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Enhancing Clinical Trials: Sample Size, Adaptive Designs, and Improving Patient Adherence

This document summarizes key insights from the "Conference on Statistical Issues in Clinical Trials" held at Duke University and the University of Pennsylvania. It discusses the challenges in determining sample size for chronic diseases, including Parkinson's, Alzheimer's, and rheumatoid arthritis, and emphasizes the appeal of adaptive designs in clinical trials. The document highlights the importance of pre-planning and the potential for reducing enrollment time and variability while addressing the complexities surrounding accrual processes and patient adherence interventions. Innovative approaches based on behavioral economics are proposed to improve adherence in clinical settings.

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Enhancing Clinical Trials: Sample Size, Adaptive Designs, and Improving Patient Adherence

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  1. Comments on Sample Size, Adaptive Designs, Accrual and Adherence Stephen L George Duke University School of Medicine University of Pennsylvania Conference on Statistical Issues in Clinical Trials April 13, 2011

  2. Sample Size(Tilley) • Disease progression in chronic diseases • Parkinson’s Disease • Alzheimer’s Disease • Rheumatoid Arthritis • Composite outcome measure (e.g., UPDRS in PD)

  3. Study Options • De Novo patients • Non-linear model • Missing data (LOCF?) • Large variability and sample size • Currently treated patients • Reduces non-linearity • Less missing data • Less variability and smaller sample size

  4. Adaptive Designs(Coffey) • Statistical issues and principles are mostly well known and developed (although methodology still needs work) • Approaches must be defined in advance (no ad hoc procedures) • Most current clinical trials are adaptive to some degree • e.g., group sequential trials

  5. Adaptive Designs (continued) • Adaptive designs are appealing in principle • Use early data from the trial itself to inform future conduct of the trial, potentially including sample size, randomization balance, test statistics, endpoints, inclusion criteria, etc • The goal is to produce reliable results in less time with fewer patients • So what is the problem?

  6. Some Questions • What is the added cost of adaptive designs? • Increased planning • Logistical issues • Information technology • Issues of reliability? • Potential for bias (e.g., reverse engineering) • How much do we gain? • Cost-benefit analysis

  7. Accrual (Zhang) • Slow-accruing trials are common • Extensive inclusion/exclusion criteria • Competing trials • Slow IRB approval • Specific treatments • NCI rules on slow-accruing trials • Even without slow accrual, accurate prediction can be useful

  8. Accrual (continued) • Various approaches compared by Zhang • Brownian motion • Poisson process • Properties are reasonable, but is the added complexity worth the effort?

  9. Number of Events at Time t • An extension of accrual models:

  10. Adherence Interventions(Kimmel-Troxel) • How can adherence be improved? • By how much? • At what cost? • What is the effect on efficacy? • Clinical trials are the best way to assess interventions

  11. Clinical Trials of Adherence Interventions • New approaches based on behavioral economics • Positive feedback • Present-based preferences • Variable reinforcement • Regret aversion • WIN2 (Warfarin trial example) • 2X2 factorial design • Lottery (yes/no); Med-eMonitor (yes/no)

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