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Activity Number: 427 - Various Challenges and Strategies in Analysis of Real-Life Data
Type: Invited
Date/Time: Thursday, August 6, 2020 : 10:00 AM to 11:50 AM
Sponsor: SSC (Statistical Society of Canada)
Abstract #308161
Title: Informative Visit Patterns in Longitudinal Health Care Claims Data
Author(s): Robert Platt*
Companies: McGill University
Keywords: Longitudinal data; Missing data; Informative censoring

In longitudinal health claims data, patients’ visit patterns usually do not follow regular patterns. These patterns may provide information about patients’ health status, and may introduce bias. In diseases with temporal variability in disease status such as multiple sclerosis, this can have an important impact. In this talk, I will describe settings in which informative visit patterns can cause bias in estimation of disease risk, time to progression, and of relative risks comparing two treatments. I will present graphical methods to describe these potential biases, and some statistical tools involving inverse probability weighting to correct for bias.

Authors who are presenting talks have a * after their name.

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