Marginal structural models with monotonicity constraints: A case study in out-of-hospital cardiac arrest patients
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Marginal structural models with monotonicity constraints : A case study in out-of-hospital cardiac arrest patients. / Starkopf, Liis; Rajan, Shahzleen; Lange, Theis; Gerds, Thomas Alexander.
In: Statistics in Medicine, Vol. 42, No. 5, 2023, p. 603-618.Research output: Contribution to journal › Journal article › Research › peer-review
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TY - JOUR
T1 - Marginal structural models with monotonicity constraints
T2 - A case study in out-of-hospital cardiac arrest patients
AU - Starkopf, Liis
AU - Rajan, Shahzleen
AU - Lange, Theis
AU - Gerds, Thomas Alexander
N1 - Publisher Copyright: © 2023 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd.
PY - 2023
Y1 - 2023
N2 - 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.
AB - 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.
KW - causal inference
KW - g-computation
KW - marginal structural models
KW - penalized splines
U2 - 10.1002/sim.9612
DO - 10.1002/sim.9612
M3 - Journal article
C2 - 36656059
AN - SCOPUS:85147016745
VL - 42
SP - 603
EP - 618
JO - Statistics in Medicine
JF - Statistics in Medicine
SN - 0277-6715
IS - 5
ER -
ID: 334808443