A stratified adaptive two-stage design with co-primary endpoints for phase II clinical oncology trials.

Background: Given the inherent challenges of conducting randomized phase III trials in older cancer patients, single-arm phase II trials which assess the feasibility of a treatment that has already been shown to be effective in a younger population may provide a compelling alternative. Such an approach would need to evaluate treatment feasibility based on a composite endpoint that combines multiple clinical dimensions and to stratify older patients as fit or frail to account for the heterogeneity of the study population to recommend an appropriate treatment approach. In this context, stratified adaptive two-stage designs for binary or composite endpoints, initially developed for biomarker studies, allow to include two subgroups whilst maintaining competitive statistical performances.

Addressing heterogeneity in the design of phase II clinical trials in geriatric oncology.

Introduction: Cancer in the elderly is a major public issue. However, older patients have long been debarred from clinical trials. There is a high unmet medical need for specific trials addressing oncology strategies adapted to older patients' conditions.

Focus on an infrequently used quantity in the context of competing risks: The conditional probability function.

In clinical studies of hematologic and oncologic diseases, the outcomes of interest are generally composite time to event endpoints which are usually defined by occurrence of different event types. Nonetheless, clinicians are interested in studying only one event type, which leads to a competing risks situation. In this context, Pepe and Mori presented a quantity directly derived from the cumulative incidence: the conditional probability.

Comparison of variable selection methods for high-dimensional survival data with competing events.

Background: In the era of personalized medicine, it's primordial to identify gene signatures for each event type in the context of competing risks in order to improve risk stratification and treatment strategy. Until recently, little attention was paid to the performance of high-dimensional selection in deriving molecular signatures in this context. In this paper, we investigate the performance of two selection methods developed in the framework of high-dimensional data and competing risks: Random survival forest and a boosting approach for fitting proportional subdistribution hazards models.

A Statistical Approach to Determine the Optimal Duration of Post-Treatment Follow-Up: Application to Metastatic Nonseminomatous Germ Cell Tumors.

Background: The objective of this study was to present a statistical method to define an optimal duration of follow-up for patients in remission after treatment for cancer, for detection of recurrences.

Patients And Methods: Surveillance duration was estimated using the 2-step approach proposed by Mould et al. Relapse-free interval was modeled using the parametric cure model proposed by Boag.

Optimal scheduling of post-therapeutic follow-up of patients treated for cancer for early detection of relapses.

Post-therapeutic surveillance is one important component of cancer care. However, there still is no evidence-based strategies to schedule patients' follow-up examinations. Our approach is based on the modeling of the probability of the onset of relapse at an early asymptotic or preclinical stage and its transition to a clinical stage.

Determining the length of posttherapeutic follow-up for cancer patients using competing risks modeling.

Background/objective: After a curative treatment for cancer, patients enter into a posttherapeutic surveillance phase. This phase aims to detect relapses as soon as possible to improve the outcome. Mould and others predicted with a simple formula, using a parametric mixture cure model, how long early-stage breast cancer patients should be followed after treatment.

Smooth conditional distribution function and quantiles under random censorship.

We consider a nonparametric random design regression model in which the response variable is possibly right censored. The aim of this paper is to estimate the conditional distribution function and the conditional alpha-quantile of the response variable. We restrict attention to the case where the response variable as well as the explanatory variable are unidimensional and continuous.