Open AccessLocal control for identifying subgroups of interest in observational research: persistence of treatment for major depressive disorder
Author(s) -
Faries Douglas E.,
Chen Yi,
Lipkovich Ilya,
Zagar Anthony,
Liu Xianchen,
Obenchain Robert L.
Publication year - 2013
Publication title -
international journal of methods in psychiatric research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.275
H-Index - 73
eISSN - 1557-0657
pISSN - 1049-8931
DOI - 10.1002/mpr.1390
Subject(s) - observational study , confounding , counterfactual thinking , population , medicine , depression (economics) , sample size determination , venlafaxine , psychiatry , psychology , statistics , mathematics , environmental health , anxiety , antidepressant , social psychology , economics , macroeconomics
Abstract Caregivers are regularly faced with decisions between competing treatments. Large observational health care databases provide a golden opportunity for research on heterogeneity in patient response to guide caregiver decisions, due to their sample size, diverse populations, and real‐world setting. Local control is a promising tool for using observational data to detect patient subgroups with differential response on one treatment relative to another. While standard data mining approaches find subgroups with optimal responses for a particular population, detecting subgroups that reveal treatment differences while also adjusting for confounding in observational data is challenging. Local control utilizes unsupervised clustering to form non‐parametric patient‐level counterfactual treatment differences and displays them as an observed distribution of effect‐size estimates. Classification and regression trees (CART) then find the factors that drive the greatest outcome differentiation between treatments. In this manuscript, we demonstrate the use of this two‐step strategy using local control plus CART to identify depression patients most (least) likely to benefit from treatment with duloxetine relative to extended‐release venlafaxine. Prior medication costs and age were found to be factors most associated with differential outcome, with prior medication costs remaining as an important factor after sensitivity analyses using a second dataset. Copyright © 2013 John Wiley & Sons, Ltd.