Economic Evaluation of Cost and Time Required for a Platform Trial vs Conventional Trials.

Importance: Platform trial design allows the introduction of new interventions after the trial is initiated and offers efficiencies to clinical research. However, limited guidance exists on the economic resources required to establish and maintain platform trials.

Objective: To compare cost (US dollars) and time requirements of conducting a platform trial vs a series of conventional (nonplatform) trials using a real-life example.

The Case for a Bayesian Approach to Benefit-Risk Assessment:: Overview and Future Directions.

The benefit-risk assessment of a new medicinal product or intervention is one of the most complex tasks that sponsors, regulators, payers, physicians, and patients face. Therefore, communicating the trade-off of benefits and risks in a clear and transparent manner, using all available evidence, is critical to ensure that the best decisions are made. Several quantitative methods have been proposed in recent years that try to provide insight into this challenging problem.

DIA's Adaptive Design Scientific Working Group (ADSWG): Best Practices Case Studies for "Less Well-understood" Adaptive Designs.

Adaptive design (AD) clinical trials use accumulating subject data to modify the parameters of the design of an ongoing study, without compromising the validity and integrity of the study. The 2010 US Food and Drug Administration (FDA) Draft Guidance on Adaptive Design Clinical Trials described a subset of 7 primary design types as "less well-understood." FDA defined these designs as those with limited regulatory experience.

Addressing Challenges and Opportunities of "Less Well-Understood" Adaptive Designs.

The draft adaptive design guidance released by FDA in 2010 included references to adaptive study designs that were described as "less well-understood." At that time, there was relatively little regulatory experience with such designs, and their properties were felt to be insufficiently understood. In order to promote greater use of adaptive designs, especially those categorized as less well-understood, the Best Practice Subteam of the DIA Adaptive Designs Scientific Working Group (ADSWG) has worked on describing and characterizing these designs, identifying challenges associated with them and suggesting improvements to design or study conduct aspects that might make them more acceptable.

The use of group sequential, information-based sample size re-estimation in the design of the PRIMO study of chronic kidney disease.

Background: Chronic kidney disease is associated with a marked increase in risk for left ventricular hypertrophy and cardiovascular mortality compared with the general population. Therapy with vitamin D receptor activators has been linked with reduced mortality in chronic kidney disease and an improvement in left ventricular hypertrophy in animal studies.

Purpose: PRIMO (Paricalcitol capsules benefits in Renal failure Induced cardia MOrbidity) is a multinational, multicenter randomized controlled trial to assess the effects of paricalcitol (a selective vitamin D receptor activator) on mild to moderate left ventricular hypertrophy in patients with chronic kidney disease.

Semiparametric estimation of treatment effects given base-line covariates on an outcome measured after a post-randomization event occurs.

We consider estimation, from a double-blind randomized trial, of treatment effect within levels of base-line covariates on an outcome that is measured after a post-treatment event E has occurred in the subpopulation 𝒫(E,E) that would experience event E regardless of treatment. Specifically, we consider estimation of the parameters γ indexing models for the outcome mean conditional on treatment and base-line covariates in the subpopulation 𝒫(E,E). Such parameters are not identified from randomized trial data but become identified if additionally it is assumed that the subpopulation 𝒫(Ē,E) of subjects that would experience event E under the second treatment but not under the first is empty and a parametric model for the conditional probability that a subject experiences event E if assigned to the first treatment given that the subject would experience the event if assigned to the second treatment, his or her outcome under the second treatment and his or her pretreatment covariates.

A consultant's perspective on the regulatory hurdles to adaptive trials.

This is a discussion of the following two papers appearing in this special issue on adaptive designs: 'A regulatory view on adaptive/flexible clinical trial design' by H. M. James Hung, Robert T.

Sensitivity analyses comparing outcomes only existing in a subset selected post-randomization, conditional on covariates, with application to HIV vaccine trials.

In many experiments, researchers would like to compare between treatments and outcome that only exists in a subset of participants selected after randomization. For example, in preventive HIV vaccine efficacy trials it is of interest to determine whether randomization to vaccine causes lower HIV viral load, a quantity that only exists in participants who acquire HIV. To make a causal comparison and account for potential selection bias we propose a sensitivity analysis following the principal stratification framework set forth by Frangakis and Rubin (2002, Biometrics58, 21-29).