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Inferential Frameworks for Clinical Trials

In: Principles and Practice of Clinical Trials

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  • James P. Long

    (Department of Biostatistics, University of Texas MD Anderson Cancer Center)

  • J. Jack Lee

    (Department of Biostatistics, University of Texas MD Anderson Cancer Center)

Abstract

Statistical inference is the process of using data to draw conclusions about unknown quantities. Statistical inference plays a large role both in designing clinical trials and in analyzing the resulting data. The two main schools of inference, frequentist and Bayesian, differ in how they estimate and quantify uncertainty in unknown quantities. Typically, Bayesian methods have clearer interpretation at the cost of specifying additional assumptions about the unknown quantities. This chapter reviews the philosophy behind these two frameworks including concepts such as p-values, Type I and Type II errors, confidence intervals, credible intervals, prior distributions, posterior distributions, and Bayes factors. Application of these ideas to various clinical trial designs including 3 + 3, Simon’s two-stage, interim safety and efficacy monitoring, basket, umbrella, and platform drug trials is discussed. Recent developments in computing power and statistical software now enable wide access to many novel trial designs with operating characteristics superior to classical methods.

Suggested Citation

  • James P. Long & J. Jack Lee, 2022. "Inferential Frameworks for Clinical Trials," Springer Books, in: Steven Piantadosi & Curtis L. Meinert (ed.), Principles and Practice of Clinical Trials, chapter 54, pages 973-1002, Springer.
    • Handle: RePEc:spr:sprchp:978-3-319-52636-2_271
    DOI: 10.1007/978-3-319-52636-2_271
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