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Adjusting for unmeasured confounding using validation data: Simplified two‐stage calibration for survival and dichotomous outcomes
Author(s) -
Hjellvik Vidar,
De Bruin Marie L.,
Samuelsen Sven O.,
Karlstad Øystein,
Andersen Morten,
Haukka Jari,
Vestergaard Peter,
Vries Frank,
Furu Kari
Publication year - 2019
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.8131
Subject(s) - confounding , statistics , data set , calibration , hazard ratio , proportional hazards model , confidence interval , computer science , outcome (game theory) , set (abstract data type) , econometrics , mathematics , mathematical economics , programming language
In epidemiology, one typically wants to estimate the risk of an outcome associated with an exposure after adjusting for confounders. Sometimes, outcome and exposure and maybe some confounders are available in a large data set, whereas some important confounders are only available in a validation data set that is typically a subset of the main data set. A generally applicable method in this situation is the two‐stage calibration (TSC) method. We present a simplified easy‐to‐implement version of the TSC for the case where the validation data are a subset of the main data. We compared the simplified version to the standard TSC version for incidence rate ratios, odds ratios, relative risks, and hazard ratios using simulated data, and the simplified version performed better than our implementation of the standard version. The simplified version was also tested on real data and performed well.