Paul Gallo

Adaptive designs in clinical drug development--an Executive Summary of the PhRMA Working Group.

A PhRMA Working Group on adaptive clinical trial designs has been formed to investigate and facilitate opportunities for wider acceptance and usage of adaptive designs and related methodologies. A White Paper summarizing the findings of the group is in preparation; this article is an Executive Summary for that full White Paper, and summarizes the findings and recommendations of the group. Logistic, operational, procedural, and statistical challenges associated with adaptive designs are addressed. Three particular areas where it is felt that adaptive designs can be utilized beneficially are discussed: dose finding, seamless Phase II/III trials designs, and sample size reestimation.

Confirmatory adaptive designs with Bayesian decision tools for a targeted therapy in oncology

The ability to select a sensitive patient population may be crucial for the development of a targeted therapy. Identifying such a population with an acceptable level of confidence may lead to an inflation in development time and cost. We present an approach that allows to decrease these costs and to increase the reliability of the population selection. It is based on an actual adaptive phase II/III design and uses Bayesian decision tools to select the population of interest at an interim analysis. The primary endpoint is assumed to be the time to some event like e.g. progression. It is shown that the use of appropriately stratified logrank tests in the adaptive test procedure guarantees overall type I error control also when using information on patients that are censored at the adaptive interim analysis. The use of Bayesian decision tools for the population selection decision making is discussed. Simulations are presented to illustrate the operating characteristics of the study design relative to a more traditional development approach. Estimation of treatment effects is considered as well.

Moving Beyond the Hazard Ratio in Quantifying the Between-Group Difference in Survival Analysis

In a longitudinal clinical study to compare two groups, the primary end point is often the time to a specific event (eg, disease progression, death). The hazard ratio estimate is routinely used to empirically quantify the between-group difference under the assumption that the ratio of the two hazard functions is approximately constant over time. When this assumption is plausible, such a ratio estimate may capture the relative difference between two survival curves. However, the clinical meaning of such a ratio estimate is difficult, if not impossible, to interpret when the underlying proportional hazards assumption is violated (ie, the hazard ratio is not constant over time). Although this issue has been studied extensively and various alternatives to the hazard ratio estimator have been discussed in the statistical literature, such crucial information does not seem to have reached the broader community of health science researchers. In this article, we summarize several critical concerns regarding this conventional practice and discuss various well-known alternatives for quantifying the underlying differences between groups with respect to a time-to-event end point. The data from three recent cancer clinical trials, which reflect a variety of scenarios, are used throughout to illustrate our discussions. When there is not sufficient information about the profile of the between-group difference at the design stage of the study, we encourage practitioners to consider a prespecified, clinically meaningful, model-free measure for quantifying the difference and to use robust estimation procedures to draw primary inferences.

The future of drug development: advancing clinical trial design

Declining pharmaceutical industry productivity is well recognized by drug developers, regulatory authorities and patient groups. A key part of the problem is that clinical studies are increasingly expensive, driven by the rising costs of conducting Phase II and III trials. It is therefore crucial to ensure that these phases of drug development are conducted more efficiently and cost-effectively, and that attrition rates are reduced. In this article, we argue that moving from the traditional clinical development approach based on sequential, distinct phases towards a more integrated view that uses adaptive design tools to increase flexibility and maximize the use of accumulated knowledge could have an important role in achieving these goals. Applications and examples of the use of these tools — such as Bayesian methodologies — in early- and late-stage drug development are discussed, as well as the advantages, challenges and barriers to their more widespread implementation.

Adaptive seamless Phase II/III designs : Background, operational aspects, and examples

Adaptive seamless designs have been considered as one possible way to shorten the time and patient exposure necessary to discover, develop, and demonstrate the benefits of a new drug. We introduce the concept of adaptive designs and describe the current statistical methodologies that relate to adaptive seamless designs. We also describe the decision process involved with seamless designs and present some illustrative examples.

Good Practices for Adaptive Clinical Trials in Pharmaceutical Product Development

This article is a summary of good adaptive practices for the planning and implementation of adaptive designs compiled from experiences gained in the pharmaceutical industry. The target audience is anyone involved in the planning and execution of clinical trials. The first step prior to planning an adaptive design is to assess the appropriateness of its use. Hence, strategic points to consider when assessing if an adaptive design is the right choice for a trial are discussed. In addition, strategic points for consideration at the design and implementation stage are included from operational, regulatory, clinical, and statistical perspectives. Good practices for trial simulation, trial documentation, and data monitoring committees are provided.

VALsartan In Acute myocardial iNfarcTion (VALIANT) trial: baseline characteristics in context.

The VALsartan In Acute myocardial iNfarcTion (VALIANT) trial compared outcomes with: (1) angiotensin‐converting enzyme inhibition (ACEI) with the reference agent captopril; (2) angiotensin‐receptor blockade (ARB) with valsartan; or (3) both in patients with heart failure (HF) and/or left ventricular systolic dysfunction (LVSD) after myocardial infarction (MI).

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.

Assessment of Consistency of Treatment Effects in Multiregional Clinical Trials

Multiregional clinical trials (MRCTs) present great opportunities but also challenges to the trial community. To address the challenges and fully realize the opportunities, a PhRMA MRCT Cross-Functional Key Issue Team (KIT) was formed in 2008. One of the work streams within the KIT particularly focuses on the assessment of consistency of treatment effects across regions. As the main objective of this work stream, this research explores a number of definitions for consistency assessments. We address the issues primarily for superiority trials with continuous endpoints, then extend briefly to noninferiority trials, random effect models, binary endpoints, and survival endpoints. Computations and simulations are used to study the properties of the proposed definitions, particularly the power for showing consistency. To illustrate applications of the methods, we use a trial example with a continuous endpoint. We discuss considerations for trial design as well as for data analysis. The consistency assessment relies heavily on the definition of regions and the number of regions. We recommend working with health authorities to define region in a manner that is sensible from a practical interpretation standpoint and also makes region consistency assessment a feasible undertaking.

Adaptive dose-response studies

Insufficient exploration of the dose-response is a shortcoming of clinical drug development, and failure to characterize dosing early is often cited as a key contributor to the high late-stage attrition rate currently faced by the industry. Adaptive methods, for example, make it feasible to design a proof-of-concept study as an adaptive dose-response trial. Efficient learning about the dose response earlier in development will ultimately reduce overall costs and provide better information on dose in the filing package. This article presents the Pharmaceutical Research and Manufacturers of America working groups main recommendations regarding adaptive dose-response studies. As background, traditional fixed and adaptive dose-response designs are briefly reviewed. Information on developing a Bayesian adaptive dose design and some monitoring and processing issues are also discussed.