Estimation of direct effects for survival data by using the Aalen additive hazards model

T. Martinussen, S. Vansteelandt, M. Gerster, J. V. Hjelmborg

Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer review

Resumé

We extend the definition of the controlled direct effect of a point exposure on a survival outcome, other than through some given, time-fixed intermediate variable, to the additive hazard scale. We propose two-stage estimators for this effect when the exposure is dichotomous and randomly assigned and when the association between the intermediate variable and the survival outcome is confounded only by measured factors, which may themselves be affected by the exposure. The first stage of the estimation procedure involves assessing the effect of the intermediate variable on the survival outcome via Aalen's additive regression for the event time, given exposure, intermediate variable and confounders. The second stage involves applying Aalen's additive model, given the exposure alone, to a modified stochastic process (i.e. a modification of the observed counting process based on the first-stage estimates). We give the large sample properties of the estimator proposed and investigate its small sample properties by Monte Carlo simulation. A real data example is provided for illustration.
OriginalsprogEngelsk
TidsskriftJournal of the Royal Statistical Society, Series B (Statistical Methodology)
Vol/bind73
Sider (fra-til)773-788
Antal sider16
ISSN1369-7412
DOI
StatusUdgivet - 2011

Citer dette

@article{9d9a01bbc8bf43888a4bcac275553167,
title = "Estimation of direct effects for survival data by using the Aalen additive hazards model",
abstract = "We extend the definition of the controlled direct effect of a point exposure on a survival outcome, other than through some given, time-fixed intermediate variable, to the additive hazard scale. We propose two-stage estimators for this effect when the exposure is dichotomous and randomly assigned and when the association between the intermediate variable and the survival outcome is confounded only by measured factors, which may themselves be affected by the exposure. The first stage of the estimation procedure involves assessing the effect of the intermediate variable on the survival outcome via Aalen's additive regression for the event time, given exposure, intermediate variable and confounders. The second stage involves applying Aalen's additive model, given the exposure alone, to a modified stochastic process (i.e. a modification of the observed counting process based on the first-stage estimates). We give the large sample properties of the estimator proposed and investigate its small sample properties by Monte Carlo simulation. A real data example is provided for illustration.",
author = "T. Martinussen and S. Vansteelandt and M. Gerster and Hjelmborg, {J. V.}",
year = "2011",
doi = "10.1111/j.1467-9868.2011.00782.x",
language = "English",
volume = "73",
pages = "773--788",
journal = "Journal of the Royal Statistical Society, Series B (Statistical Methodology)",
issn = "1369-7412",
publisher = "Wiley",

}

Estimation of direct effects for survival data by using the Aalen additive hazards model. / Martinussen, T.; Vansteelandt, S.; Gerster, M.; Hjelmborg, J. V.

I: Journal of the Royal Statistical Society, Series B (Statistical Methodology), Bind 73, 2011, s. 773-788.

Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer review

TY - JOUR

T1 - Estimation of direct effects for survival data by using the Aalen additive hazards model

AU - Martinussen, T.

AU - Vansteelandt, S.

AU - Gerster, M.

AU - Hjelmborg, J. V.

PY - 2011

Y1 - 2011

N2 - We extend the definition of the controlled direct effect of a point exposure on a survival outcome, other than through some given, time-fixed intermediate variable, to the additive hazard scale. We propose two-stage estimators for this effect when the exposure is dichotomous and randomly assigned and when the association between the intermediate variable and the survival outcome is confounded only by measured factors, which may themselves be affected by the exposure. The first stage of the estimation procedure involves assessing the effect of the intermediate variable on the survival outcome via Aalen's additive regression for the event time, given exposure, intermediate variable and confounders. The second stage involves applying Aalen's additive model, given the exposure alone, to a modified stochastic process (i.e. a modification of the observed counting process based on the first-stage estimates). We give the large sample properties of the estimator proposed and investigate its small sample properties by Monte Carlo simulation. A real data example is provided for illustration.

AB - We extend the definition of the controlled direct effect of a point exposure on a survival outcome, other than through some given, time-fixed intermediate variable, to the additive hazard scale. We propose two-stage estimators for this effect when the exposure is dichotomous and randomly assigned and when the association between the intermediate variable and the survival outcome is confounded only by measured factors, which may themselves be affected by the exposure. The first stage of the estimation procedure involves assessing the effect of the intermediate variable on the survival outcome via Aalen's additive regression for the event time, given exposure, intermediate variable and confounders. The second stage involves applying Aalen's additive model, given the exposure alone, to a modified stochastic process (i.e. a modification of the observed counting process based on the first-stage estimates). We give the large sample properties of the estimator proposed and investigate its small sample properties by Monte Carlo simulation. A real data example is provided for illustration.

U2 - 10.1111/j.1467-9868.2011.00782.x

DO - 10.1111/j.1467-9868.2011.00782.x

M3 - Journal article

VL - 73

SP - 773

EP - 788

JO - Journal of the Royal Statistical Society, Series B (Statistical Methodology)

JF - Journal of the Royal Statistical Society, Series B (Statistical Methodology)

SN - 1369-7412

ER -