Estimation of average causal effect using the restricted mean residual lifetime as effect measure

Research output: Contribution to journal › Journal article › Research › peer-review

Standard

Estimation of average causal effect using the restricted mean residual lifetime as effect measure. / Mansourvar, Zahra; Martinussen, Torben.

In: Lifetime Data Analysis, Vol. 23, No. 3, 07.2017, p. 426–438.

Research output: Contribution to journal › Journal article › Research › peer-review

Harvard

Mansourvar, Z & Martinussen, T 2017, 'Estimation of average causal effect using the restricted mean residual lifetime as effect measure', Lifetime Data Analysis, vol. 23, no. 3, pp. 426–438. https://doi.org/10.1007/s10985-016-9366-z

APA

Mansourvar, Z., & Martinussen, T. (2017). Estimation of average causal effect using the restricted mean residual lifetime as effect measure. Lifetime Data Analysis, 23(3), 426–438. https://doi.org/10.1007/s10985-016-9366-z

Vancouver

Mansourvar Z, Martinussen T. Estimation of average causal effect using the restricted mean residual lifetime as effect measure. Lifetime Data Analysis. 2017 Jul;23(3):426–438. https://doi.org/10.1007/s10985-016-9366-z

Author

Mansourvar, Zahra ; Martinussen, Torben. / Estimation of average causal effect using the restricted mean residual lifetime as effect measure. In: Lifetime Data Analysis. 2017 ; Vol. 23, No. 3. pp. 426–438.

Bibtex

@article{19e244ef1fe54f869ae64767f44a019c,
title = "Estimation of average causal effect using the restricted mean residual lifetime as effect measure",
abstract = "Although mean residual lifetime is often of interest in biomedical studies, restricted mean residual lifetime must be considered in order to accommodate censoring. Differences in the restricted mean residual lifetime can be used as an appropriate quantity for comparing different treatment groups with respect to their survival times. In observational studies where the factor of interest is not randomized, covariate adjustment is needed to take into account imbalances in confounding factors. In this article, we develop an estimator for the average causal treatment difference using the restricted mean residual lifetime as target parameter. We account for confounding factors using the Aalen additive hazards model. Large sample property of the proposed estimator is established and simulation studies are conducted in order to assess small sample performance of the resulting estimator. The method is also applied to an observational data set of patients after an acute myocardial infarction event.",
author = "Zahra Mansourvar and Torben Martinussen",
year = "2017",
month = jul,
doi = "10.1007/s10985-016-9366-z",
language = "English",
volume = "23",
pages = "426–438",
journal = "Lifetime Data Analysis",
issn = "1380-7870",
publisher = "Springer",
number = "3",

}

RIS

TY - JOUR

T1 - Estimation of average causal effect using the restricted mean residual lifetime as effect measure

AU - Mansourvar, Zahra

AU - Martinussen, Torben

PY - 2017/7

Y1 - 2017/7

N2 - Although mean residual lifetime is often of interest in biomedical studies, restricted mean residual lifetime must be considered in order to accommodate censoring. Differences in the restricted mean residual lifetime can be used as an appropriate quantity for comparing different treatment groups with respect to their survival times. In observational studies where the factor of interest is not randomized, covariate adjustment is needed to take into account imbalances in confounding factors. In this article, we develop an estimator for the average causal treatment difference using the restricted mean residual lifetime as target parameter. We account for confounding factors using the Aalen additive hazards model. Large sample property of the proposed estimator is established and simulation studies are conducted in order to assess small sample performance of the resulting estimator. The method is also applied to an observational data set of patients after an acute myocardial infarction event.

AB - Although mean residual lifetime is often of interest in biomedical studies, restricted mean residual lifetime must be considered in order to accommodate censoring. Differences in the restricted mean residual lifetime can be used as an appropriate quantity for comparing different treatment groups with respect to their survival times. In observational studies where the factor of interest is not randomized, covariate adjustment is needed to take into account imbalances in confounding factors. In this article, we develop an estimator for the average causal treatment difference using the restricted mean residual lifetime as target parameter. We account for confounding factors using the Aalen additive hazards model. Large sample property of the proposed estimator is established and simulation studies are conducted in order to assess small sample performance of the resulting estimator. The method is also applied to an observational data set of patients after an acute myocardial infarction event.

U2 - 10.1007/s10985-016-9366-z

DO - 10.1007/s10985-016-9366-z

M3 - Journal article

C2 - 27037915

VL - 23

SP - 426

EP - 438

JO - Lifetime Data Analysis

JF - Lifetime Data Analysis

SN - 1380-7870

IS - 3

ER -

ID: 161793096