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Tom Ten Have's contributions to causal inference and biostatistics: review and future research directions
Author(s) -
Small Dylan S.,
Joffe Marshall M.,
Lynch Kevin G.,
Roy Jason A.,
Russell Localio A.
Publication year - 2012
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.5708
Subject(s) - causal inference , biostatistics , categorical variable , inference , mediation , randomized experiment , causal model , econometrics , psychology , computer science , medicine , artificial intelligence , machine learning , public health , sociology , mathematics , social science , nursing , pathology
Tom Ten Have made many contributions to causal inference and biostatistics before his untimely death. This paper reviews Tom's contributions and discusses potential related future research directions. We focus on Tom's contributions to longitudinal/repeated measures categorical data analysis and particularly his contributions to causal inference. Tom's work on causal inference was primarily in the areas of estimating the effect of receiving treatment in randomized trials with nonadherence and mediation analysis. A related area to mediation analysis he was working on at the time of his death was posttreatment effect modification with applications to designing adaptive treatment strategies. Copyright © 2012 John Wiley & Sons, Ltd.