Subgroup identification in clinical trials via the predicted individual treatment effect

Abstract

Identifying subgroups of treatment responders through the different phases of clinical trials has the potential to increase success in drug development. Recent developments in subgroup analysis consider subgroups that are defined in terms of the predicted individual treatment effect, i.e. the difference between the predicted outcome under treatment and the predicted outcome under control for each individual, which in turn may depend on multiple biomarkers. In this work, we study the properties of different modelling strategies to estimate the predicted individual treatment effect. We explore linear models and compare different estimation methods, such as maximum likelihood and the Lasso with and without randomized response. For the latter, we implement confidence intervals based on the selective inference framework to account for the model selection stage. We illustrate the methods in a dataset of a treatment for Alzheimer disease (normal response) and in a dataset of a treatment for prostate cancer (survival outcome). We also evaluate via simulations the performance of using the predicted individual treatment effect to identify subgroups where a novel treatment leads to better outcomes compared to a control treatment.

Bayesian credible subgroup identification for treatment effectiveness in time-to-event data

“…Our methods are also tested on two time-to-event datasets: One from a trial in patients with prostate cancer, and another from a simulated Merck Sharp & Dohme, London, UK (MSD) clinical trial in patients with myocardial infarctions. The first dataset is publicly available [21] and has been analyzed in Ballarini et al's study [9]. Our findings are similar to those found by Ballarini et al [9], but, in addition, our methods also identify the non-benefiting subgroups to the treatment.…”

“…The first dataset is publicly available [21] and has been analyzed in Ballarini et al's study [9]. Our findings are similar to those found by Ballarini et al [9], but, in addition, our methods also identify the non-benefiting subgroups to the treatment. The second dataset is a simulated data based on a randomized and placebo-controlled study on the effect of vorapaxar in addition with aspirin for secondary prevention of thrombotic events.…”

“…Many tree-based and model-based methods have been developed for time-to-event subgroup analysis [5][6][7][8]. Ballarini et al [9] recently introduced a multiple regression model with a Lasso-type penalty to estimate benefiting PLOS ONE | https://doi.org/10.1371/journal.pone.0229336 February 26, 2020 1 / 19 a1111111111 a1111111111 a1111111111 a1111111111 a1111111111…”

Are Novel, Nonrandomized Analytic Methods Fit for Decision Making? The Need for Prospective, Controlled, and Transparent Validation

“…Returning, for example, to the controversial question of whether RWD can ever replace RCTs (Table 1 and above), we believe the Methodology Potential benefit for drug developers and decision makers Current limitations How to validate prospectively Predictive approaches to heterogeneous treatment effects [35][36][37] (Positive) RCTs can only help predict that at least some patients similar to those enrolled in the trial will likely benefit from the intervention ("reference class forecasting"). However, determining the best treatment for an individual patient is different from determining the best average treatment, because of heterogeneity of treatment effects.…”

“…The ultimate test of a predictive approach is to compare decisions or outcomes in settings that use such predictions with usual care in a prospectively planned experiment. 36 HTA, health technology assessment; MTC, mixed treatment comparison; PK/PD, pharmacokinetic/pharmacodynamic; RCT, randomized controlled trial; REA, relative effectiveness assessment; RWD, real-world data. STATE of the ART answer at this point in time should be neither a categorical no or yes but an open-minded, prospective exploration to identify scenarios where RWD analysis can provide sufficiently robust, decision-relevant supportive evidence.…”