ArticleStatistical science : a review journal of the Institute of Mathematical Statistics2020
Linear mixed models with endogenous covariates: modeling sequential treatment effects with application to a mobile health study.
Article in Statistical science : a review journal of the Institute of Mathematical Statistics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
22 citing papers in PubMed, 24 citations in OpenAlex.
- Just-in-Time Adaptive Intervention for Stabilizing Sleep Hours of Japanese Workers: Microrandomized Trial.Journal of medical Internet research · 2024Trial
- Weight gain trajectories and obesity rates in intensive and conventional treatments of type 1 diabetes from the DCCT compared with a control population without diabetes.Diabetic medicine : a journal of the British Diabetic Association · 2022Trial
- Reminder Strategies to Improve Meal-Logging Adherence: Protocol for a Microrandomized Trial.JMIR research protocols · 2026Article
- Dynamic Predictions and Predictimands for Salvage Therapy in Recurrent Prostate Cancer Using Joint Models.Statistics in medicine · 2026Article
- Bayesian multivariate linear mixed-effects models with varied association structures.Statistical methods in medical research · 2026Article
- Antecedents and consequences of different indicators of engagement with a digital intervention for tobacco cessation.NPJ digital medicine · 2026Article
- Analyzing Gait Dynamics and Recovery Trajectory in Lower Extremity Fractures Using Linear Mixed Models and Gait Analysis Variables.Bioengineering (Basel, Switzerland) · 2025Article
- Designing digital health interventions with causal inference and multi-armed bandits: a review.Frontiers in digital health · 2025Review
- Nonlinear mixed-effects modeling as a method for causal inference to predict exposures under desired within-subject dose titration schemes.CPT: pharmacometrics & systems pharmacology · 2025Article
- Causal effects of sedentary breaks on affective and cognitive parameters in daily life: a within-person encouragement design.Npj mental health research · 2024Article
- Article
- A Robust Mixed-Effects Bandit Algorithm for Assessing Mobile Health Interventions.Advances in neural information processing systems · 2024Article
- Article
- A comparative analysis of the effects of containment policies on the epidemiological manifestation of the COVID-19 pandemic across nine European countries.Scientific reports · 2023Article
- Microrandomized Trials: Developing Just-in-Time Adaptive Interventions for Better Public Health.American journal of public health · 2023Article
- Reinforcement learning as an innovative model-based approach: Examples from precision dosing, digital health and computational psychiatry.Frontiers in pharmacology · 2022Article
- IntelligentPooling: Practical Thompson Sampling for mHealth.Machine learning · 2021Article
- Smoking Cessation Using Wearable Sensors: Protocol for a Microrandomized Trial.JMIR research protocols · 2021Article
- Personalized Policy Learning using Longitudinal Mobile Health Data.Journal of the American Statistical Association · 2021Article
- Notifications to Improve Engagement With an Alcohol Reduction App: Protocol for a Micro-Randomized Trial.JMIR research protocols · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 1 institution in 1 country.
Funding
Abstract
Mobile health is a rapidly developing field in which behavioral treatments are delivered to individuals via wearables or smartphones to facilitate health-related behavior change. Micro-randomized trials (MRT) are an experimental design for developing mobile health interventions. In an MRT the treatments are randomized numerous times for each individual over course of the trial. Along with assessing treatment effects, behavioral scientists aim to understand between-person heterogeneity in the treatment effect. A natural approach is the familiar linear mixed model. However, directly applying linear mixed models is problematic because potential moderators of the treatment effect are frequently endogenous-that is, may depend on prior treatment. We discuss model interpretation and biases that arise in the absence of additional assumptions when endogenous covariates are included in a linear mixed model. In particular, when there are endogenous covariates, the coefficients no longer have the customary marginal interpretation. However, these coefficients still have a conditional-on-the-random-effect interpretation. We provide an additional assumption that, if true, allows scientists to use standard software to fit linear mixed model with endogenous covariates, and person-specific predictions of effects can be provided. As an illustration, we assess the effect of activity suggestion in the HeartSteps MRT and analyze the between-person treatment effect heterogeneity.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.