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Treatment effect estimates are often available from randomized controlled trials as a single average treatment effect for a certain patient population. Estimates of the conditional average treatment effect (CATE) are more useful for individualized treatment decision-making, but randomized trials are often too small to estimate the CATE. Examples in medical literature make use of the relative treatment effect (e.g. an odds ratio) reported by randomized trials to estimate the CATE using large observational datasets. One approach to estimating these CATE models is by using the relative treatment effect as an offset, while estimating the covariate-specific untreated risk. We observe that the odds ratios reported in randomized controlled trials are not the odds ratios that are needed in offset models because trials often report the marginal odds ratio. We introduce a constraint or a regularizer to better use marginal odds ratios from randomized controlled trials and find that under the standard observational causal inference assumptions, this approach provides a consistent estimate of the CATE. Next, we show that the offset approach is not valid for CATE estimation in the presence of unobserved confounding. We study if the offset assumption and the marginal constraint lead to better approximations of the CATE relative to the alternative of using the average treatment effect estimate from the randomized trial. We empirically show that when the underlying CATE has sufficient variation, the constraint and offset approaches lead to closer approximations to the CATE.

Prediction models are used among others to inform medical decisions on interventions. Typically, individuals with high risks of adverse outcomes are advised to undergo an intervention while those at low risk are advised to refrain from it. Standard prediction models do not always provide risks that are relevant to inform such decisions: for example, an individual may be estimated to be at low risk because similar individuals in the past received an intervention which lowered their risk. Therefore, prediction models supporting decisions should target risks belonging to defined intervention strategies. Previous works on prediction under interventions assumed that the prediction model was used only at one time point to make an intervention decision. In clinical practice, intervention decisions are rarely made only once: they might be repeated, deferred, and reevaluated. This requires estimated risks under interventions that can be reconsidered at several potential decision moments. In the current work, we highlight key considerations for formulating estimands in sequential prediction under interventions that can inform such intervention decisions. We illustrate these considerations by giving examples of estimands for a case study about choosing between vaginal delivery and cesarean section for women giving birth. Our formalization of prediction tasks in a sequential, causal, and estimand context provides guidance for future studies to ensure that the right question is answered and appropriate causal estimation approaches are chosen to develop sequential prediction models that can inform intervention decisions., (© 2024 The Author(s). Biometrical Journal published by Wiley‐VCH GmbH.)

Oncologists are faced with choosing the best treatment for each patient, based on the available evidence from randomized controlled trials (RCTs) and observational studies. RCTs provide estimates of the average effects of treatments on groups of patients, but they may not apply in many real-world scenarios where for example patients have different characteristics than the RCT participants, or where different treatment variants are considered. Causal inference defines what a treatment effect is and how it may be estimated with RCTs or outside of RCTs with observational - or 'real-world' - data. In this review, we introduce the field of causal inference, explain what a treatment effect is and what important challenges are with treatment effect estimation with observational data. We then provide a framework for conducting causal inference studies and describe when in oncology causal inference from observational data may be particularly valuable. Recognizing the strengths and limitations of both RCTs and observational causal inference provides a way for more informed and individualized treatment decision-making in oncology., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)