Translating Evidence-Based Self-Management Interventions Using a Stepped-Care Approach for Patients With Cancer and Their Caregivers: A Pilot Sequential Multiple Assignment Randomized Trial Design.

Background: Self-directed interventions are cost-effective for patients with cancer and their family caregivers, but barriers to use can compromise adherence and efficacy.

Aim: Pilot a Sequential Multiple Assignment Randomized Trial (SMART) to develop a time-varying dyadic self-management intervention that follows a stepped-care approach in providing different types of guidance to optimize the delivery of Coping-Together, a dyadic self-directed self-management intervention.

Methods: 48 patients with cancer and their caregivers were randomized in Stage 1 to: (a) Coping-Together (included a workbook and 6 booklets) or (b) Coping-Together + lay telephone guidance.

An adaptive enrichment design using Bayesian model averaging for selection and threshold-identification of predictive variables.

Precision medicine is transforming healthcare by offering tailored treatments that enhance patient outcomes and reduce costs. As our understanding of complex diseases improves, clinical trials increasingly aim to detect subgroups of patients with enhanced treatment effects. Biomarker-driven adaptive enrichment designs, which initially enroll a broad population and later restrict to treatment-sensitive patients, are gaining popularity.

Differentially Private Outcome-Weighted Learning for Optimal Dynamic Treatment Regime Estimation.

Precision medicine is a framework for developing evidence-based medical recommendations that seeks to determine the optimal sequence of treatments tailored to all of the relevant patient-level characteristics which are observable. Because precision medicine relies on highly sensitive, patient-level data, ensuring the privacy of participants is of great importance. Dynamic treatment regimes (DTRs) provide one formalization of precision medicine in a longitudinal setting.

The time-dependent Poisson-gamma model in practice: Recruitment forecasting in HIV trials.

Despite a growing body of literature in the area of recruitment modeling for multicenter studies, in practice, statistical models to predict enrollments are rarely used and when they are, they often rely on unrealistic assumptions. The time-dependent Poisson-Gamma model (tPG) is a recently developed flexible methodology which allows analysts to predict recruitments in an ongoing multicenter trial, and its performance has been validated on data from a cohort study. In this article, we illustrate and further validate the tPG model on recruitment data from randomized controlled trials.

Revisiting the effects of maternal education on adolescents' academic performance: Doubly robust estimation in a network-based observational study.

In many contexts, particularly when study subjects are adolescents, peer effects can invalidate typical statistical requirements in the data. For instance, it is plausible that a student's academic performance is influenced both by their own mother's educational level as well as that of their peers. Since the underlying social network is measured, the Add Health study provides a unique opportunity to examine the impact of maternal college education on adolescent school performance, both direct and indirect.

Variable selection for individualised treatment rules with discrete outcomes.

An individualised treatment rule (ITR) is a decision rule that aims to improve individuals' health outcomes by recommending treatments according to subject-specific information. In observational studies, collected data may contain many variables that are irrelevant to treatment decisions. Including all variables in an ITR could yield low efficiency and a complicated treatment rule that is difficult to implement.

Using an intersectionality-based approach to evaluate mental health services use among gay, bisexual and other men who have sex with men in Montreal, Toronto and Vancouver.

Aims: To cope with homonegativity-generated stress, gay, bisexual and other men who have sex with men (GBM) use more mental health services (MHS) compared with heterosexual men. Most previous research on MHS among GBM uses data from largely white HIV-negative samples. Using an intersectionality-based approach, we evaluated the concomitant impact of racialization and HIV stigma on MHS use among GBM, through the mediating role of perceived discrimination (PD).

Gabapentinoids and Risk for Severe Exacerbation in Chronic Obstructive Pulmonary Disease : A Population-Based Cohort Study.

Background: North American and European health agencies recently warned of severe breathing problems associated with gabapentinoids, including in patients with chronic obstructive pulmonary disease (COPD), although supporting evidence is limited.

Objective: To assess whether gabapentinoid use is associated with severe exacerbation in patients with COPD.

Design: Time-conditional propensity score-matched, new-user cohort study.

Principal stratification for quantile causal effects under partial compliance.

Within the principal stratification framework in causal inference, the majority of the literature has focused on binary compliance with an intervention and modelling means. Yet in some research areas, compliance is partial, and research questions-and hence analyses-are concerned with causal effects on (possibly high) quantiles rather than on shifts in average outcomes. Modelling partial compliance is challenging because it can suffer from lack of identifiability.

On "Reflections on the concept of optimality of single decision point treatment regimes".

This is a discussion of "Reflections on the concept of optimality of single decision point treatment regimes" by Trung Dung Tran, Ariel Alonso Abad, Geert Verbeke, Geert Molenberghs, and Iven Van Mechelen. The authors propose a thoughtful consideration of optimization targets and the implications of such targets for the resulting optimal treatment rule. However, we contest the assertation that targets of optimization have been overlooked and suggest additional considerations that researchers must contemplate as part of a complete framework for learning about optimal treatment regimes.

Variable selection in high dimensions for discrete-outcome individualized treatment rules: Reducing severity of depression symptoms.

Despite growing interest in estimating individualized treatment rules, little attention has been given the binary outcome setting. Estimation is challenging with nonlinear link functions, especially when variable selection is needed. We use a new computational approach to solve a recently proposed doubly robust regularized estimating equation to accomplish this difficult task in a case study of depression treatment.

Dynamic Treatment Regimes Using Bayesian Additive Regression Trees for Censored Outcomes.

To achieve the goal of providing the best possible care to each individual under their care, physicians need to customize treatments for individuals with the same health state, especially when treating diseases that can progress further and require additional treatments, such as cancer. Making decisions at multiple stages as a disease progresses can be formalized as a dynamic treatment regime (DTR). Most of the existing optimization approaches for estimating dynamic treatment regimes including the popular method of Q-learning were developed in a frequentist context.

Role of fatty liver in the epidemic of advanced chronic liver disease among people with HIV: protocol for the Canadian LIVEHIV multicentre prospective cohort.

Introduction: Advanced chronic liver disease (ACLD) is a major cause of death for people with HIV (PWH). While viral hepatitis coinfections are largely responsible for this trend, metabolic dysfunction-associated steatotic liver disease (MASLD) is an emerging concern for PWH. We aimed to assess the contribution of MASLD to incident ACLD in PWH.

A time-dependent Poisson-Gamma model for recruitment forecasting in multicenter studies.

Forecasting recruitments is a key component of the monitoring phase of multicenter studies. One of the most popular techniques in this field is the Poisson-Gamma recruitment model, a Bayesian technique built on a doubly stochastic Poisson process. This approach is based on the modeling of enrollments as a Poisson process where the recruitment rates are assumed to be constant over time and to follow a common Gamma prior distribution.

Bayesian inference for optimal dynamic treatment regimes in practice.

In this work, we examine recently developed methods for Bayesian inference of optimal dynamic treatment regimes (DTRs). DTRs are a set of treatment decision rules aimed at tailoring patient care to patient-specific characteristics, thereby falling within the realm of precision medicine. In this field, researchers seek to tailor therapy with the intention of improving health outcomes; therefore, they are most interested in identifying DTRs.

Monte Carlo sensitivity analysis for unmeasured confounding in dynamic treatment regimes.

Data-driven methods for personalizing treatment assignment have garnered much attention from clinicians and researchers. Dynamic treatment regimes formalize this through a sequence of decision rules that map individual patient characteristics to a recommended treatment. Observational studies are commonly used for estimating dynamic treatment regimes due to the potentially prohibitive costs of conducting sequential multiple assignment randomized trials.

Benefits of the PRISM Shelter-Based Program for Attainment of Stable Housing and Functional Outcomes by People Experiencing Homelessness and Mental Illness: A Quantitative Analysis.

Objective: To explore the housing trajectory, personal recovery, functional level, and quality of life of clients at discharge and 1 year after completing (PRISM), a shelter-based mental health and rehabilitation program intended to provide individuals experiencing homelessness and severe mental illness with transition housing and to reconnect them with mental health and social services.

Method: Housing status, psychiatric follow-up trajectory, personal recovery (Canadian Personal Recovery Outcome Measure), functional level (Multnomah Community Ability Scale), and quality of life (Lehman Quality of Life Interview) were assessed at program entry, at program discharge and 1 year later.

Results: Of the 50 clients who participated in the study from May 31, 2018, to December 31, 2019, 43 completed the program.

Using Pilot Data for Power Analysis of Observational Studies for the Estimation of Dynamic Treatment Regimes.

Significant attention has been given to developing data-driven methods for tailoring patient care based on individual patient characteristics. Dynamic treatment regimes formalize this approach through a sequence of decision rules that map patient information to a suggested treatment. The data for estimating and evaluating treatment regimes are ideally gathered through the use of Sequential Multiple Assignment Randomized Trials (SMARTs), though longitudinal observational studies are commonly used due to the potentially prohibitive costs of conducting a SMART.