Instrumental variables estimation under a structural Cox model

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Standard

Instrumental variables estimation under a structural Cox model. / Martinussen, Torben; Nørbo Sørensen, Ditte; Vansteelandt, Stijn.

I: Biostatistics, Bind 20, Nr. 1, 2019, s. 65-79.

Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt

Harvard

Martinussen, T, Nørbo Sørensen, D & Vansteelandt, S 2019, 'Instrumental variables estimation under a structural Cox model', Biostatistics, bind 20, nr. 1, s. 65-79. https://doi.org/10.1093/biostatistics/kxx057

APA

Martinussen, T., Nørbo Sørensen, D., & Vansteelandt, S. (2019). Instrumental variables estimation under a structural Cox model. Biostatistics, 20(1), 65-79. https://doi.org/10.1093/biostatistics/kxx057

Vancouver

Martinussen T, Nørbo Sørensen D, Vansteelandt S. Instrumental variables estimation under a structural Cox model. Biostatistics. 2019;20(1):65-79. https://doi.org/10.1093/biostatistics/kxx057

Author

Martinussen, Torben ; Nørbo Sørensen, Ditte ; Vansteelandt, Stijn. / Instrumental variables estimation under a structural Cox model. I: Biostatistics. 2019 ; Bind 20, Nr. 1. s. 65-79.

Bibtex

@article{15070f771725478da64fe98dc2dae563,

title = "Instrumental variables estimation under a structural Cox model",

abstract = "Instrumental variable (IV) analysis is an increasingly popular tool for inferring the effect of an exposure on an outcome, as witnessed by the growing number of IV applications in epidemiology, for instance. The majority of IV analyses of time-to-event endpoints are, however, dominated by heuristic approaches. More rigorous proposals have either sidestepped the Cox model, or considered it within a restrictive context with dichotomous exposure and instrument, amongst other limitations. The aim of this article is to reconsider IV estimation under a structural Cox model, allowing for arbitrary exposure and instruments. We propose a novel class of estimators and derive their asymptotic properties. The methodology is illustrated using two real data applications, and using simulated data.",

author = "Torben Martinussen and {N{\o}rbo S{\o}rensen}, Ditte and Stijn Vansteelandt",

year = "2019",

doi = "10.1093/biostatistics/kxx057",

language = "English",

volume = "20",

pages = "65--79",

journal = "Biostatistics",

issn = "1465-4644",

publisher = "Oxford University Press",

number = "1",

}

RIS

TY - JOUR

T1 - Instrumental variables estimation under a structural Cox model

AU - Martinussen, Torben

AU - Nørbo Sørensen, Ditte

AU - Vansteelandt, Stijn

PY - 2019

Y1 - 2019

N2 - Instrumental variable (IV) analysis is an increasingly popular tool for inferring the effect of an exposure on an outcome, as witnessed by the growing number of IV applications in epidemiology, for instance. The majority of IV analyses of time-to-event endpoints are, however, dominated by heuristic approaches. More rigorous proposals have either sidestepped the Cox model, or considered it within a restrictive context with dichotomous exposure and instrument, amongst other limitations. The aim of this article is to reconsider IV estimation under a structural Cox model, allowing for arbitrary exposure and instruments. We propose a novel class of estimators and derive their asymptotic properties. The methodology is illustrated using two real data applications, and using simulated data.

AB - Instrumental variable (IV) analysis is an increasingly popular tool for inferring the effect of an exposure on an outcome, as witnessed by the growing number of IV applications in epidemiology, for instance. The majority of IV analyses of time-to-event endpoints are, however, dominated by heuristic approaches. More rigorous proposals have either sidestepped the Cox model, or considered it within a restrictive context with dichotomous exposure and instrument, amongst other limitations. The aim of this article is to reconsider IV estimation under a structural Cox model, allowing for arbitrary exposure and instruments. We propose a novel class of estimators and derive their asymptotic properties. The methodology is illustrated using two real data applications, and using simulated data.

U2 - 10.1093/biostatistics/kxx057

DO - 10.1093/biostatistics/kxx057

M3 - Journal article

C2 - 29165631

VL - 20

SP - 65

EP - 79

JO - Biostatistics

JF - Biostatistics

SN - 1465-4644

IS - 1

ER -

ID: 189098281