Contains fulltext : 203476.pdf (publisher's version ) (Open Access
Background: The effectiveness of treatment for people with substance use disorders is usually examin...
Longitudinal studies, where data are repeatedly collected on subjects over a period, are common in m...
Much of epidemiology and clinical medicine is focused on the estimation of treatments or interventio...
Many exposures of epidemiological interest are time varying, and the values of potential confounders...
Longitudinal studies in which exposures, confounders, and outcomes are measured repeatedly over time...
According to the authors, time-modified confounding occurs when the causal relation between a time-f...
This thesis and related research is motivated by my interest in understanding the use of time-varyin...
<p>In many longitudinal studies, repeated response and predictors are not directly observed, but can...
Abstract Estimation of causal effects of time-varying exposures using longitudinal data is a common ...
Development and application of statistical models for medical scientific researc
Much of epidemiology and clinical medicine is focused on estimating the effects of treatments or int...
In longitudinal settings, causal inference methods usually rely on a discretization of the patient ...
In this paper, we investigate the impact of time-invariant covariates when fitting transition mixed ...
Causal inference uses observations to infer the causal structure of the data generating system. We s...
Intensive longitudinal data has been widely used to examine reciprocal or causal relations between v...
Background: The effectiveness of treatment for people with substance use disorders is usually examin...
Longitudinal studies, where data are repeatedly collected on subjects over a period, are common in m...
Much of epidemiology and clinical medicine is focused on the estimation of treatments or interventio...
Many exposures of epidemiological interest are time varying, and the values of potential confounders...
Longitudinal studies in which exposures, confounders, and outcomes are measured repeatedly over time...
According to the authors, time-modified confounding occurs when the causal relation between a time-f...
This thesis and related research is motivated by my interest in understanding the use of time-varyin...
<p>In many longitudinal studies, repeated response and predictors are not directly observed, but can...
Abstract Estimation of causal effects of time-varying exposures using longitudinal data is a common ...
Development and application of statistical models for medical scientific researc
Much of epidemiology and clinical medicine is focused on estimating the effects of treatments or int...
In longitudinal settings, causal inference methods usually rely on a discretization of the patient ...
In this paper, we investigate the impact of time-invariant covariates when fitting transition mixed ...
Causal inference uses observations to infer the causal structure of the data generating system. We s...
Intensive longitudinal data has been widely used to examine reciprocal or causal relations between v...
Background: The effectiveness of treatment for people with substance use disorders is usually examin...
Longitudinal studies, where data are repeatedly collected on subjects over a period, are common in m...
Much of epidemiology and clinical medicine is focused on the estimation of treatments or interventio...
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