Download An Alternative to Data Imputation in Analgesic Clinical Trials

An Alternative to Data Imputation in
Analgesic Clinical Trials
David Petullo, Thomas Permutt, Feng Li
Division of Biometrics II, Office of Biostatistics
Office of Translational Sciences, Center for Drug Evaluation and Research
U.S. Food & Drug Administration
Chronic Pain Trial - Not Missing Data
• If patient discontinues study drug, the pain
score at planned endpoint becomes irrelevant
• Completers with bad outcomes
• All considered equally bad outcomes
• You have an outcome just not a numerical score
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Continuous Responder Curve
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Continuous Responder Curve
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Continuous Responder Curve
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Why Half?
• Doesn’t have to be half
• Can be adaptive
– Trim only dropouts for the group with more
dropouts
– Dropouts plus others for the other group to
make fractions equal
• Why isn’t this completers analysis?
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Trim Same Fraction
• If equal fractions
– It is difference in completer means
• If active has more completers
– Bonus: compare your best completers to all placebo
completers
– Because more completers is good drug effect
• If placebo has more completers
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Test drug not “penalized” or disqualified
But you picked your best
So it’s fair to compare to placebo best
This is an insoluble problem with MAR methods
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Permutation Test
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Calculate difference in trimmed means
Rerandomize and recalculate, many times
Use rerandomization distribution as reference
See how far real value is in tail of reference
distribution
• Exact, randomization-based test
– That’s easy, even completers analysis can do that
– But based on interpretable statistic
• Especially in the case of more dropouts in the active
group
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Case Study 1
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Lyrica- Efficacy
• Indication: management of pain associated with spinal
cord injury (SCI)
• Two randomized, multi-center, placebo-controlled,
double-blind trials
– Study 1107: 4-week dose escalation, 12-week fixed, 1-week
taper
– Study 125: 3-week dose escalation, 9-week fixed
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Study 1107 - Efficacy
• Primary efficacy parameter was duration adjusted average
change (DAAC)
– A significant result on DAAC by itself would not
support efficacy
– Must use a conservative imputation strategy
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Disposition
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Results
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Difference in Better Half
Mean change from baseline pain at Week 16
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Permutation Test
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Case 1: Summary
• Discontinuations – 15% placebo, 17% active
• BOCF and mBOCF – treatment effect
• Better Half estimand – treatment effect
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Case Study 2
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Drug X ( approved 2010)
• Drug X
• Management of pain severe enough to require daily,
around the clock, long term, opioid treatment
• Two studies provided evidence of efficacy
• Study 1: DB, R, PC, AC study in subjects with
moderate to severe chronic low back pain
• Study 2: DB, R, PC, AC study in subjects with
OA of the knee
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Study 2: Efficacy
• Change from baseline to the end of the maintenance period in the
average pain
• Pain measured using 11-point NRS scale during the past 12hours
• Analysis
• ANCOVA with treatment, baseline pain score and pooled
center
• Analysis population was all randomized subjects that
received at least 1 dose of study drug
• Missing data imputed using LOCF
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Disposition
Active
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Results
Active
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Sensitivity Analyses
Active
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Difference in Better Half
Average change from baseline pain at Week 12
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Permutation Test
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Case 2: Summary
• Discontinuations – 39% placebo, 43% active
• LOCF –
treatment effect
• BOCF and mBOCF – no treatment effect
• Better Half –
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no treatment effect
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Conclusion
• Complete observations – just not numerical
• Exact test for hypothesis of no drug effect
• All randomized patients
• Accounts for
– Excess placebo dropouts for lack of efficacy (bonus)
– Excess active dropouts for toxicity
• Guaranteed fair comparison in an adherent subset
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