scholarly journals A graphical perspective of marginal structural models: An application for the estimation of the effect of physical activity on blood pressure

2016 ◽  
Vol 27 (8) ◽  
pp. 2428-2436
Author(s):  
Denis Talbot ◽  
Amanda M Rossi ◽  
Simon L Bacon ◽  
Juli Atherton ◽  
Geneviève Lefebvre
Keyword(s):  
Physical Activity ◽  
Blood Pressure ◽  
Repeated Measures ◽  
Structural Equation ◽  
Structural Equation Models ◽  
Causal Effect ◽  
Structural Models ◽  
Marginal Structural Models ◽  
Time Varying ◽  
Causal Graph

Estimating causal effects requires important prior subject-matter knowledge and, sometimes, sophisticated statistical tools. The latter is especially true when targeting the causal effect of a time-varying exposure in a longitudinal study. Marginal structural models are a relatively new class of causal models that effectively deal with the estimation of the effects of time-varying exposures. Marginal structural models have traditionally been embedded in the counterfactual framework to causal inference. In this paper, we use the causal graph framework to enhance the implementation of marginal structural models. We illustrate our approach using data from a prospective cohort study, the Honolulu Heart Program. These data consist of 8006 men at baseline. To illustrate our approach, we focused on the estimation of the causal effect of physical activity on blood pressure, which were measured at three time points. First, a causal graph is built to encompass prior knowledge. This graph is then validated and improved utilizing structural equation models. We estimated the aforementioned causal effect using marginal structural models for repeated measures and guided the implementation of the models with the causal graph. By employing the causal graph framework, we also show the validity of fitting conditional marginal structural models for repeated measures in the context implied by our data.

Instrumental variable estimation of the marginal structural Cox model for time-varying treatments

Biometrika ◽  
2021 ◽  
Author(s):  
Y Cui ◽  
H Michael ◽  
F Tanser ◽  
E Tchetgen Tchetgen
Keyword(s):  
Instrumental Variable ◽  
Causal Effect ◽  
Cox Model ◽  
Sufficient Conditions ◽  
Structural Models ◽  
Model Parameters ◽  
Marginal Structural Models ◽  
Time Varying ◽  
Unmeasured Confounding ◽  
Sequential Randomization

Summary Robins (1998) introduced marginal structural models, a general class of counterfactual models for the joint effects of time-varying treatments in complex longitudinal studies subject to time-varying confounding. Robins (1998) established the identification of marginal structural model parameters under a sequential randomization assumption, which rules out unmeasured confounding of treatment assignment over time. The marginal structural Cox model is one of the most popular marginal structural models to evaluate the causal effect of time-varying treatments on a censored failure time outcome. In this paper, we establish sufficient conditions for identification of marginal structural Cox model parameters with the aid of a time-varying instrumental variable, when sequential randomization fails to hold due to unmeasured confounding. Our instrumental variable identification condition rules out any interaction between an unmeasured confounder and the instrumental variable in its additive effects on the treatment process, the longitudinal generalization of the identifying condition of Wang & Tchetgen Tchetgen (2018). We describe a large class of weighted estimating equations that give rise to consistent and asymptotically normal estimators of the marginal structural Cox model, thereby extending the standard inverse probability of treatment weighted estimation of marginal structural models to the instrumental variable setting. Our approach is illustrated via extensive simulation studies and an application to estimate the effect of community antiretroviral therapy coverage on HIV incidence.

Mental distress and its associations with behavioral outcomes during the COVID-19: A national survey of Chinese adults (Preprint)

2020 ◽  
Author(s):  
Lu Ma ◽  
Liwang Gao ◽  
Joseph Tak-Fai Lau ◽  
Rahman Atif ◽  
Blair T. Johnson ◽  
...  
Keyword(s):  
Physical Activity ◽  
Body Weight ◽  
Educational Attainment ◽  
Structural Equation ◽  
Online Survey ◽  
Mental Distress ◽  
Structural Equation Models ◽  
Behavioral Outcomes ◽  
National Sample ◽  
Negative Consequences

BACKGROUND This study primarily aimed to evaluate the associations between mental distress and COVID-19-related changes in behavioral outcomes, and potential modifiers (age, gender, and educational attainment) of such associations. OBJECTIVE The COVID -19 pandemic has led to elevated levels of mental distress attributed to prolonged lockdowns, business closures, and social isolation. Its impact on behavioral outcomes is however less known. This study is designed to primarily evaluate the associations between mental distress and COVID-19-related changes in drinking, smoking, physical activity and body weight, and potential modifiers of such associations including age, gender, and educational attainment. METHODS An online survey using anonymous network sampling was conducted in China during April-May, 2020 using a 74-item questionnaire distributed through social media. A national sample of 10,545 adults in 31 provinces provided data on socio-demographic characteristics, COVID-19-related mental distress, and changes in behavioral outcomes. Structural equation models were used for data analyses. RESULTS About 13% of adults reported experiencing at least one symptom of mental distress. After adjusting for age, education, gender, ethnicity, marital status, residence, and number of chronic conditions, greater mental distress was associated with increased smoking and alcohol consumption (among current smokers and drinkers) and with both increased and decreased physical activity. Underweight adults were more likely to lose body weight (≥1 kg) whereas overweight adults were more likely to gain weight by the same amount. The association between mental distress and change in physical activity was stronger in adults aged 40 and above and those with high education. Mental distress was significantly associated with an increase in smoking in males but not females. CONCLUSIONS Mental distress was associated with increased smoking in males but not females. These findings inform the design of tailored public health interventions aimed to mitigate long-term negative consequences of mental distress on outcomes.

scholarly journals Causal inference in continuous time: an example on prostate cancer therapy

Biostatistics ◽  
2018 ◽  
Vol 21 (1) ◽  
pp. 172-185 ◽  
Author(s):  
Pål Christie Ryalen ◽  
Mats Julius Stensrud ◽  
Sophie Fosså ◽  
Kjetil Røysland
Keyword(s):  
Prostate Cancer ◽  
Continuous Time ◽  
Causal Effect ◽  
Structural Models ◽  
Marginal Structural Models ◽  
Cancer Treatments ◽  
Discrete Parameter ◽  
Inverse Probability Weights ◽  
Using Data ◽  
Prostate Cancer Treatments

Abstract In marginal structural models (MSMs), time is traditionally treated as a discrete parameter. In survival analysis on the other hand, we study processes that develop in continuous time. Therefore, Røysland (2011. A martingale approach to continuous-time marginal structural models. Bernoulli 17, 895–915) developed the continuous-time MSMs, along with continuous-time weights. The continuous-time weights are conceptually similar to the inverse probability weights that are used in discrete time MSMs. Here, we demonstrate that continuous-time MSMs may be used in practice. First, we briefly describe the causal model assumptions using counting process notation, and we suggest how causal effect estimates can be derived by calculating continuous-time weights. Then, we describe how additive hazard models can be used to find such effect estimates. Finally, we apply this strategy to compare medium to long-term differences between the two prostate cancer treatments radical prostatectomy and radiation therapy, using data from the Norwegian Cancer Registry. In contrast to the results of a naive analysis, we find that the marginal cumulative incidence of treatment failure is similar between the strategies, accounting for the competing risk of other death.

scholarly journals The Effect of the Mobile ‘Blood Pressure Management Application’ on Hypertension Self-Management Enhancement: A Randomised Controlled Trial

2019 ◽  
Author(s):  
Ali Bozorgi ◽  
Hamed Hosseini ◽  
Hassan Eftekhar ◽  
Reza Majdzadeh ◽  
Ali Yoonessi ◽  
...  
Keyword(s):  
Physical Activity ◽  
Blood Pressure ◽  
Clinical Trial ◽  
Repeated Measures ◽  
Patient Adherence ◽  
Intervention Group ◽  
Public Hospitals ◽  
Self Management ◽  
Control Group ◽  
Management Application

Abstract Background : Self-management of blood pressure is of great significance given the increasing incidence of hypertension and associated disabilities. With the increased use of mobile health in medicine, the present study evaluated the effect of the self-management application on patient adherence to hypertension treatment. Methods : This clinical trial was performed on 120 hypertensive patients who were provided with a mobile intervention for 8 weeks and followed-up to 24 th weeks. Data on the primary outcome (adherence to treatment) and secondary outcomes (adherence to the DASH diet, regular monitoring of blood pressure, and physical activity) were collected using a questionnaire and a mobile application, respectively. The inter-group change difference over time was analyzed using repeated measures ANOVA (General Linear Model). Results : The treatment adherence score increased by an average of 5.9 (95%CI: 5.0-6.7) in the intervention group compared to the control group. Scores of adherence to the low-fat and low-salt diet plans were 1.7 (95%CI: 1.3-2.1) and 1.5 (95%CI: 1.2-1.9), respectively. Moreover, moderate physical activity increased to 100.0 minutes (95%CI: 61.7-138.3) per week in the intervention group. Conclusion: The treatment and control of blood pressure require a multifaceted approach given its complexity and multifactorial nature. Considering the widespread use of smartphones , mhealth interventions can be effective in self-management and better patient adherence to treatments. Our results showed that this application can be used as a successful tool for hypertension self-management in patients attending public hospitals in developing countries. Trial registration: This study was registered in the Iran Randomized Clinical Trial Center under the number IRCT2015111712211N2 on 1 January 2016.

scholarly journals The causal effect of dynamic fertility treatment strategies on the probability of pregnancy: a novel application of marginal structural models (MSMs)

2019 ◽  
Vol 112 (3) ◽  
pp. e178
Author(s):  
Soudeh Ansari ◽  
Michael P. LaValley ◽  
Sara Lodi ◽  
Brooke Hayward ◽  
Gilbert L. Mottla ◽  
...  
Keyword(s):  
Causal Effect ◽  
Treatment Strategies ◽  
Structural Models ◽  
Fertility Treatment ◽  
Marginal Structural Models

scholarly journals Expectancy-Value Model Related to Physical Activity Behaviors in Chilean and Spanish Adolescents

2020 ◽  
Vol 17 (21) ◽  
pp. 8219
Author(s):  
Laura O. Gallardo ◽  
Alberto Abarca-Sos ◽  
Alberto Moreno Doña
Keyword(s):  
Physical Activity ◽  
Structural Equation ◽  
Structural Equation Models ◽  
Perceived Competence ◽  
Group Model ◽  
Physically Active ◽  
Expectancy Value ◽  
Essential Variable ◽  
Value Model ◽  
Weight Teasing

The purpose of the study is to comparatively test the expectancy-value model in Chilean and Spanish samples. The model proposes: a social world (composed of social support, physical activity teasing, and weight teasing), expectancy (composed of perceived competence and appearance), task values (composed of enjoyment and stress) to predict physical activity and intention to be physically active. Participants were 497 (Chilean) and 1365 (Spanish) adolescents. Structural equation models and multi-group modelling were used. All the models presented adequate fit to the data. The results show that physical activity teasing is a contextual and essential variable; perceived competence and enjoyment influenced physical activity and intentions to be physically active; some differences appeared in the prediction of physical activity and intentions to be physically active when the multi-group model was run. Culturally tailored interventions are key to improving physical activity (PA) behaviors.

Sign in / Sign up

Export Citation Format

Share Document