Comparison of causal effect estimators under exposure misclassification

Babanezhad, Manoochehr; Vansteelandt, Stijn; Goetghebeur, Els

doi:10.1016/j.jspi.2009.11.015

Cited by 24 publications

(15 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We consider the case where ignorability and the probability of response depend on the error-free covariates. In addition to this work, there is literature on related issues such as inverse probability-weighted estimation with missing regressors (Robins et al, 1994; Tan, 2011), error in the treatment or exposure measure (Babanezhad et al, 2010; and a 2010 unpublished report from The Pennsylvania State University by J. Kang and J. Schafer), and estimation of propensity scores when covariates are measured with error (D’Agostino & Rubin, 2000; Raykov, 2012). Pearl (2010) developed a general framework for causal inference in the presence of error-prone covariates.…”

Section: Introductionmentioning

confidence: 99%

Inverse probability weighting with error-prone covariates

2013

View full text Add to dashboard Cite

Summary Inverse probability-weighted estimators are widely used in applications where data are missing due to nonresponse or censoring and in the estimation of causal effects from observational studies. Current estimators rely on ignorability assumptions for response indicators or treatment assignment and outcomes being conditional on observed covariates which are assumed to be measured without error. However, measurement error is common for the variables collected in many applications. For example, in studies of educational interventions, student achievement as measured by standardized tests is almost always used as the key covariate for removing hidden biases, but standardized test scores may have substantial measurement errors. We provide several expressions for a weighting function that can yield a consistent estimator for population means using incomplete data and covariates measured with error. We propose a method to estimate the weighting function from data. The results of a simulation study show that the estimator is consistent and has no bias and small variance.

show abstract

Section: Introductionmentioning

confidence: 99%

Inverse probability weighting with error-prone covariates

2013

View full text Add to dashboard Cite

show abstract

“…The proposed method can be adapted to handle the situation where all participants have the error‐prone ‘proxy’ exposure measure as a special case ( R = 0 for all). A recent simulation study demonstrating the biasing effects of exposure misclassification on propensity score estimators supports the need for such methods .…”

Section: Discussionmentioning

confidence: 96%

Statistical analysis with missing exposure data measured by proxy respondents: a misclassification problem within a missing‐data problem

Shardell

Hicks

2014

Statistics in Medicine

View full text Add to dashboard Cite

In studies of older adults, researchers often recruit proxy respondents, such as relatives or caregivers, when study participants cannot provide self-reports (e.g., due to illness). Proxies are usually only sought to report on behalf of participants with missing self-reports; thus, either a participant self-report or proxy report, but not both, is available for each participant. Furthermore, the missing-data mechanism for participant self-reports is not identifiable and may be nonignorable. When exposures are binary and participant self-reports are conceptualized as the gold standard, substituting error-prone proxy reports for missing participant self-reports may produce biased estimates of outcome means. Researchers can handle this data structure by treating the problem as one of misclassification within the stratum of participants with missing self-reports. Most methods for addressing exposure misclassification require validation data, replicate data, or an assumption of nondifferential misclassification; other methods may result in an exposure misclassification model that is incompatible with the analysis model. We propose a model that makes none of the aforementioned requirements and still preserves model compatibility. Two user-specified tuning parameters encode the exposure misclassification model. Two proposed approaches estimate outcome means standardized for (potentially) high-dimensional covariates using multiple imputation followed by propensity-score methods. The first method is parametric and uses maximum likelihood to estimate the exposure misclassification model (i.e., the imputation model) and the propensity score model (i.e., the analysis model); the second method is non-parametric and uses boosted classification and regression trees to estimate both models. We apply both methods to a study of elderly hip-fracture patients.

show abstract

“…[10][11][12][13][14] Many correction methods for dealing with measurement error have been developed for regression settings, where describing the association relationship is the focus. There has been little work on causal inference with measurement error, although this area has recently received increasing attention with some methods available to handle error-prone covariates, [15][16][17] misclassified treatment, 18,19 and mismeasured outcomes. 20,21 Measurement error and misclassification arise ubiquitously in applications and present a considerable challenge to statistical inference.…”

Section: Introductionmentioning

confidence: 99%

Weighted causal inference methods with mismeasured covariates and misclassified outcomes

Shu

2019

Statistics in Medicine

View full text Add to dashboard Cite

Inverse probability weighting (IPW) estimation has been widely used in causal inference. Its validity relies on the important condition that the variables are precisely measured. This condition, however, is often violated, which distorts the IPW method and thus yields biased results. In this paper, we study the IPW estimation of average treatment effects for settings with mismeasured covariates and misclassified outcomes. We develop estimation methods to correct for measurement error and misclassification effects simultaneously. Our discussion covers a broad scope of treatment models, including typically assumed logistic regression models and general treatment assignment mechanisms. Satisfactory performance of the proposed methods is demonstrated by extensive numerical studies.

show abstract

Comparison of causal effect estimators under exposure misclassification

Cited by 24 publications

References 31 publications

Inverse probability weighting with error-prone covariates

Inverse probability weighting with error-prone covariates

Statistical analysis with missing exposure data measured by proxy respondents: a misclassification problem within a missing‐data problem

Weighted causal inference methods with mismeasured covariates and misclassified outcomes

Contact Info

Product

Resources

About