Analysis Issues in Assessing Efficacy in Randomized Clinical Trials “Intention to Treat” and...
-
Upload
violet-anthony -
Category
Documents
-
view
220 -
download
4
Transcript of Analysis Issues in Assessing Efficacy in Randomized Clinical Trials “Intention to Treat” and...
![Page 1: Analysis Issues in Assessing Efficacy in Randomized Clinical Trials “Intention to Treat” and Compliance Elizabeth Garrett-Mayer Oncology Biostatistics.](https://reader036.fdocuments.in/reader036/viewer/2022062421/56649dd05503460f94ac4d3f/html5/thumbnails/1.jpg)
Analysis Issues in Assessing Efficacy in Randomized Clinical
Trials“Intention to Treat”
and Compliance
Elizabeth Garrett-MayerOncology Biostatistics
April 26, 2004
![Page 2: Analysis Issues in Assessing Efficacy in Randomized Clinical Trials “Intention to Treat” and Compliance Elizabeth Garrett-Mayer Oncology Biostatistics.](https://reader036.fdocuments.in/reader036/viewer/2022062421/56649dd05503460f94ac4d3f/html5/thumbnails/2.jpg)
Randomized Clinical Trials
• Why are randomized trials the “gold-standard” for assessing treatment efficacy?
• Randomization!
• Balances factors that might be related to treatment effects across groups
• Controls confounding.
• Avoids selection bias in forming groups.
![Page 3: Analysis Issues in Assessing Efficacy in Randomized Clinical Trials “Intention to Treat” and Compliance Elizabeth Garrett-Mayer Oncology Biostatistics.](https://reader036.fdocuments.in/reader036/viewer/2022062421/56649dd05503460f94ac4d3f/html5/thumbnails/3.jpg)
General Problem
• Study subjects do not always adhere to protocol– Drop-out– Switch treatments– Take only a portion of assigned treatment
• How do we account for ‘compliance’?
• Most would say: “we don’t and we shouldn’t!”
![Page 4: Analysis Issues in Assessing Efficacy in Randomized Clinical Trials “Intention to Treat” and Compliance Elizabeth Garrett-Mayer Oncology Biostatistics.](https://reader036.fdocuments.in/reader036/viewer/2022062421/56649dd05503460f94ac4d3f/html5/thumbnails/4.jpg)
Example: Coronary Drug Project
• Total mortality using clofibrate vs. placebo in men with history of myocardial infarction– Good adherers, clofibrate: 15% mortality– Poor adherers, clofibrate: 25% mortality– Good adherers, placebo: 15% mortality– Poor adherers, placebo: 28%
mortality
• Tried to ‘adjust’ but it didn’t help.
![Page 5: Analysis Issues in Assessing Efficacy in Randomized Clinical Trials “Intention to Treat” and Compliance Elizabeth Garrett-Mayer Oncology Biostatistics.](https://reader036.fdocuments.in/reader036/viewer/2022062421/56649dd05503460f94ac4d3f/html5/thumbnails/5.jpg)
Intention to Treat (ITT)
• What is “intention to treat”?• Analyze the data based purely on the
randomization• Ignore the following:
– Cross-overs– Non-compliance/Drop-outs
• Sounds illogical, but, in principle, it isn’t.• Some encourage ‘supplementary
analyses’ which look at compliers only
![Page 6: Analysis Issues in Assessing Efficacy in Randomized Clinical Trials “Intention to Treat” and Compliance Elizabeth Garrett-Mayer Oncology Biostatistics.](https://reader036.fdocuments.in/reader036/viewer/2022062421/56649dd05503460f94ac4d3f/html5/thumbnails/6.jpg)
Examples of Violation of ITT
• Compare only patients who actually received assigned treatment.
• Assign patients to comparison groups based on the treatment they received.
• Exclude patients with low adherence/compliance
![Page 7: Analysis Issues in Assessing Efficacy in Randomized Clinical Trials “Intention to Treat” and Compliance Elizabeth Garrett-Mayer Oncology Biostatistics.](https://reader036.fdocuments.in/reader036/viewer/2022062421/56649dd05503460f94ac4d3f/html5/thumbnails/7.jpg)
What do we know about compliance?
• In general, compliance …..• Is not random
– Individuals who are not compliant might also have other ‘factors’ which are related to the outcome
• Is not dichotomous– Non-compliers can have varying levels of non-compliance– E.g. might only take ½ of prescribed medications, might only take
¼.• Can fluctuate over time
– Often, compliance is good early in study and then tapers off.– Sometimes, patients will take lots of meds close to office visit to
‘make-up’ for non-compliance.• Is hard to measure
– Reliability– Completeness– Inequality of follow-up across arms
![Page 8: Analysis Issues in Assessing Efficacy in Randomized Clinical Trials “Intention to Treat” and Compliance Elizabeth Garrett-Mayer Oncology Biostatistics.](https://reader036.fdocuments.in/reader036/viewer/2022062421/56649dd05503460f94ac4d3f/html5/thumbnails/8.jpg)
So…..
• It is potentially “hazardous” to rely on analyses that allow for non-compliance
• ITT is unbiased: it measures ‘effect’ in global sense– If people are non-compliant on trial, they are likely to
be non-compliant in “real-life”– If people switch medications, or self-medicate on trial,
they are likely to do that in “real-life”
• And, compliance analyses are usually an afterthought:– Not part of the clinical trial protocol– Ad hoc analyses decided after the study is over.
![Page 9: Analysis Issues in Assessing Efficacy in Randomized Clinical Trials “Intention to Treat” and Compliance Elizabeth Garrett-Mayer Oncology Biostatistics.](https://reader036.fdocuments.in/reader036/viewer/2022062421/56649dd05503460f94ac4d3f/html5/thumbnails/9.jpg)
Tempting…
• It is tempting to analyze by ‘treatment received’, BUT!– The groups are no longer comparable– Effectiveness of treatment should incorporate
compliance (outside trial people may be even LESS compliant)
![Page 10: Analysis Issues in Assessing Efficacy in Randomized Clinical Trials “Intention to Treat” and Compliance Elizabeth Garrett-Mayer Oncology Biostatistics.](https://reader036.fdocuments.in/reader036/viewer/2022062421/56649dd05503460f94ac4d3f/html5/thumbnails/10.jpg)
But, ITT is not always ideal
• Supplementary analyses are often warranted
• They can provide additional information
• But, by and large, experts agree:
ANALYSIS BY “INTENTION TO TREAT” SHOULD REMAIN THE MAIN STATISTICAL
APPROACH FOR PRESENTING COMPARATIVE RESULTS FROM RANDOMIZED CLINICAL TRIALS.
![Page 11: Analysis Issues in Assessing Efficacy in Randomized Clinical Trials “Intention to Treat” and Compliance Elizabeth Garrett-Mayer Oncology Biostatistics.](https://reader036.fdocuments.in/reader036/viewer/2022062421/56649dd05503460f94ac4d3f/html5/thumbnails/11.jpg)
Example
• Serum Cholesterol in elderly hypertension trial• Patients were randomized to either (A) diuretic,
(B) beta-blockers, or (C) placebo• 1 year post randomization:
– (A) vs. (C): +0.12 mmol/l change in serum cholesterol (p=0.001)
– (B) vs. (C): +0.08 mmol/l change in serum cholesterol (p=0.003)
• SURPRISING: Why would there be a lipid effect of beta-blockers?
![Page 12: Analysis Issues in Assessing Efficacy in Randomized Clinical Trials “Intention to Treat” and Compliance Elizabeth Garrett-Mayer Oncology Biostatistics.](https://reader036.fdocuments.in/reader036/viewer/2022062421/56649dd05503460f94ac4d3f/html5/thumbnails/12.jpg)
Compliance issues
• 30% of beta-blocker group were also receiving diuretic by 1 year either instead of or in addition to beta-blocker.
• Alternative analysis: Consider 3 groups– Diuretic alone– Beta-blocker alone– Both
• Results:– Diuretic alone: +0.11 (p<0.001)– Beta-blocker alone: +0.03 (p=0.20)
![Page 13: Analysis Issues in Assessing Efficacy in Randomized Clinical Trials “Intention to Treat” and Compliance Elizabeth Garrett-Mayer Oncology Biostatistics.](https://reader036.fdocuments.in/reader036/viewer/2022062421/56649dd05503460f94ac4d3f/html5/thumbnails/13.jpg)
How to interpret these results?
• ITT is not “wrong” analysis
• But, the additional analysis provides insight.
• Sometimes, however, it gets messy and hard to interpret.
![Page 14: Analysis Issues in Assessing Efficacy in Randomized Clinical Trials “Intention to Treat” and Compliance Elizabeth Garrett-Mayer Oncology Biostatistics.](https://reader036.fdocuments.in/reader036/viewer/2022062421/56649dd05503460f94ac4d3f/html5/thumbnails/14.jpg)
Example: Febrile Seizures
• Use of phenobarbitol for the prevention of recurrence of febrile seizures in children.
• Question: it might help seizures, but does it hurt child’s cognition?
• Randomized double blind placebo controlled trial• Outcomes: Seizure recurrence, change in IQ• Some failed compliance• Some crossed-over• Depending on how adherence is defined,
different results and different inferences.
![Page 15: Analysis Issues in Assessing Efficacy in Randomized Clinical Trials “Intention to Treat” and Compliance Elizabeth Garrett-Mayer Oncology Biostatistics.](https://reader036.fdocuments.in/reader036/viewer/2022062421/56649dd05503460f94ac4d3f/html5/thumbnails/15.jpg)
Strange results???
![Page 16: Analysis Issues in Assessing Efficacy in Randomized Clinical Trials “Intention to Treat” and Compliance Elizabeth Garrett-Mayer Oncology Biostatistics.](https://reader036.fdocuments.in/reader036/viewer/2022062421/56649dd05503460f94ac4d3f/html5/thumbnails/16.jpg)
Strange results???
![Page 17: Analysis Issues in Assessing Efficacy in Randomized Clinical Trials “Intention to Treat” and Compliance Elizabeth Garrett-Mayer Oncology Biostatistics.](https://reader036.fdocuments.in/reader036/viewer/2022062421/56649dd05503460f94ac4d3f/html5/thumbnails/17.jpg)
Sometimes ITT is not an option
• Two kinds of outcomes (generally):– Visit-related: quantitative lab measures, symptoms– Events: death, relapse, development of disease.
• Visit-related endpoints are harder for follow-up • Patients may drop out between the baseline and
follow-up visit.– Non-compliance with treatment is related to non-
compliance with follow-up.– Non-compliance is not independent of treatment
group.
![Page 18: Analysis Issues in Assessing Efficacy in Randomized Clinical Trials “Intention to Treat” and Compliance Elizabeth Garrett-Mayer Oncology Biostatistics.](https://reader036.fdocuments.in/reader036/viewer/2022062421/56649dd05503460f94ac4d3f/html5/thumbnails/18.jpg)
Example: Incomplete Follow-Up
• MAAS: Multicentre Anti-Atheroma Study• Simvastin versus placebo• N = 381 patients with coronary artery disease
(CAD)• Outcomes: Mean change in 4 year mean and
minimum lumen diameter of preselected segments of coronary arteries
• Study planners realized four year follow-up would only be achieved by a subset of patients
![Page 19: Analysis Issues in Assessing Efficacy in Randomized Clinical Trials “Intention to Treat” and Compliance Elizabeth Garrett-Mayer Oncology Biostatistics.](https://reader036.fdocuments.in/reader036/viewer/2022062421/56649dd05503460f94ac4d3f/html5/thumbnails/19.jpg)
Example: Incomplete Follow-Up
• How can we plan ahead for that?• Options:
– Increase sample size? – Use 4 year data on completers only?– Use LOCF (last observation carried forward)?
• Problems:– Sample size increase will still not help with the bias– Completers only analysis introduces bias– LOCF has validity issues: assumes that patients
observation at, for example, 2 years is the same as at 4 years.
![Page 20: Analysis Issues in Assessing Efficacy in Randomized Clinical Trials “Intention to Treat” and Compliance Elizabeth Garrett-Mayer Oncology Biostatistics.](https://reader036.fdocuments.in/reader036/viewer/2022062421/56649dd05503460f94ac4d3f/html5/thumbnails/20.jpg)
Example: Incomplete Follow-Up
• Planners decided to use LOCF
• Preserved the ITT approach
• Introduced bias into the measurements
![Page 21: Analysis Issues in Assessing Efficacy in Randomized Clinical Trials “Intention to Treat” and Compliance Elizabeth Garrett-Mayer Oncology Biostatistics.](https://reader036.fdocuments.in/reader036/viewer/2022062421/56649dd05503460f94ac4d3f/html5/thumbnails/21.jpg)
Another example: Differential Dropout
• Inhaled corticosteroids vs. placebo• 116 kids with asthma• Outcome measure is FEV (forced expiratory
volume)• More patients withdrew on placebo arm than on
corticosteroid arm (26 vs. 3).• Dropout due to exaccerbation of symptoms (so,
maybe treatment works!)• Difficult to interpret quantitative results• “Informative censoring”
![Page 22: Analysis Issues in Assessing Efficacy in Randomized Clinical Trials “Intention to Treat” and Compliance Elizabeth Garrett-Mayer Oncology Biostatistics.](https://reader036.fdocuments.in/reader036/viewer/2022062421/56649dd05503460f94ac4d3f/html5/thumbnails/22.jpg)
Using Compliance Data
• Example: Obesity study• European multi-centre double-blind randomized
trial of dexfenfluramine (dF) versus placebo.• 1 year follow-up of 822 obese patients• Compliance data:
– Plasma concentrations of fenfluramine(F) and its metabolite norfenflurmaine (nF) taken at 6 and 12 months.
– Compliance “outcome” is nF+F.• Original study found significant effect of dF, but
wanted to address the issue of compliance
![Page 23: Analysis Issues in Assessing Efficacy in Randomized Clinical Trials “Intention to Treat” and Compliance Elizabeth Garrett-Mayer Oncology Biostatistics.](https://reader036.fdocuments.in/reader036/viewer/2022062421/56649dd05503460f94ac4d3f/html5/thumbnails/23.jpg)
Using Compliance Data
![Page 24: Analysis Issues in Assessing Efficacy in Randomized Clinical Trials “Intention to Treat” and Compliance Elizabeth Garrett-Mayer Oncology Biostatistics.](https://reader036.fdocuments.in/reader036/viewer/2022062421/56649dd05503460f94ac4d3f/html5/thumbnails/24.jpg)
So, now what?
• How can we use the compliance information in assessing efficacy?
• Think of a regression approach: Pocock et al.
Y F nF ei i i i i 0 1 2( ) p lacebo
![Page 25: Analysis Issues in Assessing Efficacy in Randomized Clinical Trials “Intention to Treat” and Compliance Elizabeth Garrett-Mayer Oncology Biostatistics.](https://reader036.fdocuments.in/reader036/viewer/2022062421/56649dd05503460f94ac4d3f/html5/thumbnails/25.jpg)
How to understand the equation:Y F nF ei i i i 0 1 ( )
Y F nF ei i i i 0 1 3( )
dF:
Placebo :
0 20 40 60 80
-14
-12
-10
-8-6
-4
F+nF level
Me
an
% w
eig
ht c
ha
nge
DrugPlacebo
![Page 26: Analysis Issues in Assessing Efficacy in Randomized Clinical Trials “Intention to Treat” and Compliance Elizabeth Garrett-Mayer Oncology Biostatistics.](https://reader036.fdocuments.in/reader036/viewer/2022062421/56649dd05503460f94ac4d3f/html5/thumbnails/26.jpg)
What does this tell us?
• It helps understand the mechanism
• Model makes certain assumptions– “Linear” change in weight loss– Placebo treated are “like” dF treated patients
• But, we can make useful inferences
• Missing data???
![Page 27: Analysis Issues in Assessing Efficacy in Randomized Clinical Trials “Intention to Treat” and Compliance Elizabeth Garrett-Mayer Oncology Biostatistics.](https://reader036.fdocuments.in/reader036/viewer/2022062421/56649dd05503460f94ac4d3f/html5/thumbnails/27.jpg)
Other compliance approaches
• Pill counts– Pros
• Easy and non-invasive approach• Can ‘blind’ the patients
– Cons• Easy for patient to pretend (by getting rid of pills)• Compliance may vary • Patient may take many pills just prior to visit
• “Mems caps”: Medication Event Monitoring System
• Diaries: interesting mechanism that not only ‘records’, but also might change the behavior.
![Page 28: Analysis Issues in Assessing Efficacy in Randomized Clinical Trials “Intention to Treat” and Compliance Elizabeth Garrett-Mayer Oncology Biostatistics.](https://reader036.fdocuments.in/reader036/viewer/2022062421/56649dd05503460f94ac4d3f/html5/thumbnails/28.jpg)
Pill Counts in Obesity Study
![Page 29: Analysis Issues in Assessing Efficacy in Randomized Clinical Trials “Intention to Treat” and Compliance Elizabeth Garrett-Mayer Oncology Biostatistics.](https://reader036.fdocuments.in/reader036/viewer/2022062421/56649dd05503460f94ac4d3f/html5/thumbnails/29.jpg)
Broader Issue
• Confounding?
compliance
treatment
outcome
Compliance associatedwith treatment.
Compliance associatedwith outcome.
Treatment associated with outcome????
?
![Page 30: Analysis Issues in Assessing Efficacy in Randomized Clinical Trials “Intention to Treat” and Compliance Elizabeth Garrett-Mayer Oncology Biostatistics.](https://reader036.fdocuments.in/reader036/viewer/2022062421/56649dd05503460f94ac4d3f/html5/thumbnails/30.jpg)
Why then perform ITT and ignore compliance?
• First, compliance is hard to measure• Second, we don’t want to make inferences where we
have to ‘condition’ on compliance.• Third, and most importantly, it is a mistake to adjust for
something that is related to treatment (e.g. compliance)! Recall “causal pathway” idea.
compliance
treatment
outcome
![Page 31: Analysis Issues in Assessing Efficacy in Randomized Clinical Trials “Intention to Treat” and Compliance Elizabeth Garrett-Mayer Oncology Biostatistics.](https://reader036.fdocuments.in/reader036/viewer/2022062421/56649dd05503460f94ac4d3f/html5/thumbnails/31.jpg)
What if compliance is not related to treatment?
• No longer have confounding!
compliance
treatment
outcome
![Page 32: Analysis Issues in Assessing Efficacy in Randomized Clinical Trials “Intention to Treat” and Compliance Elizabeth Garrett-Mayer Oncology Biostatistics.](https://reader036.fdocuments.in/reader036/viewer/2022062421/56649dd05503460f94ac4d3f/html5/thumbnails/32.jpg)
Notice directionality of arrows
compliance
treatment
outcome
compliance
treatment
outcome
?
Compliance is on causal pathwaybetween treatment and outcome.
Compliance is NOT on causalpathway.What could give rise to this figure?
If treatment can be self-selected, non-compliers might choose differenttreatment.
CONFOUNDING!
![Page 33: Analysis Issues in Assessing Efficacy in Randomized Clinical Trials “Intention to Treat” and Compliance Elizabeth Garrett-Mayer Oncology Biostatistics.](https://reader036.fdocuments.in/reader036/viewer/2022062421/56649dd05503460f94ac4d3f/html5/thumbnails/33.jpg)
Broader Issue: Adjustment
• My favorite confounding example
• Observational study of the effects of coffee on lung cancer
coffee
cancer
smoking associatedwith coffee.
smoking associatedwith cancer.
But, coffee NOT associated with cancer.
smoking?
![Page 34: Analysis Issues in Assessing Efficacy in Randomized Clinical Trials “Intention to Treat” and Compliance Elizabeth Garrett-Mayer Oncology Biostatistics.](https://reader036.fdocuments.in/reader036/viewer/2022062421/56649dd05503460f94ac4d3f/html5/thumbnails/34.jpg)
What if?
• What if coffee consumption was causally associated with smoking (i.e. coffee causes smoking?)
coffee
cancer
coffee causes smoking.
smoking causescancer.
Does coffee cause cancer?
smoking?
![Page 35: Analysis Issues in Assessing Efficacy in Randomized Clinical Trials “Intention to Treat” and Compliance Elizabeth Garrett-Mayer Oncology Biostatistics.](https://reader036.fdocuments.in/reader036/viewer/2022062421/56649dd05503460f94ac4d3f/html5/thumbnails/35.jpg)
Adjustment
• Attempt to remove effect of differences in baseline composition of groups on the outcome of interest.
• Analytic procedure• Only for observational studies?
– No: randomized studies might have imbalance that can be adjusted
• How to adjust?– stratification or subgroup analyses– regression approaches (e.g. linear or logistic regression)
• Adjustment factors SHOULD be measured prior to treatment assignment
• Do not want to adjust for factors that are a result of protocol!