Bayesian randomized clinical trials: From fixed to adaptive design

Yin, Guosheng; Lam, Chi Kin; Shi, Haolun

doi:10.1016/j.cct.2017.04.010

Cited by 37 publications

(26 citation statements)

References 43 publications

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“…HRV as a research and clinical tool is still underrecognized. It should be implemented in several clinical areas within a Bayesian paradigm to improve prediction, diagnosis, and therapy ( 182 ).…”

Section: Discussionmentioning

confidence: 99%

Hidden Signals—The History and Methods of Heart Rate Variability

Ernst

2017

Front. Public Health

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The understanding of heart rate variability (HRV) has increased parallel with the development of modern physiology. Discovered probably first in 1847 by Ludwig, clinical applications evolved in the second part of the twentieth century. Today HRV is mostly used in cardiology and research settings. In general, HRV can be measured over shorter (e.g., 5–10 min) or longer (12 or 24 h) periods. Since 1996, most measurements and calculations are made according to the standard of the Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology. As the first step, the series of times between successive R-peaks in the ECG are in milliseconds. It is crucial, however, to identify and remove extrasystoles and artifacts according to standard protocols. The series of QRS distances between successive heartbeats can be analyzed with simple or more sophisticated algorithms, beginning with standard deviation (SDNN) or by the square root of the mean of the sum of squares of differences between adjacent normal RR (rMSSD). Short-term HRV is frequently analyzed with the help of a non-parametric fast Fourier transformation quantifying the different frequency bands during the measurement period. In the last decades, various non-linear algorithms have been presented, such as different entropy and fractal measures or wavelet analysis. Although most of them have a strong theoretical foundation, their clinical relevance is still debated.

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Section: Discussionmentioning

confidence: 99%

Hidden Signals—The History and Methods of Heart Rate Variability

Ernst

2017

Front. Public Health

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“…Those subsequent actions may depend on whether or not the regulatory agencies might take given future observations. This type of sequential decision-theoretic model has been explored by various authors [ 31 – 35 ].…”

Section: Discussionmentioning

confidence: 99%

Value of information methods to design a clinical trial in a small population to optimise a health economic utility function

Pearce

Hee

Madan

et al. 2018

BMC Med Res Methodol

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BackgroundMost confirmatory randomised controlled clinical trials (RCTs) are designed with specified power, usually 80% or 90%, for a hypothesis test conducted at a given significance level, usually 2.5% for a one-sided test. Approval of the experimental treatment by regulatory agencies is then based on the result of such a significance test with other information to balance the risk of adverse events against the benefit of the treatment to future patients. In the setting of a rare disease, recruiting sufficient patients to achieve conventional error rates for clinically reasonable effect sizes may be infeasible, suggesting that the decision-making process should reflect the size of the target population.MethodsWe considered the use of a decision-theoretic value of information (VOI) method to obtain the optimal sample size and significance level for confirmatory RCTs in a range of settings. We assume the decision maker represents society. For simplicity we assume the primary endpoint to be normally distributed with unknown mean following some normal prior distribution representing information on the anticipated effectiveness of the therapy available before the trial. The method is illustrated by an application in an RCT in haemophilia A. We explicitly specify the utility in terms of improvement in primary outcome and compare this with the costs of treating patients, both financial and in terms of potential harm, during the trial and in the future.ResultsThe optimal sample size for the clinical trial decreases as the size of the population decreases. For non-zero cost of treating future patients, either monetary or in terms of potential harmful effects, stronger evidence is required for approval as the population size increases, though this is not the case if the costs of treating future patients are ignored.ConclusionsDecision-theoretic VOI methods offer a flexible approach with both type I error rate and power (or equivalently trial sample size) depending on the size of the future population for whom the treatment under investigation is intended. This might be particularly suitable for small populations when there is considerable information about the patient population.Electronic supplementary materialThe online version of this article (10.1186/s12874-018-0475-0) contains supplementary material, which is available to authorized users.

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“…Making frequentist statistical inferences and interpreting results (from a frequentist standpoint) in these response‐adaptive trials is difficult. Many authors have investigated this topic, discussed examples, and provided possible solutions . In this paper, we propose a simple way to analyze and interpret the results from response‐adaptive trials and use this method to study the statistical power of the OBA design.…”

Section: Methodsmentioning

confidence: 99%

Frequentist operating characteristics of Bayesian optimal designs via simulation

Zhang

Trippa

Parmigiani

2019

Statistics in Medicine

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Bayesian adaptive designs have become popular because of the possibility of increasing the number of patients treated with more beneficial treatments, while still providing sufficient evidence for treatment efficacy comparisons. It can be essential, for regulatory and other purposes, to conduct frequentist analyses both before and after a Bayesian adaptive trial, and these remain challenging. In this paper, we propose a general simulation-based approach to compare frequentist designs with Bayesian adaptive designs based on frequentist criteria such as power and to compute valid frequentist p-values. We illustrate our approach by comparing the power of an equal randomization (ER) design with that of an optimal Bayesian adaptive (OBA) design. The Bayesian design considered here is the dynamic programming solution of the optimization of a specific utility function defined by the number of successes in a patient horizon, including patients whose treatment will be affected by the trial's results after the end of the trial.While the power of an ER design depends on treatment efficacy and the sample size, the power of the OBA design also depends on the patient horizon size. Our results quantify the trade-off between power and the optimal assignment of patients to treatments within the trial. We show that, for large patient horizons, the two criteria are in agreement, while for small horizons, differences can be substantial. This has implications for precision medicine, where patient horizons are decreasing as a result of increasing stratification of patients into subpopulations defined by molecular markers. KEYWORDSBayesian adaptive designs, dynamic programming, frequentist analyses, optimal strategy Spiegelhalter et al 5 and Cellamare et al 6 have illustrated the use of Bayesian approaches in clinical trials and discussed their advantages. Ethical concerns 7 associated with adaptive designs are also relevant in Bayesian adaptive settings. On the other hand, frequentist criteria remain the mainstay in regulatory decision-making and in the medical literature. This applies to both trial design requirements, such as the power at a given type I error level, and reported 4026

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Bayesian randomized clinical trials: From fixed to adaptive design

Cited by 37 publications

References 43 publications

Hidden Signals—The History and Methods of Heart Rate Variability

Hidden Signals—The History and Methods of Heart Rate Variability

Value of information methods to design a clinical trial in a small population to optimise a health economic utility function

Frequentist operating characteristics of Bayesian optimal designs via simulation

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