Individualized treatment effects of a digital alcohol intervention and their associations with participant characteristics and engagement.

AIMS: Conditional average treatment effects are often reported in intervention studies, in which assumptions are made regarding how effects are similar across a heterogeneous sample. Nonetheless, differing factors, such as genetics, age, and sex, can impact an intervention's effect on outcomes. The study aimed to estimate the individualized effects of a digital alcohol intervention among individuals looking online to reduce their drinking. METHODS: We used data from a randomized controlled trial (RCT), including 2129 adults from the Swedish general population. The RCT concerned a text message-based alcohol intervention that sought to engender change through increasing knowledge on how to change and instilling confidence in changing behaviour. Outcomes were total weekly alcohol consumption and monthly heavy episodic drinking. Individualized treatment effects were modelled using baseline characteristics (age, gender, alcohol consumption, and psychosocial variables) and engagement with the intervention content. RESULTS: We found evidence that the effects of the digital alcohol intervention were heterogeneous concerning participants' age, baseline alcohol consumption, confidence, and importance. For heavy episodic drinking, there was evidence that effects were heterogeneous concerning age, sex, and baseline alcohol consumption. Overall, women, older individuals, and heavier drinkers benefitted more from the intervention in terms of effect size. In addition, participants who engaged more with the goal-setting and screening content reported better outcomes. CONCLUSIONS: The results highlight how different individuals respond differently to a digital alcohol intervention. This allows insight into who benefits the most and least from the intervention and highlights the potential merit of designing interventions adapted to different individuals' needs.

Estimating individualized treatment effects using an individual participant data meta-analysis.

BACKGROUND: One key aspect of personalized medicine is to identify individuals who benefit from an intervention. Some approaches have been developed to estimate individualized treatment effects (ITE) with a single randomized control trial (RCT) or observational data, but they are often underpowered for the ITE estimation. Using individual participant data meta-analyses (IPD-MA) might solve this problem. Few studies have investigated how to develop risk prediction models with IPD-MA, and it remains unclear how to combine those methods with approaches used for ITE estimation. In this article, we compared different approaches using both simulated and real data with binary and time-to-event outcomes to estimate the individualized treatment effects from an IPD-MA in a one-stage approach. METHODS: We compared five one-stage models: naive model (NA), random intercept (RI), stratified intercept (SI), rank-1 (R1), and fully stratified (FS), built with two different strategies, the S-learner and the T-learner constructed with a Monte Carlo simulation study in which we explored different scenarios with a binary or a time-to-event outcome. To evaluate the performance of the models, we used the c-statistic for benefit, the calibration of predictions, and the mean squared error. The different models were also used on the INDANA IPD-MA, comparing an anti-hypertensive treatment to no treatment or placebo ( N = 40 237 , 836 events). RESULTS: Simulation results showed that using the S-learner led to better ITE estimation performances for both binary and time-to-event outcomes. None of the risk models stand out and had significantly better results. For the INDANA dataset with a binary outcome, the naive and the random intercept models had the best performances. CONCLUSIONS: For the choice of the strategy, using interactions with treatment (the S-learner) is preferable. For the choice of the method, no approach is better than the other.

Variational Temporal Deconfounder for Individualized Treatment Effect Estimation with Longitudinal Observational Data.

Purpose: This paper proposes a new approach, Variational Temporal Deconfounder (VTD), for estimating individualized treatment effects (ITE) from longitudinal observational data, where we address the hidden confounding issues by using proxies (i.e., surrogate variables that serve for unobservable variables). Methods: We build VTD by incorporating a variational recurrent autoencoder that learns the latent encodings of hidden confounders from observed proxies and an ITE estimation network that takes the learned hidden encodings to predict the probability of receiving treatments and potential outcomes. Results: We test VTD on both synthetic and real-world clinical data, and the results from synthetic data experiments demonstrate VTD's effectiveness in deconfounding by outperforming existing methods, while results from two real-world datasets (i.e., Medical Information Mart for Intensive Care version III [MIMIC-III] and the National Alzheimer's Coordinating Center [NACC] database) suggest that the performance of the VTD model outperforms existing baseline models, however, varies depending on the assumptions of underlying causal structures and availability of proxies for hidden confounders. Conclusion: The VTD offers a unique solution to address the confounding bias without the "unconfoundedness" assumption when estimating the ITE from longitudinal observational data. The elimination of the requirement for the "unconfoundedness" assumption makes the VTD more versatile and practical in real-world clinical applications of personalized medicine.

High Resolution Treatment Effects Estimation: Uncovering Effect Heterogeneities with the Modified Causal Forest.

There is great demand for inferring causal effect heterogeneity and for open-source statistical software, which is readily available for practitioners. The mcf package is an open-source Python package that implements Modified Causal Forest (mcf), a causal machine learner. We replicate three well-known studies in the fields of epidemiology, medicine, and labor economics to demonstrate that our mcf package produces aggregate treatment effects, which align with previous results, and in addition, provides novel insights on causal effect heterogeneity. For all resolutions of treatment effects estimation, which can be identified, the mcf package provides inference. We conclude that the mcf constitutes a practical and extensive tool for a modern causal heterogeneous effects analysis.

Case-only trees and random forests for exploring genotype-specific treatment effects in randomized clinical trials with dichotomous endpoints.

Discovering gene-treatment interactions in clinical trials is of rising interest in the era of precision medicine. Nonparametric statistical learning methods such as trees and random forests are useful tools for building prediction rules. In this article, we introduce trees and random forests to the recently proposed case-only approach for discovering gene-treatment interactions and estimating marker-specific treatment effects for a dichotomous trial endpoints. The motivational example is a case-control genetic association study in the Prostate Cancer Prevention Trial (PCPT), which tested the hypothesis whether finasteride can prevent prostate cancer. We compare this novel approach to the interaction tree method previously proposed. Because of the modeling simplicity - directly targeting at interaction - and the statistical efficiency of the case-only approach, case-only trees and random forests yield more accurate prediction of heterogeneous treatment effects and better measure of variable importance, relative to the interaction tree method which uses data from both cases and controls. Application of the proposed case-only trees and random forests to the PCPT study yielded a discovery of genotypes that may influence the prevention effect of finasteride.

Random forests of interaction trees for estimating individualized treatment effects in randomized trials.

Assessing heterogeneous treatment effects is a growing interest in advancing precision medicine. Individualized treatment effects (ITEs) play a critical role in such an endeavor. Concerning experimental data collected from randomized trials, we put forward a method, termed random forests of interaction trees (RFIT), for estimating ITE on the basis of interaction trees. To this end, we propose a smooth sigmoid surrogate method, as an alternative to greedy search, to speed up tree construction. The RFIT outperforms the "separate regression" approach in estimating ITE. Furthermore, standard errors for the estimated ITE via RFIT are obtained with the infinitesimal jackknife method. We assess and illustrate the use of RFIT via both simulation and the analysis of data from an acupuncture headache trial.