2. WHAT IS A RANDOMIZED CLINICAL
TRIAL?
• A planned experiment in humans
• Designed to assess efficacy of treatment or
intervention
• Comparison of outcomes in a group of patients
treated with new therapy and those in a comparable
group of patients with a control therapy
• Patients in both groups enrolled, treated and
followed over same time period.
3. USE OF CLINICAL TRIAL
• Evaluating :
– New drugs, other treatment for disease
– New medical / healthcare technology
– New methods of prevention
– New programmes for screening
– New methods of providing healthcare
– New healthcare policies
4. BASIC CLINICAL TRIAL DESIGN
• What is the question?
• Who is the population of interest?
• What is the intervention?
• How will the efficacy of the intervention be
assessed?
5. TRIAL DESIGN CLASSIFICATIONS
• How patients assign?
– Parallel
– Crossover
– Cluster
• What to prove?
– Superiority
– Equivalence
– Non-inferiority
• To explore or confirm?
– Exploratory
– Confirmatory
9. • Parallel
– Patients are randomized to new
treatment/intervention and follow up to the
interest outcome
• Crossover
– Randomize patients to different sequences of
treatments, but all patients eventually get all
treatments in varying order( patient is his/her own
control)
• Cluster
– When larger groups are randomized instead of
individual patients
10. RANDOMIZATION
• Method of assigning patients to treatment group
• Principle reason is to avoid predictability in the treatment
assignment
– Avoid bias on the part of the investigator or patient
• Tends to create comparison groups that are similar
• Stratification before randomization (eg by gender, age
group, disease stage or prognostic factors) may be used as
an additional means of ensuring balance in treatment
groups for known variables
11. DATA COLLECTION
• Baseline characteristics
• Treatment
– As assigned
– As received
• Outcome
– Beneficial and adverse effects
12. SELECTING THE PRIMARY OUTCOME
• ‘Objective’ vs ‘Subjective outcome’
– Objective outcome that are well-defined and can be
observed directly are preferred
• Death (or survival), disease recurrence, changes in
blood pressure
– Subjective may be equally important but are more subject
to bias
• Pain reduction, quality of life, psychological status
• Ease and accuracy of diagnosis
13. SELECTING THE PRIMARY OUTCOME
• Measured independently of treatment assignment
– All patients should be followed up in the same way,
with the same test and at the same intervals after
treatment
• Clinically relevant
– Although change in lipid profile may be an objective,
easily measured outcome; it may be of little relevance
if there is no concomitant change in risk of heart
disease or death
15. SAMPLE SIZE
• How many people do I need to study in order
to determine whether there is a difference
between treatment
• Must be planned carefully to ensure that the
research time, patients effort and cost
invested are not wasted
16. COMPONENTS of SAMPLE SIZE
CALCULATION
• Power
– The ability to detect a true difference in outcome between
the standard or control arm and the intervention arm
– Usually 80% (ie : 20% of false-negative result)
• Level of significance
– Likelihood of detecting a treatment effect when no effect
exists
– Usually p=0.05 (ie: 5% of false-positive result)
17. COMPONENTS of SAMPLE SIZE
CALCULATION
• Underlying event rate in the population under study
– Usually established from previous study (including
observational cohorts)
• Size of treatment effects sought
– The difference between the rate of the event in the control
and intervention groups @ as a relative reduction
(proportional change in the intervention group)
– Eg : control group 6.3%, intervention group 4.2%
• Absolute difference = 2.1%
• Relative reduction = 2.1/6.3 = 33%
18. COMPONENTS of SAMPLE SIZE
CALCULATION
• Effect of compliance
– Sample size must be adjusted for non-compliance
– Adjusted n per arm
• n=N/([c1+c2-1]2) ; c1 and c2 are average compliance
rates per arm
• Allocation ratio
22. NON COMPLIANCE
• The preferred method to handle non-
compliance (or treatment cross-over) is to
analyze the data according to the assigned
treatment (intention to treat analysis)
– Secondary analysis may be included to evaluate
treatment effect in patients receiving the assigned
treatment only
24. LOSS TO FOLLOW UP
• Loss of contact with a patient, so that there is no
opportunity to assess outcomes
• If the proportion surviving in the loss to follow-up
group is different from the group for which survival is
known, the conclusion that the two treatments are
equally beneficial in misleading and incorrect
25. VALIDITY OF RESULT
• Internal vs external validity
• Internal validity
– Conclusion supported by study design
– High when trial is technically well-design, conducted and
analyzed properly
• Differences can be attributed to the treatment under study
• External validity
– Generalization to reference population
26. ETHICAL ISSUES
• Randomization
– Is it ethical to randomize patients to treatment?
– Is it ethical not to randomize when a promising new
treatment is available bit has unknown efficacy?
• Informed Consent
– Can truly informed consent ever be obtained?
• Use of placebo
– When is the use of a placebo appropriate?
27. ETHICAL ISSUES
• Data monitoring
– Should treatment effects (beneficial or adverse)
be monitored during the trial?
• Scientifically valid vs politically correct
research
– How should interim results be used?
28. Number needed to treat
• Number of patients who need to be treated in
order to prevent one additional bad outcome.
• Inverse of the Absolute Risk Reduction (ARR)
• NNT=1/ARR
• AAR=CER-EER
– CER=control group event rate
– EER=experimental group event rate
29. • E.g:
– NNT=2.7
• A doctor will see a lot of events in very little time
– NNT=800
• A doctor will have to treat a large number of patients in
order to see a very few events
30. Intention to treat
• A method that includes noncompliant patients
in the groups to which they were originally
randomized into
• Reason why use this approach:
– Preserves the effects of randomization
– Often provides an assessment of the practical
impact of a treatment
34. 34
Introduction
Parallel design
Each patient one treatment
i.e. if a patient receives treatment A, he/she
will not receive treatment B.
2 types of parallel design
Group comparison (parallel group) design
The matched pairs parallel group
(Chow & Liu 2004 ;Norleans 2001)
35. 35
Introduction… cont
Primary outcome –related to the aim of the
study, used in the sample size calculations &
primary data analysis
Secondary outcome - arise as relation to the
primary outcome (subgroup analysis), useful
in exploratory investigation to generate future
study analysis
RANDOMISATION & BLINDING
36. 36
Introduction… cont
2 types of control
Placebo control -difficult to design long-term studies and may not be ethical to
continue if treatment drastically changes the course of disease in either
direction.
Active control - if placebo-controlled studies may not be ethically accepted
38. 38
Statistical Analysis
The type of outcome
Whether adjustment for baseline variables is
needed
Whether subgroup analyses are being
conducted
In the case of survival outcomes, whether
the proportional hazards assumption is
satisfied
(Kirkwood & Sterne 2003)
39.
40. INTRODUCTION
•Cross-over trial - special type of “repeated
measurements”
• Subjects divided into two or more treatment groups and
after a specified period they are given a different treatment
to the first one.
•The subjects therefore act as their own controls
40
CROSSOVER-INTRO
41. INTRODUCTION(CONT..)
Requirements for conducting crossover design :
Disease under study must be chronic and treatable but not curable
Disease must be stable over course of study
Patients: population well defined, at similar point in disease
Intervention: randomized and double-blinded assignment to treatment
sequence
Outcome: blinded assessment
41
CROSSOVER-INTRO
42. Advantages
• Need fewer patients,
multiple use of the
same subjects
throughout the trial
• Precision is increased
• Decreased cost
Disadvantages
• Period effect
• Carryover effect
(residual effect)
• Length of trial
• Missing data/
Dropouts/outliers
42
CROSSOVER-INTRO
43. 1)PERIOD EFFECT
One that occurs in a given period, irrespective of order of treatments
to avoid period effect, one experimental unit will receive A→B and another B→A,
administered at same time
Can occurs if :
-disease changes (deteriorates or improves) over the study, respond differently
to treatment in period 1 compared to period 2.
-disease process unstable. e.g cancer patients deteriorate over time. Effect can
be balanced by randomization.
This condition effects size of differences between
treatments.
43
CROSSOVER-INTRO
44. 2)CARRYOVER EFFECT
Manifestation of treatment in subsequent periods; effects
treatment persist into later time period and influence/modify
effects of subsequent treatment
Carryover effects lead to false interpretation of joint effect of
two treatments to single effect of one.
44
CROSSOVER-INTRO
45. 2)CARRYOVER EFFECT(CONT..)
The effect depends on design, setting, treatment and
response
Arise in number of ways:
- pharmacological carryover - active ingredients of drug still present in following
period
-Learning effects: lead to positive effect on the response
- Fatigue effects: lead to negative effect
- Psychological effects: lead to positive or negative
effect
(Jones & Kenward 2003)
45
CROSSOVER-INTRO
46. 3)LENGTH OF TRIAL
Duration of conducting trials may be longer than parallel group
study - studied during at least 2 study periods.
4)MISSING DATA / DROPOUTS / OUTLIERS
Greater effect on analysis as compared to parallel design.
Compliance is threats to validity, fail to take medication as
prescribed, and drop out if treatment unpleasant.
46
CROSSOVER-INTRO
48. STATISTICAL CONSIDERATION
Detail of variables & how will be reported
Detail plans for statistical of primary and secondary outcome
-summary measures to be reported
-methods of analysis
-plans for handling missing data, non-complier and withdrawal in analysis
-plans for predefined subgroup analysis
-statement regarding use of intention to treat analysis
-how to handle carryover effects in analysis
48
CROSSOVER-ANALYSIS
50. DEALING WITH PROBLEMS OF
CROSS-OVER DESIGN
Minimize carry over effect
- Adequate wash out - time interval long enough between two periods to
eliminate effect of a formulation administered before.
- length of washout period usually half-life (t1/2) of study medicine within
population of interest. Generally, washout is at least 5 times of the t1/2.
50
CROSSOVER-PROBLEM
Source :Shen, D.
51. DEALING WITH PROBLEMS OF
CROSS-OVER DESIGN(CONT..)
Randomization
Randomized to the sequence
Half subjects are assigned to receive A/B while other half receive B/A.
Considerations regarding outliers
In bioavailability/bioequivalence study, subjects (outliers) differ from other
subjects when comparing test and reference product in subject himself
Existence of an outlier without violation of protocol may indicate:
i) failure of product: abnormal response present both for test product and
reference product
ii) subpopulation: occur when an individual represents a population, the
bioavailability of two products is different from majority of population.
In general, the exclusion of outliers is not recommended, mainly for designs that
are not replicated.
51
CROSSOVER-PROBLEM
52. DEALING WITH PROBLEMS OF
CROSS-OVER DESIGN(CONT..)
Intention-to-treat
Intention-to-treat (ITT) principle appropriate for most RCT
An ITT analysis requires all data to be analyzed according to randomized group
assignment, regardless of whether some participants violated the protocol, not
compliant, or received incorrect treatment.
Restricting analysis to compliant subjects lead to biased results. If non-
compliance related to outcome, misrepresent differences between treatment
groups..
52
CROSSOVER-PROBLEM
53. DEALING WITH PROBLEMS OF
CROSS-OVER DESIGN(CONT..)
Latin square design
‘Diagram-balanced’ Latin square designs considered good designs for
estimation of carryover effects
Each treatment preceded and followed equally by each other
treatment.
A Latin square design is composed of ‘n’ treatment, ‘n’ rows and ‘n’
columns.
‘n’ treatment is the number of treatment under trial;
‘n’ rows represent different sequence or subjects;
‘n’ columns represent different time periods.
53
CROSSOVER-PROBLEM
54. DEALING WITH PROBLEMS OF
CROSS-OVER DESIGN(CONT..)
For even value of ‘n’, balance for the estimation of residual effects of treatment
achieved with single Latin square.
For odd value of ‘n’, achieved with a pair of Latin squares.
Example:
n=2, where 2 treatments under trial, 2 sequences and 2 time periods :
A B
B A
n=3, where 3 treatments under trial, a pair of 3x3 diagrams required to enable
estimation of carryover effects, the rows represent different sequences or
subjects, columns represent different time periods:
A B C A C B
B C A B A C
C A B C B A
54
CROSSOVER-PROBLEM
55. Advantages of Crossover design
• The treatment differences can be based on
within-subject comparisons instead of
between-subject: so less variability within a
subject-increase precision
• Small sample size
56. EFFECT SIZE
• Important for practical significant
• The real difference in the comparison,
regardless of the sample size
• Also known as the standardized difference, its
value ranges from 0 to infinity
• The computation of effect size for different
statistics may vary and the cut points also may
differ
57. • For test of means-Cohen’s d is widely used
( d>0.8-big, d<0.2-small, d=0.5-moderate)
• P value answer- Does it work or not?
• Effect size answer-How well does it work?
58. Why is effect size computation
important?
Knowing the magnitude of an effect allows us
to ascertain the practical significance of
statistical significance
Practical Significance
Meta-analysis
59. Types of effect of Effect Sizes
• Mean Difference between groups:-Cohen’s d
• Correlation/Regression:- Cohen’s d or
Pearson’s r and R2
• Contingency tables:OR, RR
• ANOVA/ GLM:- Eta-squared, Omega squared,
ICC
• Chi-square tests:- Phi( 2 binary variables),
Cramer’s Phi or V( categorical variables)
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