Interrupted Time Series and its applications

1:00pm - 2:00pm EST

This webinar is part of the Advanced Methods Webinar Series

Interrupted Time Series (ITS) is a powerful quasi-experimental time series tool for evaluating temporal effects of interventions on an outcome of interest.

In this webinar we will provide an introduction to regression models with autocorrelated residuals and interrupted time series analysis using segmented regression and rational functions.

Administrative data will be used for illustrating these methods.

View recorded presentation below.

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Rahim MoineddinDr Rahim Moineddin is Professor of Biostatistics at the Department of Family and Community Medicine, University of Toronto, Canada. He has Cross-Appointments with the Biostatistics Division, Dalla Lana School of Public Health and the University of Toronto. Dr Moineddin is also a Senior Adjunct Scientist with the Institute for Clinical Evaluative Sciences (ICES).

Dr. Moineddin has performed extensive research in biostatistics and primary care research. He has a strong theoretical background in statistics, biostatistics and methodology and has collaborated with many senior health science researchers over his career.

Dr. Moineddin has extensive experience with large administrative data bases, specifically those located at the Institute for Clinical Evaluative Sciences, and the Canadian Community Health Surveys which are linked to ICES health services data. He has in-depth knowledge of Time Series analysis, multi-level modelling, analysis of correlated data, randomized and clustered randomized controlled clinical trials and was lead biostatistician on several clinical trials research projects.

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