scholarly journals Optimal, two-stage, adaptive enrichment designs for randomized trials, using sparse linear programming

2020 ◽  
Vol 82 (3) ◽  
pp. 749-772
Author(s):  
Michael Rosenblum ◽  
Ethan X. Fang ◽  
Han Liu
Keyword(s):  
Linear Programming ◽  
Randomized Trials ◽  
Two Stage ◽  
Enrichment Designs ◽  
Adaptive Enrichment Designs

scholarly journals Optimized adaptive enrichment designs

2017 ◽  
Vol 28 (7) ◽  
pp. 2096-2111 ◽  
Author(s):  
Thomas Ondra ◽  
Sebastian Jobjörnsson ◽  
Robert A Beckman ◽  
Carl-Fredrik Burman ◽  
Franz König ◽  
...  
Keyword(s):  
Net Present Value ◽  
Single Stage ◽  
Programming Approach ◽  
Theoretic Approach ◽  
Bayesian Decision ◽  
Two Stage ◽  
Dynamic Programming Approach ◽  
Enrichment Designs ◽  
Adaptive Enrichment Designs ◽  
The One

Based on a Bayesian decision theoretic approach, we optimize frequentist single- and adaptive two-stage trial designs for the development of targeted therapies, where in addition to an overall population, a pre-defined subgroup is investigated. In such settings, the losses and gains of decisions can be quantified by utility functions that account for the preferences of different stakeholders. In particular, we optimize expected utilities from the perspectives both of a commercial sponsor, maximizing the net present value, and also of the society, maximizing cost-adjusted expected health benefits of a new treatment for a specific population. We consider single-stage and adaptive two-stage designs with partial enrichment, where the proportion of patients recruited from the subgroup is a design parameter. For the adaptive designs, we use a dynamic programming approach to derive optimal adaptation rules. The proposed designs are compared to trials which are non-enriched (i.e. the proportion of patients in the subgroup corresponds to the prevalence in the underlying population). We show that partial enrichment designs can substantially improve the expected utilities. Furthermore, adaptive partial enrichment designs are more robust than single-stage designs and retain high expected utilities even if the expected utilities are evaluated under a different prior than the one used in the optimization. In addition, we find that trials optimized for the sponsor utility function have smaller sample sizes compared to trials optimized under the societal view and may include the overall population (with patients from the complement of the subgroup) even if there is substantial evidence that the therapy is only effective in the subgroup.

scholarly journals Planning of Electric Power Systems Considering Virtual Power Plants with Dispatchable Loads Included: An Inexact Two-Stage Stochastic Linear Programming Model

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Changyu Zhou ◽  
Guohe Huang ◽  
Jiapei Chen
Keyword(s):  
Linear Programming ◽  
Power System ◽  
Electric Power ◽  
Power Supply ◽  
Power Plants ◽  
Electricity Demand ◽  
Virtual Power ◽  
Stochastic Linear Programming ◽  
Two Stage ◽  
Virtual Power Plants

In this study, an inexact two-stage stochastic linear programming (ITSLP) method is proposed for supporting sustainable management of electric power system under uncertainties. Methods of interval-parameter programming and two-stage stochastic programming were incorporated to tackle uncertainties expressed as interval values and probability distributions. The dispatchable loads are integrated into the framework of the virtual power plants, and the support vector regression technique is applied to the prediction of electricity demand. For demonstrating the effectiveness of the developed approach, ITSLP is applied to a case study of a typical planning problem of power system considering virtual power plants. The results indicate that reasonable solutions for virtual power plant management practice have been generated, which can provide strategies in mitigating pollutant emissions, reducing system costs, and improving the reliability of power supply. ITSLP is more reliable for the risk-aversive planners in handling high-variability conditions by considering peak-electricity demand and the associated recourse costs attributed to the stochastic event. The solutions will help decision makers generate alternatives in the event of the insufficient power supply and offer insight into the tradeoffs between economic and environmental objectives.

scholarly journals Adaptive enrichment designs for confirmatory trials

2018 ◽  
Vol 38 (4) ◽  
pp. 613-624 ◽  
Author(s):  
Tze Leung Lai ◽  
Philip W. Lavori ◽  
Ka Wai Tsang
Keyword(s):  
Enrichment Designs ◽  
Adaptive Enrichment Designs ◽  
Confirmatory Trials

Optimized adaptive enrichment designs for three-arm trials: learning which subpopulations benefit from different treatments

Biostatistics ◽  
2019 ◽  
Author(s):  
Jon Arni Steingrimsson ◽  
Joshua Betz ◽  
Tianchen Qian ◽  
Michael Rosenblum
Keyword(s):  
Sample Size ◽  
Disease Severity ◽  
Adaptive Design ◽  
Type I Error ◽  
Type I ◽  
Expected Sample Size ◽  
Common Control ◽  
Enrichment Designs ◽  
Adaptive Enrichment Designs ◽  
Standard Designs

Summary We consider the problem of designing a confirmatory randomized trial for comparing two treatments versus a common control in two disjoint subpopulations. The subpopulations could be defined in terms of a biomarker or disease severity measured at baseline. The goal is to determine which treatments benefit which subpopulations. We develop a new class of adaptive enrichment designs tailored to solving this problem. Adaptive enrichment designs involve a preplanned rule for modifying enrollment based on accruing data in an ongoing trial. At the interim analysis after each stage, for each subpopulation, the preplanned rule may decide to stop enrollment or to stop randomizing participants to one or more study arms. The motivation for this adaptive feature is that interim data may indicate that a subpopulation, such as those with lower disease severity at baseline, is unlikely to benefit from a particular treatment while uncertainty remains for the other treatment and/or subpopulation. We optimize these adaptive designs to have the minimum expected sample size under power and Type I error constraints. We compare the performance of the optimized adaptive design versus an optimized nonadaptive (single stage) design. Our approach is demonstrated in simulation studies that mimic features of a completed trial of a medical device for treating heart failure. The optimized adaptive design has $25\%$ smaller expected sample size compared to the optimized nonadaptive design; however, the cost is that the optimized adaptive design has $8\%$ greater maximum sample size. Open-source software that implements the trial design optimization is provided, allowing users to investigate the tradeoffs in using the proposed adaptive versus standard designs.

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