Centre for Biostatistics Seminar
|Dates:||11 June 2018|
|Times:||14:00 - 14:00|
|What is it:||Seminar|
|Organiser:||Faculty of Biology, Medicine and Health|
|Who is it for:||University staff|
Title: Application/Applicability of Marginal Structural Models (MSMs) to irregular healthcare data
The uptake of Marginal Structural Models (MSMs) as a methodological alternative for causal inference particularly when treatments are time varying and confounded by time varying covariates has been increasing and continues to do so. This increase in use of this methodological approach is attributed to its ability to give unbiased estimates when treatments are time varying and confounded by time varying covariates. However, MSMs have been widely used in clinical settings where individuals are observed and have their treatments reviewed at regular time intervals. In healthcare settings, individuals can be observed and treated at irregular time intervals and this irregularity may well be related to previous treatments and it can also influence future treatment. Not much is known about performance of MSMs in irregular data settings. On the basis of the foregoing, this research sought to investigate application of MSMs to irregular data settings.
The aim of this research was to investigate applicability and performance of Marginal Structural Models to estimate causal effects in health data settings where individual patients may be observed at different (or “irregular”) time intervals, and these intervals themselves may be related to treatment and confounder histories. A new class of Inverse Probability of Treatment Weighted (IPTW) estimators for irregular data was proposed to address the aim of the thesis. In this talk, I present the proposed IPTW estimator for irregular data and results from a ‘blinded simulation study’ that demonstrate how the estimators can be used in a ‘real life’ healthcare data setting.
Role: Postgraduate research student
Travel and Contact Information
Jean McFarlane Building