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   Articles with "missing covariates" as a keyword



Robust best linear weighted estimator with missing covariates in survival analysis

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recommendations! Published in 2024 at "Statistics in Medicine"

DOI: 10.1002/sim.10044

Abstract: Missing data in covariates can result in biased estimates and loss of power to detect associations. We consider Cox regression in which some covariates are subject to missing. The inverse probability weighted approach is often… read more here.

Keywords: missing covariates; robust best; weighted estimator; best linear ... See more keywords

Model validation and influence diagnostics for regression models with missing covariates.

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recommendations! Published in 2018 at "Statistics in medicine"

DOI: 10.1002/sim.7584

Abstract: Missing covariate values are prevalent in regression applications. While an array of methods have been developed for estimating parameters in regression models with missing covariate data for a variety of response types, minimal focus has… read more here.

Keywords: regression; validation; regression models; missing covariates ... See more keywords

Strategies for imputing missing covariates in accelerated failure time models.

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recommendations! Published in 2018 at "Statistics in medicine"

DOI: 10.1002/sim.7809

Abstract: Missing covariates often occur in biomedical studies with survival outcomes. Multiple imputation via chained equations (MICE) is a semi-parametric and flexible approach that imputes multivariate data by a series of conditional models, one for each… read more here.

Keywords: time; missing covariates; accelerated failure; time models ... See more keywords

Testing for parametric component of partially linear models with missing covariates

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recommendations! Published in 2019 at "Statistical Papers"

DOI: 10.1007/s00362-016-0848-6

Abstract: This paper considers the testing problem of partially linear models with missing covariates. The inverse probability weighted restricted estimator for the parametric component under linear constraint is derived and proven to share asymptotically normal distribution.… read more here.

Keywords: parametric component; partially linear; linear models; missing covariates ... See more keywords

Estimation in the Semiparametric Accelerated Failure Time Model With Missing Covariates: Improving Efficiency Through Augmentation

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recommendations! Published in 2017 at "Journal of the American Statistical Association"

DOI: 10.1080/01621459.2016.1205500

Abstract: ABSTRACT This article considers linear regression with missing covariates and a right censored outcome. We first consider a general two-phase outcome sampling design, where full covariate information is only ascertained for subjects in phase two… read more here.

Keywords: time; missing covariates; augmented estimators; model ... See more keywords

Augmented inverse probability weighted estimation and prediction for cause-specific proportional hazards regression with missing covariates

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recommendations! Published in 2024 at "Statistics"

DOI: 10.1080/02331888.2024.2343925

Abstract: This paper describes estimation of the regression parameters and prediction of the cumulative incidence functions under the cause-specific proportional hazards model when some of covariates are not fully observed. Assuming that missingness mechanism is missing… read more here.

Keywords: missing covariates; proportional hazards; regression; specific proportional ... See more keywords

Imputation of Missing Covariates in Randomized Controlled Trials with Continuous Outcomes: Simple, Unbiased and Efficient Methods

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recommendations! Published in 2022 at "Journal of Biopharmaceutical Statistics"

DOI: 10.1080/10543406.2021.2011898

Abstract: ABSTRACT The literature on dealing with missing covariates in nonrandomized studies advocates the use of sophisticated methods like multiple imputation (MI) and maximum likelihood (ML)-based approaches over simple methods. However, these methods are not necessarily… read more here.

Keywords: simple methods; imputation missing; randomized controlled; missing covariates ... See more keywords

Changepoint inference in the presence of missing covariates for principal surrogate evaluation in vaccine trials

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recommendations! Published in 2020 at "Biometrika"

DOI: 10.1093/biomet/asaa100

Abstract: We consider the use of threshold-based regression models to evaluate immune response biomarkers as principal surrogate markers of a vaccine’s protective effect. Threshold-based regression models, which allow the relationship between a clinical outcome and a… read more here.

Keywords: vaccine efficacy; missing covariates; principal surrogate; vaccine ... See more keywords

Non-parametric approach for frequentist multiple imputation in survival analysis with missing covariates

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recommendations! Published in 2021 at "Statistical Methods in Medical Research"

DOI: 10.1177/09622802211011197

Abstract: In clinical and epidemiological studies using survival analysis, some explanatory variables are often missing. When this occurs, multiple imputation (MI) is frequently used in practice. In many cases, simple parametric imputation models are routinely adopted… read more here.

Keywords: imputation; multiple imputation; missing covariates; survival analysis ... See more keywords