Clinical Trial Design Issues and Options for the Study of Rare Diseases

Clinical Trial DesignIssues and Optionsfor the Study of Rare DiseasesJeffrey Krischer, Ph.D.Data Management and Coordination CenterRare Diseases Clinical Research NetworkOctober 2, 2012

• NoneFinancial Disclosure

The Issue The Implication• Few patients.• Few patients.• Few patients.• Few patients.• Multi-site studies.• Difficulty getting drugsfor limited indications.• Longer studies.• Limited design optionsfor clinical trials.

The Basic Issue• To test efficacy, need a control group.• Choices: literature control, historical controls,concurrent controls, patients as own controls.• Typical design is to recruit/assign to a treatmentgroup/observe each group and compareoutcomes.• BuBut what if you could design astudy using the same patient morethan once?

Possible Design Options• Parallel group design• Cross-over design• Factorial design• Historical controls design• Randomized withdrawal design• Early escape design• n-of-1 design• Group sequential design• Case-Control design• Prospective cohort design• Decision analysis-based design• Ranking and selection design• Adaptive design• Risk-based allocation design• Bayesian designs• Enhanced designsReduce the number ofstudy subjects neededby using enrolled subjectsmore than once.

Crossover DesignsA1A2Difficulty getting drugs for limited indications.B1B2washoutTime

Crossover DesignsA1A2Difficulty getting drugs for limited indications.B1B2A1+A2 vs. B1+B2

Crossover Design Analysis

Treatment Effects

Carryover Effects

Using Crossover Designs• Chronic condition.• Relatively short study period.• No carryover effect expected.

Advantages and Limitations• Advantages:• Doubles the effective sample size.• Limitations:• Study takes twice as long.• Refusal bias.• Carryover effects make interpreting studyvery complex.

Factorial DesignsCADifficulty getting drugs for limited indications.BDCDTime

Factorial Design AnalysisA+C + A+D vs. B+C + B+DandC+A + C+B vs. D+A + D+B

Factorial Design Analysis

Example of an InteractionA active , B active : 60% successA active , B placebo : 30% successA placebo , B active : 30% successA placebo , B placebo : 30% success

InteractionsAABBCD

Placebo Controlled Experiments(B and D are placebo)If both questions are placebo controlled, thenA C A+C PlaceboSecondary comparison of combined effects

Advantages and Limitations• Advantages:• Doubles the effective sample size, and it ispossible for the treatments to runconcurrently.• Limitations:• Study could take twice as long.• Refusal bias.• Interaction effects make interpretingstudy very complex.

N-of-1 Randomized Trial DesignABBAABDrug and placebo administeredsequentially in a randomsequence, generally 3 or moredrug-placebo pairs.

N-of-1 Randomized Trial Design• If patient’s response was poor, that treatmentstopped and the next treatment in the sequencebegun immediately without breaking therandomized sequence.• At the end of the study, the mean values for allmeasures and the mean differences betweentreatments are computed.

n-of-1 Randomized Trial• Disorder should be chronic, i.e. relativelyunchanging.• Treatment effect rapid.• Treatment duration for optimal effect should bewell known.

Advantages and Limitations• Advantages:• Every patient receives every treatment.• Treatment is evaluated in each patient.• Limitations:• Study could take very long.• Refusal bias.• Difficult to know whether design assumptionsare met (duration of treatment foroptimal effect ).

Enhanced Trial DesignsUsing patients more than once,but using the trial itself to select which group tore-use.

. Conventional designRandomizeTreatmentPhase 11 2ActiveTreatmentPlaceboResultsResponderNonResponderResponderNonResponderEfficacy analysis is based on comparing theresults of the two treatment arms

Sample Size Required for a Given PowerResponse rateDrug Placebo DifferenceSingle Phase Design 60% 45% 15%Total nPower70%80%90%SinglePhase Design274346462

Single Enhanced DesignRandomizeTreatmentPhase 11 2ActiveTreatmentPlaceboResultsResponderNonResponderResponderNonResponderRandomizeTreatmentPhase 23 4ActiveTreatmentPlacebo

Comparison of Sample Size Required fora Given PowerResponse rateDrug Placebo DifferenceSingle Phase Design or SPCD Phase 1 60% 45% 15%SPCD Phase 2 50% 25% 25%1.6XPower70%80%90%Total nSinglePhase Design274346462SPCD157199266

Comparison of Sample sizes between Parallel and SPDdesigns for three scenarios with low placebo response rate.Stage 1 Stage 2 Parallel SPDDrug Pbo Drug Pbo n nScenario 1 .40 .20 .30 .10 164 101Scenario 2 .30 .15 .20 .05 242 134Scenario 3 .25 .10 .20 .05 200 116All calculations are based on two-tailed alpha = 0.05, 80% power. Forthe SPD, the estimates are based on a 2:1 initial allocation ofplacebo:drug, and assumed retention of placebo patients from Stage 1to Stage 2 = 90%.

Issues• 2 chances to receive the active agent.• Study takes at least twice as long.• Efficiency depends upon response rate of thosereceiving placebo (placebo effect)• Savings in sample size since all patients areused once and some are used twice.

. Conventional designRandomizeTreatmentPhase 11 2ActiveTreatmentPlaceboResultsResponderNonResponderResponderNonResponder

Double Enhanced DesignRandomizeTreatmentPhase 11ActiveTreatment2PlaceboResultsResponderRandomize5 6ActiveTreat PlacebomentNonResponderTreatmentPhase 2ResponderRandomize3 4ActiveTreatmentNonResponderPlacebo

Summary• When the number of study subjects is limited, itis possible to design studies that “re-use” thesubjects enrolled to increase study power .• Such designs require a number of assumptionsthat may or may not be verifiable.• For some assumptions, it may be possible to testfor their effect, but this is done after the studyconcludes and may complicate reporting andinterpretation of results.• Analyses are more complex and require goodstatistical advice at the time the study is beingdesigned.

ReferencesTamura R., Huang X.: An examination of theefficiency of the sequential parallel design inpsychiatric clinical trials; Clinical Trials 2007;4:309-317.Ivanova A., Qaqish B., Schoenfeld D.:Optimality, sample size and power calculationsfor the sequential parallel comparison design;Statistics in Medicine 2011; 30: 2793-2803.Tamura R., Xuang X., Boos D.: Estimation ofTreatment Effect for the Sequential Parallel Design;Statistics in Medicine 2011; Accepted forpublication.Power calculator for a Sequential Parallel Design | MGHBiostatistics Center

References• Guyatt, GH, Sackett DL, Adachi, JD, et al. Aclinician’s guide for conducting randomized trialsin individual patients. Can Med Assoc j. 1988;139:497-503.

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