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A statistical approach to identify optimal inclusion criteria: An application to acute stroke clinical trials.

ABSTRACT: Purpose:To develop a statistical approach that compares patient selection strategies across clinical trials and apply this approach to acute ischemic stroke clinical trials to identify the optimal inclusion criteria. Methods:We developed a statistical approach that compares the number needed to treat to achieve one success (NNT) along with the number needed to screen to achieve one success (NNS) and assesses if there are significant differences in inclusion criteria, treatment course, and clinical outcome among patients that may have been included/excluded in the trials. We applied this approach to the study population from four recent positive acute stroke clinical trials: MR CLEAN, EXTEND-IA, ESCAPE, and SWIFT PRIME, applying published trial criteria to an independent registry of 612 acute stroke patients, since we did not have access to the complete trial data. Results:Although reported NNT were similar for EXTEND-IA, SWIFT PRIME and ESCAPE, and somewhat higher for MR CLEAN, NNS varied across the trials from 21 for EXTEND-IA, 27 for MR CLEAN, to 46 for ESCAPE and 64 for SWIFT PRIME, reflecting less and more stringent inclusion criteria, respectively. Although there were significant differences in imaging biomarkers and other clinical characteristics among patients that may have been included/excluded in the trials, these differences did not translate to significant differences in treatment course or clinical outcomes. Conclusions:Our study proposes a robust statistical approach that can be applied to a larger pooled trial dataset, if made available, to objectively compare across clinical trials and inform inclusion criteria of future trials. Pooled analysis of the acute stroke trial data is needed to determine which imaging biomarker inclusion criteria are critical and which may be relaxed. If this procedure were applied across the pooled trial data, it could decrease costs and refine the design of future trials to be the most efficacious for the greatest number of patients.

SUBMITTER: Ball RL

PROVIDER: S-EPMC6463814 | biostudies-literature | 2019 Jun

REPOSITORIES: biostudies-literature

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A statistical approach to identify optimal inclusion criteria: An application to acute stroke clinical trials.

Ball Robyn L RL Jiang Bin B Desai Manisha M Michel Patrik P Eskandari Ashraf A Jovin Tudor T Wintermark Max M

Contemporary clinical trials communications 20190409

<h4>Purpose</h4>To develop a statistical approach that compares patient selection strategies across clinical trials and apply this approach to acute ischemic stroke clinical trials to identify the optimal inclusion criteria.<h4>Methods</h4>We developed a statistical approach that compares the number needed to treat to achieve one success (NNT) along with the number needed to screen to achieve one success (NNS) and assesses if there are significant differences in inclusion criteria, treatment cou ...[more]

PMID: 31011658

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Project description:BACKGROUND:Tralokinumab is an anti-interleukin (IL)-13 monoclonal antibody investigated for the treatment of severe, uncontrolled asthma in two Phase III clinical trials, STRATOS 1 and 2. The STRATOS 1 biomarker analysis plan was developed to identify biomarker(s) indicative of IL-13 activation likely to predict tralokinumab efficacy and define a population in which there was an enhanced treatment effect; this defined population was then tested in STRATOS 2. METHODS:The biomarkers considered were blood eosinophil counts, fractional exhaled nitric oxide (FeNO), serum dipeptidyl peptidase-4, serum periostin and total serum immunoglobulin E. Tralokinumab efficacy was measured as the reduction in annualised asthma exacerbation rate (AAER) compared with placebo (primary endpoint measure of STRATOS 1 and 2). The biomarker analysis plan included negative binomial and generalised additive models, and the Subgroup Identification based on Differential Effect Search (SIDES) algorithm, supported by robustness and sensitivity checks. Effects on the key secondary endpoints of STRATOS 1 and 2, which included changes from baseline in standard measures of asthma outcomes, were also investigated. Prior to the STRATOS 1 read-out, numerous simulations of the methodology were performed with hypothetical data. RESULTS:FeNO and periostin were identified as the only biomarkers potentially predictive of treatment effect, with cut-offs chosen by the SIDES algorithm of >?32.3?ppb and?>?27.4?ng/ml, respectively. The FeNO >?32.3?ppb subgroup was associated with greater AAER reductions and improvements in key secondary endpoints compared with the periostin >?27.4?ng/ml subgroup. Upon further evaluation of AAER reductions at different FeNO cut-offs, ?37?ppb was chosen as the best cut-off for predicting tralokinumab efficacy. DISCUSSION:A rigorous statistical approach incorporating multiple methods was used to investigate the predictive properties of five potential biomarkers and to identify a participant subgroup that demonstrated an enhanced tralokinumab treatment effect. Using STRATOS 1 data, our analyses identified FeNO at a cut-off of ?37?ppb as the best assessed biomarker for predicting enhanced treatment effect to be tested in STRATOS 2. Our findings were inconclusive, which reflects the complexity of subgroup identification in the severe asthma population. TRIAL REGISTRATION:STRATOS 1 and 2 are registered on ClinicalTrials.gov ( NCT02161757 registered on June 12, 2014, and NCT02194699 registered on July 18, 2014).

Dataset Information

A statistical approach to identify optimal inclusion criteria: An application to acute stroke clinical trials.

Publications

A statistical approach to identify optimal inclusion criteria: An application to acute stroke clinical trials.

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