Transcript LECTURE 21

Intention-to-Treat (ITT)
What is it and why should it be used?
Invoked Population Model –
Randomization Model
Nonrandom Selection of Clinics in a
Nonrandom Selection of Communities
Undefined Sampling Procedure for Patients
(a variety of sources are used)
N = NA + NB patients
Randomization
NA patients
Source: Lachin J. Cont Clin Trials, 1988.
NB patients
Realities of Clinical Trials
• Enrollment of ineligible participants
Incorrect diagnosis, e.g., TB
Errors in applying eligibility criteria
Knowing violation of eligibility criteria
• Incorrect treatment assigned
• Incorrect treatment given
• Less than 100% adherence to study treatment
Refusal to take study treatment after randomization
Withdrawal from study treatment during the study
Failure to adhere to instructions for taking study
medication
• Use of prohibited concomitant treatments
• Missing data (losses to follow-up)
What do you do with these deviations?
Many opinions on this.
ITT: Origin of Term
Bradford Hill, Principles of Medical Statistics, 7th Edition, 1961
• Hill raised the question “have any patients
after admission to the treated or control group
been excluded from further observation?”
– Patients who cannot be retained on treatment.
– Patients for whom a surgical procedure cannot be
performed.
• “Unless the losses are very few and therefore
unimportant, we may inevitably have to keep
such patients in the comparison and thus
measure the intention to treat in a given way
rather than actual treatment”.
– Concerned about bias resulting from groups that
were no longer comparable.
Strict Definition of ITT
“Includes all randomized patients in the groups
to which they were randomly assigned,
regardless of the treatment they actually
received, and regardless of subsequent
withdrawal from treatment or deviation from
the protocol”.
Working group of Biopharmaceutical Section of the
American Statistical Association (Fisher LD et al, in
Statistical Issues in Drug Development, Peace KE,
editor, 1990.
Two General Approaches
“Intention to treat” (also referred to as “full analysis” set
and “as randomized”)
Analyze all randomized subjects in their assigned
groups utilizing as much information from each as
possible
“Treatment received” (also referred to as “per protocol”
or “as treated”)
Analyze only fully eligible and compliant subjects with
no missing data, e.g., “valid” and “evaluable” subjects
There May Be a Middle Ground
• Modified Intention to Treat (MITT) population
– Participants included in assigned treatment group
regardless of treatment actually received.
– Ineligible participants based on measures made
before randomization, but delayed, are excluded
(e.g., patients with HIV who are in enrolled in an HIV
prevention trial or patients without TB who are
enrolled in a TB treatment trial).
• Safety population
– Participants who take the experimental treatment
even if assigned control treatment.
Example: Randomized Trial of Prevention of HIV
with Acyclovir in Couples Where One Partner is
Co-infected with HIV and HSV-2
“The primary analysis was a modified intention-totreat analysis of linked transmissions of HIV-1;
unlinked transmissions, seroconversions that
occurred among men when their female partners
who were infected with HIV-1 were pregnant and not
taking the study drug, and seroconversions that
occurred after the death of the HIV-1 infected partner
were excluded. The secondary analysis was
intention-to-treat”
N Engl J Med 2010; 362:427-439.
Arguments for Intention to Treat
• Consistent with randomization – get the right
significance probability for hypothesis testing.
• Addresses the question of practical interest – a
comparison of treatment policies.
• If the objective is to understand the implication
of using a specific intervention in practice, this
is the right analysis (e.g., non-adherence is a
consequence of using a strategy in practice).
Arguments for Per Protocol (As Treated)
Analysis
• Better estimate of pure pharmaceutical effect of
treatment (i.e., including non-compliers dilutes the
treatment difference).
• The relevant question is whether the treatment can
work when used as intended, e.g., is it effective among
patients who can tolerate it?
• In a non-inferiority/equivalence study (as opposed to a
superiority study) , this may be a more conservative
analysis (less dilution toward no difference)
Key Point: Make sure you discuss the question
before you start the study.
Arguments for Both
• Trials can ask two questions:
– “Can Drug A reduce tumor size”? (explanatory)
– “Does prescribing Drug A to patients with tumors
do more harm than good”? (management)
– Can it work versus does it work study designs. Also
referred to pragmatic and explanatory approaches
by Shwartz and Lellouch (J Chronic Dis, 1967)
Sackett and Gent, N Engl J Med 1979.
In non-inferiority studies you should consider both
ITT and per protocol analyses.
Pre-Exposure Prophylaxis (PrEP) Trials to
Prevent HIV Acquisition
• Several trials, results not all consistent, does
adherence explain all/part of the difference?
– IprEx trial in men who have sex with men (36 vs 64
infections -- 44% reduction in incidence with FTCTDF; 95% self-reported adherence but about 50%
based on drug levels.)
– FEM-PrEP trial in heterosexual women (33 vs 35
infections with FTC-TDF; 95% self-reported
adherence but about 38% based on drug levels)
– Partners trial in discordant couples (13 vs 52
infections – 75% reduction in incidence with FTCTDF; 97% self-reported adherence and 82%
adherence based on blood levels)
Obstacles to Intention to Treat (ITT)
• Missing data
– Due to the way the protocol was written
– Losses to follow-up
– Withdrawal of consent
Important to note that ITT not only requires all
randomized participants be included in the
analysis, but also requires that all randomized
participants be followed and have the
outcomes of interest measured no matter
adherence to the protocol.
Some Protocols Define Situations When Patients
Should No Longer Be Followed: Off Study
•
•
•
•
•
•
•
Did not start treatment
Ineligible
Unacceptable toxicity
Disease progression
Incarceration
Lost to follow-up
Withdrawal of consent
Bad idea if ITT is goal – follow everyone until the end of the
study or until some defined follow-up period
has been achieved. “Off study” is a confusing and bad term.
Obstacles to Per Protocol Analysis
• Defining adherence to treatment
• What is an acceptable level of adherence and
how do you measure it?
– Do you count events that occurred within 2 days, 7
days, 30 days of treatment discontinuation?
– Depends on the study and it may not always be
clear where to draw the line.
Treatment Received
Advantage:
Undiluted treatment effect
Disadvantage:
Comparison of groups may be
biased and it is not predictable
in which direction.
Intention-to-Treat
Advantage:
Comparability of treatment
groups; no bias resulting from
exclusions.
Disadvantage: Possible dilution of treatment
effect; loss of power unless
sample size was increased to
account for it.
Intent-to-Treat May be More Powerful
• Not only larger sample size, but…
• If the treatment under study has an effect even
after discontinuation (e.g., disease progression
slowed, lingering pharmacologic effect)
ICH Guidelines – Full Analysis Set
• Exclusions may occur for failure to meet major
entry criteria, failure to take at least one dose
of medication, and for lack of any data after
randomization
• Exclusion of ineligibles may only occur if:
–
–
–
–
Criterion measured prior to randomization
Eligibility can be objectively assessed
There equal scrutiny for all patients
All violations of specific type are excluded
ICH Guidelines – Per Protocol Set
Typical Criteria
• Completion of pre-specified minimum
exposure to treatment
• Availability of measurements of primary
outcomes
• Absence of major protocol violations
Examples of Eligibility Errors
in AIDS Trials
• Liver enzyme tests are mixed up for Patient X and
Patient Y; 2 weeks after randomization it is determined
study drugs are contraindicated for Patient X
• Patients have CD4+ cell counts mixed up and the
wrong patient is randomized.
• Patient X is randomized and is discovered 4 weeks
later to be HIV negative
• Qualifying lab measurements made 45 days before
randomization instead of within 30 days
Policies for Handling Eligibility Errors
1st Priority is Prevention
• Simple inclusion/exclusion criteria
• Eligibility checks before randomization
• Regular summary reports to monitor performance
Possible Policies
• Don’t enroll until eligibility is verified
• Enroll those possibly eligible and withdraw later if ineligible;
decision to withdraw is blinded to treatment group and based on
pre-randomization measurements
• Enroll those possibly eligible and keep them
Peto R et al., Br J. Cancer 1976; 34:585-612.
What do you do about eligibility
errors?
• Document them.
• Determine whether it is safe for patients to continue
treatment.
• If safe, assess whether patient should be allowed to
continue treatment.
• In most cases, follow the patients like other
randomized patients so that an intent to treat analysis
can be carried out.
• Pre-specify a plan for handling them in the protocol.
Examples of “Adherence” Problems
in AIDS Trials
• Patient X reports taking a study medication which is not allowed
by the protocol
• Patient X dies after randomization but before study drug is picked
up from pharmacy
• Patient X quits taking study treatment 2 weeks after randomization
because she decides he does not want to participate in a placebo
controlled study
• Patient X quits taking study drug 8 weeks after randomization due
to side effects
• Patient X stops taking study drug before outcome assessment
because their condition is worsening.
• Patient X is randomized twice because he did not like the first
assignment
Anturane Trial
Anturane
Total
Ineligible patients
Eligible patients
Placebo
No. of
Patients
No. of
Events
No. of
Patients
No. of
Events
P-value
813
74
816
89
0.28
38
10
33
4
775
64
783
85
0.10
Nonanalyzable deaths
20
23
Analyzable deaths
44
62
0.08
43
62
0.06
22
37
0.04
6
24
0.003
Analyzable cardiac deaths
Analyzable sudden cardiac
Analyzable sudden cardiac
deaths in 1st 6 months
N Engl J Med 1980; 302:250-256.
Coronary Drug Project
Mortality Results
Clofibrate
Placebo
1103
2789
No. deaths in 5 years
221
583
Percent dead
20.0
20.9
No. patients
p-value = 0.55
JAMA 231:360-81, 1975.
Coronary Drug Project –Adherence to Clofibrate
(3 capsules, 3 times per day)
40
35
Percent
30
25
20
15
10
5
0
0-19
20-39
40-59
60-79
80-89
90+
Adherence*
* (No. of capsules taken/No. that should have been taken) x 100
Averaged over all 4 month visits for 5 years for those alive after 5 years.
JAMA 231:360-81, 1975.
Coronary Drug Project
Mortality According to Adherence to
Clofibrate
Adherence
Percent Dead
<80%
24.6
p=0.0001
80%+
15.0
Overall
20.0
NEJM 303:1038-41, 1980.
The Obvious, But Naïve, Solution
Percent dead
Clofibrate
Adherers
Placebo
15.0
20.9
p = 0.04
Coronary Drug Project
Adherence to Clofibrate and Placebo
(3 capsules, 3 times per day)
Clofibrate
Percent
40
35
30
25
20
15
10
5
0
0-19
20-39
40-59
60-79
80-89
90+
Percent
Adherence
Placebo
45
40
35
30
25
20
15
10
5
0
0-19
20-39
40-59
60-79
Adherence
JAMA 231:360-81, 1975.
80-89
90+
Coronary Drug Project
Mortality According to Adherence
to Clofibrate and Placebo
Adherence
Clofibrate
Placebo
<80%
24.6
28.2
p=0.0001
p=0.0000001
80%+
15.0
15.1
Overall
20.0
20.9
NEJM 303:1038-41, 1980.
Summary / Recommendations
• Primary analysis should usually be ITT (need to continue collecting
data to do this right) – it addresses a pragmatic policy/management
question which is always relevant.
• ITT analysis requires excellent trial conduct.
• It is appropriate to carry out secondary “per protocol” or “as
treated” analyses but these have to be interpreted with caution.
• For analyses which are not intent-to-treat it is often
difficult/impossible to quantify bias resulting from not comparing like
with like
• If exclusions after randomization are to be made as part of
secondary “per protocol” analyses, they should be specified in the
protocol
• Think about what you want to estimate in advance.