arXiv subjects

Florie Bouvier

Publications and source records attributed to Florie Bouvier.

2 recordsLinked to original sources

arXiv2026

Evaluating the influence of treatment-effect heterogeneity on discrimination

Analyzing the heterogeneity of treatment effects is crucial in personalized medicine to identify which patients will benefit from specific treatments. The performance of a conditional average treatment effects model to guide treatment decisions can be assessed in different ways, with an important one being the model's ability to effectively discriminate between individuals who benefit from the treatment and those who do not. While many methods and algorithms have been proposed to develop conditional average treatment effects models and individualized treatment rules, little is known about the discriminative ability that can be achieved according to the population's underlying distribution of treatment effects. In this work, we computed the discrimination that can be achieved under oracle CATE for a panel of 20 distributions with varying average treatment effects and levels of heterogeneity. The assessment included the following discrimination metrics: the c-statistic for benefit, the concentration of benefit, and the population average prescriptive effect (PAPE). Results showed that the three metrics employed in this study did not require the same levels of treatment effect heterogeneity to lead to high discrimination results. Notably, achieving high c-statistic for benefit and PAPE values required greater heterogeneity than obtaining high concentration of benefit values. The three metrics considered behave very differently across the distributions. For instance, the concentration of benefit can indicate perfect discrimination in settings with negligible treatment-effects heterogeneity.

stat.AP↗

arXiv2024

Do machine learning methods lead to similar individualized treatment rules? A comparison study on real data

Identifying patients who benefit from a treatment is a key aspect of personalized medicine, which allows the development of individualized treatment rules (ITRs). Many machine learning methods have been proposed to create such rules. However, to what extent the methods lead to similar ITRs, i.e., recommending the same treatment for the same individuals is unclear. In this work, we compared 22 of the most common approaches in two randomized control trials. Two classes of methods can be distinguished. The first class of methods relies on predicting individualized treatment effects from which an ITR is derived by recommending the treatment evaluated to the individuals with a predicted benefit. In the second class, methods directly estimate the ITR without estimating individualized treatment effects. For each trial, the performance of ITRs was assessed by various metrics, and the pairwise agreement between all ITRs was also calculated. Results showed that the ITRs obtained via the different methods generally had considerable disagreements regarding the patients to be treated. A better concordance was found among akin methods. Overall, when evaluating the performance of ITRs in a validation sample, all methods produced ITRs with limited performance, suggesting a high potential for optimism. For non-parametric methods, this optimism was likely due to overfitting. The different methods do not lead to similar ITRs and are therefore not interchangeable. The choice of the method strongly influences for which patients a certain treatment is recommended, drawing some concerns about their practical use.

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