Estimation of time-specific intervention effects on continuously distributed time-to-event outcomes by targeted maximum likelihood estimation.

This work considers targeted maximum likelihood estimation (TMLE) of treatment effects on absolute risk and survival probabilities in classical time-to-event settings characterized by right-censoring and competing risks. TMLE is a general methodology combining flexible ensemble learning and semiparametric efficiency theory in a two-step procedure for substitution estimation of causal parameters. We specialize and extend the continuous-time TMLE methods for competing risks settings, proposing a targeting algorithm that iteratively updates cause-specific hazards to solve the efficient influence curve equation for the target parameter. As part of the work, we further detail and implement the recently proposed highly adaptive lasso estimator for continuous-time conditional hazards with L1 -penalized Poisson regression. The resulting estimation procedure benefits from relying solely on very mild nonparametric restrictions on the statistical model, thus providing a novel tool for machine-learning-based semiparametric causal inference for continuous-time time-to-event data. We apply the methods to a publicly available dataset on follicular cell lymphoma where subjects are followed over time until disease relapse or death without relapse. The data display important time-varying effects that can be captured by the highly adaptive lasso. In our simulations that are designed to imitate the data, we compare our methods to a similar approach based on random survival forests and to the discrete-time TMLE.

Targeted maximum likelihood estimation for causal inference in survival and competing risks analysis.

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.

New drug candidates for bipolar disorder-A nation-wide population-based study.

OBJECTIVE: Drug repurposing is an increasingly promising idea in many fields of medicine. We systematically used Danish nation-wide population-based registers to investigate whether continued use of non-aspirin non-steroidal anti-inflammatory drugs (NSAIDs), low-dose aspirin, high-dose aspirin, statins, allopurinol, and angiotensin agents decrease the rate of incident mania/bipolar disorder. METHODS: A nation-wide population-based longitudinal study using Poisson regression analyses including all persons in Denmark who purchased the exposure medication of interest and a random sample of 30% of the Danish population. The follow-up period comprised a 10 years period from 2005 to 2015. Two different outcome measures were included, (1) a diagnosis of mania/bipolar disorder at a psychiatric hospital contact as inpatient or outpatient and (2) a combined measure of a diagnosis of mania/bipolar disorder or initiation of lithium use. RESULTS: A total of 1,605,365 subjects were exposed to one of the six drugs of interest during the exposure period from 2005 to 2015, median age 57 years [quartiles: 43;69], and female proportion of 53.1%. Continued use of low-dose aspirin, statins, and angiotensin agents were associated with decreased rates of incident mania/bipolar disorder on both outcome measures. Continued uses of non-aspirin NSAIDs as well as high-dose aspirin were associated with an increased rate of incident bipolar disorder. There were no statistically significant associations for allopurinol. CONCLUSIONS: The study supports the potential of agents acting on inflammation and the stress response system in bipolar disorder and illustrates that population-based registers can be used to systematically identify drugs with repurposing potentials.