Estimation of time-specific intervention effects on continuously distributed time-to-event outcomes by targeted maximum likelihood estimation
Publikation: Working paper › Preprint › Forskning
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Estimation of time-specific intervention effects on continuously distributed time-to-event outcomes by targeted maximum likelihood estimation. / Rytgaard, Helene Charlotte Wiese; Eriksson, Frank; Laan, Mark van der.
2022.Publikation: Working paper › Preprint › Forskning
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TY - UNPB
T1 - Estimation of time-specific intervention effects on continuously distributed time-to-event outcomes by targeted maximum likelihood estimation
AU - Rytgaard, Helene Charlotte Wiese
AU - Eriksson, Frank
AU - Laan, Mark van der
PY - 2022
Y1 - 2022
N2 - Targeted maximum likelihood estimation is a general methodology combining flexible ensemble learning and semiparametric efficiency theory in a two-step procedure for estimation of causal parameters. Proposed targeted maximum likelihood procedures for survival and competing risks analysis have so far focused on events taken values in discrete time. We here present a targeted maximum likelihood estimation procedure for event times that take values in R+. We focuson the estimation of intervention-specific mean outcomes with stochastic interventions on a time-fixed treatment. For data-adaptive estimation of nuisance parameters, we propose a new flexible highly adaptive lasso estimation method for continuous-time intensities that can be implemented with L1-penalized Poisson regression. In a simulation study the targeted maximum likelihood estimator based on the highly adaptive lasso estimator proves to be unbiased and achieve proper coverage in agreement with the asymptotic theory and further displays efficiency improvements relative to a Kaplan-Meier approach.
AB - Targeted maximum likelihood estimation is a general methodology combining flexible ensemble learning and semiparametric efficiency theory in a two-step procedure for estimation of causal parameters. Proposed targeted maximum likelihood procedures for survival and competing risks analysis have so far focused on events taken values in discrete time. We here present a targeted maximum likelihood estimation procedure for event times that take values in R+. We focuson the estimation of intervention-specific mean outcomes with stochastic interventions on a time-fixed treatment. For data-adaptive estimation of nuisance parameters, we propose a new flexible highly adaptive lasso estimation method for continuous-time intensities that can be implemented with L1-penalized Poisson regression. In a simulation study the targeted maximum likelihood estimator based on the highly adaptive lasso estimator proves to be unbiased and achieve proper coverage in agreement with the asymptotic theory and further displays efficiency improvements relative to a Kaplan-Meier approach.
KW - stat.ME
M3 - Preprint
T3 - arXiv
BT - Estimation of time-specific intervention effects on continuously distributed time-to-event outcomes by targeted maximum likelihood estimation
ER -
ID: 306104552