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Douglas K Owens - One of the best experts on this subject based on the ideXlab platform.

  • missing data strategies for time varying confounders in comparative effectiveness studies of non missing time varying exposures and right censored outcomes
    Statistics in Medicine, 2019
    Co-Authors: Manisha Desai, Maria E Montezrath, Kristopher Kapphahn, Vilija R Joyce, Maya B Mathur, Ariadna Garcia, Natasha Purington, Douglas K Owens
    Abstract:

    : The treatment of missing data in comparative effectiveness studies with right-censored outcomes and time-varying covariates is challenging because of the multilevel structure of the data. In particular, the performance of an accessible method like multiple Imputation (MI) under an Imputation model that ignores the multilevel structure is unknown and has not been compared to complete-case (CC) and Single Imputation methods that are most commonly applied in this context. Through an extensive simulation study, we compared statistical properties among CC analysis, last value carried forward, mean Imputation, the use of missing indicators, and MI-based approaches with and without auxiliary variables under an extended Cox model when the interest lies in characterizing relationships between non-missing time-varying exposures and right-censored outcomes. MI demonstrated favorable properties under a moderate missing-at-random condition (absolute bias <0.1) and outperformed CC and Single Imputation methods, even when the MI method did not account for correlated observations in the Imputation model. The performance of MI decreased with increasing complexity such as when the missing data mechanism involved the exposure of interest, but was still preferred over other methods considered and performed well in the presence of strong auxiliary variables. We recommend considering MI that ignores the multilevel structure in the Imputation model when data are missing in a time-varying confounder, incorporating variables associated with missingness in the MI models as well as conducting sensitivity analyses across plausible assumptions.

  • Missing data strategies for time-varying confounders in comparative effectiveness studies of non-missing time-varying exposures and right-censored outcomes.
    Statistics in medicine, 2019
    Co-Authors: Manisha Desai, Kristopher Kapphahn, Vilija R Joyce, Maya B Mathur, Ariadna Garcia, Natasha Purington, Maria E. Montez-rath, Douglas K Owens
    Abstract:

    The treatment of missing data in comparative effectiveness studies with right-censored outcomes and time-varying covariates is challenging because of the multilevel structure of the data. In particular, the performance of an accessible method like multiple Imputation (MI) under an Imputation model that ignores the multilevel structure is unknown and has not been compared to complete-case (CC) and Single Imputation methods that are most commonly applied in this context. Through an extensive simulation study, we compared statistical properties among CC analysis, last value carried forward, mean Imputation, the use of missing indicators, and MI-based approaches with and without auxiliary variables under an extended Cox model when the interest lies in characterizing relationships between non-missing time-varying exposures and right-censored outcomes. MI demonstrated favorable properties under a moderate missing-at-random condition (absolute bias

Manisha Desai - One of the best experts on this subject based on the ideXlab platform.

  • missing data strategies for time varying confounders in comparative effectiveness studies of non missing time varying exposures and right censored outcomes
    Statistics in Medicine, 2019
    Co-Authors: Manisha Desai, Maria E Montezrath, Kristopher Kapphahn, Vilija R Joyce, Maya B Mathur, Ariadna Garcia, Natasha Purington, Douglas K Owens
    Abstract:

    : The treatment of missing data in comparative effectiveness studies with right-censored outcomes and time-varying covariates is challenging because of the multilevel structure of the data. In particular, the performance of an accessible method like multiple Imputation (MI) under an Imputation model that ignores the multilevel structure is unknown and has not been compared to complete-case (CC) and Single Imputation methods that are most commonly applied in this context. Through an extensive simulation study, we compared statistical properties among CC analysis, last value carried forward, mean Imputation, the use of missing indicators, and MI-based approaches with and without auxiliary variables under an extended Cox model when the interest lies in characterizing relationships between non-missing time-varying exposures and right-censored outcomes. MI demonstrated favorable properties under a moderate missing-at-random condition (absolute bias <0.1) and outperformed CC and Single Imputation methods, even when the MI method did not account for correlated observations in the Imputation model. The performance of MI decreased with increasing complexity such as when the missing data mechanism involved the exposure of interest, but was still preferred over other methods considered and performed well in the presence of strong auxiliary variables. We recommend considering MI that ignores the multilevel structure in the Imputation model when data are missing in a time-varying confounder, incorporating variables associated with missingness in the MI models as well as conducting sensitivity analyses across plausible assumptions.

  • Missing data strategies for time-varying confounders in comparative effectiveness studies of non-missing time-varying exposures and right-censored outcomes.
    Statistics in medicine, 2019
    Co-Authors: Manisha Desai, Kristopher Kapphahn, Vilija R Joyce, Maya B Mathur, Ariadna Garcia, Natasha Purington, Maria E. Montez-rath, Douglas K Owens
    Abstract:

    The treatment of missing data in comparative effectiveness studies with right-censored outcomes and time-varying covariates is challenging because of the multilevel structure of the data. In particular, the performance of an accessible method like multiple Imputation (MI) under an Imputation model that ignores the multilevel structure is unknown and has not been compared to complete-case (CC) and Single Imputation methods that are most commonly applied in this context. Through an extensive simulation study, we compared statistical properties among CC analysis, last value carried forward, mean Imputation, the use of missing indicators, and MI-based approaches with and without auxiliary variables under an extended Cox model when the interest lies in characterizing relationships between non-missing time-varying exposures and right-censored outcomes. MI demonstrated favorable properties under a moderate missing-at-random condition (absolute bias

David Haziza - One of the best experts on this subject based on the ideXlab platform.

  • Multiply robust bootstrap variance estimation in the presence of singly imputed survey data
    Journal of Survey Statistics and Methodology, 2020
    Co-Authors: Sixia Chen, David Haziza, Zeinab Mashreghi
    Abstract:

    Abstract Item nonresponse in surveys is usually dealt with through Single Imputation. It is well known that treating the imputed values as if they were observed values may lead to serious underestimation of the variance of point estimators. In this article, we propose three pseudo-population bootstrap schemes for estimating the variance of imputed estimators obtained after applying a multiply robust Imputation procedure. The proposed procedures can handle large sampling fractions and enjoy the multiple robustness property. Results from a simulation study suggest that the proposed methods perform well in terms of relative bias and coverage probability, for both population totals and quantiles.

  • Recent Developments in Dealing with Item Non‐response in Surveys: A Critical Review
    International Statistical Review, 2018
    Co-Authors: Sixia Chen, David Haziza
    Abstract:

    The most common way for treating item non‐response in surveys is to construct one or more replacement values to fill in for a missing value. This process is known as Imputation. We distinguish Single from multiple Imputation. Single Imputation consists of replacing a missing value by a Single replacement value, whereas multiple Imputation uses two or more replacement values. This article reviews various Imputation procedures used in National Statistical Offices as well as the properties of point and variance estimators in the presence of imputed survey data. It also provides the reader with newer developments in the field.

  • Multiply robust Imputation procedures for zero-inflated distributions in surveys
    METRON, 2017
    Co-Authors: Sixia Chen, David Haziza
    Abstract:

    Item nonresponse in surveys is usually treated by some form of Single Imputation. In practice, the survey variable subject to missing values may exhibit a large number of zero-valued observations. In this paper, we propose multiply robust Imputation procedures for treating this type of variable. Our procedures may be based on multiple Imputation models and/or multiple nonresponse models. An Imputation procedure is said to be multiply robust if the resulting estimator is consistent when all models but one are misspecified. The variance of the imputed estimators is estimated through a generalized jackknife variance estimation procedure. Results from a simulation study suggest that the proposed procedures perform well in terms of bias, efficiency and coverage rate.

  • Doubly robust inference for the distribution function in the presence of missing survey data
    Scandinavian Journal of Statistics, 2015
    Co-Authors: Hélène Boistard, Guillaume Chauvet, David Haziza
    Abstract:

    Item non-response in surveys occurs when some, but not all, variables are missing. Unadjusted estimators tend to exhibit some bias, called the non-response bias, if the respondents differ from the non-respondents with respect to the study variables. In this paper, we focus on item non-response, which is usually treated by some form of Single Imputation. We examine the properties of doubly robust Imputation procedures, which are those that lead to an estimator that remains consistent if either the outcome variable or the non-response mechanism is adequately modelled. We establish the double robustness property of the imputed estimator of the finite population distribution function under random hot-deck Imputation within classes. We also discuss the links between our approach and that of Chambers and Dunstan. The results of a simulation study support our findings.

Thomas Nittner - One of the best experts on this subject based on the ideXlab platform.

  • The additive model affected by missing completely at random in the covariate
    Computational Statistics, 2004
    Co-Authors: Thomas Nittner
    Abstract:

    The main purpose of this paper is a comparison of several Imputation methods within the simple additive model ty =f(x) + e where the independent variable X is affected by missing completely at random. Besides the well-known complete case analysis, mean Imputation plus random noise, Single Imputation and two kinds of nearest neighbor Imputations are used. A short introduction to the model, the missing mechanism, the inference, the Imputation methods and their implementation is followed by the main focus—the simulation experiment. The methods are compared within the experiment based on the sample mean squared error, estimated variances and estimated biases of f(x) at the knots.

  • missing at random mar in nonparametric regression a simulation experiment
    Statistical Methods and Applications, 2003
    Co-Authors: Thomas Nittner
    Abstract:

    The additive model $y = s(x) + \epsilon$ is considered when some observations on x are missing at random but corresponding observations on y are available. Especially for this model, missing at random is an interesting case because the complete case analysis is expected to be no more suitable. A simulation experiment is reported and the different methods are compared based on their superiority with respect to the sample mean squared error. Some focus is also given on the sample variance and the estimated bias. In detail, the complete case analysis, a kind of stochastic mean Imputation, a Single Imputation and the nearest neighbor Imputation are discussed.

  • The Additive Model with Missing Values in the Independent Variable - Theory and Simulation
    2002
    Co-Authors: Thomas Nittner
    Abstract:

    After a short introduction of the model, the missing mechanism and the method of inference some Imputation procedures are introduced with special focus on the simulation experiment. Within this experiment, the simple additive model y = f(x) + e is assumed to have missing values in the independent variable according to MCAR. Besides the well-known complete case analysis, mean Imputation plus random noise, a Single Imputation and two ways of nearest neighbor Imputation are used. These methods are compared within a simulation experiment based on the average mean square error, variances and biases of \hat{f}(x) at the knots.

  • Linear Regression Models with Incomplete Categorical Covariates
    Computational Statistics, 2002
    Co-Authors: Helge Toutenburg, Thomas Nittner
    Abstract:

    We present three different methods based on the conditional mean Imputation when binary explanatory variables are incomplete. Apart from the Single Imputation and multiple Imputation especially the so-called pi Imputation is presented as a new procedure. Seven procedures are compared in a simulation experiment when missing data are confined to one independent binary variable: complete case analysis, zero order regression, categorical zero order regression, pi Imputation, Single Imputation, multiple Imputation, modified first order regression. After a brief theoretical description of the simulation experiment, MSE-ratio, variance and bias are used to illustrate differences within and between the approaches.

  • The Classical Linear Regression Model with one Incomplete Binary Variable
    1999
    Co-Authors: Helge Toutenburg, Thomas Nittner
    Abstract:

    We present three different methods based on the conditional mean Imputation when binary explanatory variables are incomplete. Apart from the Single Imputation and multiple Imputation especially the so-called pi Imputation is presented as a new procedure. Seven procedures are compared in a simulation experiment when missing data are confined to one independent binary variable: complete case analysis, zero order regression, categorical zero order regression, pi Imputation, Single Imputation, multiple Imputation, modified first order regression. After a brief theoretical description of the simulation experiment, MSE-ratio, variance and bias are used to illustrate differences within and between the approaches.

Kristopher Kapphahn - One of the best experts on this subject based on the ideXlab platform.

  • missing data strategies for time varying confounders in comparative effectiveness studies of non missing time varying exposures and right censored outcomes
    Statistics in Medicine, 2019
    Co-Authors: Manisha Desai, Maria E Montezrath, Kristopher Kapphahn, Vilija R Joyce, Maya B Mathur, Ariadna Garcia, Natasha Purington, Douglas K Owens
    Abstract:

    : The treatment of missing data in comparative effectiveness studies with right-censored outcomes and time-varying covariates is challenging because of the multilevel structure of the data. In particular, the performance of an accessible method like multiple Imputation (MI) under an Imputation model that ignores the multilevel structure is unknown and has not been compared to complete-case (CC) and Single Imputation methods that are most commonly applied in this context. Through an extensive simulation study, we compared statistical properties among CC analysis, last value carried forward, mean Imputation, the use of missing indicators, and MI-based approaches with and without auxiliary variables under an extended Cox model when the interest lies in characterizing relationships between non-missing time-varying exposures and right-censored outcomes. MI demonstrated favorable properties under a moderate missing-at-random condition (absolute bias <0.1) and outperformed CC and Single Imputation methods, even when the MI method did not account for correlated observations in the Imputation model. The performance of MI decreased with increasing complexity such as when the missing data mechanism involved the exposure of interest, but was still preferred over other methods considered and performed well in the presence of strong auxiliary variables. We recommend considering MI that ignores the multilevel structure in the Imputation model when data are missing in a time-varying confounder, incorporating variables associated with missingness in the MI models as well as conducting sensitivity analyses across plausible assumptions.

  • Missing data strategies for time-varying confounders in comparative effectiveness studies of non-missing time-varying exposures and right-censored outcomes.
    Statistics in medicine, 2019
    Co-Authors: Manisha Desai, Kristopher Kapphahn, Vilija R Joyce, Maya B Mathur, Ariadna Garcia, Natasha Purington, Maria E. Montez-rath, Douglas K Owens
    Abstract:

    The treatment of missing data in comparative effectiveness studies with right-censored outcomes and time-varying covariates is challenging because of the multilevel structure of the data. In particular, the performance of an accessible method like multiple Imputation (MI) under an Imputation model that ignores the multilevel structure is unknown and has not been compared to complete-case (CC) and Single Imputation methods that are most commonly applied in this context. Through an extensive simulation study, we compared statistical properties among CC analysis, last value carried forward, mean Imputation, the use of missing indicators, and MI-based approaches with and without auxiliary variables under an extended Cox model when the interest lies in characterizing relationships between non-missing time-varying exposures and right-censored outcomes. MI demonstrated favorable properties under a moderate missing-at-random condition (absolute bias