The utility of Bayesian predictive probabilities for interim monitoring of clinical trials (original) (raw)
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Bayesian predictive approach to interim monitoring in clinical trials
Statistics in medicine, 2006
This paper reviews Bayesian strategies for monitoring clinical trial data. It focuses on a Bayesian stochastic curtailment method based on the predictive probability of observing a clinically significant outcome at the scheduled end of the study given the ...
2016
Bayesian approach has been increasingly applied to various aspects of design and analysis of clinical trials. We present one application concerning an interim futility analysis of a trial. Longitudinal data were collected for a range of the studied doses. Bayesian analysis was first conducted to predict observations at the end of treatment for patients not yet followed through treatment, based on all interim observed data. The predicted data in combination with observed data at the end of treatment were then analyzed using a Bayesian normal dynamic linear model for dose response inference. Summary of the Bayesian analysis was used to aid an interim futility decision.
Statistics in Medicine, 2007
The most common Bayesian methods for sample size determination (SSD) are reviewed in the non‐sequential context of a confirmatory phase III trial in drug development. After recalling the regulatory viewpoint on SSD, we discuss the relevance of the various priors applied to the planning of clinical trials. We then investigate whether these Bayesian methods could compete with the usual frequentist approach to SSD and be considered as acceptable from a regulatory viewpoint. Copyright © 2007 John Wiley & Sons, Ltd.
Bayesian Design of Single-arm Phase II Clinical Trials with Continuous Monitoring
Clinical Trials, 2009
Background Bayesian designs are increasingly used to conduct phase II clinical trials. However, stopping boundaries in most Bayesian designs are defined from posterior credible intervals. The use of designs based on posterior credible intervals results in a loss of efficiency when compared to formal stopping rules based on Bayesian hypothesis tests. Such designs also introduce an unnecessary element of subjectivity in the interpretation of trial results. Methods We derive a new class of Bayesian designs based on formal hypothesis tests. The prior densities used to define the alternative hypotheses in these tests assign no mass to parameter values that are consistent with the null hypotheses and are called nonlocal alternative prior densities. Results We show that Bayesian designs based on hypothesis tests and nonlocal alternative prior densities are more efficient than common Bayesian designs based on posterior credible intervals and common frequentist designs. In contrast to trial ...
The aim of an exploratory clinical trial is to determine whether a new intervention is promising for further testing in confirmatory clinical trials. Most exploratory clinical trials are designed as single-arm trials using a binary outcome with or without interim monitoring for early stopping. In this context, we propose a Bayesian adaptive design denoted as predictive sample size selection design (PSSD). The design allows for sample size selection following any planned interim analyses for early stopping of a trial, together with sample size determination before starting the trial. In the PSSD, we determine the sample size using the method proposed by Sambucini (Statistics in Medicine 2008; 27:1199-1224), which adopts a predictive probability criterion with two kinds of prior distributions, that is, an 'analysis prior' used to compute posterior probabilities and a 'design prior' used to obtain prior predictive distributions. In the sample size determination of the PSSD, we provide two sample sizes, that is, N and N max , using two types of design priors. At each interim analysis, we calculate the predictive probabilities of achieving a successful result at the end of the trial using the analysis prior in order to stop the trial in case of low or high efficacy (Lee et al., Clinical Trials 2008; 5:93-106), and we select an optimal sample size, that is, either N or N max as needed, on the basis of the predictive probabilities. We investigate the operating characteristics through simulation studies, and the PSSD retrospectively applies to a lung cancer clinical trial. (243) Furthermore, the predictive distribution of S is beta-binomial, as is well known.
Bayesian clinical trials in action
Statistics in Medicine, 2012
Although the frequentist paradigm has been the predominant approach to clinical trial design since the 1940s, it has several notable limitations. Advancements in computational algorithms and computer hardware have greatly enhanced the alternative Bayesian paradigm. Compared with its frequentist counterpart, the Bayesian framework has several unique advantages, and its incorporation into clinical trial design is occurring more frequently. Using an extensive literature review to assess how Bayesian methods are used in clinical trials, we find them most commonly used for dose finding, efficacy monitoring, toxicity monitoring, diagnosis/decision making, and studying pharmacokinetics/pharmacodynamics. The additional infrastructure required for implementing Bayesian methods in clinical trials may include specialized software programs to run the study design, simulation and analysis, and web-based applications, all of which are particularly useful for timely data entry and analysis. Trial success requires not only the development of proper tools but also timely and accurate execution of data entry, quality control, adaptive randomization, and Bayesian computation. The relative merit of the Bayesian and frequentist approaches continues to be the subject of debate in statistics. However, more evidence can be found showing the convergence of the two camps, at least at the practical level. Ultimately, better clinical trial methods lead to more efficient designs, lower sample sizes, more accurate conclusions, and better outcomes for patients enrolled in the trials. Bayesian methods offer attractive alternatives for better trials. More Bayesian trials should be designed and conducted to refine the approach and demonstrate their real benefit in action.
Special Issue Paper Bayesian clinical trials in action
2020
Although the frequentist paradigm has been the predominant approach to clinical trial design since the 1940s, it has several notable limitations. Advancements in computational algorithms and computer hardware have greatly enhanced the alternative Bayesian paradigm. Compared with its frequentist counterpart, the Bayesian framework has several unique advantages, and its incorporation into clinical trial design is occurring more frequently. Using an extensive literature review to assess how Bayesian methods are used in clinical trials, we find them most commonly used for dose finding, efficacy monitoring, toxicity monitoring, diagnosis/decision making, and studying pharmacokinetics/pharmacodynamics. The additional infrastructure required for implementing Bayesian methods in clinical trials may include specialized software programs to run the study design, simulation and analysis, and web-based applications, all of which are particularly useful for timely data entry and analysis. Trial ...
Practical guidelines for adaptive seamless phase II/III clinical trials that use Bayesian methods
Statistics in Medicine, 2012
Journal; 45:581-589) proposed a flexible testing procedure for seamless phase II/III clinical trials. Schmidli et al. (Statistics in Medicine; 26:4925-4938), Kimani et al. (Statistics in Medicine; 28:917-936) and Brannath et al. (Statistics in Medicine; 28:1445-1463) exploited the flexible testing of Hommel to propose adaptation in seamless phase II/III clinical trials that incorporate prior knowledge by using Bayesian methods.
Bayesian methods for phase I clinical trials
Statistics in Medicine, 1992
Phase I clinical trials are conducted to determine the dose-response curve of a new drug with respect to toxic side effects and, in particular, to estimate the maximum tolerated dose (MTD). In this paper we take a Bayesian approach to the problem of making inferences about the MTD. Working with broad classes of priors, we obtain the posterior distribution of the MTD and study its properties. We also address the question of providing updated assessments of the risk of toxicity for new patients entering the study at a specific dose level. These assessments would be useful in deciding issues of study management and ethics. Our analysis pays particular attention to the sensitivity of the inferences and risk assessments to the choice of prior and the choice of model for the dose-response relationship.