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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 www.fda.gov 2 Continuous Responder Curve www.fda.gov 3 Continuous Responder Curve www.fda.gov 4 Continuous Responder Curve www.fda.gov 5 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? www.fda.gov 6 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 – – – – 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 www.fda.gov 7 Permutation Test • • • • 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 www.fda.gov 8 Case Study 1 www.fda.gov 9 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 www.fda.gov 10 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 www.fda.gov 11 Disposition www.fda.gov 12 Results www.fda.gov 13 Difference in Better Half Mean change from baseline pain at Week 16 www.fda.gov 14 Permutation Test www.fda.gov 15 Case 1: Summary • Discontinuations – 15% placebo, 17% active • BOCF and mBOCF – treatment effect • Better Half estimand – treatment effect www.fda.gov 16 Case Study 2 www.fda.gov 17 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 www.fda.gov 18 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 www.fda.gov 19 Disposition Active www.fda.gov 20 Results Active www.fda.gov 21 Sensitivity Analyses Active 22 Difference in Better Half Average change from baseline pain at Week 12 www.fda.gov 23 Permutation Test www.fda.gov 24 Case 2: Summary • Discontinuations – 39% placebo, 43% active • LOCF – treatment effect • BOCF and mBOCF – no treatment effect • Better Half – www.fda.gov no treatment effect 25 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 www.fda.gov 26