Summary The analysis of data from dose‐response studies has long been divided according to two major strategies: multiple comparison procedures and model‐based approaches. Model‐based approaches assume a functional relationship between the response and the dose, taken as a quantitative factor, according to a prespecified parametric model. The fitted model is then used to estimate an adequate dose to achieve a desired response but the validity of its conclusions will highly depend on the correct choice of the a priori unknown dose‐response model. Multiple comparison procedures regard the dose as a qualitative factor and make very few, if any, assumptions about the underlying dose‐response model. The primary goal is often to identify the minimum effective dose that is statistically significant and produces a relevant biological effect. One approach is to evaluate the significance of contrasts between different dose levels, while preserving the family‐wise error rate. Such procedures are relatively robust but inference is confined to the selection of the target dose among the dose levels under investigation. We describe a unified strategy to the analysis of data from dose‐response studies which combines multiple comparison and modeling techniques. We assume the existence of several candidate parametric models and use multiple comparison techniques to choose the one most likely to represent the true underlying dose‐response curve, while preserving the family‐wise error rate. The selected model is then used to provide inference on adequate doses.

The use of historical control information is a valuable option and may lead to more efficient clinical trials. The proposed approach is attractive for nonconfirmatory trials, but under certain circumstances extensions to the confirmatory setting could be envisaged as well.

The Bayesian approach to finding the maximum-tolerated dose in phase I cancer trials is discussed. The suggested approach relies on a realistic dose-toxicity model, allows one to include prior information, and supports clinical decision making by presenting within-trial information in a transparent way. The modeling and decision-making components are flexible enough to be extendable to more complex settings. Critical aspects are emphasized and a comparison with the continual reassessment method (CRM) is performed with data from an actual trial and a simulation study. The comparison revealed similar operating characteristics while avoiding some of the difficulties encountered in the actual trial when applying the CRM.

Integrating selection and confirmation phases into a single trial can expedite the development of new treatments and allows to use all accumulated data in the decision process. In this paper we review adaptive treatment selection based on combination tests and propose overall adjusted p-values and simultaneous confidence intervals. Also point estimation in adaptive trials is considered. The methodology is illustrated in a detailed example based on an actual planned study.

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.

The power prior by Ibrahim and Chen (Statist. Sci. 2000; 15:46-60) is one of several methods to incorporate historical data in the analysis of a clinical trial. The power prior raises the likelihood of the historical data to the power parameter a(0) which quantifies the discounting of the historical data due to heterogeneity between trials. It is shown that the standard method of estimating the power parameter from the historical and current data is inappropriate, and we therefore suggest to use a modified power prior approach or to consider alternative methods instead.

For disease indications such as Acquired Immune Deficiency Syndrome (AIDS) and various cancers, randomization to a pure control treatment may be scientifically desirable but not ethically acceptable. Clinicians may insist that the experimental treatment be made available, at least as a rescue medication, for all patients in the control arm. A method for estimating a treatment effect in survival data from randomized clinical trials of this type is developed under an accelerated failure time model. This approach retains all patients in the groups to which they were randomized and is not based on an ad hoc subgroup analysis. By conditioning on having observed patient switch times, this method avoids the need to model patient switching patterns in the analysis. This new approach is evaluated using simulation studies, and is illustrated through analysing data from a Medical Research Council lung cancer trial.

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.