• Corpus ID: 237434340

Estimating nuisance parameters often reduces the variance (with consistent variance estimation)

@inproceedings{Lok2021EstimatingNP,
  title={Estimating nuisance parameters often reduces the variance (with consistent variance estimation)},
  author={Judith J Lok},
  year={2021}
}
  • J. Lok
  • Published 6 September 2021
  • Mathematics
In many applications, to estimate a parameter or quantity of interest ψ∗, a finite-dimensional nuisance parameter θ∗ is estimated first. For example, many estimators in causal inference depend on the estimated propensity score: the probability of (possibly time-dependent) treatment given the past. These nuisance parameters θ∗ are often estimated in a first step, which can affect the variance of the estimator for ψ∗. θ∗ is often estimated by maximum (partial) likelihood. Inverse Probability… 

References

SHOWING 1-10 OF 23 REFERENCES
Estimation of Regression Coefficients When Some Regressors are not Always Observed
Abstract In applied problems it is common to specify a model for the conditional mean of a response given a set of regressors. A subset of the regressors may be missing for some study subjects either
Doubly robust estimation in missing data and causal inference models.
TLDR
The results of simulation studies are presented which demonstrate that the finite sample performance of DR estimators is as impressive as theory would predict and the proposed method is applied to a cardiovascular clinical trial.
Variance estimation in inverse probability weighted Cox models
TLDR
A new variance estimator is proposed that combines the estimation procedures for the hazard ratio and weights using stacked estimating equations, with additional adjustments for the sum of non-independent and identically distributed terms in a Cox partial likelihood score equation.
Semiparametric regression estimation in the presence of dependent censoring
SUMMARY We propose a semiparametric estimation procedure for estimating the regression of an outcome Y, measured at the end of a fixed follow-up period, on baseline explanatory variables X, measured
Variance estimation when using inverse probability of treatment weighting (IPTW) with survival analysis
  • P. Austin
  • Mathematics
    Statistics in medicine
  • 2016
TLDR
An extensive set of Monte Carlo simulations to examine different methods of variance estimation when using a weighted Cox proportional hazards model to estimate the effect of treatment found that the use of a bootstrap estimator resulted in approximately correct estimates of standard errors and confidence intervals with the correct coverage rates.
Statistical modeling of causal effects in continuous time
This article studies the estimation of the causal effect of a time-varying treatment on time-to-an-event or on some other continuously distributed outcome. The paper applies to the situation where
The central role of the propensity score in observational studies for causal effects
Abstract : The results of observational studies are often disputed because of nonrandom treatment assignment. For example, patients at greater risk may be overrepresented in some treatment group.
Stratification and weighting via the propensity score in estimation of causal treatment effects: a comparative study.
Estimation of treatment effects with causal interpretation from observational data is complicated because exposure to treatment may be confounded with subject characteristics. The propensity score,
Marginal Structural Models and Causal Inference in Epidemiology
In observational studies with exposures or treatments that vary over time, standard approaches for adjustment of confounding are biased when there exist time-dependent confounders that are also
Marginal structural models to estimate the causal effect of zidovudine on the survival of HIV-positive men.
TLDR
The marginal structural Cox proportional hazards model is described and used to estimate the causal effect of zidovudine on the survival of human immunodeficiency virus-positive men participating in the Multicenter AIDS Cohort Study.
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