Citations

Abstract Prevention trials in oncology are some of the most important cancer clinical trials that can be designed, implemented, analyzed, and interpreted. They are pivotal in the goal of stopping the development of cancer before it starts. Prevention trials are unique in that they not only have some of the same requirements and challenges as treatment trials but also have challenges that may make their design more complex. This paper aims to discuss some types of prevention trials and highlight their most common design challenges, including large sample size requirements, slow accrual rates with long accrual duration, extended follow-up periods with trial adherence issues and missing data, endpoints that require extended follow-up or have a high level of confounding, and problems with optimizing study design. This article provides real study examples and suggestions for designing prevention clinical trials while mitigating the known issues they face.

Abstract Interpreting cancer clinical trial results often depends on addressing issues of multiplicity. When testing multiple hypotheses, unreliable findings can occur by chance due to the inflation of the type I error rate, the probability of mistakenly rejecting the null hypothesis when the null hypothesis is true. In this setting, researchers may often set the type I error rate (or the alpha level) low to limit false positive findings and the interpretation of a causal relationship where none exists. Conversely, overly conservative type I error control may result in declaring findings, that do not meet multiplicity-adjusted alpha levels, as false when they are actually true, reducing opportunities for new discovery. This presentation focuses on multiplicity adjustment in the context of clinical trials conducted within the NCI’s Community Oncology Research Program (NCORP). Because federally sponsored trials often require long-term participation from patients and represent a substantial investment by taxpayers, striking the right balance between optimizing what is learned from these trials, while avoiding false positive results, should be a priority.

Abstract Examining treatment effects in subgroups of patients defined by demographic, genetic, or clinical characteristics is increasingly of interest given the pursuit of personalized medicine and the importance of representation and equity in treatment decisions. The magnitude or even the direction of the treatment effect may vary across subgroups, and these differential treatment effects could have clinical implications. Subgroup analyses require caution in their interpretation, however, because of the high probability of a false-positive or false-negative conclusion. We outline study design and analysis considerations for responsibly investigating and reporting differential treatment effects across subgroups in oncology trials, with examples from the National Cancer Institute’s National Clinical Trials Network and Community Oncology Research Program. Recommendations include ensuring appropriate representation of patients from subgroups of interest, recognizing power and multiplicity limitations, and treating exploratory subgroup analyses as hypothesis generating rather than practice changing.

AbstractPurposeThis paper reports the efficacy of the poly (ADP‐ribose) polymerase inhibitor olaparib alone and in combination with the antiangiogenesis agent cediranib compared with cediranib alone in patients with advanced endometrial cancer.MethodsThis was open‐label, randomized, phase 2 trial (NCT03660826). Eligible patients had recurrent endometrial cancer, received at least one (<3) prior lines of chemotherapy, and were Eastern Cooperative Oncology Group performance status 0 to 2. Patients were randomly assigned (1:1:1), stratified by histology (serous vs. other) to receive cediranib alone (reference arm), olaparib, or olaparib and cediranib for 28‐day cycles until progression or unacceptable toxicity. The primary end point was progression‐free survival in the intention‐to‐treat population. Homologous repair deficiency was explored using the BROCA‐GO sequencing panel.ResultsA total of 120 patients were enrolled and all were included in the intention‐to‐treat analysis. Median age was 66 (range, 41–86) years and 47 (39.2%) had serous histology. Median progression‐free survival for cediranib was 3.8 months compared with 2.0 months for olaparib (hazard ratio, 1.45 [95% CI, 0.91‐2.3] p = .935) and 5.5 months for olaparib/cediranib (hazard ratio, 0.7 [95% CI, 0.43–1.14] p = .064). Four patients receiving the combination had a durable response lasting more than 20 months. The most common grade 3/4 toxicities were hypertension in the cediranib (36%) and olaparib/cediranib (33%) arms, fatigue (20.5% olaparib/cediranib), and diarrhea (17.9% cediranib). The BROCA‐GO panel results were not associated with response.ConclusionThe combination of cediranib and olaparib demonstrated modest clinical efficacy; however, the primary end point of the study was not met. The combination was safe without unexpected toxicity.

Abstract Clinical trials typically collect longitudinal data, data that are collected repeated over time, such as laboratories, scans, or patient-reported outcomes. Due to a variety of reasons, this data can be missing, whether a patient stops attending clinical visits (ie, dropout) or misses assessments intermittently. Understanding the reasons for missing data as well as predictors of missing data can aid in determination of the missing data mechanism. The analysis methods used are dependent on the missing data mechanism and may make certain assumptions about the missing data itself. Methods for nonignorable missing data, which assumes that the missing data depend on the missing data itself, make stronger assumptions and include pattern-mixture models and shared parameter models. Missing data that are ignorable after adjusting for other covariates can be analyzed using methods that adjust for covariates, such as mixed-effects models or multiple imputation. Missing data that are ignorable can be analyzed using standard approaches that require complete case data, such as change from baseline or proportion of patients who declined at a specified time point. In clinical trials, truly ignorable data are rare, resulting in additional analysis methods required for proper interpretation of the results. Conducting several analyses under different assumptions, called sensitivity analyses, can determine the extent of the impact of the missing data.