Interim Analyses In Clinical Trials Research Articles

Interim analysis in clinical trials

The early development of experimental design discouraged a sequential approach to the analysis of data, yet this seems a natural form of scientific enquiry. Clinical trial investigators should continuously monitor the quality of their techniques, but will often wish to delegate data monitoring to an independent group. The history and functions of data monitoring committees (DMCs) are reviewed. DMCs come in many shapes and sizes. They will need to consider many aspects of the data before making recommendations to the investigators, who have ultimate responsibility for early termination or protocol changes. Statistical issues form part of the assessment, and will involve management, safety and efficacy. Two broad approaches to early stopping are (i) the demonstration of strong evidence that a treatment effect falls above or below some critical value (not necessarily zero); (ii) stochastic curtailment based on prediction of final results. The latter is examined somewhat critically. Most trials will involve group sequential analyses at discrete time points. The effect of repeated data inspection on (i) is well known, although its relevance is debatable. Bayesian and likelihood methods do not entirely remove the difficulty.

Some extensions to a new approach for interim analysis in clinical trials

There already exists many methods for group sequential analysis of clinical trials. A new approach suggests to preserve the overall type I error alpha by requiring that a succession of interim analyses be significant at the alpha level. In this paper, this procedure is extended. A rule for early acceptance of H0 is presented, with an extension for flexible number and times of analyses. A real example is given.

Discussion of the Paper by Jennison and Turnbull

Discussion of the Paper by Jennison and Turnbull

Monitoring versus interim analysis of clinical trials: A perspective from the pharmaceutical industry

The definitions of “interim analysis” and “monitoring” of clinical trials are often ambiguous in the current literature. The resulting confusion can lead to erroneous conclusions and misguided decisions, especially when activities that are operational or observational are evaluated in a probabilistic sense as inferential. The authors seek to define “interim analysis” and “monitoring” in a mutually exclusive fashion. These definitions will then provide the opportunity to review and categorize existing clinical trial practices and procedures. This will clarify such issues as “when to look” and “when to pay a price” (e.g., test size and power) and characterize such issues in the context of pharmaceutical industry drug development.

Interim Analysis in Clinical Trials: The Role of the Likelihood Principle

Abstract In the classical statistical approach, whether or not a clinician looks at accumulating data with the possibility of stopping the trial can affect any inferences drawn from the trial. This is in contrast to the Bayesian and likelihood approaches to statistics. I give examples to illustrate the differences in these approaches and discuss designing clinical trials from a Bayesian point of view.

Interim Analysis in Clinical Trials: The Role of the Likelihood Principle

Interim Analysis in Clinical Trials: The Role of the Likelihood Principle

Interim analyses in clinical trials: classical vs. Bayesian approaches.

This paper concerns interim analysis in clinical trials involving two treatments from the points of view of both classical and Bayesian inference. I criticize classical hypothesis testing in this setting and describe and recommend a Bayesian approach in which sampling stops when the probability that one treatment is the better exceeds a specified value. I consider application to normal sampling analysed in stages and evaluate the gain in average sample number as a function of the number of interim analyses.

On choosing the number of interim analyses in clinical trials.

Small but important therapeutic effects of new treatments can be most efficiently detected through the study of large randomized prospective series of patients. Such large scale clinical trials are nowadays commonplace. The alternative is years of polemic and debate surrounding several trials each too small to detect plausible differences with any certainty. Such trials produce equivocal and contradictory results, which could be predicted from power calculations based upon sensible pre-trial estimates of treatment differences. Unfortunately such calculations often lead to sample sizes of several thousands. It is not surprising that investigators tend to be over-optimistic in their estimation of treatment effects (which are necessarily uncertain) especially when the sample size requirements are so stark. In this paper a method is outlined for incorporating into the sample size calculations the uncertainty of the estimate made at the design stage of a clinical trial. In particular a formal scheme is described for deciding how many interim analyses should be performed to satisfy ethical and pragmatic requirements of large clinical trial design. Although the argument will be 'Bayesian', the criteria for assessment and comparison will be strictly of a Neyman-Pearson (i.e. significance testing) kind.