arXiv subjects

Shaun R Seaman

Publications and source records attributed to Shaun R Seaman.

3 recordsLinked to original sources

arXiv2026

Doubly robust integration of nonprobability and probability survey data

Doubly robust (DR) estimators of the population mean of an outcome have been proposed for integrating outcome and covariate data from a nonprobability sample with covariate data from a probability survey. These estimators combine inverse probability weighting with mass imputation. We review these DR estimators and extend them to allow estimation of a domain (subpopulation) mean, possibly using data from individuals outside the domain to improve estimation when the domain is small. For situations where an outcome is observed in both samples, we describe estimators that efficiently combine one of these DR estimators with a Horvitz-Thompson or Hajek estimator that uses only the probability survey data. We investigate the asymptotic relative efficiency of these combined estimators compared to their two component estimators, and carry out a simulation study to assess relative efficiency in finite samples. The relative efficiency depends on the ratio of the variances of the two component estimators and on how predictive the covariates are of the outcome. We illustrate the use of all the estimators by analysing data from the Natsal-3 probability survey and four contemporaneous online nonprobability surveys. Our extensions will enable greater uptake and practical use of these combination methods in the survey statistics field.

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arXiv2025

Simulating from marginal structural models for hazards, cause-specific hazards and subdistribution hazards using general copulas

Seaman and Keogh (Biometrical Journal 2024) proposed a method for simulating data compatible with a marginal structural model (MSM) for the hazard of a survival time outcome. In this short report, I propose two extensions of this method. First, Seaman and Keogh favoured the use of a Gaussian copula, because this enables the function of the confounder history through which the hazard of failure depends on confounders to be interpreted as a risk score. Here, I describe how this interpretation can be preserved even when a non-Gaussian copula is used. Second, I extend Seaman and Keogh's method to allow simulation of data compatible with a MSM for a cause-specific or subdistribution hazard of failure in the presence of a competing event.

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arXiv2023

Simulating data from marginal structural models for a survival time outcome

Marginal structural models (MSMs) are often used to estimate causal effects of treatments on survival time outcomes from observational data when time-dependent confounding may be present. They can be fitted using, e.g., inverse probability of treatment weighting (IPTW). It is important to evaluate the performance of statistical methods in different scenarios, and simulation studies are a key tool for such evaluations. In such simulation studies, it is common to generate data in such a way that the model of interest is correctly specified, but this is not always straightforward when the model of interest is for potential outcomes, as is an MSM. Methods have been proposed for simulating from MSMs for a survival outcome, but these methods impose restrictions on the data-generating mechanism. Here we propose a method that overcomes these restrictions. The MSM can be a marginal structural logistic model for a discrete survival time or a Cox or additive hazards MSM for a continuous survival time. The hazard of the potential survival time can be conditional on baseline covariates, and the treatment variable can be discrete or continuous. We illustrate the use of the proposed simulation algorithm by carrying out a brief simulation study. This study compares the coverage of confidence intervals calculated in two different ways for causal effect estimates obtained by fitting an MSM via IPTW.

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