Christy Chuang-Stein

Sample Size Reestimation: A Review and Recommendations:

Despite best efforts, some crucial information used to design a confirmatory trial may not be available, or may be available but with a high degree of uncertainty, at the design stage. When this happens, it may be prudent to check the validity of those assumptions using interim data from the study and make midcourse adjustment if necessary. One such adjustment is to modify the sample size. In this article, we focus on sample size reestimation (SSR) for phase III and IV studies. The discussion is relevant to both continuous and binary endpoints even though the basis for SSR might differ for those two cases. We review commonly used approaches to adjust sample size and provide recommendations on how SSR should be implemented to achieve the objectives and maintain the integrity of the trial. The recommendations cover scientific, procedural, and logistic considerations.

The impact and implication of regression to the mean on the design and analysis of medical investigations

We have examined the regression effect and its magnitude under the Gaussian distributional assumption. The impact and implication of regression to the mean on the analysis of medical investigations was discussed. For simplicity, we called the approach adjusting for the regression effect a two-stage procedure and noted its relationship to the analysis of covariance model for comparing treatment groups. We also proposed to examine the correlation structure among repeated measurements in the absence of any external interventions through a model more realistic than the one assuming equal correlations. The proposed structure led us to investigate ways to reduce or eliminate regression effect via study designs when patient selection is inevitable. Two examples were given to help illustrate the discussion in this paper.

Tutorial in Biostatistics A review of tests for detecting a monotone dose–response relationship with ordinal response data

This tutorial reviews methods for testing independence between discrete levels of a dose and an ordered categorical response variable. The tests are designed to be powerful for cases in which the response improves monotonically as dosage level increases. First, we show how to apply some standard tests for doubly-ordered contingency tables. Then, we show how to construct tests as part of a model-building strategy. Other topics discussed include generalizations to stratified data, small-sample methods, and sample size and power considerations.

Summarizing Laboratory Data with Different Reference Ranges in Multi-Center Clinical Trials

In this paper, we propose a procedure to summarize the lab data obtained from laboratories which have different reference ranges in a multi-center clinical trial. The procedure calls for first standardizing lab values relative to their respective reference ranges. Unit-free intermediate summary statistics are obtained based on the standardized values. The procedure then restores the unit by using the reference ranges from the center with the highest patient enrollment. Two methods to standardize the lab values and the relationship between them are discussed. Two examples are given to illustrate the proposed procedure and to demonstrate why procedures such as the one proposed in this paper are necessary.

A new proposal for benefit-less-risk analysis in clinical trials

In this paper, we propose a method to discount the observed benefit of a treatment by the observed risk in order to facilitate the benefit-less-risk comparison of treatments in a clinical trial. The discounting, applied to each individual in a trial, utilizes a method proposed by Chuang-Stein and co-authors to consolidate the safety data collected in the trial. The collating of the safety information allows one to estimate quantitatively the risk experienced by each individual, and therefore enables the construction of a risk-adjusted benefit measure for the same individual. We discuss the rationale for the adjusting method and examine its impact on the inference. When the discounting process reflects an individuals choice, the results should be interpreted at the individual level. An example is given to illustrate the approach.

Adaptive and Model-Based Dose-Ranging Trials: Quantitative Evaluation and Recommendations. White Paper of the PhRMA Working Group on Adaptive Dose-Ranging Studies

Poor dose-regimen selection remains a key cause of the high attrition rate of investigational drugs in confirmatory trials, being directly related to the escalating costs of drug development. This article is a follow-up to the first white paper put forward by the PhRMA Working Group (WG) on Adaptive Dose-Ranging Studies (Bornkamp et al. 2007). It presents results and conclusions from a new round of simulation-based evaluations conducted by the WG, proposing a new set of recommendations to improve the accuracy and efficiency of dose-finding in clinical drug development.

Safety Analysis in Controlled Clinical Trials

This paper examines the objectives of collecting safety data and discusses the evolution of regulatory requirements for safety summarization over the last 30 years. A quick review of the current practice in analyzing safety data is provided and some recent proposals and trends in this area are discussed. The question that must he considered, is whether the analysis that is currently being performed is relevant and whether safety data are being summarized in a way most beneficial to study monitors and investigators in understanding the safety outlook of a new treatment and consequently in selecting a treatment based on the safety data provided in package inserts. Some thoughts are offered.

Laboratory data in clinical trials: a statistician's perspective.

Even though laboratory data provide the best indicators for systemic toxicities in clinical trials of investigational medications, many applied statisticians lack a basic understanding of the interpretation of such data. Understanding is essential to a statisticians ability to help evaluate a patients overall safety experience in a trial, the latter being the primary objective for collecting laboratory data in the trial. In this paper, we discuss the purpose of conducting laboratory evaluations as well as some hidden issues concerning the current practice of laboratory data analysis. The issues include the use of reference ranges, the one-parameter-at-a-time approach, and the exploratory nature of safety data analyses.

The Regression Fallacy

This paper examines mathematically the origin of the regression toward the mean phenomenon. The magnitude of regression effect in terms of mean and mean percentage change for a selected target population was calculated under the assumption of a bivariate normal distribution. The impact of regression effect on the statistical estimation of a treatment effect was addressed. Methods to reduce and to adjust for regression effect were reviewed and discussed. Questions concerning the designs and the analyses of clinical trials in light of the presence of regression effect were posed.

PISC Expert Team White Paper: Toward a Consistent Standard of Evidence When Evaluating the Efficacy of an Experimental Treatment From a Randomized, Active-Controlled Trial

The double-blind placebo-controlled trial is the established standard for determining the efficacy of an experimental treatment. However, there are circumstances where the use of a placebo is unethical or impractical, and active-controlled trials are a common alternative. In an active-controlled trial, the objective is typically to show that the effect of the experimental treatment is within some prespecified margin of the control effect. The margin is often chosen specifically to guarantee 50% or 75% preservation of the control effect over placebo. An implicit assumption is that a higher standard of efficacy is required when a new treatment is evaluated in an active-controlled trial. In this article, we argue that standards based on margins and/or percent preservation are inherently arbitrary and lacking in objective clinical or scientific justification. The use of these ad hoc standards introduces logical inconsistencies for regulatory evaluation such that safe and effective treatments may be denied regulatory approval. We therefore argue that active-controlled trials should not require an arbitrarily higher standard of evidence than placebo-controlled trials. We discuss how “synthesis” analyses combining the results of both an active-controlled trial and one or more historical trials can address methodological challenges and provide for adequate estimation and testing of an experimental treatment effect relative to placebo.