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Although adults aged 65 years or older make up a strong majority of cancer patients, their underrepresentation in cancer clinical trials leads to the lack of representative data to guide evidence-based therapeutic decisions in this patient population. The Trial Design Working Group, convened as part of the workshop titled, Engaging Older Adults in the National Cancer Institute Clinical Trials Network: Challenges and Opportunities, recommended study designs and design elements that could improve accrual of older adults in National Cancer Institute–funded clinical trials. These include trials that are specifically designed to enroll older adults, trials that include a cohort of older patients (parallel cohort, stratified cohort, or embedded cohort), and trials with pragmatic design elements to facilitate enrollment of older adults. This manuscript provides brief descriptions of the recommended designs, examples of successful trials, and considerations for implementation of these designs. As with any clinical trial, the scientific questions and trial objectives should drive the study design, the selection of endpoints and intervention, and eligibility criteria. When designing trials that include older adults, the heterogeneity of fitness levels is an important consideration as fitness can influence accrual rates and outcomes. Appropriately incorporating geriatric assessments can help identify the optimal subset of older patients for inclusion and minimize selection bias. Incorporating pragmatic design elements to reduce the burden on trial participants as well as on accruing sites and retaining essential elements to ensure that the main goal of the trial can be accomplished can enhance enrollment without compromising the integrity of trials.

Supplemental material, Supplemental_Material for Designing clinical trials with (restricted) mean survival time endpoint: Practical considerations by Anne Eaton, Terry Therneau and Jennifer Le-Rademacher in Clinical Trials

Supplemental material, supp_mat for Application of multi-state models in cancer clinical trials by Jennifer G Le-Rademacher, Ryan A Peterson, Terry M Therneau, Ben L Sanford, Richard M Stone and Sumithra J Mandrekar in Clinical Trials

In the last two decades, principal component analysis (PCA) was extended to interval-valued data; several adaptations of the classical approach are known from the literature. Our approach is based on the symbolic covariance matrix Cov for the interval-valued variables proposed by Billard (2008). Its crucial advantage, when compared to other approaches, is that it fully utilizes all the information in the data. The symbolic covariance matrix can be decomposed into a within part CovW and a between part CovB. We propose a further insight into the PCA results: the proportion of variance explained due to the within information and the proportion of variance explained due to the between information can be calculated. Additionally, we suggest PCA on CovB and CovW to be done to obtain deeper insights into the data under study. In the case study presented, the information gain when performing PCA on the intervals instead of the interval midpoints (conditionally the means) is about 45%. It turns out that, for these data, the uniformity assumption over intervals does not hold and so analysis of the data represented by histogram-valued variables is suggested.