experimental study: clinical trial - interfetpthailand€¦ · assignment of subjects to study...

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Experimental Study:Clinical Trial

Kamol Udol, MD, MSc

Faculty of Medicine Siriraj Hospital

Mahidol University

[email protected]

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Classification of Study Designs in Clinical Medicine and Epidemiology

Descriptive Study(No specific hypothesis)

● Case report● Case series● Descriptive study based on

rates (e.g. Prevalence or Incidence study)

Analytic study(Specific hypothesis)

● Experiment

● Non-experiment Quasi-experiment

Cohort

Retrospective

Prospective

Case-Control

Cross-sectional

Longitudinal

Ecological

Other

Unit of study can be either individual people or groups of people2

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Concepts

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Experiment

● A set of observations, conducted under controlled circumstances, in which the scientist manipulates the conditions to ascertain what effect, if any, such manipulation has on the observations

Rothman KJ, Greenland S, Lash TL. Modern Epidemiology 3rd ed, 2008.4

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Experiment

A study of

CAUSE-EFFECT association

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Validity for Causal Inference

Validity Ranking Types of Study Design

Highest Experimental study

Quasi-experimental study

Prospective cohort study

Retrospective cohort study

Nested case-control study

Time-series analysis

Cross-sectional study

Ecologic study

Case study

Lowest Anecdote

Adapted from Environmental Health Perspectives 1997;15:1079.6

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Health Sciences

Human subjects

Health outcomes

Exposure

Causal association?

Manipulable? Ethical?

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Experimental Study

● Exposure conditions must be amenable to manipulation

● To be ethical, exposure assignments must be expected to cause more good than harm

Therapeutic or preventive interventions

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● Possible factors other than drug A that may be related to cholesterol reduction (Extraneous or confounding factors)

Other co-interventions: E.g., Lifestyle modifications

Regression to the mean phenomenon

Subjects with hypercholesterolemia

Reduction of cholesterol

Drug A

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Subjects Outcomes

Intervention/Exposure

● Extraneous Factors Other co-interventions

Regression to the mean phenomenon

Natural course

Learning effect if measurement is done more than once

Change in properties of the measurement tools over time

Placebo effect

Various unknown factors

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Experimental Study

Main features

● There must be a control group

Not a unique feature of experimental study

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Physicians’ Health Study

Side effects Aspirin (%)

GI symptoms 34.8

Upper GI ulcers 1.5

Bleeding problems 27.0

N Engl J Med 1989;321:129-35.12

Without a control group, one might conclude that aspirin causes GI symptoms, upper GI ulcers and bleeding problems.

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Physicians’ Health Study

Side effects Aspirin (%)

GI symptoms 34.8

Upper GI ulcers 1.5

Bleeding problems 27.0

Control (%) p value

34.2 0.48

1.3 0.08

20.4 <0.001

N Engl J Med 1989;321:129-35.13

With a control group, it’s only bleeding problems that are related to aspirin.

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Experimental group

Control group

Intervention

Outcome

● Difference in outcome can be attributed to the intervention only if both groups have similar probability of developing the outcome at the beginning

● Similar prognosis

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Experimental group

Control group

Intervention

Outcome

● Difference in outcome cannot be attributed solely to the intervention as both groups have different prognosis at the beginning

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Experimental Study

Main features● There must be a control group

Not a unique feature of experimental study

● Experimental and control groups must have similar pre-intervention risk of developing the outcomes (i.e. similar prognosis)

● Investigators manipulate the assignment of exposure to study participants to ensure similar prognosis between groups The unique feature of experimental study

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Experimental Study: Basic Structure

Population

Sample

Assignment

Experiment Control

Outcome Experimental Group

OutcomeControl Group

Definition of population

Selection of subjects to be studied

Assignment of subjects to study group

Administration of intervention

Measurement of outcome

Controlled by investigator

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Experimental Study

● Clinical trials

Patients as subjects

● Field trials

Healthy individuals as subjects

● Community intervention trials

Groups of people in communities as subjects

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Phases of Clinical Trials

● Phase I Dose ranging Small group of healthy subjects

● Phase II Efficacy and side effects Small group of patients with specific diseases

● Phase III Confirmation of efficacy, effectiveness and safety Large group of patients with specific diseases

● Phase IV Postmarketing surveillance

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Methodology

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Validity of Research

● The extent to which the research result (answer) represents the truth

● Research result = Truth + Error

● Error

Random error (Chance)

Remedy: Appropriate sample size

Systematic error (Bias)

Remedy: Appropriate methodology

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Validity of Research

● Internal validity

Accuracy

● External validity

Generalizability, Practical utility

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Design Considerations

● Research question

● Selection of participants

● Selection of intervention and control

● Selection of outcomes (endpoints)

● Data analysis and sample size

● Measures to reduce bias

● Ethical considerations

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Research Question

● Effectiveness of therapeutic or preventive interventions Pharmacotherapy

Surgical procedures

Physical therapy

Lifestyle interventions

Educational programs

Vaccine

Etc.

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Research Question

● Primary research question The most important question the study wants to

answer The answer can be conclusive There must be at least one primary research

question (there is usually one)

● Secondary research question Less important questions The answers can only be hypothesis generation There can be any number of secondary research

question (there are usually many)

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Good Research Question: FINER

● Feasible Adequate number of subjects Adequate technical expertise Affordable in time and money Manageable in scope

● Interesting● Novel

Provides new findings Confirms, refutes or extends previous findings

● Ethical● Relevant

To scientific knowledge To clinical and health policy To future research

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Research Question: Components

PICO

● Patients / Population

● Intervention

● Comparison intervention (Control)

● Outcome

● Each component must be defined as specifically and clearly as possible

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Research Question

● Should be as clear and specific as possible

Not “Is drug I better than drug C?”

But “In population P, does drug I at daily dose X reduce outcome O over a period of time T more than drug C at daily dose Y by the magnitude M?”

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Selection of Participants

● Eligibility criteria

Identify a population in which it is feasible, ethical and relevant to study the impact of the interventions on outcomes

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Selection of Participants

● Eligibility criteria

Inclusion criteria

Define the main characteristics of the target population that pertain to the research question

Exclusion criteria

Specific characteristics rendering individuals not suitable to enter the study despite fulfilling the inclusion criteria

Should be parsimonious (enhance generalizability)

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Exclusion criteria should fit either one of these 4 reasons

1.Conditions in which the study treatment is clearly indicated ACEI is indicated in congestive heart failure

(CHF); a study of ACEI in other population must exclude patients with CHF

2.Conditions in which the study treatment is contraindicated or would be harmful Unacceptable risk of adverse reaction, e.g. history

of severe allergic reaction to the study treatment Pregnant or lactating women Severe renal or hepatic impairment

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Exclusion criteria should fit either one of these 4 reasons

3. Subjects unlikely to benefit form study treatment Short life expectancy

High likelihood of non-adherence or being lost to follow-up

Not likely to respond to treatment due to some characteristics

4. Practical problems with participating in the protocol Impaired mental status

Language barrier

Concurrent participation in another study

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Selection of Intervention

● Intensity, duration and frequency

Balance effectiveness and safety

Serious conditions: Effectiveness may be the primary concern

Highest tolerable dose

Mild conditions or prevention: Safety may be the primary concern

Lowest effective dose

Generalizability

Simple interventions are better

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Selection of Control

● Concurrent standard of care

No treatment

Placebo

Active treatment control

● Allowance or restriction of co-interventions (for both intervention and control groups)

+ Supportive care

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Outcome (Endpoint)

● Primary outcome

To address primary research question

● Secondary outcome

To address secondary research question(s)

● Adverse outcomes

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Outcome

● Single event

Total mortality, Cause-specific mortality, hospitalization, occurrence of a condition e.g. MI, stroke, recurrence of cancer, etc.

● Combination of events: Composite outcome

Should be capable of meaningful interpretation such as being related through a common underlying conditions

Example: Cardiovascular death, non-fatal MI, recurrent severe angina or unplanned coronary revascularization

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Composite Outcome

● Advantages

Reduce sample size

Reduce bias

● Disadvantages

Assume equal importance of each component

Death=MI=Angina

Assume similar effect of the intervention on each component

Complicate interpretation of the result

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Choice of Outcome (1)

● Responsiveness to intervention

● Relevance Clinical or “hard” outcome preferable to

“surrogate” or “physiologic” outcome (relevant to patients & physicians)

Quality of life (relevant to patients & physicians)

Economic outcome (relevant to government, policy makers, payers of health care cost)

● Credibility Assessment can be objective and unambiguous

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Choice of Outcome (2)

● Capability of being assessed in all participants, in an unbiased fashion, and as completely as possible

Similar outcome for all subjects

Similar technique of measurement

Outcome with objective criteria

Requiring no or minimal co-operation of subjects

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Adverse Outcomes

● Hardly any intervention is 100% safe

● Though rarely specified as primary outcome in most clinical trials, adverse outcome is very important

● Most clinical trials are not designed for the purpose of addressing adverse outcomes Inadequate sample size, short duration of follow-

up, restricted subject selection

Statistical requirements for comparison of adverse outcome should not be too strict

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Adverse Outcomes

● Definitions Difficult to define clearly as there are many

possibilities and some are unexpected However, important adverse outcomes should be

well-defined

● Ascertainment Systematic elicitation using checklist and lab tests

Standardization

Spontaneous report by subjects Clinically important events Unexpected events

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Outcome Measurements

● Clear, appropriate and acceptable definitions

● Standardized measurement tools

Reliability

Validity

● Consistent measurement procedures

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Is the intervention effective?(Does the intervention make any difference?)

Study population

Intervention group

Control group

Outcome Intervention

group

OutcomeControl group

Comparison

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Comparison of Outcome Between Groups

● Absolutely no difference

The intervention is not effective

● Some degree of difference

What is the probability that the observed difference is only a “chance” finding?

This probability is called “p value” (obtained by the approach of “Hypothesis testing”)

If this probability (p) is very small (< 5%), the observed difference is unlikely to be a chance finding There is a true difference

The intervention is effective

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Comparison of Outcome Between Groups

Type of Outcome Statistical Test

CategoricalFisher’s ExactChi-Square (Approximate)

Time-to-Event Log-rank (Survival analysis)

Continuous Student’s t

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What is the magnitude of effect?

● Measure of effect

Parameter estimation

Point estimate

Interval estimate (95% confidence interval)

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Magnitude of effect: Binary outcome

Treatment (n = NT) Control (n = NC)

Outcome+ pT = nT/NT pC = nC/NC

Measures Formula

Risk Difference (RD) orAbsolute risk reduction (ARR)

pC - pT

Relative Risk (RR) pT/pC

Relative Risk Reduction (RRR) 1 - RR

Number needed to treat (NNT) 100/ARR (%)

Odds Ratio (OR) [pT/(1-pT)]/[pC/(1-pC)]

Hazard Ratio (HR) Cox regression model

Relative Hazard Reduction(Frequently presented as RRR)

1 - HR

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Subgroup Analysis

● Examine if treatment effects differ (in direction or magnitude) among different subgroups Male vs. Female

Young vs. Old

Severe vs. Mild

etc.

● By statistical test for heterogeneity / interaction / subgroup effect

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● Apixaban is more effective than warfarin in subjects without heart failure (CI excludes 1), but not in subjects with heart failure (CI includes 1)

● Misconception of subgroup analysis!!!

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● Comparison between subgroups, not looking at each subgroup separately (inadequate sample size)

● Are HRs for subjects with and without heart failure too different to be a chance finding?

● Look for p value for interaction / heterogeneity / subgroup effect● If this p is < 0.05, then there is subgroup effect (HR in subjects with HF is

different from HR in subjects without HF), which has to be viewed as hypothesis generation rather than a definitive conclusion.

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Sample Size Estimation

● Too small sample size can prove nothing Probability of type II error

Wasteful, unethical, may mislead conclusions

● A clinical trial should have sufficient statistical power to detect clinically important effect

● Calculation of sample size with provision for adequate levels of significance and power is an essential part of planning

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Sample Size Estimation

● Parameters required for calculation

Acceptable type I () error (level of significance)

Acceptable type II () error (complement of “power”)

Smallest clinically important effect that needs to be detected

Expected variability of outcome variable

From other trials or a pilot study

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Sample Size Formula

● Binary outcome, 2 groups, equal sample size in each group

𝑛 𝑝𝑒𝑟 𝑔𝑟𝑜𝑢𝑝 =𝑧 ൗ𝛼 2

2ത𝜋 1 − ത𝜋 + 𝑧𝛽 𝜋1 1 − 𝜋1 + 𝜋2 1 − 𝜋22

𝜋1 − 𝜋22

• (1 - ) represents the variance of binary outcome• 1 = Proportion of outcome in group 1• 2 = Proportion of outcome in group 2• 1 - 2 = Smallest clinically important difference• ഥπ = (1 + 2)/2

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Example

● Drug A to reduce mortality of avian influenza

● Current mortality rate = 60%

● Gr 1 = Control (No drug A)

● Gr 2 = Drug A

● = 0.05 z/2 = 1.96

● Power = 90% = 0.1 z = 1.28


2ത𝜋 1 − ത𝜋 + 𝑧𝛽 𝜋1 1 − 𝜋1 + 𝜋2 1 − 𝜋22

𝜋1 − 𝜋22

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● 1 = 0.6, 2 = ?

Define 1 – 2

If 1 – 2 = 0.1, then 2 = 0.5

● ഥπ = (0.6 + 0.5)/2 = 0.55


2ത𝜋 1 − ത𝜋 + 𝑧𝛽 𝜋1 1 − 𝜋1 + 𝜋2 1 − 𝜋22

𝜋1 − 𝜋22

𝑛 𝑝𝑒𝑟 𝑔𝑟𝑜𝑢𝑝

=1.96 2 × 0.55 × 0.45 + 1.28 0.6 × 0.4 + 0.5 × 0.5

2

0.6 − 0.5 2

= 517.5 = 518Total N = 2 x 518 = 1036

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What if both 1 and 2 are unknown?

1– (1–)

0.1 0.9 0.09

0.2 0.8 0.16

0.3 0.7 0.21

0.4 0.6 0.24

0.5 0.5 0.25

0.6 0.4 0.24

0.7 0.3 0.21

0.8 0.2 0.16

0.9 0.1 0.09

The greatest value of (1 –) = 0.25

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● Set all the variance term (1 - ) to be 0.25 (the highest possible value)

● Define 1 – 2 (smallest clinically important difference) = 0.1


2ത𝜋 1 − ത𝜋 + 𝑧𝛽 𝜋1 1 − 𝜋1 + 𝜋2 1 − 𝜋22

𝜋1 − 𝜋22

𝑛 𝑝𝑒𝑟 𝑔𝑟𝑜𝑢𝑝 =1.96 2 × 0.25 + 1.28 0.25 + 0.25

2

0.1 2

= 524.9 = 525Total N = 2 x 525 = 1050

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Sample Size Formula

● Continuous outcome, 2 groups, equal sample size in each group

𝑛 𝑝𝑒𝑟 𝑔𝑟𝑜𝑢𝑝 = 2𝑧 ൗ𝛼 2

+ 𝑧𝛽 𝜎

𝜇1 − 𝜇2

2

• = Standard deviation of outcome

• 1 - 2 = Smallest clinically important difference (of means)

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Example

● Drug B to reduce cholesterol in hypercholesterolemia

● Mean SD cholesterol = 250 40 mg/dL

● Group 1 = Control (No drug B)

● Group 2 = Drug B

● = 0.05 z/2 = 1.96

● Power = 90% = 0.1 z = 1.28

● = 40


+ 𝑧𝛽 𝜎

𝜇1 − 𝜇2

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● Define 1 – 2 = 30


+ 𝑧𝛽 𝜎

𝜇1 − 𝜇2

2

𝑛 𝑝𝑒𝑟 𝑔𝑟𝑜𝑢𝑝 = 21.96 + 1.28 × 40

30

2

= 37.3 = 38Total N = 2 x 38 = 76

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Measures to Reduce Bias

● Treatment allocation by randomization

● Concealment of randomization

● Blinding of treatment allocation

● Maximizing adherence and follow-up

● Analysis using intention-to-treat principle

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Randomization

● A process analogous to coin tossing

Subjects are equally likely to be assigned to either the intervention or control group

The best method for obtaining comparability of groups

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Randomization

● Merits of randomization More likely than other methods to balance the

distribution of prognostic factors (both known and unknown) between the intervention and control groups

Avoid “selection bias”: allocation of treatment based on physician or patient preference, which (either consciously or unconsciously) is usually based on many prognostic factors

Guarantee the validity of statistical tests

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Experimental Study = Randomized Controlled Trial (RCT)

Population

Sample

Randomization

Experiment Control

Outcome Experimental Group

OutcomeControl Group

Definition of population

Selection of subjects to be studied

Assignment of subjects to study group

Administration of intervention

Measurement of outcome

Controlled by investigator

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Randomized Controlled Trial

● The best study design to evaluate efficacy/effectiveness of a therapeutic / preventive intervention

● Matching instead of randomization?

Possible only for known prognostic factors

Low feasibility with increasing numbers of prognostic factors to be matched

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Randomization Process

● Simple randomization

● Blocked randomization

● Stratified randomization

● Adaptive randomization

● Cluster randomization

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Simple Randomization

● Procedures equivalent to “coin tossing”

Random-number table

Computer generated randomization list

● Advantage

Easy

● Disadvantages (esp. for small sample size)

Risk of imbalance in number of subjects between groups

Risk of imbalance in prognosis between groups

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Blocked Randomization

● Avoid serious imbalance in the number of subjects assigned to each group

● Guarantee that

at no time during randomization will the imbalance be large (maximum difference = 0.5 x block size)

at certain points the number of subjects in each group will be equal

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Blocked randomization

● At the end of each block the number of subjects in each group will be equal

● Order of treatment within the block and order of consecutive blocks are at random

● Assignment to the last person in each block can be known if treatment is not blind

Solution: randomly varying block size

Block of 4

Block of 6

TCTC

TCCTCT

CTTC CCTT CTCT TTCC TCCT . . . .

CCTCTT TTTCCC TCTCCT . . . . . .

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Stratified Randomization

● Avoid imbalance in prognosis (baseline characteristics) between groups

● Subjects are classified into stratum based on stratification factor(s)

Important prognostic factors regarding the outcome

● Blocked randomization is then performed for each stratum separately

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Example● Suppose there are 2 important prognostic

factors that should not be imbalance between groups Age

40-49 50-59 60+

History of MI Yes No

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Strata Age History of MI Assignment

1 40-49 Yes TCCT CTCT

2 40-49 No TCTC CCTT

3 50-59 Yes etc.

4 50-59 No

5 60+ Yes

6 60+ No

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● Numbers of prognostic factors used for stratification should be kept minimum, otherwise there would be too many strata and some of them would contain only a few subjects if the sample size is small

● In most multi-center clinical trials, randomization is usually stratified by centers

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Adaptive Randomization

● Randomization procedures that change allocation probability as the study progresses, based on imbalance in numbers of subjects, in prognosis, or in response to treatment Baseline adaptive randomization Correct imbalance in number or prognosis

Response adaptive randomization Maximize the number of subjects on the superior

intervention

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Cluster randomization

● Randomization with naturally occurring groups or cluster of participants as units of randomization (schools, physicians’ practices, communities, etc.)

All subjects in the same cluster receive the same intervention

Suitable for interventions that cannot be delivered to individual subjects

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Concealment of randomization

● Concealed randomization

Inability to predict the next assignment

● Unconcealed randomization

Awareness of the next assignment may result in sicker or less sick patients being systematically enrolled to either intervention or control groups, resulting in groups with different prognosis

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● Commonly used methods of concealed randomization

1. Central (or remote) randomization, usually by using a telephone call or internet

2. Preparation of blinded medication in a pharmacy

3. Sequentially numbered, opaque, sealed envelopes (SNOSE)

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Pseudo- (or Quasi-) Randomization

● Superficially similar to randomization, but it is systematic alternate treatment allocation

allocation according to birth date, chart or ID number, day of the week, attending physicians, etc.

● Lack of concealment of randomization

● The factor used for assignment of intervention may somehow relate to prognosis Patients presenting on Monday may in general be

sicker than those presenting on other days

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Blinding of treatment allocation

● Unawareness of treatment assignment after randomization

● Ensure comparability between intervention and control groups after randomization

● Should be considered whenever it is possible to blind participants Blinding is not always possible surgical trials, devices, behavioral intervention,

etc.

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Blinding of Treatment Allocation

Avoid bias due to Placebo effect

Fake response in order to please the physician or investigators

Differential co-intervention

Differential interpretation of lab results

Differential follow-up procedure and schedule

Differential effort to detect outcomes

Differential verification of outcomes

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Participants Purposes

Patients To avoid placebo effects

CliniciansTo prevent differential administration of therapies that affect the outcome of interest (co-intervention)

Data collectors To prevent bias in data collection

Adjudicators of outcomeTo prevent bias in decisions about whether or not a patient has had an outcome of interest

Data analystsTo avoid bias in decisions regarding data analysis

Five groups that should be blind to treatment assignment, if possible

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Blinding of Treatment Allocation

● Achieved by

Placebo

Sham procedures

Blind adjudication committee

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Difference between Blinding and Concealment of Randomization

● Blinding

Unawareness of treatment allocation afterrandomization


Unawareness of treatment allocation beforerandomization

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Adherence and Follow-up

● Nonadherence (noncompliance) and lost to follow-up Loss of power of the trial

Biased results

● Subjects not adherent to the protocol or lost to follow-up have different prognosis from others

● Efforts to maximize adherence and follow-up should be carried out

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Coronary Drug Project

Compliance5-Y Mortality (%)

Clofibrate

Total group 20.0

Good (≥ 80%) 15.0

Poor (< 80%) 24.6

Placebo

28.3

15.1

20.9

N Engl J Med 1980;303:1038-1041.

Even in the placebo group, those with good compliance had better prognosis than those with poor compliance.

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Follow-up

● In general, if lost to follow-up is ≥20%, the study validity is usually considerably compromised

● If lost to follow-up is <20%, the study validity may or may not be considerably compromised

If want to be sure, do the sensitivity analysis assuming the worst case scenario

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When does loss to F/U seriously threaten validity?

Trial A

Treatment Control

Number of patients randomized 1000 1000

Number (%) lost to F/U 30 (3%) 30 (3%)

Number (%) of deaths 200 (20%) 400 (40%)

RR not counting patients loss to F/U 0.2/0.4 = 0.5

RR for worst case scenario 0.23/0.4 = 0.57

Trial B

Treatment Control

1000 1000

30 (3%) 30 (3%)

30 (3%) 60 (6%)

0.03/0.06 = 0.5

0.06/0.06 = 1.0

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Follow-up

● Consider the number lost to follow-up to the number of outcome events

No. lost to F/U << No. outcome events

Small effect of bias

No. lost to F/U ≈ No. outcome events

Considerable effect of bias

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Adherence and Follow-up

● Efforts to maximize adherence and follow-up should be carried out

Run-in period

Placebo run-in

Active treatment run-in

Incentives

Monetary and non-monetary

Social events

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Analysis

● Intention-to-treat analysis

Analysis of outcomes based on the treatment arm to which patients were randomized rather than which treatment they actually received

● Preserve the value of randomization

● Results reflecting true population effect when the intervention is widely used in real practice

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Analysis

● Per-Protocol analysis

Analysis restricted to subjects who comply to the study protocol

Can introduce bias (See example)

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Analysis

Example

● Drug A in acute myocardial infarction to reduce in-hospital mortality

● Drug A can cause severe hypotension in subjects with poor left ventricular function

● Truth: Drug A has no effect in acute myocardial infarction regarding in-hospital mortality.

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R

350

350

150

150

Placebo

Drug A

Death

70Mortality 20%

Mortality 40%60

130/500 = 26%

150

Mortality 20%70

60

70/350 = 20%

p = 0.04

Per-Protocol Analysis

Drug A reduces

mortality!!!

N = 500

N = 500Mortality 40%

Discontinue due to

severe hypotension 102

Poor LV function

Good LV function

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R

350

350

150

150

PlaceboDeath

70Mortality 20%

Mortality 40%60

130/500 = 26%

150

Mortality 20%70

60

130/500 = 26%

p = 1.00

N = 500

N = 500Mortality 40%

Get it right

Drug A

Discontinue due to

severe hypotension 103

Poor LV function

Good LV functionIntention-to-treat Analysis

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Ethical Issues

● Is randomization ethical?

● Is using placebo ethical?

● IRB or EC approval

● Informed consent

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Ethical Issues

● Is randomization ethical? Withholding a drug from patients

● “Randomization is ethical only when we do not know whether drug A is better than drug B”

● Equipoise The state of uncertainty about the relative merits

of new treatment and standard treatment

Current evidence does not prove that either arm is superior

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Ethical Issues

● “Studies are only ethical if they have a reasonable likelihood of producing the correct answer to the research question, and randomized studies are more likely to lead to a conclusive and correct result than nonrandomized designs”

● Is it ethical “not to randomize”?

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Ethical Issues

● Is using placebo ethical?

“Pure placebo” is not ethical if there is a standard treatment for the condition

Standard treatment must be provided to all patients; experimental treatment is then compared to placebo on top of the standard treatment

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IRB or EC Approval

● Risk minimization

● Risks are reasonable in relation to anticipated benefits

● Informed consent

● Maintenance of confidentiality

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Informed Consent

● Consent provided after subjects fully understand the objectives, procedures, anticipated benefits and risks of the study

● “Written” consent is standard

● “Verbal” consent is acceptable in some situations

● Decision to participate must be made free of any influence

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Other Forms of Experiment

● Randomized cross-over design

● Factorial design

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Cross-Over Design

Study population

Intervention

Control

Control

Intervention

R Period 1 Period 2

● Each subject serves as his/her own control

● Reduced variability, resulting in smaller sample size

● Suitable for

Chronic, stable diseases, episodic conditions

Treatment is non-curative

● Based on the assumption that there is no “carry over”effect

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Factorial Design

● Evaluate 2 or more interventions in a single trial (save cost and time)

● Interaction between the 2 (or more) interventions

Absence: much smaller sample size than conducting 2 separate trials

Presence: the only trial design that enables assessment of interaction

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Factorial Design: ISIS-2 Trial

● Evaluate the effect of 2 drugs, namely Aspirin (ASA) and Streptokinase (SK), in patients with acute myocardial infarction

● Patients were randomized 2 times

Randomized to receive ASA or ASA Placebo, then

Randomized to receive SK or SK Placebo

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Factorial design: ISIS-2 Trial

ASA ASA Placebo

SK SK + ASA SK only

SK Placebo ASA only Nothing

Eventually, there were 4 groups of patients SK + ASA

SK only

ASA only

Nothing

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ASA ASA Placebo

SK SK + ASA SK only


Effect of SK was estimated by comparing the outcome between SK group (SK+ASA combined with SK only) and SK Placebo group (ASA only combined with Nothing)

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ASA ASA Placebo

SK SK + ASA SK only


Effect of ASA was estimated by comparing the outcome between ASA group (SK+ASA combined with ASA only) and ASA Placebo group (SK only combined with Nothing)

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Conclusions (1)

● Experimental study (= RCT) is most suitable for studying the effectiveness of a therapeutic or preventive intervention

● Main features Control group

Similar prognosis between experimental and control groups

Investigators manipulate or control assignment of subjects to groups using randomization technique (unique for experiment study)

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Conclusions (2)

● Measures used to reduce bias

Treatment allocation by randomization


Blinding of treatment allocation

Maximizing adherence and follow-up

Analysis using intention-to-treat principle

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experimental study: clinical trial - interfetpthailand€¦ · assignment of subjects to study...

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