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.

Both combinations were active, with acceptable safety profiles. Irinotecan/5-FU/FA was selected as the most effective combination for investigation in a phase III trial in advanced gastric cancer.

Clinical trials in oncology often allow patients randomized to placebo to cross over to the active treatment arm after disease progression, leading to underestimation of the treatment effect on overall survival as per the intention-to-treat analysis. We illustrate the statistical aspects and practical use of the rank-preserving structural failure time (RPSFT) model with the Fleming-Harrington family of tests to estimate the crossover-corrected treatment effect, and to assess its sensitivity to various weighting schemes in the RECORD-1 trial. The results suggest that the benefit demonstrated in progression-free survival is likely to translate into a robust overall survival benefit.

The COVID-19 pandemic has a global impact on the conduct of clinical trials of medical products. This article discusses implications of the COVID-19 pandemic on clinical research methodology aspects and provides points to consider to assess and mitigate the risk of seriously compromising the integrity and interpretability of clinical trials. The information in this article will support discussions that need to occur cross-functionally on an ongoing basis to "integrate all available knowledge from the ethical, the medical, and the methodological perspective into decision making. " This article aims at facilitating: (i) risk assessments of the impact of the pandemic on trial integrity and interpretability; (ii) identification of the relevant data and information related to the impact of the pandemic on the trial that needs to be collected; (iii) short-term decision making impacting ongoing trial operations; (iv) ongoing monitoring of the trial conduct until completion, including the possible involvement of data monitoring committees, and adequately documenting all measures taken to secure trial integrity throughout and after the pandemic, and (v) proper analysis and interpretation of the eventual interim or final trial data.

Traditional phase III non-inferiority trials require compelling evidence that the treatment vs control effect bfθ is better than a pre-specified non-inferiority margin θ(NI) . The standard approach compares this margin to the 95 per cent confidence interval of the effect parameter. In the phase II setting, in order to declare Proof of Concept (PoC) for non-inferiority and proceed in the development of the drug, different criteria that are specifically tailored toward company internal decision making may be more appropriate. For example, less evidence may be needed as long as the effect estimate is reasonably convincing. We propose a non-inferiority design that addresses the specifics of the phase II setting. The requirements are that (1) the effect estimate be better than a critical threshold θ(C), and (2) the type I error with regard to θ(NI) is controlled at a pre-specified level. This design is compared with the traditional design from a frequentist as well as a Bayesian perspective, where the latter relies on the Level of Proof (LoP) metric, i.e. the probability that the true effect is better than effect values of interest. Clinical input is required to establish the value θ(C), which makes the design transparent and improves interactions within clinical teams. The proposed design is illustrated for a non-inferiority trial for a time-to-event endpoint in oncology.