Causal effect estimation strategies in a longitudinal study with complex time-varying confounders: A tutorial
- Mertens, B.J.A.
- Datta, S.
- Brand, R.
- Peul, W.
DOIAbstract
Development and application of statistical models for medical scientific researc
Development and application of statistical models for medical scientific researc
Causal inference with observational longitudinal data and time-varying exposures is complicated due ...
When estimating the effect of treatment on HIV using data from observational studies, standard metho...
Much of epidemiology and clinical medicine is focused on the estimation of treatments or interventio...
We develop an approach to identifying and estimating causal ef-fects in longitudinal settings with t...
Causal inference methods are statistical techniques used to analyse the causal effect of a treatment...
Abstract Estimation of causal effects of time-varying exposures using longitudinal data is a common ...
Longitudinal studies in which exposures, confounders, and outcomes are measured repeatedly over time...
This thesis and related research is motivated by my interest in understanding the use of time-varyin...
Many exposures of epidemiological interest are time varying, and the values of potential confounders...
In health services research, it is vital to know whether an event, such as treatment or modifiable e...
To estimate causal effects, analysts performing observational studies in health settings utilize sev...
This dissertation considers statistical methodology for causal effect moderation in both experimenta...
Observational studies aiming to estimate causal effects often rely on conceptual frameworks that are...
Summary. This article considers the problem of assessing causal effect moderation in longitudinal se...
Bayesian statistical methods are becoming increasingly in demand in clinical and public health resea...
Causal inference with observational longitudinal data and time-varying exposures is complicated due ...
When estimating the effect of treatment on HIV using data from observational studies, standard metho...
Much of epidemiology and clinical medicine is focused on the estimation of treatments or interventio...
We develop an approach to identifying and estimating causal ef-fects in longitudinal settings with t...
Causal inference methods are statistical techniques used to analyse the causal effect of a treatment...
Abstract Estimation of causal effects of time-varying exposures using longitudinal data is a common ...
Longitudinal studies in which exposures, confounders, and outcomes are measured repeatedly over time...
This thesis and related research is motivated by my interest in understanding the use of time-varyin...
Many exposures of epidemiological interest are time varying, and the values of potential confounders...
In health services research, it is vital to know whether an event, such as treatment or modifiable e...
To estimate causal effects, analysts performing observational studies in health settings utilize sev...
This dissertation considers statistical methodology for causal effect moderation in both experimenta...
Observational studies aiming to estimate causal effects often rely on conceptual frameworks that are...
Summary. This article considers the problem of assessing causal effect moderation in longitudinal se...
Bayesian statistical methods are becoming increasingly in demand in clinical and public health resea...
Causal inference with observational longitudinal data and time-varying exposures is complicated due ...
When estimating the effect of treatment on HIV using data from observational studies, standard metho...
Much of epidemiology and clinical medicine is focused on the estimation of treatments or interventio...
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