Targeted maximum likelihood estimation for causal inference in survival and competing risks analysis
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Targeted maximum likelihood estimation for causal inference in survival and competing risks analysis. / Rytgaard, Helene C. W.; van der Laan, Mark J.
In: Lifetime Data Analysis, Vol. 30, 2024, p. 4–33.Research output: Contribution to journal › Journal article › Research › peer-review
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TY - JOUR
T1 - Targeted maximum likelihood estimation for causal inference in survival and competing risks analysis
AU - Rytgaard, Helene C. W.
AU - van der Laan, Mark J.
PY - 2024
Y1 - 2024
N2 - Targeted maximum likelihood estimation (TMLE) provides a general methodology for estimation of causal parameters in presence of high-dimensional nuisance parameters. Generally, TMLE consists of a two-step procedure that combines data-adaptive nuisance parameter estimation with semiparametric efficiency and rigorous statistical inference obtained via a targeted update step. In this paper, we demonstrate the practical applicability of TMLE based causal inference in survival and competing risks settings where event times are not confined to take place on a discrete and finite grid. We focus on estimation of causal effects of time-fixed treatment decisions on survival and absolute risk probabilities, considering different univariate and multidimensional parameters. Besides providing a general guidance to using TMLE for survival and competing risks analysis, we further describe how the previous work can be extended with the use of loss-based cross-validated estimation, also known as super learning, of the conditional hazards. We illustrate the usage of the considered methods using publicly available data from a trial on adjuvant chemotherapy for colon cancer. R software code to implement all considered algorithms and to reproduce all analyses is available in an accompanying online appendix on Github.
AB - Targeted maximum likelihood estimation (TMLE) provides a general methodology for estimation of causal parameters in presence of high-dimensional nuisance parameters. Generally, TMLE consists of a two-step procedure that combines data-adaptive nuisance parameter estimation with semiparametric efficiency and rigorous statistical inference obtained via a targeted update step. In this paper, we demonstrate the practical applicability of TMLE based causal inference in survival and competing risks settings where event times are not confined to take place on a discrete and finite grid. We focus on estimation of causal effects of time-fixed treatment decisions on survival and absolute risk probabilities, considering different univariate and multidimensional parameters. Besides providing a general guidance to using TMLE for survival and competing risks analysis, we further describe how the previous work can be extended with the use of loss-based cross-validated estimation, also known as super learning, of the conditional hazards. We illustrate the usage of the considered methods using publicly available data from a trial on adjuvant chemotherapy for colon cancer. R software code to implement all considered algorithms and to reproduce all analyses is available in an accompanying online appendix on Github.
KW - TMLE
KW - Semiparametric efficiency
KW - Survival analysis
KW - Competing risks
KW - Super learning
KW - Highly adaptive lasso
KW - Causal inference
KW - Average treatment effects
KW - REGULARIZATION PATHS
KW - CUMULATIVE INCIDENCE
KW - ADJUVANT THERAPY
KW - FLUOROURACIL
KW - LEVAMISOLE
KW - OUTCOMES
KW - HAZARDS
KW - TRIALS
KW - MODELS
U2 - 10.1007/s10985-022-09576-2
DO - 10.1007/s10985-022-09576-2
M3 - Journal article
C2 - 36336732
VL - 30
SP - 4
EP - 33
JO - Lifetime Data Analysis
JF - Lifetime Data Analysis
SN - 1380-7870
ER -
ID: 325819672