Randomized trial seminar

0 Background
0 Experimental studies
0 Designs of RCT
0 Types of RCT
0 Steps in conducting RCT
0 Ethical issues
0 Pros & Cons
0 Example

0 Both in public health and in clinical practice, the
main objective is to modify the natural history of
disease so as to prevent or delay death or disability
and to improve the health of patient or the
population.
0 The challenge is to select the best available
preventive or therapeutic measure to achieve this
goal.
0 To do so, studies are carried out that determine the
value of these measures.

Galen(129-c.199ce) gave one of his potions to a lot of patients:
some recovered, some died. He thought that was evidence that
the potion worked.
All who drink of this treatment recover in short time, Except
those whom it did not help, who will die ,It is obvious, therefore,
that it fails only in incurable cases.
0 The problem with Galen’s line of reasoning is that no
experiment could disprove it . He could call any treatment
superior by claiming that evidence against it doesn’t count.

0 In 1537,AMBROISE PARÉ conducted unplanned trials in the
treatment of battle wounds.
0 One day after winning a battle, there were numerous wounded
for him to treat.
0 Due to lack of boiling oil which was a standard treatment at
that time, he used digestive made of egg yolk, rose oil &
turpentine and found it to work better than boiling oil.
0 Such trials have been the instruments of development of
medicine from a long time.

0 The first instance of random allocation of patients to experimental
and control conditions is attributed to James Lind, a naval surgeon,
in 1747.
0 Lind randomly assigned 12 sailors to 6 different treatments for
scurvy. The two patients who were given lemons and oranges
recovered most quickly, suggesting a beneficial effect of citrus.

Randomization was
Contributed by statistician
R.A. Fisher in agriculture
in 1923.
Randomized plots of crops
to receive different
treatments

0 The first RCT in medicine is credited to Sir A. Bradford Hill,
an epidemiologist for England's Medical Research Council.
0 The trial, published in the British Medical Journal in 1948,
tested whether streptomycin is effective in treating
tuberculosis.
0 First, he simply alternated the assignment of hospital
admissions to drug versus control .Later he recognized it led
to selection bias because the sequence was too easy to predict.
That realization led to the use of a random numbers table to
generate the numeric series by which patients would be
assigned to conditions.

0 Involve an active attempt
0 Types
 With patients as unit of studyCLINICAL TRIALS
With healthy people as unit of
study
FIELD TRIALS/
COMMUNITY
INTERVENTION
STUDIES
With communities as unit of
study
COMMUNITY TRIALS

0 Types
RANDOMIZED
CONTROLLED
TRIALS
Those involving a process of
‘RANDOM ALLOCATION’
Non RANDOMIZED
CONTROLLED
TRIALS
Those departing from ‘strict
randomization’ for practical
purposes
a. Cross-sectional comparison:
e.g. to comparable
communities or groups
b. Temporal comparison:
e.g. before and after the
intervention
c. Combinations of the above:
e.g. time-series analysis in
community trial.

In a randomized controlled trial,
Participants are assigned to treatment conditions at random
(i.e., they have an equal probability of being assigned to any
group).
Procedures are controlled to ensure that all participants in all
study groups are treated the same except for the factor that is
unique to their group. The unique factor is the type of
intervention they receive.

Primary goal
0 to test whether an
intervention works by
comparing it to a control
condition, usually either
no intervention or an
alternative intervention.
Secondary goals may include:
0 identify factors that
influence the effects of the
intervention (i.e.,
moderators)
0 understand the processes
through which an
intervention influences
change (i.e., mediators or
change mechanisms that
bring about the intervention
effect)

Study
population
Current
interventio
n
Improve
Do not
Improve
New
Interventio
n
Improve
Do not
Improve
R
A
N
D
O
M
IZ
A
TI
O
N

0 Concurrent parallel study design
0 Cross-over type of study design
0 Factorial design
0 Cluster design

0 Classical clinical trial
approach
0 Two study groups

0 Assumption of ‘no carryover
effects’ is difficult to test
0 Needs to determine appropriate
length of washout period.
0 “Period”effects : Progression of
disease, Dropouts
0 Will patient do better on drug A or
drug B?
0 Removes “patient effect” reducing
variability and increasing precision
of estimation

0 Major concern: interaction of
interventions
0 Patients must be willing and able to
take any of the treatment
combinations
0 Optimal dose modification strategy
for toxicity may be hard to
determine
0 Evaluates multiple factors
simultaneously

0 Groups or clusters randomly
assigned, not individuals. E.g. :
villages, classrooms , platoons
0 focus on effectiveness,outcomes
under conditions of actual use.
0 more appropriate for the
evaluation of interventions such
as family-based dietary
interventions,community-based
health promotion initiatives
0 or educational interventions.

0 Clinical trials
0 Preventive trials
0 Risk factor trials
0 Cessation experiments
0 Trial of etiological agents
0 Evaluation of health services

1. Drawing up a protocol
2. Selecting reference and experimental populations
3. Allocation of study subjects: Randomization
4. Intervention / manipulation
5. Follow –Up
6. Assessment of Outcome

0 One of the essential feature of RCT is that it is
conducted under a strict Protocol.
0 Once protocol is evolved, it should be strictly adhered to
throughout the study.
Prevents bias and reduces source of errors in the
study.
0 Preliminary or pilot test runs of protocol can be held
so see whether it contains any flaw.
0 Final version of protocol should be agreed upon by all
concerned before the trial begins.

0 Aims & Objectives of the study
0 Questions to be answered
0 Criteria for selection of study & control group
0 Size of Sample
0 Procedure for allocation of subjects into study &
control Group
0 Interventions to be applied-when ,where ,how and
what kind of subjects
0 Standardization of working procedures and schedules
0 Responsibilities of parties involved in trial
0 Evaluation of outcome of study

I. Reference /Target
Population:
 It is the population to
which findings of the trial
,if found successful , are
applicable.
 e.g. whole population,
population of school
children, population of a
city, industrial workers or
social groups
II. Experimental /Study
Population:
 derived from reference
population.
 Ideally, it should be chosen
randomly from reference
population so that it is
representation of reference
population.
Otherwise it may not be possible
to generalize the findings of the
study to reference population.

0 Inclusion & exclusion criteria : for determining who will or
will not be included in the study must be spelled out with great
precision, and in writing.
To ensure the replicability by others, just like with laboratory
experiments

0 To derive a causal inference regarding relationship of
intervention and outcome, comparison is important.
0 Types of controls :
Historical
Simultaneous nonrandomized controls
Randomized controls

0 Comparison group from past.
0 We go back to records of patients who were treated before
new treatment became available.
0 simple
Demerit:
0 Comparability can not be assured
0 Quality : Data for medical purpose not for research purpose
0 Changes in many factors over calendar time
0 Recall bias

0 Day of month of admission-odd/even days
0 Alternate assignment into study &control group
Predictable by investigator- selection bias

0 Randomization - a statistical procedure
by which the participants are allocated to
“Study” and “Control” groups.
0 The critical element of randomization is
the unpredictability of the next
assignment.
0 It makes RCT the gold standard design
for performing clinical trials.

Flipping a coin?
Rolling dice?
Shuffling cards?
Table of random numbers
computer random number
generators
• Random
• Reproducible
Preferable & recommended
• Random but tempt investigators
toward non-randomness
• Adequate methods but not
optimal
• Cannot be checked – no audit
trail
Not recommended

0 the probability of being
assigned to any
intervention stays
constant over the course
of the trial
0 the allocation probability
changes in response to the
balance, composition, or
outcomes of the groups
0 controversial because they
allocate patients not purely at
random
0 Aim: increase the sample's
probability of being assigned
to the best treatment
FIXED ALLOCATION
PROCEDURES
ADAPTIVE PROCEDURES

1. Simple(complete)
randomization
2. Permuted block
randomization
3. Balanced permuted
block randomization
4. Stratified randomization
1. Minimization Adaptive
Randomization
i. Biased Coin
Randomization
ii. Urn Randomization
2. Response Adaptive
Randomization
i. "Play-the-winner"
procedure
FIXED ALLOCATION
PROCEDURES
ADAPTIVE PROCEDURES

0 Elementary form of randomization, in which, every time when
there is an eligible participant, the investigator flips a coin to
determine whether the participant goes into the intervention or
control group.
0 A limitation is that random assignment is truly random.
A random process can result in the study winding up with
different numbers of subjects in each group. This is more
likely to happen if sample size is small

• Characters of ‘A completely random sequence of digits’
• Each digit occurs equally frequently in the whole sequence
• Adjacent (set of) digits are completely independent of one
another
• A table of random digits
• Behaves as a finite section from a completely random series
• Randomness is a property of the table as a whole
• Different numbers in the table are independent

00–04 05–09 10–14 15–19
00 56348 01458 36236 07253
01 09372 27651 30103 37004
02 44782 54023 61355 71692
03 04383 90952 57204 57810
04 98190 89997 98839 76129
05 16263 35632 88105 59090
06 62032 90741 13468 02647
07 48457 78538 22759 12188
08 36782 06157 73084 48094
09 63302 55103 19703 74741
Take Any Direction
…have to mention
this chosen
direction in
procedureMention
corresponding row
& column in
procedure

 Suppose we want to compare 2 treatments(A & B)
Random Number Table can be used in a no. of ways:
a) we will consider every odd number an assignment to
A and every even number an assignment to B
b) we could say that digits 0 to 4 would be treatment A,
and digits 5 to 9 treatment B.
c) If we are studying three groups, we could say that
digits 1 to 3 are treatment A, digits 4 to 6 treatment
B, digits 7 to 9 treatment C, and digit 0 would be
ignored.
d) Prepare a series of opaque envelopes that are
numbered sequentially on the outside: 1, 2, 3, 4, 5,
and so on.

542-04-#37
0 Simple randomization does not guarantee balance over time
in each realization.
0 Patient characteristics can change during recruitment (e.g.
early pts sicker than later).
0 Restricted randomizations guarantee balance
1. Permuted-block
2. Biased coin (Efron)
3. Urn design (LJ Wei)

0 Blocked randomization reduces the risk that different numbers
of people will be assigned to the treatment (T) and control (C)
groups.
0 Patients are randomized by blocks.
0 The order is chosen randomly at the beginning of the block.
0 In randomly permuted blocks, there are several block sizes
(e.g., 4, 6, and 8), and the block size and specific order are
chosen randomly at the beginning of each block.

Example
0 Block size 2m = 4
2 Trts A,B }  4C2 = 6 possible
0 Write down all possible assignments
0 For each block, randomly choose one of the six possible
arrangements
0 {AABB, ABAB, BAAB, BABA, BBAA, ABBA}
ABAB BABA ......
Pts 1 2 3 4 5 6 7 8 9 10 11 12

Advantage
0 A balance in the number
of cases assigned to T
versus C at any point in
the trial (which could be
valuable if the trial needs
to be stopped early).
Disadvantage
0 with fixed blocks,
predictability of the group
assignment of patients
being randomized late in
the block by research staff .
reduced by using the
method of randomly
permuted blocks and blind
ing of research staff to the
randomization process .

0 To ensure that the treatment and control groups are balanced
on important prognostic factors that can influence the study
outcome (e.g., gender, ethnicity, age, socioeconomic status).
0 Before doing the trial, the investigator decides which strata are
important and how many stratification variables can be
considered given the proposed sample size.
0 A separate simple or blocked randomization schedule is
developed for each stratum.
0 Large trials often use randomly permuted blocks within
stratification groups.

o Minimization corrects (minimizes) imbalances that arise over
the course of the study in the numbers of people allocated to
the treatment and control.
o An attempt to resolve the problem of empty strata when
trying to balance on many factors with a small number of
subjects
o Balances Trt assignment simultaneously over many strata.
o Used when the number of strata is large relative to sample
size as stratified randomization would yield sparse strata.
o Logistically more complicated

o In this procedure, if the imbalance in treatment assignments
passes some threshold, the allocation is changed from chance
to a bias in favor of the under-represented group.
o For example... If after 10 randomizations, there are 7 patients
assigned to intervention and 3 assigned to control, the coin
toss will become biased.
o Then, rather than having 50/50 chance of being assigned to
either condition, the next patient will be given a 2/3 chance of
being assigned to the under-represented condition and a 1/3
chance of being assigned to the overrepresented one.
o This procedure requires keeping track of imbalances
throughout the trial. In smaller trials, imbalances can still
result

0 This procedure tries to correct imbalances after each
allocation.
0 For example...
0 The investigator starts with off with an urn containing a red
ball and a blue ball to represent each condition.
0 If the first draw pulls the red ball, then the red ball is replaced
together with a blue ball, increasing the odds that blue will be
chosen on the next draw. This continues, replacing the chosen
ball and one of the opposite color on each draw.
0 The procedure works best at preventing imbalance when final
sample size is small.

542-04-#46
0 Stratified Randomization
0 Covariate Adaptive
0 Pocock & Simon

542-04-#47
0Goal is to balance on a number of factors but with "small"
numbers of subjects
0Example .., if at some point Trt A has more older patients that Trt
B, next few older patients should more likely be given Trt B until
"balance" is achieved
0Several risk factors can be incorporated into a score for degree of
imbalance B(t) for placing next patient on treatment t (A or B)
0More complicated to implement

Study type Randomization
Large studies Blocked
Large, Multicentre studies Stratified by centre
Small studies Blocked and Stratified
by centre
Large number of
Prognostic factors
Minimization
Large studies Stratified analysis
without stratified
randomization

• Actual randomization should be delayed until just prior to
initiation of therapy after consenting.
• This prevents randomizing participants who drop out
before participating in any of the study.
• This is important because everyone who gets randomized
needs to be included in the study's analysis.

1. Sequenced sealed envelopes (prone to tampering!)
2. Sequenced bottles/packets
3. Phone call to central location
- Live response
- Voice Response System
4. One site PC system
5. Web based
Best plans can easily be messed up in the implementation

0 Allocation concealment means that the
person who generates the random assignment
remains blind to what condition the person
will enter.
0 Preferably, randomization should be
completed by someone who has no other
study responsibilities. Often, the study
statistician assumes this role.
0 If allocation is not concealed, research staff is
prone to assign "better" patients to
intervention rather than control, which can
bias the treatment effect upward by 20-30%

• Deliberate application or withdrawal or reduction of the
suspected causal factor as laid down in the protocol
• This manipulation creates :
Independent variable (drug, vaccine or a new procedure):
whose effect is determined by measurement of final
outcome.
Dependent variable: final outcome (incidence of disease ,
survival time , recovery time)

0 Prognostic profile at entry
0 Treatment (Assigned & Received)
0 Follow-up
 Attrition
0 Outcome
 Outcome measurement
 Reliability and validity of outcome measures

0 Attrition: refers to a rate of loss of
participants from the study that differs
between the intervention and control
groups.
0 It may compromise
 internal validity by altering the random composition
of groups and their equivalence.
 statistical validity by reducing sample size and
power or by systematically altering the variability
within samples.
 External validity due to the potential for attrition to
limit the generalizability of results to only those who
are retained in a study.

0 An adverse event (AE) is an undesirable health occurrence
that occurs during the trial and that may or may not have a
causal relationship to the treatment.
0 A serious adverse event (SAE) is defined as something life-
threatening, requiring or prolonging hospitalization and/or
creating significant disability.
0 E.g. A suicide attempt -- an SAE in a study of any treatment.
0 The SAE needs to be reported regardless of whether it bears
any relationship to the treatment or the problem being studied
0 Depending on the severity and frequency of adverse events,
investigators and data safety monitors may have to decide to
terminate the trial prematurely.

Every Adverse event needs to be reported!

0 Which participants will be analyzed?
 Intention to treat
 Per protocol analysis
0 Subgroup Analyses
0 Statistical Power of study
0 Data Analytic Techniques
 Continuous Outcome Variables
 Categorial Outcome Variables

0 Basic Principle - “As randomized, so analyzed”
0 Includes all randomized patients in the groups to which they
were randomly assigned, regardless of their adherence with
the entry criteria, regardless of the treatment they actually
received, and regardless of subsequent withdrawal from
treatment or deviation from the protocol…..(Lloyd) Fisher et
al., 1990.
0 The ITT analysis addresses the question of pragmatic
hypothesis– the effectiveness of therapy i.e whether the study
treatment, if made available to the population, would be
superior to an alternative intervention.

0 the opposite end of the spectrum from ITT analysis
0 includes in the analysis only those cases who completed
treatment.
0 Its results represent the best case treatment results that could
be achieved if the study sample were retained and remained
compliant with treatment.
0 Should not be used alone/main analysis

0 Planned subgroup analyses. In a few instances, a study may
have been designed and powered to test whether a treatment
works better for one demographic group (e.g., females) than
another (e.g., males). In that case, testing a hypothesized
treatment by demographic group interaction would be a
primary aim that definitely needs be tested.
0 Exploratory subgroup analyses. More often, many different
treatment-by-subgroup interactions will be explored. Those
analyses can support hypothesis generation. The important
caveat is the need to remember that unplanned subgroup
analyses are done in the context of discovery rather than
confirmation. Any findings require replication in another trial.

0 Power is ability to find a difference when a real difference
exists.
0 The power of a study is determined by three factors:
 Alpha level.
 Sample size.
 Effect size:
• Association between DV and IV
• Separation of Means relative to error variance.

0 Sometimes we will incorrectly fail to reject the null hypothesis
– a type II error.
0 There really is an effect but we did not find it
0 Statistical power is the probability of detecting a real effect
0 More formally, power is given by:
1- 
where  is the probability of making a type II error
0 In other words, it is the probability of not making a type II
error

0 By making alpha less strict, we can increase power.
(e.g. p < 0.05 instead of 0.01)
0 However, we increase the chance of a Type I error.

0 One of the most useful aspects of power analysis is the
estimation of the sample size required for a particular study
0 Too small a sample size and an effect may be missed
0 Too large a sample size too expensive a study
0 Different formulae/tables for calculating sample size are
required according to experimental design

0 n = the sample size required in each group (double this for
total sample)
0 σ = standard deviation, of the primary outcome variable
0 δ= size of difference of clinical importance
0 π 1 = first proportion
0 π 2 = second proportion
0 zơ/2=depends on desired significance level
0 Zβ=depends on desired power

0 As the separation of
means increases, the
power of study increases.
0 As the variability about a
mean decreases power
also increases

Continuous outcome
measures((e.g. symptom
severity)
0 Analysis of Variance
(ANOVA) and its Variants
and Extensions
Alternative Analyses
0 Latent Growth Models
0 Growth Mixture Models
0 Multi-level Models
Categorial outcome
measures(e.g. being
hospitalized or quitting
smoking )
0 Generalized estimating
equations (GEE)
0 Survival analysis

• Dichotomization: Common analyses of RCT data require
outcomes to be classified as either "present" or "absent."
• Some outcomes such as death, stroke, or pregnancy are
naturally dichotomous (present or not).
• Sometimes events are dichotomized as a "success" or a
"failure" instead of "present" and "absent."
• Other outcomes that are naturally continuous (e.g., length of
hospital stay, blood pressure, pain score) can be dichotomized
by the selection of a “cutoff” score that separates successes
from failures.
• A "two by two" table often summarizes results

Blinding is an attempt to reduce bias arising out of errors
of assessment
a) Open trials:All participants and investigators know who is
getting which interventionE.g. medical vs. surgical treatments
b) Single blind trial: Participant not aware
c) Double blind trial: Neither doctor/investigator nor the
participant is aware.
d) Triple blind trial: Doctor/investigator , participant and the
person analyzing the data are all not aware of the assigned tt

Concealment of
allocation Blinding/Masking
0 to protect the randomization
process before the subject
enters the trial.
0 ALWAYS feasible
0 If not done, results in
selection bias(treatment
assignment is no longer truly
random)
0 After trial begins.
0 Blinding is not always feasible
0 If not done, can result in
patients biasing their responses
because of their knowledge of
treatment;
biased outcome assessment
because investigators have
knowledge of treatment

0 Estimation with Statistical Significance Testing
0 Assessing the Effect Size of an Intervention
0 Assessing Clinical Significance

Estimation with Statistical Significance Testing
0 Whether the effect of a treatment reaches a conventional
significance level (p <.05) depends heavily on factors such as
sample size.
Assessing the Effect Size of an Intervention
0 An effect size describes the magnitude of an intervention's
effect on the study outcome.
0 In the case of RCTs, the effect size represents the magnitude
of the difference between the control and intervention
conditions on a key outcome variable adjusted for the standard
deviation of either group.

Assessing Clinical Significance
0 When testing interventions that address health problems
The proportion of patients who move from exhibiting clinical
or dysfunctional levels of symptoms/behavior to functional
levels.
The Number Needed to Treat (NNT) expresses the number of
patients who need to receive the intervention to produce one
good outcome compared to control. NNT is a widely used
index of clinical significance
NNT= 1_ _____
(Rate in untreated gp)-(Rate in treated gp)

0 When testing interventions that address health problems
0 Once outcomes have been dichotomized, six statistical
measures are used to describe the treatment effect:-
• Control Event Rate (CER) & Experimental Event Rate
(EER)
• Absolute Risk Reduction (ARR) & Number Needed to
Treat (NNT)
• Relative Risk (RR) & Relative Risk Reduction (RRR)

0 CER = number of events in ctrl group/number of subjects in
ctrl group
0 EER = number of events in exp group/number of subjects in
exp group
0 ARR = CER – EER
0 The Number Needed to Treat (NNT) expresses the number of
patients who need to receive the intervention to produce one
good outcome compared to control. NNT= 1/ARR
0 RR represents the fraction of the original risk that remains with
intervention.RR=EER/CER
0 RRR represents the fraction of the original risk that is removed
with intervention.RRR=ARR/CER

CER & EERExp
(MgSO4)
Ctrl
(placebo)
Row
totals:
CP (+) 20 38 58
CP (-) 1021 1057 2078
Column
totals: 1041 1095 2136
Interpretation:
3.5% of the infants born in the control
group (without MgSO4) developed
moderate to severe cerebral palsy.
CER =
number of events in ctrl group
number of subjects in ctrl group
CER =
38
1095
= 0.035 = 3.5%
Interpretation:
1.9% of the infants born in the
experimental group (with MgSO4)
developed moderate to severe
cerebral palsy.
EER =
number of events in exp group
number of subjects in exp group
EER =
20
1021
= 0.019 = 1.9%
Control Event Rate
Experimental Event Rate

ARR & NNTExp
(MgSO4)
Ctrl
(placebo)
Row
totals:
CP (+) 20 38 58
CP (-) 1021 1057 2078
Column
totals: 1041 1095 2136
Interpretation:
Use of MgSO4 reduced the rate of
moderate to severe cerebral palsy by
1.6 percentage points.
Interpretation:
Between 62 and 63 pre-term mothers
need to be treated in order to avoid
one additional case of moderate to
severe cerebral palsy.
NNT=
1
ARR
NNT =
1
0.016
= 62.5
Absolute Risk Reduction
Number Needed to Treat
ARR = CER – EER
ARR = 3.5% – 1.9%
ARR = 1.6%
Notice how the units for CER & EER are "events per person" and the units for NNT is
"people per event." This matches the intuitive interpretation of ARR & NNT.

RR & RRRExp
(MgSO4)
Ctrl
(placebo)
Row
totals:
CP (+) 20 38 58
CP (-) 1021 1057 2078
Column
totals: 1041 1095 2136
Interpretation:
Use of MgSO4 reduced the risk of
moderate to severe CP by 46% of its
original value.
CER
RRR =
ARR
0.035
0.016
RRR = = 0.46 = 46%
Relative Risk
Relative Risk Reduction
Interpretation:
Use of MgSO4 reduced the risk of
moderate to severe CP to 54% of its
original value.
CER
RR=
EER
0.035
0.019
RR = = 0.54 = 54%
RR represents the fraction of the original risk that remains with MgSO4.
RRR represents the fraction of the original risk that is removed with MgSO4.

0 The Consolidated Standards of Reporting Trials (CONSORT)
has become the gold standard for reporting the results of
RCTs.
0 A checklist and flow diagram.
0 first published in 1996 and updated in 2001 and 2010.
0 Extensions of the CONSORT Statement have been developed
for other types of study designs, interventions and data.

0 Investigators are responsible to uphold ethical standards and
guidelines
0 Declaration of Helsinki. Developed by the World Medical
Association, this set of ethical principles guides medical
researchers in conducting research on human subjects.

Some procedures for safeguarding human subjects include:
0 Informed consent procedures
0 Procedures to safeguard confidentiality
0 Protocols to preserve safety and address adverse events
0 Reporting study results

0 Random assignment and the use of a control condition ensure
that any extraneous variation not due to the intervention is
either controlled experimentally or randomized. That allows
the study's results to be causally attributed to differences
between the intervention and control conditions.
0 In sum, the use of an RCT design gives the investigator
confidence that differences in outcome between treatment and
control were actually caused by the treatment, since random
assignment (theoretically) equalizes the groups on all other
variables.

Drawbacks of conducting an RCT are:
0 Time- and energy- intensive, Expensive
0 Losses to follow-up, lack of perfect blinding, and other
problems often affect RCTs in the field of public health,
0 “effect modification”--when the intervention–outcome
association varies according to the presence of external
characteristics.
0 “behavioral effect modification,”(institutional, provider, and
recipient behaviors)“biological effect modification.”

0Unnecessary if:
0 Clearly successful intervention
0 Previous RCT’s or meta-analyses
0Impractical when:
0 Unethical to randomise
0 Large number needed
0Inappropriate when:
0 Looking at prognosis
0 Looking at validity of diagnostic tests
0 Looking at quality of care

Intervention : Modeled On The Baby-friendly
Hospital Initiative

0 two-week zinc therapy for diarrhoea on morbidity and mortality due to
diarrhoea and ALRI in a community-based cluster randomized trial (3) in the
Matlab field area of ICDDR,B: Centre for Health and Population Research in
Bangladesh. Thirty service areas (clusters) around Matlab Treatment Centre
each with about 200 children aged 3-59 months were randomly allocated to
intervention or comparison areas. One community health worker served each
cluster
0 In this trial, the availability of zinc supplements, along with ORT and
appropriate education programmes, was associated with significantly higher
use of ORT and lower use of antibiotics.
J HEALTH POPUL NUTR 2004 Dec;22(4):440-442

0 “Evidence-based public health” calls for a solid knowledge
base on disease frequency and distribution, on the
determinants and consequences of disease, and on the safety,
efficacy, and effectiveness of interventions and their costs.
0 Existing standards and methods must be adapted to meet the
methodological challenges of evaluating large-scale public
health interventions.

0 Instead of having a “no treatment” control, equivalence trials test
whether a new intervention is as good as (or not worse than) an
established treatment with proven efficacy.
0 New intervention may have some advantage (convenience, cost)
over established, but is it at least as efficacious?

0 People may be carrying out equivalence trials without
realising it.
0 Analysis with respect to a pre-stated margin of non-inferiority
(smallest clinically interesting difference)
0 ITT analysis may increase risk of type 1 error
0 Choice of outcomes important
Piaggio et al., 2006, JAMA,295,1152-1160

0 Need to reference established efficacy of “standard” treatment
0 Hypotheses should be framed in terms of non-inferiority
0 “Margins of equivalence” should be reported

0 Various approaches used:
0Checklist approach
0Quality scoring system approach
0 Quality scores are complicated and tend to vary depending
on the instrument used –so, not encouraged

Randomized trial seminar

More Related Content

What's hot

Similar to Randomized trial seminar

More from Manisha Malik

Recently uploaded

Randomized trial seminar