Adjusting for unmeasured confounding using validation data: Simplified two-stage calibration for survival and dichotomous outcomes

Vidar Hjellvik, Marie L De Bruin, Sven O Samuelsen, Øystein Karlstad, Morten Andersen, Jari Haukka, Peter Vestergaard, Frank de Vries, Kari Furu

Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer review

Resumé

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.

OriginalsprogEngelsk
TidsskriftStatistics in Medicine
Vol/bind38
Udgave nummer15
Sider (fra-til)2719-2734
ISSN0277-6715
DOI
StatusUdgivet - jul. 2019

Fingeraftryk

Confounding
Calibration
Odds Ratio
Epidemiology
Subset
Relative Risk
Incidence
Large Data Sets
Hazard
Datasets
Estimate

Bibliografisk note

© 2019 John Wiley & Sons, Ltd.

Citer dette

Hjellvik, Vidar ; De Bruin, Marie L ; Samuelsen, Sven O ; Karlstad, Øystein ; Andersen, Morten ; Haukka, Jari ; Vestergaard, Peter ; de Vries, Frank ; Furu, Kari. / Adjusting for unmeasured confounding using validation data : Simplified two-stage calibration for survival and dichotomous outcomes. I: Statistics in Medicine. 2019 ; Bind 38, Nr. 15. s. 2719-2734.
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Hjellvik, V, De Bruin, ML, Samuelsen, SO, Karlstad, Ø, Andersen, M, Haukka, J, Vestergaard, P, de Vries, F & Furu, K 2019, 'Adjusting for unmeasured confounding using validation data: Simplified two-stage calibration for survival and dichotomous outcomes', Statistics in Medicine, bind 38, nr. 15, s. 2719-2734. https://doi.org/10.1002/sim.8131

Adjusting for unmeasured confounding using validation data : Simplified two-stage calibration for survival and dichotomous outcomes. / Hjellvik, Vidar; De Bruin, Marie L; Samuelsen, Sven O; Karlstad, Øystein; Andersen, Morten; Haukka, Jari; Vestergaard, Peter; de Vries, Frank; Furu, Kari.

I: Statistics in Medicine, Bind 38, Nr. 15, 07.2019, s. 2719-2734.

Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer review

TY - JOUR

T1 - Adjusting for unmeasured confounding using validation data

T2 - Simplified two-stage calibration for survival and dichotomous outcomes

AU - Hjellvik, Vidar

AU - De Bruin, Marie L

AU - Samuelsen, Sven O

AU - Karlstad, Øystein

AU - Andersen, Morten

AU - Haukka, Jari

AU - Vestergaard, Peter

AU - de Vries, Frank

AU - Furu, Kari

N1 - © 2019 John Wiley & Sons, Ltd.

PY - 2019/7

Y1 - 2019/7

N2 - 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.

AB - 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.

KW - bias correction

KW - epidemiology

KW - two-stage calibration

KW - unmeasured confounding

KW - validation data

U2 - 10.1002/sim.8131

DO - 10.1002/sim.8131

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SP - 2719

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JO - Statistics in Medicine

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SN - 0277-6715

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ER -