Abstract

BackgroundPhase III randomized controlled trials (RCT) typically exclude certain patient subgroups, thereby potentially jeopardizing estimation of a drug’s effects when prescribed to wider populations and under routine care (“effectiveness”). Conversely, enrolling heterogeneous populations in RCTs can increase endpoint variability and compromise detection of a drug’s effect. We developed the “RCT augmentation” method to quantitatively support RCT design in the identification of exclusion criteria to relax to address both of these considerations. In the present manuscript, we describe the method and a case study in schizophrenia.MethodsWe applied typical RCT exclusion criteria in a real-world dataset (cohort) of schizophrenia patients to define the “RCT population” subgroup, and assessed the impact of re-including each of the following patient subgroups: (1) illness duration 1–3 years; (2) suicide attempt; (3) alcohol abuse; (4) substance abuse; and (5) private practice management. Predictive models were built using data from different “augmented RCT populations” (i.e., subgroups where patients with one or two of such characteristics were re-included) to estimate the absolute effectiveness of the two most prevalent antipsychotics against real-world results from the entire cohort. Concurrently, the impact on RCT results of relaxing exclusion criteria was evaluated by calculating the comparative efficacy of those two antipsychotics in virtual RCTs drawing on different “augmented RCT populations”.ResultsData from the “RCT population”, which was defined with typical exclusion criteria, allowed for a prediction of effectiveness with a bias < 2% and mean squared error (MSE) = 5.8–6.8%. Compared to this typical RCT, RCTs using augmented populations provided improved effectiveness predictions (bias < 2%, MSE = 5.3–6.7%), while returning more variable comparative effects. The impact of augmentation depended on the exclusion criterion relaxed. Furthermore, half of the benefit of relaxing each criterion was gained from re-including the first 10–20% of patients with the corresponding real-world characteristic.ConclusionsSimulating the inclusion of real-world subpopulations into an RCT before running it allows for quantification of the impact of each re-inclusion upon effect detection (statistical power) and generalizability of trial results, thereby explicating this trade-off and enabling a controlled increase in population heterogeneity in the RCT design.

Highlights

  • Phase III randomized controlled trials (RCT) typically exclude certain patient subgroups, thereby potentially jeopardizing estimation of a drug’s effects when prescribed to wider populations and under routine care (“effectiveness”)

  • An even higher proportion of screened patients (73–93%) has been reported as not participating in clinical trials [4,5,6,7]. It is often unknown how trial exclusion criteria impact estimations of drug effects, possibly exaggerating or underestimating them [8, 9] and, in general, there is no evidence at time of launch to support treatment guidance for patients who were excluded from trials conducted as part of the process of drug approval [10]

  • The magnitude of symptoms improvement was greater in the Schizophrenia Outpatients Health Outcomes (SOHO) population compared to the “RCT population” (Drug D1: − 0.88 (− 0.90, − 0.85) vs. -0.78 (− 0.83, − 0.72); Drug D2: − 0.71 (− 0.75, − 0.67) vs. -0.64 (− 0.72, − 0.57), see Table 1)

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Summary

Introduction

Phase III randomized controlled trials (RCT) typically exclude certain patient subgroups, thereby potentially jeopardizing estimation of a drug’s effects when prescribed to wider populations and under routine care (“effectiveness”). Patient populations recruited in phase III randomized-controlled trials (RCT) tend to be more homogeneous compared to those likely to be prescribed the drug under investigation in real-world clinical practice. Such homogeneity is the result of excluding patients with certain characteristics, such as comorbidities, risk factors or co-medications. An even higher proportion of screened patients (73–93%) has been reported as not participating in clinical trials [4,5,6,7] It is often unknown how trial exclusion criteria impact estimations of drug effects, possibly exaggerating or underestimating them [8, 9] and, in general, there is no evidence at time of launch to support treatment guidance for (real-world) patients who were excluded from trials conducted as part of the process of drug approval [10]. Considerations of RCT design, such as constant sample sizes and the necessary trade-off between minimizing this “RCT generalizability bias” and retaining enough RCT statistical power remains unexplored

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