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Full-Text Articles in Biostatistics
Novel Methods For Analyzing Longitudinal Data With Measurement Error In The Time Variable, Caroline Munindi Mulatya
Novel Methods For Analyzing Longitudinal Data With Measurement Error In The Time Variable, Caroline Munindi Mulatya
Theses and Dissertations
In some longitudinal studies, the observed time points are often confounded with measurement error due to the sampling conditions, resulting into data with measurement error in the time variable. This type of data occurs mainly in observational studies when the onset of a longitudinal process is unknown or in clinical trials when individual visits do not take place as specified by the study protocol, but are often rounded to coincide with the study protocol. Methodological and inferential implications of error in time varying covariates for both linear and nonlinear models have been studied widely. In this dissertation, we shift attention …
Using Validation Data To Adjust The Inverse Probability Weighting Estimator For Misclassified Treatment, Danielle Braun, Corwin Zigler, Francesca Dominici, Malka Gorfine
Using Validation Data To Adjust The Inverse Probability Weighting Estimator For Misclassified Treatment, Danielle Braun, Corwin Zigler, Francesca Dominici, Malka Gorfine
Harvard University Biostatistics Working Paper Series
The inverse probability weighting (IPW) estimator is widely used to estimate the treatment effect in observational studies in which patient characteristics might not be balanced by treatment group. The estimator assumes that treatment assignment, is error-free, but in reality treatment assignment can be measured with error. This arises in the context of comparative effectiveness research, using administrative data sources in which accurate procedural or billing codes are not always available. We show the bias introduced to the estimator when using error-prone treatment assignment, and propose an adjusted estimator using a validation study to eliminate this bias. In simulations, we explore …
A Cautionary Note On The Effect Of Treatment Misclassification On The Average Treatment Effect, Danielle Braun, Corwin Zigler, Malka Gorfine, Francesca Dominici
A Cautionary Note On The Effect Of Treatment Misclassification On The Average Treatment Effect, Danielle Braun, Corwin Zigler, Malka Gorfine, Francesca Dominici
Harvard University Biostatistics Working Paper Series
Comparative effectiveness research often relies on large administrative data, such as claims data. Methods to estimate treatment effects assume that treatment assignment is error-free, but in reality the inaccuracy of procedural or billing codes frequently misclassifies patients into treatment groups. Propensity score methods are widely used to analyze observational studies in which patient characteristics might not be balanced by treatment group. We evaluate the impact of treatment misclassification on 1) propensity score estimation; 2) treatment effect estimation conditional on propensity score estimation and implementation. We focus on three common propensity score implementations: subclassification, matching, and inverse probability of treatment weighting …
Causal Inference In Observational Studies With Clustered Data, Meng Wu
Causal Inference In Observational Studies With Clustered Data, Meng Wu
Legacy Theses & Dissertations (2009 - 2024)
In this thesis, we study causal inference in observational studies with clustered data.