Incorporating intermediate binary responses into interim analyses of clinical trials: A comparison of four methods

2008 ◽  
Vol 27 (10) ◽  
pp. 1646-1666 ◽  
Author(s):  
Anne Whitehead ◽  
Marina Roshini Sooriyarachchi ◽  
John Whitehead ◽  
Kim Bolland
Keyword(s):  
Clinical Trials ◽  
Binary Responses ◽  
Interim Analyses

Adaptive Learning of Drug Quality and Optimization of Patient Recruitment for Clinical Trials with Dropouts

2021 ◽  
Author(s):  
Zhili Tian ◽  
Weidong Han ◽  
Warren B. Powell
Keyword(s):  
Clinical Trial ◽  
Clinical Trials ◽  
Dynamic Programming ◽  
Value Function ◽  
Standard Treatment ◽  
Treatment Effectiveness ◽  
Interim Analyses ◽  
Clinical Trial Program ◽  
Patient Enrollment ◽  
The Value Function

Problem definition: Clinical trials are crucial to new drug development. This study investigates optimal patient enrollment in clinical trials with interim analyses, which are analyses of treatment responses from patients at intermediate points. Our model considers uncertainties in patient enrollment and drug treatment effectiveness. We consider the benefits of completing a trial early and the cost of accelerating a trial by maximizing the net present value of drug cumulative profit. Academic/practical relevance: Clinical trials frequently account for the largest cost in drug development, and patient enrollment is an important problem in trial management. Our study develops a dynamic program, accurately capturing the dynamics of the problem, to optimize patient enrollment while learning the treatment effectiveness of an investigated drug. Methodology: The model explicitly captures both the physical state (enrolled patients) and belief states about the effectiveness of the investigated drug and a standard treatment drug. Using Bayesian updates and dynamic programming, we establish monotonicity of the value function in state variables and characterize an optimal enrollment policy. We also introduce, for the first time, the use of backward approximate dynamic programming (ADP) for this problem class. We illustrate the findings using a clinical trial program from a leading firm. Our study performs sensitivity analyses of the input parameters on the optimal enrollment policy. Results: The value function is monotonic in cumulative patient enrollment and the average responses of treatment for the investigated drug and standard treatment drug. The optimal enrollment policy is nondecreasing in the average response from patients using the investigated drug and is nonincreasing in cumulative patient enrollment in periods between two successive interim analyses. The forward ADP algorithm (or backward ADP algorithm) exploiting the monotonicity of the value function reduced the run time from 1.5 months using the exact method to a day (or 20 minutes) within 4% of the exact method. Through an application to a leading firm’s clinical trial program, the study demonstrates that the firm can have a sizable gain of drug profit following the optimal policy that our model provides. Managerial implications: We developed a new model for improving the management of clinical trials. Our study provides insights of an optimal policy and insights into the sensitivity of value function to the dropout rate and prior probability distribution. A firm can have a sizable gain in the drug’s profit by managing its trials using the optimal policies and the properties of value function. We illustrated that firms can use the ADP algorithms to develop their patient enrollment strategies.

scholarly journals A web-based endpoint adjudication system for interim analyses in clinical trials

2009 ◽  
Vol 6 (1) ◽  
pp. 60-66 ◽  
Author(s):  
Tracy L Nolen ◽  
Bill F Dimmick ◽  
Luis Ostrosky-Zeichner ◽  
Amy S Kendrick ◽  
Carole Sable ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Web Based ◽  
Interim Analyses

scholarly journals Prospective comparison of Bayesian and frequentist adaptive clinical trials : The SHADOW - SHINE project

2021 ◽  
Author(s):  
Kristine Broglio ◽  
William Meurer ◽  
Valerie Durkalski ◽  
Qi Pauls ◽  
Jason Connor ◽  
...  
Keyword(s):  
Clinical Trial ◽  
Clinical Trials ◽  
Acute Stroke ◽  
Sample Size ◽  
Trial Design ◽  
Interim Analyses ◽  
Patient Level Data ◽  
Patient Level ◽  
Prospective Comparison ◽  
Level Data

Importance: Bayesian adaptive trial design has the potential to create more efficient clinical trials. However, one of the barriers to the uptake of Bayesian adaptive designs for confirmatory trials is limited experience with how they may perform compared to a frequentist design. Objective: Compare the performance of a Bayesian and a frequentist adaptive clinical trial design. Design: Prospective observational study comparing two trial designs using individual patient level data from a completed stroke trial, including the timing and order of enrollments and outcome ascertainment. The implemented frequentist design had group sequential boundaries for efficacy and futility interim analyses when 90-days post-randomization was met for 500, 700, 900, and 1,100 patients. The Bayesian alternative utilized predictive probability of trial success to govern early termination for efficacy and futility with a first interim analysis at 500 randomized patients, and subsequent interims after every 100 randomizations. Setting: Multi-center, acute stroke study conducted within a National Institutes of Health neurological emergencies clinical trials network. Participants: Patient level data from 1,151 patients randomized in a clinical trial comparing intensive insulin therapy to standard in acute stroke patients with hyperglycemia. Main Outcome(s) and Measure(s): Sample size at end of study. This was defined as the sample size at which each of the studies stopped accrual of patients. Results: As conducted, the frequentist design passed the futility boundary after 936 participants were randomized. Using the same sequence and timing of randomization and outcome data, the Bayesian alternative crossed the futility boundary about 3 months earlier after 800 participants were randomized. Conclusions and Relevance: Both trial designs stopped for futility prior to reaching the planned maximum sample size. In both cases, the clinical community and patients would benefit from learning the answer to the trial's primary question earlier. The common feature across the two designs was frequent interim analyses to stop early for efficacy or for futility. Differences between how this was implemented between the two trials resulted in the differences in early stopping.

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