Editing and imputation

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  • Articles and reports: 12-001-X200800210756
    Description:

    In longitudinal surveys nonresponse often occurs in a pattern that is not monotone. We consider estimation of time-dependent means under the assumption that the nonresponse mechanism is last-value-dependent. Since the last value itself may be missing when nonresponse is nonmonotone, the nonresponse mechanism under consideration is nonignorable. We propose an imputation method by first deriving some regression imputation models according to the nonresponse mechanism and then applying nonparametric regression imputation. We assume that the longitudinal data follow a Markov chain with finite second-order moments. No other assumption is imposed on the joint distribution of longitudinal data and their nonresponse indicators. A bootstrap method is applied for variance estimation. Some simulation results and an example concerning the Current Employment Survey are presented.

    Release date: 2008-12-23

  • Articles and reports: 11-522-X200600110408
    Description:

    Despite advances that have improved the health of the United States population, disparities in health remain among various racial/ethnic and socio-economic groups. Common data sources for assessing the health of a population of interest include large-scale surveys that often pose questions requiring a self-report, such as, "Has a doctor or other health professional ever told you that you have health condition of interest?" Answers to such questions might not always reflect the true prevalences of health conditions (for example, if a respondent does not have access to a doctor or other health professional). Similarly, self-reported data on quantities such as height and weight might be subject to reporting errors. Such "measurement error" in health data could affect inferences about measures of health and health disparities. In this work, we fit measurement-error models to data from the National Health and Nutrition Examination Survey, which asks self-report questions during an interview component and also obtains physical measurements during an examination component. We then develop methods for using the fitted models to improve on analyses of self-reported data from another survey that does not include an examination component. The methods, which involve multiply imputing examination-based data values for the survey that has only self-reported data, are applied to the National Health Interview Survey in examples involving diabetes, hypertension, and obesity. Preliminary results suggest that the adjustments for measurement error can result in non-negligible changes in estimates of measures of health.

    Release date: 2008-03-17

  • Articles and reports: 11-522-X200600110442
    Description:

    The District of Columbia Healthy Outcomes of Pregnancy Education (DC-HOPE) project is a randomized trial funded by the National Institute of Child Health and Human Development to test the effectiveness of an integrated education and counseling intervention (INT) versus usual care (UC) to reduce four risk behaviors among pregnant women. Participants were interviewed at baseline and three additional time points. Multiple imputation (MI) was used to estimate data for missing interviews. MI was done twice: once with all data imputed simultaneously, and once with data for women in the INT and UC groups imputed separately. Analyses of both imputed data sets and the pre-imputation data are compared.

    Release date: 2008-03-17

  • Articles and reports: 12-001-X200700210493
    Description:

    In this paper, we study the problem of variance estimation for a ratio of two totals when marginal random hot deck imputation has been used to fill in missing data. We consider two approaches to inference. In the first approach, the validity of an imputation model is required. In the second approach, the validity of an imputation model is not required but response probabilities need to be estimated, in which case the validity of a nonresponse model is required. We derive variance estimators under two distinct frameworks: the customary two-phase framework and the reverse framework.

    Release date: 2008-01-03
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  • Articles and reports: 12-001-X200800210756
    Description:

    In longitudinal surveys nonresponse often occurs in a pattern that is not monotone. We consider estimation of time-dependent means under the assumption that the nonresponse mechanism is last-value-dependent. Since the last value itself may be missing when nonresponse is nonmonotone, the nonresponse mechanism under consideration is nonignorable. We propose an imputation method by first deriving some regression imputation models according to the nonresponse mechanism and then applying nonparametric regression imputation. We assume that the longitudinal data follow a Markov chain with finite second-order moments. No other assumption is imposed on the joint distribution of longitudinal data and their nonresponse indicators. A bootstrap method is applied for variance estimation. Some simulation results and an example concerning the Current Employment Survey are presented.

    Release date: 2008-12-23

  • Articles and reports: 11-522-X200600110408
    Description:

    Despite advances that have improved the health of the United States population, disparities in health remain among various racial/ethnic and socio-economic groups. Common data sources for assessing the health of a population of interest include large-scale surveys that often pose questions requiring a self-report, such as, "Has a doctor or other health professional ever told you that you have health condition of interest?" Answers to such questions might not always reflect the true prevalences of health conditions (for example, if a respondent does not have access to a doctor or other health professional). Similarly, self-reported data on quantities such as height and weight might be subject to reporting errors. Such "measurement error" in health data could affect inferences about measures of health and health disparities. In this work, we fit measurement-error models to data from the National Health and Nutrition Examination Survey, which asks self-report questions during an interview component and also obtains physical measurements during an examination component. We then develop methods for using the fitted models to improve on analyses of self-reported data from another survey that does not include an examination component. The methods, which involve multiply imputing examination-based data values for the survey that has only self-reported data, are applied to the National Health Interview Survey in examples involving diabetes, hypertension, and obesity. Preliminary results suggest that the adjustments for measurement error can result in non-negligible changes in estimates of measures of health.

    Release date: 2008-03-17

  • Articles and reports: 11-522-X200600110442
    Description:

    The District of Columbia Healthy Outcomes of Pregnancy Education (DC-HOPE) project is a randomized trial funded by the National Institute of Child Health and Human Development to test the effectiveness of an integrated education and counseling intervention (INT) versus usual care (UC) to reduce four risk behaviors among pregnant women. Participants were interviewed at baseline and three additional time points. Multiple imputation (MI) was used to estimate data for missing interviews. MI was done twice: once with all data imputed simultaneously, and once with data for women in the INT and UC groups imputed separately. Analyses of both imputed data sets and the pre-imputation data are compared.

    Release date: 2008-03-17

  • Articles and reports: 12-001-X200700210493
    Description:

    In this paper, we study the problem of variance estimation for a ratio of two totals when marginal random hot deck imputation has been used to fill in missing data. We consider two approaches to inference. In the first approach, the validity of an imputation model is required. In the second approach, the validity of an imputation model is not required but response probabilities need to be estimated, in which case the validity of a nonresponse model is required. We derive variance estimators under two distinct frameworks: the customary two-phase framework and the reverse framework.

    Release date: 2008-01-03
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