L Abenhaim

The "efficacy-effectiveness gap": historical background and current conceptualization

BACKGROUNDnThe concept of the efficacy-effectiveness gap (EEG) has started to challenge confidence in decisions made for drugs when based on randomized controlled trials alone. Launched by the Innovative Medicines Initiative, the GetReal project aims to improve understanding of how to reconcile evidence to support efficacy and effectiveness and at proposing operational solutions.nnnOBJECTIVESnThe objectives of the present narrative review were 1) to understand the historical background in which the concept of the EEG has emerged and 2) to describe the conceptualization of EEG.nnnMETHODSnA focused literature review was conducted across the gray literature and articles published in English reporting insights on the EEG concept. The identification of different paradigms was performed by simple inductive analysis of the documents content.nnnRESULTSnThe literature on the EEG falls into three major paradigms, in which EEG is related to 1) real-life characteristics of the health care system; 2) the method used to measure the drugs effect; and 3) a complex interaction between the drugs biological effect and contextual factors.nnnCONCLUSIONSnThe third paradigm provides an opportunity to look beyond any dichotomy between standardized versus real-life characteristics of the health care system and study designs. Namely, future research will determine whether the identification of these contextual factors can help to best design randomized controlled trials that provide better estimates of drugs effectiveness.

Trial exclusion criteria and their impact on the estimation of antipsychotic drugs effect: A case study using the SOHO database

OBJECTIVESnTo explore the impact upon estimation of drug effect as a result of applying exclusion criteria in randomized-controlled trials (RCT) measuring the efficacy of antipsychotics (AP) in schizophrenia.nnnMETHODSnThree characteristics which may act as effect-modifiers of AP, while also common exclusion criteria in RCTs, were identified through literature review: schizophrenia duration, substance use disorder and poor adherence. The SOHO cohort was used to estimate the effect of initiating antipsychotic drugs A, B or C (pooled) upon symptom evolution at 3months from baseline (CGI-S scale). Estimated effectiveness and estimated efficacy were drawn from the SOHO and RCT-like (patients with none of the above-listed exclusion criteria) samples, respectively. Effect-modification and impact of each exclusion criterion on AP effect estimates were explored using non-adjusted statistics.nnnRESULTSnThe SOHO sample included 8250 patients initiating drug A, B or C at baseline, whose AP estimated effectiveness was ΔCGI-S=-0.78 (95% CI=-0.80, -0.76). The RCT-like sub-sample included 5348 (65%) patients whose AP estimated efficacy was ΔCGI-S=-0.73 (95% CI=-0.75, -0.70). Patients with short illness duration (≤3years since first AP; n=2436) experienced significant symptom improvement (ΔCGI-S=-0.89; 95%CI=-0.93, -0.85) compared to patients with duration >3years (mean ΔCGI-S=-0.73; 95%CI=-0.76, -0.71). Excluding patients with short illness duration led to a change in AP effect estimates but this was not the case for substance use disorder or poor adherence.nnnCONCLUSIONnUsing certain exclusion criteria in RCTs may impact the drugs effect estimate, particularly when exclusion criteria are AP effect-modifiers representing frequent characteristics among patients with schizophrenia.

The “RCT augmentation”: a novel simulation method to add patient heterogeneity into phase III trials

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 1–3xa0years; (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 <u20092% and mean squared error (MSE)u2009=u20095.8–6.8%. Compared to this typical RCT, RCTs using augmented populations provided improved effectiveness predictions (bias <u20092%, MSEu2009=u20095.3–6.7%), 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.

A Practical Guide To Adding Patient Heterogeneity Into Phase Iii Trials: Results from Imi Getreal Wp2

We used mean uf044CGI-S at 3 months (change from baseline) as outcome. This outcome was evaluated in patients taking the most frequently used drug (blinded). CGI-S score (Clinical Global Impression-Severity): • Assesses severity of patient’s mental illness at time of rating with one question • 7-point scale: from 1 (not at all ill) to 7 (extremely ill) • In SOHO cohort study, most patients have CGI-S values of 4 or 5