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Ankur Barua

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Understand the importance of obtaining

Ethical Committee clearance, InformedWritten Consent, Registration in ClinicalTrial Registry.

Interpret and apply the concepts ofRandomization, Blinding and Cross-overtechnique.

At the end of the session, the learner will be able to:

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Ethical Committee clearance,

Informed Written Consent,

Registration in Clinical Trial Registry

Procedure of Randomization

Procedure of Blinding

Cross-over technique

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Autonomy (right of patients for self-governance)To exercise this right, a patient must be informed on thebenefits and potential risks of the newtreatment/procedure (related to the requirement of

obtaining informed consent from patients).

Beneficence is the patients right to be benefited fromtherapy, and the physicians duty not to harm the patient.

Justice or fairness of distribution of the burdens andbenefits of the research. For example, testing on poorpeople or minorities, and then distributing to theprivileged would be in direct violation of this principle.

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Clearance from Ethical Committee of

the institution or organization.

Informed Written Consent for all

human experiments.

Registration in Clinical Trial Registry.

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Moher D, Hopewell S, Schulz KF, Montori V, Gtzsche PC, Devereaux PJ, et.al. CONSORT 2010Explanation and Elaboration: updated guidelines for reporting parallel group randomised trials.BMJ 2010; 340: c869. 6

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Moher D, Hopewell S, Schulz KF, Montori V, Gtzsche PC, Devereaux PJ, et.al. CONSORT 2010Explanation and Elaboration: updated guidelines for reporting parallel group randomised trials.BMJ 2010; 340: c869. 7

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Clearly stated objectives.

Well-defined endpoints or quantifiable measures derivedfrom these objectives.

A priori stated decision rules for success or failure of theexperimental treatment based on statistical testsinvolving these endpoints.

When necessary, a clearly presented calculation of thesample size and its associated power.

Well-described patient inclusion and exclusion criteria, andpatient screening and randomization.

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A system ofdata monitoring; this includes: Safety and efficacy monitoring, possibly by an external

body (e.g., a Data Safety Monitoring Board or DSMB),with possibly explicit rules for early study termination.

Data quality monitoring and error correction.

All examinations, tests, and evaluations described indetail along with a schedule of when they are to beperformed.

A data collection system which is based on data collectioninstruments called Case Report Forms (CRFs) as well as asystem of digital data entry.

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Blinded Not blinded

Randomised Not randomised

Controlled Not controlled

Trial

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True experiments (randomized controlledtrials) are contrasted with non randomizedcontrolled trials and observational studies.

In non randomized controlled trials, thecontrol group is predetermined (withoutrandom assignment) to be comparable to

the program group

Non randomized controlled trials are alsocalledquasi experiments.

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Non randomized controlled trials rely onparticipants who -

1) volunteer to join the study

2) reside geographically close to the study site3) conveniently turn up (at a clinic, school) while the studyis being conducted

Because the study groups are opportunistically

rather than randomly composed, study groupcharacteristics (age, sex) may not be balancedbefore the study begins.

Baseline differences between groups may

confound the results.

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Typical confounding variables include age, educationallevel, motivation, severity of illness, social structure, andincome.

Evaluation researchers worry that study groups in non-randomized trials will differ from one another at baseline,and the studys findings will be compromised.

They aim to create study groups that are as similar to oneanother as possible (equivalent) at baseline or beforetreatment.

Among the strategies commonly used to ensure equivalenceis one called matching.

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Matching requires selecting pairs of participants orclusters of individuals who are comparable to one anotheron important variables.

A researcher who is interested in comparing the acuity of visionamong smokers and non smokers can try to balance the twogroups by selecting pairs of smokers and non smokers who aresame age, sex and have the same medical history.

Statistical methods such as analysis ofcovariance andpropensity score analysis are sometimes used to dealwith the problem of confounding after the data arecollected for the study.

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Time-series Designs Time-series designs are longitudinal studies that enable the

researcher to monitor change from one time to the next.

They are sometimes called repeated measures analyses.

Interrupted or Single Time-series The interrupted or single time-series design without a

control group involves repeated measurement of a variable

(e.g., reported crime) before and after implementation of aprogram.

The goal is to evaluate whether the program has"interrupted" or changed a pattern established before itsimplementation.

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Self-Controlled or Pretest-Post Test Designs

Each participant is measured on some important programvariable and serves as his or her own control.

Participants are usually measured twice (at baseline andafter program participation), but they many be measuredmultiple times afterward as well.

Historical Controls

Investigators compare outcomes among participants whoreceive a new program with outcomes among a previousgroup of participants who received the standard program.

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Involves some action, intervention or manipulation such as

deliberate application or withdrawal of suspected cause.

1. Drawing up a protocol2. Selecting reference and experimental population

3. Randomization

4. Blinding

5. Manipulation or intervention

6. Follow- up

7. Assessment of outcome

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Clinical Trials:Phases

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EXPERIMENTAL STUDY DESIGNS(Randomised or Non-randomised trials) Salient Features Study Unit

i.

Lab or Animal Trials (Phase-I trial)

(a) Laboratory Experiments

(b) Animal Experiments

(c) Drug trials with small number ofpatients

Comparison / Control group

is chosen after

Randomisation, following

informed consent, wherever

feasible.

Studies could be multi-

centric when >2 centres are

involved.

Application of Statistical

Tests of Significance is

mandatory. These follow-up

studies are conducted for

demonstration of cause &

effect relationships.

1st group - Intervention

2nd group Control

- Done on animals or

experiments under lab

conditions

ii.

Clinical Trials with large number of

patients (Phase-II trial)(a) Preventive (Vaccine / Drug trials)(b) Therapeutic ( Drug trials)

1st group - Intervention

2nd group Control

- Done on diseased

individuals (patients)

iii.Field Trials (Phase-III trial)

(a) Preventive (Vaccine & Nutritional

Supplementation trials)

1st group - Intervention

2nd group Control

- Done on apparently healthy

individuals

iv.

Community Trials (Phase-IV trial)

Also known as Post-Marketing Trials

(a) Preventive (Vaccine & NutritionalSupplementation trials)

1st group - Intervention

2nd group Control

- Done on community as a unit

EXPERIMENTAL STUDY DESIGNS

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Selection by defined criteria

Potential participants

(Meet selection criteria)Non-participants

(do not meet selection criteria)

Invitation to participate Non-participants(do not give consent)

Participants

Randomization &double blinding

Experimental group Manipulation,Follow up

&

Assessment

Control group

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J

The CONSORT Flow Chart JAMA. 2001;285:1987-1991

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Random Allocation

known chance receiving a treatment

cannot predict the treatment to be given

Eliminate Selection Bias

Similar Treatment Groups

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Randomization tries to ensure that ONEfactor is different between two or more

groups.

Observe the Consequences

Attribute Causality

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Standard ways:

Random number tables

Computer programs

NOTlegitimate:

Birth date

Last digit of the medical record number

Odd/even room number

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542-04-#25

A. Sampling-BasedPopulation Model

B. RandomizationModel

Population ay~G(y| a)

Study SamplePopulation by~G(y| b)

Sample atRandom

Sample atRandom

nb patients

ybj~G(y| b)

na patients

yaj~G(y| a)

n = nb + nb patients

Randomization

na patients nb patients

Statistical Properties of Randomization

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Simple

Blocked Randomization

Stratified Randomization

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Randomize each patient to a treatmentwith a known probability

Corresponds to flipping a coin

Could have imbalance in proportion orgroup or trends in group assignment

Could have different distributions of a traitlike gender in the two arms

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Insure the proportion of patients assignedto each treatment is not far out of balance

Variable block size

An additional layer of blindness

Different distributions of a trait like genderin the two arms possible

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To improve balance between treatment assignments we can usepermuted blocks.

Within each block, the two treatments are balanced, and the order oftreatment allocation is changed (permuted) from block to block. Atthe end of each block both treatments are balanced.

Permutation number

Within-block

assignment 1 2 3 4 5 6

1 A A A B B B

2 A B B B A A

3 B A B A B A

4 B B A A A B

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A prioritize certain factors likely important(e.g., Age, Gender).

Randomize so different levels of the factorare balanced between treatment groups.

Cannot evaluate the stratification variable.

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For each subgroup or strata perform aseparate block randomization

Common strata

Clinical center, Age, Gender

Stratification MUST be taken into accountin the data analysis Subgroup Analysis.

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Every prognostic factor or prognostic factor combination (in the case ofmultiple prognostic factors) is made into a separate stratum. Treatment assignment is then balanced in each stratum yielding

balanced representation of each stratum in each treatment. Then the blocks shown in the previous table, can be allocated at

random into each stratum (thus balancing treatment allocation intoeach stratum). For example,

Treatment assignment w/ a block size of 6

Stratum

1 2 3 4 5 6

Old male A A A B B B

Young male A B B B A A

Old female B A B A B A

Young female B B A A A B

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Randomization is used to -

Control the variability of clinical outcome.

Combat treatment selection bias (where a certain typeof patient maybe more likely to be selected for onetreatment versus the other).

Create homogeneous risk strata.

Stratification is used to -

Balance known risk factors between the treatments undercomparison.

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Parallel Group

Sequential Trials Group Sequential trials

Cross-over

Factorial Designs

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Randomize patients to one of k treatments

Response

Measure at end of study

Delta or % change from baseline

Repeated measures

Function of multiple measures

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Randomised Parallel Group

Participants satisfying

entry criteria

Randomly allocated to

receive A or B

A B

Participants followed up

exactly the same way

Example: Digoxin vs Placebo DIG study

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Not for a fixed period Terminates when

One treatment shows a clear superiority or

It is highly unlikely any important difference will

be seen

Special statistical design methods

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Popular Analyze data after certain proportions of

results available Early stopping

If one treatment clearly superior

Adverse events

Careful planning and statistical design

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(C)Stephen Senn 40

Cross-over Trial

A cross-over trial is one

in which subjects are

given sequences withthe object of studying

differences between

individual treatments.

Two period two treatment

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Two-period, two-treatment

cross-over trial

Participants

satisfying

entry criteria

sometimesfollowed by

run-in period

A

B

B

A

Randomised toA followed by

B or vice-versa

Usuallywashout in

between

Example: Aspergesic (A) vs ibuprofen (B) in rheumatoid arthritis.

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E.g. 2 treatments: 2 periodcrossover

Use each patient as own control

Must eliminate carryover effects

Need sufficient washout period

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Each level of a factor (treatment or condition)

occurs with every level of every other factor

Selenomethionine and Celecoxib Gastroenterology

2002; 122:A71

Placebo

Placebo

Selenium

PlaceboPlacebo

Celecoxib

Selenium

Celecoxib

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Factorial design

Participants satisfying

entry criteria

Participants randomlyallocated to one of four

groups. 2x2 factorial

design

Example: Heart Protection Study. =Simvastatin;

=Vitamins; =Placebo

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The simplest way to randomize patients in twotreatments is by flipping a coin (the first

treatment takes the heads and the second

treatment the tails).

However, there is no guarantee of equal numberof subjects assigned to the two treatments.

For example, for N=100, the probability of equaltreatment allocation is about 8% only.

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Single blind trial: Here, the participant is not aware whether he

belongs to study group or control group.

Double blind trial: Here, neither the administrator of intervention

(doctor / nurse) nor the participant are aware ofthe group allocation and the treatment received.

Triple blind trial: Here, the participant, the administrator of

intervention (doctor / nurse) and the analyzer(statistician) are all blind.

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(1)p { Type I error } = , less p { type I error } more subjects(2)p { Type II error } = , less p { type II error } more subjects, More power (1- )

PROCESS OF HYPOTHESIS TESTING

POSSIBLE CONDITIONS OF NULL HYPOTHESIS

True False

POSSIBLE ACTIONS ON

NULL HYPOTHESIS

Accept Correct action Type II error ( )

Reject Type I error ( ) Correct action

Probability of committing {Type I error} = = level of significance (Rejecting a true null

hypothesis)

Probability of committing {Type II error} = (Accepting a false null hypothesis)

(1 - ) = Power of the test. It is Probability of not committing a type II ( ) error or ability of

a test to reject a false null hypothesis.

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1.The level of power ( 1- )

2.The effect size (d)

3.The significance level ()

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Sample size determination 1. level of power (1- )

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1. What level of power is required?

A rule of thumb is that the power of a test should be

at

least 1 4,

e.g. for a 5% significance level the power of the test

should be at least 80%.

= 1- (4X 0.5) = 0.8

This means that there is an 80% chance of detecting

the required order of difference b/t population means.

p 1. level of power (1 )

Sample

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d = The effect size (i.e clinicallyimportant differences or important

difference)

d= PA-PB

PA = proportion expected in the treatment group

PB = proportion expected in the control group

Samplesize

Sample size

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m = 16 (1- )/(PA-PB)2

= (PA-PB)/2

Treatment group = 0.05

Placebo group = 0.2

= (PA-PB)/2 = (0.05-0.2)/2 = 0.15/2 = 0.075

m = 16 (1- )/(PA-PB)2

= 16 X 0.075 ( 1-0.075)/0.0752

= 74

Sample sizeLehrs formula

Sample size 2 Effect size1 level of power 3 Statistical

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2. The effect size (i.e clinically important differences or

important difference)

d= T C

= SD of the populations that the two samples are taken

(assuming that it is the same for both)

T = mean of the treatment group; C mean of the control group

3. Significance level of 5% (i.e = 0.05)

Sample sizedetermination

2. Effect size1. level of power( 1- )

3. Statisticalsignificance

Difference between means

Sample size determination

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For a 2 sided significance level of 5% & powerof 80%

m = 16/d2

m = the number required in each group

Source: MJ Campbell, SA Julious & DG Altman (1995). Estimating sample sizes

for binary, ordered categorical, and continuous outcomes in two group

comparisons, BMJ:311:1145-1148.

Sample size determination

Sample size determination Example:

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If we wish to detect a difference in means of 5 mm Hg between the

group with antihypertensive drug & those with placebo.

The systolic BP of the drug group = 136 mm Hg

The systolic BP of placebo group = 144 mm Hg

Assume SD of BP in each group = 17 mm Hg

= 17

d= t C

d = 5/17 = 0.294

For 80% power, using a significance level of 5%

m = 16/d2

= 16/0.2942 = 185

Sample size determination Examp e

Sample size required per group at the 2 sided 5% significance for the

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p q p g p g

difference value of effect size & power

MJ Campbell, S AJulious, D GAltman (1995). Estimating sample sizes for binary, ordered

categorical, and continuous outcomes in two group comparisons, BMJ; 311: 1145-1148.

Sample size to detect a difference between two proportions at the

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Sample size to detect a difference between two proportions at the

5% significance level with 80% power

MJ Campbell, S AJulious, D GAltman (1995). Estimating sample sizes for binary, ordered

categorical, and continuous outcomes in two group comparisons, BMJ; 311: 1145-1148.

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A) Should be random assignment

Each individual has the same chance of

receiving each possible treatment.

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B) Some examples of random allocation -

I. Random number table: as each subject

enrolled, assigned a number from therandom number table; assign evennumbers to treatment A and odd totreatment B

II. Toss a coin for each subject: heads=A,tails=B

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C) Some examples of Non-random allocation -

I. Alternate assignment of treatments

II. Assignment by day of the week

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Triple blind

Randomized with adequate

allocation concealment

Parallel groups

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Abramson JH. Survey Methods in Community Medicine. 4th Edition, Ch 1. UK: Churchill Livingstone; 1990.

The World Health Organization. Health research methodology - a guide for training in research methods. Geneva:The WHO ; 1992.

K.Park. Parks text book of preventive and social medicine. 19th edition. Jabalpur, India: Benarasidas BhanotPublishers; 2007.

Berry D. Bayesian Clinical Trials. Nature Reviews 2006; 5:27-36.

OBrien PC, Fleming TR. A multiple testing procedure for clinical trials. Biometrics 1979; 35:549-56.

Piantadosi S. Clinical Trials. A Methodologic Perspective. New York: John Wiley and Sons; 1997.

Porta M. A dictionary of epidemiology. 5th edition. Oxford, New York: Oxford University Press, 2008.

Rothman KJ, Greenland S, eds. Modern epidemiology. 2nd ed. Philadelphia, PA: Lippincott - Raven Publishers,1998.

Gordis L. Epidemiology. 2nd ed. Philadelphia, PA: W. B. Saunders Co; 2000.

Altman DG. Practical statistics for medical research. London: Chapman & Hall;1991.

DS Moore and GP McCabe. Introduction to the practice of statistics. 3rd ed. New York: WH Freeman and Company;1999.

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