Efficiency of positive event dependence models for self-controlled case series designs

Shin, Gwangseop (2022) Efficiency of positive event dependence models for self-controlled case series designs. Masters thesis, Memorial University of Newfoundland.

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The self-controlled case series (SCCS) design is an outcome-dependent sampling design developed to investigate potential associations between time-varying exposures and adverse recurrent events in clinical settings. It is a case-only design, in which only cases, who are individuals experienced the event-of-interest at least one time, are included. The cases serve as their own controls. As a result, the SCCS design implicitly controls for all time-fixed confounders. The standard SCCS method is based on a conditional Poisson process model, which cannot be used to model the effects of past event occurrences. A new method, called positive event dependence self-controlled case series (PD-SCCS), has been proposed to deal with this issue. This method adjusts the baseline intensity function of an event process with the number of previous events, and maintains all features of the SCCS method. In this study, we consider rare recurrent events settings, where the events are generated from mixed nonhomogeneous pure birth models with immigration. We investigate the relative efficiency of the PD-SCCS design compared to other SCCS and cohort designs in the estimation of the relative incidence parameter, as well as impacts of some model misspecifications and violations of model assumptions required for the PD-SCCS design through extensive Monte Carlo simulation studies. We illustrate the methods by analyzing a dataset from a clinical vaccine study, as well as two synthetic datasets based on a postmarketing drug safety surveillance study.

Item Type: Thesis (Masters)
URI: http://research.library.mun.ca/id/eprint/15557
Item ID: 15557
Additional Information: Includes bibliographical references (pages 96-99)
Keywords: recurrent events, study designs, outcome-dependent samples, self-controlled case series method, likelihood-based inference, relative efficiency, model misspecification
Department(s): Science, Faculty of > Mathematics and Statistics
Date: June 2022
Date Type: Submission
Digital Object Identifier (DOI): https://doi.org/10.48336/0JS0-HD35
Library of Congress Subject Heading: Recurrent sequences (Mathematics); Sampling (Statistics); Inference; Input design, Computer

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