은 동일한 선 상에서 관찰되
고 있는가?
• Comparability of information
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Strategy for obtaining comparability
 Control group (Placebo)
• Masking of treatment for patient / physician
• Comparability of extraneous factors
 Randomization
• Random assignment of treatments
• Comparability of prognostic factors
 Blinding of the observer
• Comparability of information on outcome
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A. Controls
- Comparability of extraneous factors -
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Types of Controls
 External
• Historical
• Concurrent, not randomized
 Internal (concurrent, randomized)
•
•
•
•
No treatment
Placebo
Dose-response
Active control (positive control)
 Multiple
• Both an Active and Placebo
• Multiple doses of test drug and of an active control
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1. Historical control
 Treatment outcome을 previous series of comparable
subjects와 비교
 Non-randomized, non-concurrent
 Rapid, inexpensive, good for initial testing of new Tx
 Two sources of historical control data
• Literature (subject to publication bias) / database and/or registry
 Problems
•
•
•
•
Bias에 취약
New treatment의 효과를 과장하는 경향
Literature controls은 특히 poor
동일한 기관 내 동일한 과거 trial의 historical controls 역시
problematic
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2. Concurrent control
 Not randomized
 Patients compared, treated by different
strategies, same period
 Advantage
• Eliminate time trend
• Data of comparable quality
 Disadvantage
• Selection Bias
• Treatment groups not comparable
 Covariance analysis not adequate
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3. Placebo control
 The “placebo effect” is well documented
 Could be
• Placebo alone (no other treatment)
• Standard care + placebo
 Matched placebos are of necessary
• Pts & researchers should not decode Tx assignment
 Randomized (concurrent) control
• Considered as a Gold Standard
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B. Randomization
- Comparability of prognosis factors -
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Comparability of prognosis
 Randomization
• Purpose
− 동일한 특성을 지닌, 비교 가능한, 환자 군들을 생성
− Clinical evaluation에 관한 valid statistical tests를 보장
− Known and unknown risk factor들에 관한 비교성 확보
• Rationale
− Treatment 처치 전에 prognostic factor들을 comparable하게
함
− 최상의 therapy를 찾을 수 있는 최선의 방법
− Current and future pts들이 harmful Tx를 받을 위험 최소화
• 집단들 간 비교가 연구 개시 시점부터 동일 선상에서 비
교될 수 있도록 보장하는 유일한 수단
– 따라서 selection bias 및 confounding bias 제거
• Allow RCT to play a key role in advancing medical
science
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Randomization
BIOSTATISTICS

The success of randomization (무작위화)
•
Two inter-related processes
1.
Random allocation (무작위 배정)
−
A random process로 allocation sequence 생성
−
Allocation sequence의 unpredictability를 보장
Allocation concealment (무작위배정 은폐)
2.
−
Trial에 involve된 사람들로 하여금 upcoming assignments를
파악하는 것을 방지
−
이에 대한 protection 없이는 investigator와 patient들은 누구
에게 다음 번 assignment가 이루어질 지 알게 될 것이고, 이
는 곧 group 간 동등한 comparison이 이루어질 수 없게 할 것
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Randomization
BIOSTATISTICS
 부적절한 무작위배정 은폐 예
• After creating an adequate allocation sequence using
a random number table, affix the list to a bulletin board
with no allocation concealment
− Admitted patients could ascertain the upcoming treatment
allocations
− Route them with better prognoses to the intervention group
and with poorer prognoses to the control group, or vice versa
• Randomization list is posted on the web.
• 해결
• Use an opaque sealed envelope
• Use IVRS / IWRS randomization
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BIOSTATISTICS
Ethics of Randomization (1)
 Biostatistician/clinical trialist must sell
benefits of randomization
 Physicians should do what he/she thinks is
best for his patient
• Two MD's might ethically treat same patient quite
differently
 Chalmers & Shaw (1970)
• Annals New York Academy of Science
1.
2.
3.
If MD "knows" best treatment, should not participate in trial
If in doubt, randomization gives each patient equal chance to
receive one of therapies (i.e. best)
More ethical way of practicing medicine
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BIOSTATISTICS
Ethics of Randomization (2)
 Byar et al. (1976) NEJM
1.
RCT: honest admission that best is not known!
2.
RCT is the best method to find out!
3.
It reduces risk of being on inferior treatment
4.
It reduces risk for future patients
 Classic Example
• Ref. Silverman (1977) Scientific Amer.
1. High dose oxygen to premature infants: common practice
↓
2. Suspicion about frequency of blindness
↓
3. RCT showed high dose cause of blindness
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BIOSTATISTICS
Types of randomization
 Fixed allocation randomization
• Simple randomization
• Block randomization
• Stratified randomization
 Adaptive randomization
• Minimization method
• Covariate adaptive randomization
– Baseline adaptive randomization
– Response adaptive randomization
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BIOSTATISTICS
Block Randomization (Example)
Method (예)
 Block size 4, Two treatments A, B
 4C2 = 6 possible blocks
 {AABB, ABAB, BAAB, BABA, BBAA, ABBA}
(1)
(2)
(3)
(4)
(5)
(6)
 Generate random numbers: 2 4 0 5 8 3…
 Treatment sequences: ABAB BABA BBAA ...
 Patient allocation: 1 2 3 4 5 6 7 8 9 10 11 12 …
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Block Randomization
 Concern : block size must be concealed
• If not, the sequence become somewhat predictable
(ex. block size = 4)
ABAB BAB?
Must be A
AA_ _
Must be B B
• This could lead to selection bias
 Simple solution to selection bias
• Do not reveal blocking mechanism
• Use random block sizes
 If double blind, no selection bias
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Stratified Block Randomization
 Ex) Center = 3, block size = 4, allocation ratio=1:1
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Implementation - Timing
BIOSTATISTICS
 Actual randomization
 Should be delayed until just prior to therapy initiation
 Example
• Alprenolol Trial, Ahlmark, et al. (1976)
–
–
–
–
Nonblinded trial for AMI
393 patients randomized at the time of admission
Alprenolol treatment was not initiated until 2 weeks later
231 excluded due to MI not documented, dead,
contraindications to therapy, etc.
– Only 162 patients treated, 69 alprenolol & 93 placebo
– Imbalance raises concerns about comparability and/or
possible bias for exclusion
 Delaying randomization until initiation
 Pt’s withdrawal로 인한 문제를 partly 해결해 줌
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Block
vs.
Stratified
randomization
BIOSTATISTICS
 Block randomization
• Treatment group들 간에 할당되는 sample size 및 patient
characteristics over time의 balance를 맞추기 위한 것
•
•
– 전체 표본수가 아주 크지 않은 경우 군 간 표본 수의 불균형을 방지
– 표본 수가 큰 경우라도 시간에 따라 환자들의 특성이 변하는 time trend
가 있다면 군 간 characteristic의 balance가 깨질 수 있으므로 이를 방지
이를 사용하지 않음으로 인해 발생되는 bias는 추후에도 통계적 보정 불가능
따라서 대부분의 임상시험에서는 block randomization을 기본적으로 사용
 Stratified randomization
•
Pt들의 baseline covariates의 차이를 보정하기 위한 것
– Pt's baseline prognostic 또는 risk factor들의 차이로 인해 발생하게 될
outcome의 variation을 줄이기 위한 것


Site별 pt들의 특성 차이를 보정하기 위해 stratified by center 실시
다른 prognostic / risk factor가 있으면(예: stage) 이에 대해서도 stratify
• 너무 많은 strata 조합을 사용하게 되면 층 내에서의 randomization이
불가능해지기 때문에 stratification factor 결정 시 trade-off 감안해야
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BIOSTATISTICS
C. Blinding
- Comparability of outcome information -
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Comparability of information
 군 간 비교 시 risk of personal bias (information
bias, detection bias)의 예방 필요
• Biased patient reporting
• Biased ascertainment of information by physician
• Biased assessment of information by physician, data
manager, biostatistician
 Solution: BLINDING (Masking)
 Blinding의 목적: dual
• To avoid bias during data collection and assessment
− Comparability of information을 보장
• To implement placebo treatment
− Comparability of extraneous effects를 보장
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Types of blinding
BIOSTATISTICS
 Open label trials
• Both patient and investigator know Tx assignment
• 이를 해결하기 위한 design issue
•
PROBE design의 사용, OAC 운용
 Single blinding
• When the investigator knows but the patient does not
• Subjects masking
 Double blinding
• Neither patient nor investigator (HC provider) knows
• Treatment team (include evaluator) masking
 Triple blinding
• Include project clinician, the biostatistician, the CRA, the
programmer, and the data coordinator
• Evaluation team masking
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BIOSTATISTICS
Types of Clinical Trials
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Types of clinical trials
 Prevention trial vs. Therapeutic trial
• Preventing dz/recurrence vs. Treating dz
• Differ in… complexity / recruitment strategies / compliance / length of
follow-up / trial size, etc.
 Confirmative trial vs. Pragmatic trial
 Randomized trial vs. Non-randomized trial
 Single center trial vs. Multi-center trial
 Bioequivalence trial, Phase I, II, III trials
 Superiority vs. Non-inferiority vs. Equivalence trial
 Adaptive trials
 Lots of other definitions and variants
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Statistical Analysis
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Analysis population
 Two major principles
 ITT principle
 PP principle
Randomized
Observed
Followed
N
N´
event
X
Event rate:
X
X
or
N
N'
Fu loss: missing
N
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통계 분석
 Commonly used approaches
• Intention-to-treat analysis (ITT analysis)
− Full Analysis Set (FAS)
− Modified ITT
• As treated (On-treatment) analysis (AT/OT analysis)
• Per-protocol analysis (PP analysis)
 Missing data 처리
• LOCF approach (NOT GOOD)
• Imputation (MI)
• Use of a statistical method (ex.: “MMRM analysis”)
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Dataset
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Raw
data
(Source
data)
BIOSTATISTICS
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Data Dictionary
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Data Management
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Data Management 업무 관련 항목들
Sample
Process
Sub process
Description of each process
 DM업무의 각 절차설명 및 담당자 명시. 사용되는 SOP와 관련 양식 지정
1. Data management plan
(DMP) 개발
 Database의 간략한 구조 설명
 기타 DM 업무과정에서 특별히 고려되어야 할 사항 명시
2. DB structure 개발
 Annotated CRF 개발
 Data entry를 위한 DB구조 개발 및 변수 설정
 Data dictionary 개발
3. Data validation
specification (DVS) 개발
 DVS개발
 자료입력 후 CRF 상의 오류를 검출하기 위한 query 정의
 SECD개발
 SEC(self-evident correction) 정의
 Dummy data생성
 Dummy data 생성. DVS와 SECD에 대한 프로그램의 무결성 검증
 DVS programming / test
4. Data entry screen (DES)
 DES개발
 자료입력을 위한 data entry screen개발
개발
 Dummy data생성
 Dummy data 이용, DB와 DES에 문제가 없는 지 검정
 DES test
 Entry person에게 자료입력을 위한 지침서 개발
 Data entry instruction
5. Data entry
6. File comparison
(unmatched check)
 Data entry
 DMP에서 정의한 입력방법에 따라 자료 입력 – single/double
 Double entry 인 경우 Unmatched data 출력하여 담당자 검토 및 수정
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Data Management process 항목들
Sample
Process
Sub process
Description of each process
7. Data validation
 Run edit check
 DCF generation
 DB correction
 Edit check을 수행하여 query생성 후 연구자에게 송부
8. Medical coding
 Coding for
AE/Medication
 Review of coded data
by reviewer
 DMP에 정의한 dictionary(AE; MedDRA, Medication; KIMS) 이용, medical
coding
 Coding 완료된 자료: medical reviewer(MD/Pharmacist)에 검토요청 후 수
정사항 반영
9. DB quality check
 DB QC 수행
 DMP에 정의한 변수에 대해 일정 비율의 피험자 자료 출력, CRF와 비교
 DBQC report작성
 오류사항과 오류율을 정리한 DBQC report 작성
(edit check)
 DBQC결과 오류율 미만일 경우 DB lock
10. DB lock
11. Blinded meeting
 수정된 query를 DB에 반영
 Violation list 작성
 Protocol 정의된 violation/deviation 사항에 해당되는 피험자 listing
 Blinded meeting 을 통해 피험자별 분석 집단 결정
12. Data transfer
13. Closing
 DB transformation /test
 DMP에 정의된 절차 사용, 통계분석에 적합한 형태로 구조변경, 무결성 검증
 DB transfer
 통계분석 담당자에게 보안을 유지하며 data 전달
 DM master file작성
 DM과정 시 생성된 모든 문서를 정리/보관
 DB backup / archive
 DM과정 동안 주기적인 자료 백업 및 업무 종료 후 DB archive
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Data Management Plan (DMP)
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Statistical analysis process 항목들
Sample
Process
Sub process
Description of each process
1. Statistical analysis
plan (SAP)개발
 SAP 개발
 DB lock 이전에 SAP를 임상시험계획서를 바탕으로 상세히 작성
 Dummy table개발
 최종결과보고서에 사용될 dummy table과 figure를 SAP에 작성
2. SAS programming
 분석 프로그램 / 매크로
작성
 SAP 따라 분석프로그램을 작성
3. Statistical analysis
report 작성
 가급적 Macro를 이용하여 프로그램 작성
 생성된 table과 figure를 이용하여 통계분석보고서 작성
4. Listing raw data
 Data listing
 최종적으로 분석에 사용된 모든 자료를 listing
5. Closing
 Stat master file 작성
 통계분석 과정 시 생성된 모든 문서를 정리/보관
Data backup / archive
 분석 도중 주기적인 자료 백업, 업무 종료 후 data/program archiving




 임상시험계획서상의 표본수 계산 과정, 시험 설계 및 통계방법론 기술
6. 기타
표본수 산출
통계방법기술
무작위 배정목록 제공
눈가림 봉투 제작
 비교 임상시험을 위한 무작위 배정 목록 생성
 눈가림 임상시험을 위한 눈가림 봉투 제작
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Statistical Analysis Plan (SAP)
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Sample Size
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

임상시험 시작 시 적정 피험자 수 산출
적절하게 계획되고 수행된 clinical trial
•

Why should we calculate the sample size?
Intervention의 effectiveness를 평가할 수 있는 powerful experimental
technique
임상시험은 군 간 차이를 detect할 수 있는, 충분한 크기의
statistical “power”가 확보되어야
•
Under-powered trial은 이후 다시 시도되지 않을 것
•
Sample size 계산은 planning의 essential part

Sample size 계산을 위해서는 해당 study의 shape이 결정되어야

Sometimes, sample size calculation is all we have to do to plan the
study
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BIOSTATISTICS
Sample
☻
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BIOSTATISTICS
 예1
Sample
“기존의 논문들에서 밝혀진 xxx의 두께는 10-30세의 건강한 남자의 경우 100±20μm이다.
본 연구에서는 두께가 10% 이상 차이가 있을 경우에 차이가 있다고 판정하기로 하며,
d=0.035, σ=0.03일 때 d/σ=1.17로서 14명이 필요하다…”
“14개 center가 참여하므로 1414=196명의 모집을 목표로 한다…”

예2

예3
•
전체 14명을 각 center가 협력해서 모집하는 것
•
두 군 간 비교를 하는 연구이므로 142=28명이 필요
“통계분석을 위해서는 30명 이상이 필요하므로 30명 모집을 목표로 함”
•
통계 분석은 2명 이상이면 됨
“중심극한정리를 만족시키기 위해 30명 이상을 모집함”
•
중심극한정리는 평균(mean)이 정규분포를 한다고 가정할 수 있는 수치에 불과
 Waist resources, non-scientific, unethical !
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What are needed to calculate the sample size?
 The primary outcome measure
 The hypothesis (단측/양측, 우월성/비열등성)
 The significance level (유의수준: 5%)
 The power of the test (검정력: 80% or 90%)
 The (standardized) effect size
(Sometimes we need to estimate)
 Follow-up loss rate
★예

두 항생제 A와 B 비교 (two-sided test)

key reference로부터 얻어진 기존 연구결과:
 A 항생제: n = 604, mean±std = 578.74±268.91
 B 항생제: n = 604, mean±std = 771.49±291.02

유의수준 5% 사용, 검정력 90% 유지  =0.05, 1-=0.90

drop-out rate 20% 가정
(n A  1)  s A2  (n B  1)  s B2 (604  1)  268.912  (604  1)  291.02 2
s 

 78502.61
n A  nB  2
2  (604  1)
2
 
m
x A  xB
578.74  771.49

 0.688
s
78502.61
2( z1 2  z1  ) 2
2
2(1.96  1.282) 2

 44.4  45
(0.688) 2
N  2m  90
N* 
N
90

 113 are needed in total
1   1  0.2
★예

Annual event rate in a control group is anticipated to be 40%

It is supposed that intervention will reduce the annual rate to 30%

Have a plan of 2-year follow-up study

Consider 5% level of significance

Want to have 90% power

Assume 10% drop-out rate
H 0 :  trt   ctrl vs. H 1 :  trt   ctrl
_
pctrl  0.4, ptrt  0.3, p  0.4  0.3 / 2  0.35
  0.05, z1  1.645 ( for one  sided test )
1    0.90, z1   1.282
_
ntotal 
_
[ z1 2 2 p(1  p)  z1  p trt (1  ptrt )  p ctrl (1  p ctrl ) ] 2
 ptrt  pctrl 2
n needed 
ntotal
388

 431 .11  432
1   1  0.1

{1.645 2(0.35)(0.65)  1.282 0.3(0.7)  0.4(0.6) }2
0.3  0.42
 387 .94  388
BIOSTATISTICS
Role of Biostatistics
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BIOSTATISTICS




Role of Biostatistics
In the design of the study
In conduct and monitoring of the study
In the analysis
In interpretation of results
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BIOSTATISTICS
Biostatistics in Design
 Good statistical planning은 absolutely essential
 Study type에 대한 계획: 하나 이상의 design 가능
 무엇을 볼 것인가? Superiority, NI, equivalence
 Primary endpoint(s)는?
 가설(귀무가설, 대립가설)을 endpoints의 형태로 정의
 Bias, precision의 고려
 Control, design, blinding, randomization type들 고려
 모집단과 관련된 issues들 (gender, age, sex 등)
 Power and sample size determination
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BIOSTATISTICS
Biostatistics in Analysis
 Trial conduct에 관한 plan
• Non-compliance, missing data
• Interim analysis를 고려하는 경우, stopping rules
 Statistical analysis에 관한 고려
• Data transfer
• Statistical Analysis Plan (SAP) 준비
• Multiplicities (endpoints, treatments, analyses)에 관한 고려
• Outliers, violation of assumptions, missing data handling
• Multicenter analysis
• Subgroup analysis
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BIOSTATISTICS
Biostatistics in Monitoring
 모든 participants들을 aggressive하게 follow-up
• To minimize the amount of missing data
 Pre-planned interim analysis
• To monitor a study
• Perhaps stop early
 Re-sizing the trial
• Planned in advance
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BIOSTATISTICS
Clinical Trials and Biostatsticians
Investigator’s
(initiation) meeting
Proceed
clinical trials
Random allocation
of the first subject
Protocol develop phase
Study design development
Defining endpoints
Inclusion/exclusion criteria
Randomization
Blinding
Define ITT/PP analysis set
Sample size calculation
Statistical analysis section
Assist CRF developing
Data
review
Observe the last
observation from
the last subject
Trial Monitoring phase
Data and Safety Monitoring
Committee (DSMC)
Interim analysis if planned
Data lock &
Breaking blind
Data Monitoring phase Analysis and
reporting phase
Independent Data
Monitoring Committee Based on the
protocol
(iDMC) if no DSMC
Blind review
SAP(statistical analysis plan)
Final follow-up visit of
the 1st subject enrolled
DMC (data management center)
Analysis and
reporting
Closeout
- Patients’ closeout
- Trial closeout
Data archiving
Post study follow-up
Trial transient
All analyses
completed
SC / DSMB
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BIOSTATISTICS
Conclusion
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BIOSTATISTICS
Conclusion

Clinical trial should have sufficient statistical power to
detect differences between groups. A calculation of
sample size is an essential part of planning.

Relevant baseline data should be measured before the
start of intervention.

Once a participant is enrolled, taking measures to
enhance and monitor subjects’ compliance is essential

During all phases of a study, sufficient effort should be
spent to ensure data high quality.

Appropriate data monitoring and proper statistical
analysis should not be underestimated to ensure the
quality of clinical trial.
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