An ROC Approach to Evaluate Interim Go/No-Go Decision-Making Quality with Application to Futility Stopping in the Clinical Trial Designs

PURPOSE Despite the fact that almost any sample of patients with a particular disease is heterogeneous, most clinical trial designs ignore the possibility that treatment or dose effects may differ between prognostic or biologically defined subgroups. This article reviews two clinical trial designs that make subgroup-specific decisions and compares each to a simpler design that ignores patient heterogeneity. The purpose is to illustrate the benefits of accounting prospectively for treatment-subgroup interactions and how utilities may be used to quantify risk-benefit trade-offs. METHODS Two Bayesian clinical trial designs that perform subgroup-specific decision making and inference based on elicited utilities of patient outcomes are reviewed. The first is a randomized comparative trial of nutritional prehabilitation for patients undergoing esophageal resection that has two prognostic subgroups and is based on postoperative morbidity score. The second is a sequentially adaptive trial of natural killer cells for treating hematologic malignancies that is based on five time-to-event outcomes and that performs safety monitoring and optimizes cell dose within six disease subgroups. Computer simulations under a range of different scenarios are presented for each design to establish its operating characteristics and compare it to a more conventional design that ignores patient heterogeneity. RESULTS Each design has attractive operating characteristics, is greatly superior to a simplified design that ignores patient subgroups, is robust to deviations from its assumed statistical model, and is feasible to use for conducting trials. CONCLUSION Bayesian designs that make subgroup-specific decisions in randomized comparative trials or sequentially adaptive early-phase dose-finding trials are superior to designs that ignore patient heterogeneity. Using elicited utilities of complex patient outcomes to quantify risk-benefit trade-offs provides a practical and ethical basis for decision making and treatment evaluation in clinical trials.

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Feasibility and efficacy of shared decision making for first-admission schizophrenia: a randomized clinical trial

10.26226/morressier.59d4913ad462b8029238a1f8 ◽

2017 ◽

Author(s):

Mio Ishii

Keyword(s):

Clinical Trial ◽

Decision Making ◽

Randomized Clinical Trial ◽

Shared Decision Making ◽

Shared Decision ◽

First Admission

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Is the Deductive Falsification Approach a Better Basis for Clinical Trial Appraisal?

Reviews on Recent Clinical Trials ◽

10.2174/1574887114666190313170400 ◽

2019 ◽

Vol 14 (3) ◽

pp. 224-228

Author(s):

Steffen Mickenautsch

Keyword(s):

Clinical Trial ◽

Decision Making ◽

Empirical Evidence ◽

Clinical Decision Making ◽

Inductive Reasoning ◽

Clinical Decision ◽

Future Trial ◽

Grading Systems ◽

Component Scores ◽

Current Clinical Trial

Background: Inductive reasoning relies on an infinite regress without sufficient factual basis and verification is at any time vulnerable to single contrary observation. Thus, appraisal based on inductive verification, as applied in current clinical trial appraisal scales, checklists or grading systems, cannot prove or justify trial validity. Discussion: Trial appraisal based on deductive falsification can identify invalid trials and give evidence for the recommendation to exclude these from clinical decision-making. Such appraisal remains agnostic towards corroborated trials that pass all appraisal criteria. The results of corroborated trials cannot be considered more robust than falsified trials since nothing within a particular set of complied trial criteria can give certainty for trial compliance with any other appraisal criterion in future. A corroborated trial may or may not reflect therapeutic truth and may thus be the basis for clinical guidance, pending results of any future trial re-appraisal. Conclusion: Trial grading following appraisal based on deductive falsification should be binary (0 = Invalid or 1 = Unclear) and single component scores should be multiplied. Appraisal criteria for the judgment of trial characteristics require a clear rationale, quantification of such rationale and empirical evidence concerning the effect of trial characteristics on trial results.

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