期刊
STATISTICS IN MEDICINE
卷 42, 期 5, 页码 603-618出版社
WILEY
DOI: 10.1002/sim.9612
关键词
causal inference; g-computation; marginal structural models; penalized splines
This paper investigates the estimation of the probability of a binary counterfactual outcome with a continuous covariate under monotonicity constraints. It focuses on studying out-of-hospital cardiac arrest patients and aims to estimate the counterfactual 30-day survival probability based on the ambulance response time and the presence of bystander cardiopulmonary resuscitation (CPR). The paper proposes a marginal structural model and B-splines to model the monotone relationship, and utilizes an auxiliary regression model for the observed 30-day survival probabilities to derive an estimating equation for the parameters of interest. The methods are demonstrated and compared with an unconstrained modeling approach using large-scale Danish registry data.
This paper deals with estimating the probability of a binary counterfactual outcome as a function of a continuous covariate under monotonicity constraints. We are motivated by the study of out-of-hospital cardiac arrest patients which aims to estimate the counterfactual 30-day survival probability if either all patients had received, or if none of the patients had received bystander cardiopulmonary resuscitation (CPR), as a function of the ambulance response time. It is natural to assume that the counterfactual 30-day survival probability cannot increase with increasing ambulance response time. We model the monotone relationship with a marginal structural model and B-splines. We then derive an estimating equation for the parameters of interest which however further relies on an auxiliary regression model for the observed 30-day survival probabilities. The predictions of the observed 30-day survival probabilities are used as pseudo-values for the unobserved counterfactual 30-day survival status. The methods are illustrated and contrasted with an unconstrained modeling approach in large-scale Danish registry data.
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