Search

It is healthy to periodically question things that we have long taken for granted. Randomized clinical trials are the gold standard for evaluating the benefits and harms of interventions, though often fails to provide the practical evidence to inform medical decision-making. A primary reason is failure to recognize the most important questions for treating patients in clinical practice, and using this as the motivation for the design, monitoring, analysis, and reporting of clinical trials. Our most promising opportunity is placing increased interest on questions of a pragmatic origin to match their clinical importance. Improvements to traditional approaches are needed. Standard approaches synthesizing information obtained from separate marginal analysis of each outcome, fail to incorporate associations between outcomes and recognize the cumulative nature of outcomes in individual patients, suffer from competing risk complexities during interpretation of individual outcomes, and since efficacy and safety analyses are often conducted on different populations, generalizability is unclear. The utility of clinical trials can be enhanced by: (i) using outcomes to analyze patients rather than patients to analyze the outcomes, and (ii) incorporating benefit:risk evaluation into standard trial design and conduct, rather than conducted as a post-hoc exercise. Benefit: risk outcomes such as the desirability of outcome ranking (DOOR), can become a standard, prespecified along with efficacy and safety outcomes. Inclusion and analyses of such endpoints provide important information regarding the effects on patients, unable to be obtained by marginal analyses of efficacy and safety. Prespecified procedures to identify subgroups of patients with a positive benefit:risk profile will further advance clinical trial science. [ABSTRACT FROM AUTHOR]

There is an innate desire to conduct research faster and cheaper. The desire is magnified by todays' business and political pressures. Though understandable, such desires can be dangerous threatening our objectivity and ability to reason, resulting in studies with lower integrity, replicability, and applicability. Modern ideas include: conducting observational rather than randomized studies rationalized by the increasing access to real world data and the belief that modeling can replace randomization, using surrogate rather than clinical outcomes, using surrogate rather than clinical diseases, using PP (Per Protocol) analyses instead of ITT (Intention-to-Treat), using assumptions regarding treatment effects in place of data to estimate those effects, and using adaptive designs that promise efficiencies. These trends are often labeled as "innovation." But is this progress...or regress...a fancy way of lowering the usual evidentiary standard and introducing greater uncertainty? Is it time for a market correction for these "products" that are often presented with a degree of commercialism rather than scientific objectivity? Indeed, our traditional approaches are in need in improvements. Rather than compromising on scientific rigor, can we redirect our motivation to find BETTER answers to the most important questions for patients and clinicians? Though often misunderstood, one such movement is pragmatism, the concept of truly getting closer to understanding the effects of interventions as they are experienced by patients and the value of diagnostics in real world practice. I propose that we place increased interest on questions of a pragmatic origin to match their clinical importance and utility. I present ideas for more pragmatic assessments of the effects of interventions and the yield of diagnostics. [ABSTRACT FROM AUTHOR]

We discuss using prediction as a flexible and practical approach for monitoring futility in clinical trials with two co-primary endpoints (CPE). This approach is appealing in that it provides quantitative evaluation of potential effect sizes and associated precision, and can be combined with flexible error-spending strategies. We extend prediction of effect size estimates and the construction of predicted intervals to the two CPE case, and illustrate interim futility monitoring of treatment effects using prediction with an example. We also discuss alternative approaches based on the conditional and predictive powers, compare these methods and provide some guidance on the use of prediction for better decision in clinical trials with CPE. [ABSTRACT FROM AUTHOR]

I thank and commend Dr. Gibson on his thoughtful and thorough discussion regarding the role of I p i -values in evaluating evidence (Gibson [8]). One could replace hypothesis testing and I p i -values with confidence interval estimation. Observed I p i -values should be reported rather than, for example, I p i < 0.05 or I p i > 0.05. Although a I p i -value of 0.06 and 0.86 each result in a failure to reject a null hypothesis when testing at 0.05, they may be interpreted quite differently in light of other evidence such as results for other outcomes. [Extracted from the article]

In the future, clinical trials will have an increased emphasis on pragmatism, providing a practical description of the effects of new treatments in realistic clinical settings. Accomplishing pragmatism requires better summaries of the totality of the evidence in ways that clinical trials consumers—patients, physicians, insurers—find transparent and allow for informed benefit:risk decision-making. The current approach to the analysis of clinical trials is to analyze efficacy and safety separately and then combine these analyses into a benefit:risk assessment. Many assume that this will effectively describe the impact on patients. But this approach is suboptimal for evaluating the totality of effects on patients. We discuss methods for benefit:risk assessment that have greater pragmatism than methods that separately analyze efficacy and safety. These include the concepts ofwithin-patient analysesand composite benefit:risk endpoints with a goal of understanding how to analyze one patient before trying to figure out how to analyze many. We discuss thedesirability of outcome ranking(DOOR) and introduce thepartial credit strategyusing an example in a clinical trial evaluating the effects of a new antibiotic. As part of the example, we introduce a strategy to engage patients as a resource to inform benefit:risk analyses consistent with the goal of measuring and weighing outcomes that are most important from the patient's perspective. We describe a broad vision for the future of clinical trials consistent with increased pragmatism. Greater focus on using endpoints to analyze patients rather than patients to analyze endpoints particularly in late-phase/stage clinical trials is an important part of this vision. [ABSTRACT FROM PUBLISHER]

The authors comment on the study "Antimicrobial Products: Statistical Challenges and Opportunities" published in the current issue of the journal.