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Before a novel treatment can be deemed a clinical success, an assessment of its risk-benefit profile must be made. One of the inherent challenges for this assessment comes from the multiplicity that arises from comparing treatment groups across multiple outcomes. Composite outcomes that summarize a patient's clinical status, or severity, across a prioritized list of safety and efficacy outcomes have become increasing popular. In this article, we review these approaches and illustrate through examples some of the challenges and complexities of a composite derived from prioritized outcomes, such as the win ratio. These challenges include the difficult tension between the analytical validity that comes from choosing a pre-specified outcome and an evaluation that is responsive to unexpected safety events that arise during the course of a trial. Other challenges include a sensitivity of the resulting test statistic to the underlying censoring distribution and other nuisance parameters. Approaches that resolve some of the difficulties of the analytical challenges associated with prioritized outcomes are then discussed. Ultimately, a composite outcome of net clinical benefit is another decision tool, but one to be used alongside more traditional analyses of efficacy and safety, and with the broader perspective that investigators, the data safety monitoring board, and regulators bring to an evaluation of risk-benefit.

Background Obesity continues to be a serious public health challenge. Rates are increasing worldwide, with nearly 70% of the US adults overweight or obese, leading to increased clinical and economic burden. While successful approaches for achieving weight loss have been identified, techniques for long-term maintenance of initial weight loss have largely been unsuccessful. Financial incentive interventions have been shown in several settings to be successful in motivating participants to adopt healthy behaviors. Purpose Keep It Off is a three-arm randomized controlled trial that compares the efficacy of a lottery-based incentive, traditional direct payment incentive, and control of daily feedback without any incentive for weight-loss maintenance. This design allows comparison of a traditional direct payment incentive with one based on behavioral economic principles that consider the underlying psychology of decision-making. Methods Participants were randomized in a 2:1 ratio for each active arm relative to control, with a targeted 188 participants in total. Eligible participants were those aged 30-80 who lost at least 11 lb (5 kg) during the first 4 months of participation in Weight Watchers, a national weight-loss program, with whom we partnered. The interventions lasted 6 months (Phase I); participants were followed for an additional 6 months without intervention (Phase II). The primary outcome is weight change from baseline to the end of Phase I, with the change at the end of Phase II a key secondary endpoint. Keep It Off is a pragmatic trial that recruited, consented, enrolled, and followed patients electronically. Participants were provided a wireless weight scale that electronically transmitted daily self-monitored weights. Weights were verified every 3 months at a Weight Watchers center local to the participant and electronically transmitted. Results Using the study web-based platform, we integrated recruitment, enrollment, and follow-up procedures into a digital platform that required little staff effort to implement and manage. We randomized 191 participants in less than 1 year. We describe the design of Keep It Off and implementation of enrollment. Lessons Learned We demonstrated that our pragmatic design was successful in rapid accrual of participants in a trial of interventions to maintain weight loss. Limitations Despite the nationwide reach of Weight Watchers, the generalizability of study findings may be limited by the characteristics of its members. The interventions under study are appropriate for settings where an entity, such as an employer or health insurance company, could offer them as a benefit. Conclusions Keep It Off was implemented and conducted with minimal staff effort. This study has the potential to identify a practical and effective weight-loss maintenance strategy.

Background: To reduce research costs in the context of pragmatic trials, consideration is given to using administrative data (Medicare claims) to ascertain clinical outcomes., Methods: In the historical context of the Women's Health Initiative, the correspondence between selected cardiovascular events derived from Medicare claims was compared to those documented and adjudicated in this large-scale prevention trial., Results: Classification performance varies somewhat by type of outcome, but hazard ratios and confidence intervals derived from the two data sources were quite comparable., Conclusion: These encouraging results provided the needed support to launch a new embedded pragmatic trial of physical activity that will rely heavily on Medicare claims to ascertain cardiovascular disease incidence in the majority of those randomized., (© The Author(s) 2016.)

Background: Audits are often performed to assess the quality of clinical trial data, but beyond detecting fraud or sloppiness, the audit data are generally ignored. In an earlier study, using data from a nonrandomized study, Shepherd and Yu developed statistical methods to incorporate audit results into study estimates and demonstrated that audit data could be used to eliminate bias., Purpose: In this article, we examine the usefulness of audit-based error-correction methods in clinical trial settings where a continuous outcome is of primary interest., Methods: We demonstrate the bias of multiple linear regression estimates in general settings with an outcome that may have errors and a set of covariates for which some may have errors and others, including treatment assignment, are recorded correctly for all subjects. We study this bias under different assumptions, including independence between treatment assignment, covariates, and data errors (conceivable in a double-blinded randomized trial) and independence between treatment assignment and covariates but not data errors (possible in an unblinded randomized trial). We review moment-based estimators to incorporate the audit data and propose new multiple imputation estimators. The performance of estimators is studied in simulations., Results: When treatment is randomized and unrelated to data errors, estimates of the treatment effect using the original error-prone data (i.e., ignoring the audit results) are unbiased. In this setting, both moment and multiple imputation estimators incorporating audit data are more variable than standard analyses using the original data. In contrast, in settings where treatment is randomized but correlated with data errors and in settings where treatment is not randomized, standard treatment-effect estimates will be biased. And in all settings, parameter estimates for the original, error-prone covariates will be biased. The treatment and covariate effect estimates can be corrected by incorporating audit data using either the multiple imputation or moment-based approaches. Bias, precision, and coverage of confidence intervals improve as the audit size increases., Limitations: The extent of bias and the performance of methods depend on the extent and nature of the error as well as the size of the audit. This study only considers methods for the linear model. Settings much different than those considered here need further study., Conclusions: In randomized trials with continuous outcomes and treatment assignment independent of data errors, standard analyses of treatment effects will be unbiased and are recommended. However, if treatment assignment is correlated with data errors or other covariates, naive analyses may be biased. In these settings, and when covariate effects are of interest, approaches for incorporating audit results should be considered.