RESUMO
BACKGROUND: Frequently a biomarker capable of defining a patient population with enhanced response to an experimental agent is not fully validated with a known threshold at the start of a phase II trial. When such candidate predictive markers are evaluated and/or validated retrospectively, over-accrual of patients less likely to benefit from the regimen may result, leading to underpowered analyses or sub-optimal patient care. PURPOSE: We propose an adaptive randomized phase II study design incorporating prospective biomarker threshold identification (or non-identification), possible early futility stopping, potential mid-trial accrual restriction to marker-positive subjects, and final marker and treatment evaluation in the patient population identified as most likely to benefit. METHODS: An interim analysis is used to determine whether an initially unselected trial should stop early for futility, continue without a promising marker, or adapt accrual and resize (up to a pre-determined maximum) according to a promising biomarker. Final efficacy analyses are performed in the target population identified at the interim as most likely to benefit from the experimental regimen. Simulation studies demonstrate control of false-positive error rates, power, reduced average sample size, and other favorable aspects. RESULTS: The design performs well at identifying a truly predictive biomarker at interim analysis, and subsequently restricting accrual to patients most likely to benefit from the experimental treatment. Type I and type II error rates are adequately controlled by restricting the range of marker prevalence via the candidate thresholds, and by careful consideration of the timing of interim analysis. CONCLUSIONS: In situations where identification and validation of a naturally continuous biomarker are desired within a randomized phase II trial, the design presented herein offers a potential solution.
RESUMO
PURPOSE: Clinical trials are expensive and lengthy, where success of a given trial depends on observing a prospectively defined number of patient events required to answer the clinical question. The point at which this analysis time occurs depends on both patient accrual and primary event rates, which typically vary throughout the trial's duration. We demonstrate real-time analysis date projections using data from a collection of six clinical trials that are part of the IDEA collaboration, an international preplanned pooling of data from six trials testing the duration of adjuvant chemotherapy in stage III colon cancer, and we additionally consider the hypothetical impact of one trial's early termination of follow-up. PATIENTS AND METHODS: In the absence of outcome data from IDEA, monthly accrual rates for each of the six IDEA trials were used to project subsequent trial-specific accrual, while historical data from similar Adjuvant Colon Cancer Endpoints (ACCENT) Group trials were used to construct a parametric model for IDEA's primary endpoint, disease-free survival, under the same treatment regimen. With this information and using the planned total accrual from each IDEA trial protocol, individual patient accrual and event dates were simulated and the overall IDEA interim and final analysis times projected. Projections were then compared with actual (previously undisclosed) trial-specific event totals at a recent census time for validation. The change in projected final analysis date assuming early termination of follow-up for one IDEA trial was also calculated. RESULTS: Trial-specific predicted event totals were close to the actual number of events per trial for the recent census date at which the number of events per trial was known, with the overall IDEA projected number of events only off by eight patients. Potential early termination of follow-up by one IDEA trial was estimated to postpone the overall IDEA final analysis date by 9 months. CONCLUSIONS: Real-time projection of the final analysis time during a trial, or the overall analysis time during a trial collaborative such as IDEA, has practical implications for trial feasibility when these projections are translated into additional time and resources required.
RESUMO
BACKGROUND: Frequently a biomarker capable of defining a patient population with enhanced response to an experimental agent is not fully validated with a known threshold at the start of a phase II trial. When such candidate predictive markers are evaluated and/or validated retrospectively, over-accrual of patients less likely to benefit from the regimen may result, leading to underpowered analyses or sub-optimal patient care. PURPOSE: We propose an adaptive randomized phase II study design incorporating prospective biomarker threshold identification (or non-identification), possible early futility stopping, potential mid-trial accrual restriction to marker-positive subjects, and final marker and treatment evaluation in the patient population identified as most likely to benefit. METHODS: An interim analysis is used to determine whether an initially unselected trial should stop early for futility, continue without a promising marker, or adapt accrual and resize (up to a pre-determined maximum) according to a promising biomarker. Final efficacy analyses are performed in the target population identified at the interim as most likely to benefit from the experimental regimen. Simulation studies demonstrate control of false-positive error rates, power, reduced average sample size, and other favorable aspects. RESULTS: The design performs well at identifying a truly predictive biomarker at interim analysis, and subsequently restricting accrual to patients most likely to benefit from the experimental treatment. Type I and type II error rates are adequately controlled by restricting the range of marker prevalence via the candidate thresholds, and by careful consideration of the timing of interim analysis. CONCLUSIONS: In situations where identification and validation of a naturally continuous biomarker are desired within a randomized phase II trial, the design presented herein offers a potential solution.
