Dataset Information

GMM nonparametric correction methods for logistic regression with error contaminated covariates and partially observed instrumental variables.

ABSTRACT: We consider logistic regression with covariate measurement error. Most existing approaches require certain replicates of the error-contaminated covariates, which may not be available in the data. We propose generalized methods of moments (GMM) non-parametric correction approaches that use instrumental variables observed in a calibration subsample. The instrumental variable is related to the underlying true covariates through a general nonparametric model, and the probability of being in the calibration subsample may depend on the observed variables. We first take a simple approach adopting the inverse selection probability weighting technique using the calibration subsample. We then improve the approach based on the GMM using the whole sample. The asymptotic properties are derived and the finite sample performance is evaluated through simulation studies and an application to a real data set.

SUBMITTER: Song X

PROVIDER: S-EPMC7517614 | biostudies-literature | 2019 Sep

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

GMM nonparametric correction methods for logistic regression with error contaminated covariates and partially observed instrumental variables.

Song Xiao X Wang Ching-Yun CY

Scandinavian journal of statistics, theory and applications 20181127 3

We consider logistic regression with covariate measurement error. Most existing approaches require certain replicates of the error-contaminated covariates, which may not be available in the data. We propose generalized methods of moments (GMM) non-parametric correction approaches that use instrumental variables observed in a calibration subsample. The instrumental variable is related to the underlying true covariates through a general nonparametric model, and the probability of being in the cali ...[more]

PMID: 32982012

Similar Datasets

Project description:Inverse probability of treatment weighting is a popular propensity score-based approach to estimate marginal treatment effects in observational studies at risk of confounding bias. A major issue when estimating the propensity score is the presence of partially observed covariates. Multiple imputation is a natural approach to handle missing data on covariates: covariates are imputed and a propensity score analysis is performed in each imputed dataset to estimate the treatment effect. The treatment effect estimates from each imputed dataset are then combined to obtain an overall estimate. We call this method MIte. However, an alternative approach has been proposed, in which the propensity scores are combined across the imputed datasets (MIps). Therefore, there are remaining uncertainties about how to implement multiple imputation for propensity score analysis: (a) should we apply Rubin's rules to the inverse probability of treatment weighting treatment effect estimates or to the propensity score estimates themselves? (b) does the outcome have to be included in the imputation model? (c) how should we estimate the variance of the inverse probability of treatment weighting estimator after multiple imputation? We studied the consistency and balancing properties of the MIte and MIps estimators and performed a simulation study to empirically assess their performance for the analysis of a binary outcome. We also compared the performance of these methods to complete case analysis and the missingness pattern approach, which uses a different propensity score model for each pattern of missingness, and a third multiple imputation approach in which the propensity score parameters are combined rather than the propensity scores themselves (MIpar). Under a missing at random mechanism, complete case and missingness pattern analyses were biased in most cases for estimating the marginal treatment effect, whereas multiple imputation approaches were approximately unbiased as long as the outcome was included in the imputation model. Only MIte was unbiased in all the studied scenarios and Rubin's rules provided good variance estimates for MIte. The propensity score estimated in the MIte approach showed good balancing properties. In conclusion, when using multiple imputation in the inverse probability of treatment weighting context, MIte with the outcome included in the imputation model is the preferred approach.

Dataset Information

GMM nonparametric correction methods for logistic regression with error contaminated covariates and partially observed instrumental variables.

Publications

GMM nonparametric correction methods for logistic regression with error contaminated covariates and partially observed instrumental variables.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets