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Adaptive Designs for Clinical Trials: Application to Healthcare Epidemiology Research.

ABSTRACT: Clinical trials with adaptive designs use data that accumulate during the course of the study to modify study elements in a prespecified manner. The goal is to provide flexibility such that a trial can serve as a definitive test of its primary hypothesis, preferably in a shorter time period, involving fewer human subjects, and at lower cost. Elements that may be modified include the sample size, end points, eligible population, randomization ratio, and interventions. Accumulating data used to drive these modifications include the outcomes, subject enrollment (including factors associated with the outcomes), and information about the application of the interventions. This review discusses the types of adaptive designs for clinical trials, emphasizing their advantages and limitations in comparison with conventional designs, and opportunities for applying these designs to healthcare epidemiology research, including studies of interventions to prevent healthcare-associated infections, combat antimicrobial resistance, and improve antimicrobial stewardship.

SUBMITTER: Huskins WC

PROVIDER: S-EPMC6018921 | biostudies-literature | 2018 Mar

REPOSITORIES: biostudies-literature

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Adaptive Designs for Clinical Trials: Application to Healthcare Epidemiology Research.

Huskins W Charles WC Fowler Vance G VG Evans Scott S

Clinical infectious diseases : an official publication of the Infectious Diseases Society of America 20180301 7

Clinical trials with adaptive designs use data that accumulate during the course of the study to modify study elements in a prespecified manner. The goal is to provide flexibility such that a trial can serve as a definitive test of its primary hypothesis, preferably in a shorter time period, involving fewer human subjects, and at lower cost. Elements that may be modified include the sample size, end points, eligible population, randomization ratio, and interventions. Accumulating data used to dr ...[more]

PMID: 29121202

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Project description:BackgroundAdaptive clinical trials are growing in popularity as they are more flexible, efficient and ethical than traditional fixed designs. However, notwithstanding their increased use in assessing treatments for COVID-19, their use in critical care trials remains limited. A better understanding of the relative benefits of various adaptive designs may increase their use and interpretation.MethodsUsing two large critical care trials (ADRENAL.Clinicaltrialsgov number, NCT01448109. Updated 12-12-2017; NICE-SUGAR.Clinicaltrialsgov number, NCT00220987. Updated 01-29-2009), we assessed the performance of three frequentist and two bayesian adaptive approaches. We retrospectively re-analysed the trials with one, two, four, and nine equally spaced interims. Using the original hypotheses, we conducted 10,000 simulations to derive error rates, probabilities of making an early correct and incorrect decision, expected sample size and treatment effect estimates under the null scenario (no treatment effect) and alternative scenario (a positive treatment effect). We used a logistic regression model with 90-day mortality as the outcome and the treatment arm as the covariate. The null hypothesis was tested using a two-sided significance level (α) at 0.05.ResultsAcross all approaches, increasing the number of interims led to a decreased expected sample size. Under the null scenario, group sequential approaches provided good control of the type-I error rate; however, the type I error rate inflation was an issue for the Bayesian approaches. The Bayesian Predictive Probability and O'Brien-Fleming approaches showed the highest probability of correctly stopping the trials (around 95%). Under the alternative scenario, the Bayesian approaches showed the highest overall probability of correctly stopping the ADRENAL trial for efficacy (around 91%), whereas the Haybittle-Peto approach achieved the greatest power for the NICE-SUGAR trial. Treatment effect estimates became increasingly underestimated as the number of interims increased.ConclusionsThis study confirms the right adaptive design can reach the same conclusion as a fixed design with a much-reduced sample size. The efficiency gain associated with an increased number of interims is highly relevant to late-phase critical care trials with large sample sizes and short follow-up times. Systematically exploring adaptive methods at the trial design stage will aid the choice of the most appropriate method.

Dataset Information

Adaptive Designs for Clinical Trials: Application to Healthcare Epidemiology Research.

Publications

Adaptive Designs for Clinical Trials: Application to Healthcare Epidemiology Research.

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