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- Articles and reports: 11-522-X200600110408Description:
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: 12-001-X20060029548Description:
The theory of multiple imputation for missing data requires that imputations be made conditional on the sampling design. However, most standard software packages for performing model-based multiple imputation assume simple random samples, leading many practitioners not to account for complex sample design features, such as stratification and clustering, in their imputations. Theory predicts that analyses of such multiply-imputed data sets can yield biased estimates from the design-based perspective. In this article, we illustrate through simulation that (i) the bias can be severe when the design features are related to the survey variables of interest, and (ii) the bias can be reduced by controlling for the design features in the imputation models. The simulations also illustrate that conditioning on irrelevant design features in the imputation models can yield conservative inferences, provided that the models include other relevant predictors. These results suggest a prescription for imputers: the safest course of action is to include design variables in the specification of imputation models. Using real data, we demonstrate a simple approach for incorporating complex design features that can be used with some of the standard software packages for creating multiple imputations.
Release date: 2006-12-21 - Articles and reports: 12-001-X20060029555Description:
Researchers and policy makers often use data from nationally representative probability sample surveys. The number of topics covered by such surveys, and hence the amount of interviewing time involved, have typically increased over the years, resulting in increased costs and respondent burden. A potential solution to this problem is to carefully form subsets of the items in a survey and administer one such subset to each respondent. Designs of this type are called "split-questionnaire" designs or "matrix sampling" designs. The administration of only a subset of the survey items to each respondent in a matrix sampling design creates what can be considered missing data. Multiple imputation (Rubin 1987), a general-purpose approach developed for handling data with missing values, is appealing for the analysis of data from a matrix sample, because once the multiple imputations are created, data analysts can apply standard methods for analyzing complete data from a sample survey. This paper develops and evaluates a method for creating matrix sampling forms, each form containing a subset of items to be administered to randomly selected respondents. The method can be applied in complex settings, including situations in which skip patterns are present. Forms are created in such a way that each form includes items that are predictive of the excluded items, so that subsequent analyses based on multiple imputation can recover some of the information about the excluded items that would have been collected had there been no matrix sampling. The matrix sampling and multiple-imputation methods are evaluated using data from the National Health and Nutrition Examination Survey, one of many nationally representative probability sample surveys conducted by the National Center for Health Statistics, Centers for Disease Control and Prevention. The study demonstrates the feasibility of the approach applied to a major national health survey with complex structure, and it provides practical advice about appropriate items to include in matrix sampling designs in future surveys.
Release date: 2006-12-21 - Articles and reports: 11-522-X20020016715Description:
This paper will describe the multiple imputation of income in the National Health Interview Survey and discuss the methodological issues involved. In addition, the paper will present empirical summaries of the imputations as well as results of a Monte Carlo evaluation of inferences based on multiply imputed income items.
Analysts of health data are often interested in studying relationships between income and health. The National Health Interview Survey, conducted by the National Center for Health Statistics of the U.S. Centers for Disease Control and Prevention, provides a rich source of data for studying such relationships. However, the nonresponse rates on two key income items, an individual's earned income and a family's total income, are over 20%. Moreover, these nonresponse rates appear to be increasing over time. A project is currently underway to multiply impute individual earnings and family income along with some other covariates for the National Health Interview Survey in 1997 and subsequent years.
There are many challenges in developing appropriate multiple imputations for such large-scale surveys. First, there are many variables of different types, with different skip patterns and logical relationships. Second, it is not known what types of associations will be investigated by the analysts of multiply imputed data. Finally, some variables, such as family income, are collected at the family level and others, such as earned income, are collected at the individual level. To make the imputations for both the family- and individual-level variables conditional on as many predictors as possible, and to simplify modelling, we are using a modified version of the sequential regression imputation method described in Raghunathan et al. ( Survey Methodology, 2001).
Besides issues related to the hierarchical nature of the imputations just described, there are other methodological issues of interest such as the use of transformations of the income variables, the imposition of restrictions on the values of variables, the general validity of sequential regression imputation and, even more generally, the validity of multiple-imputation inferences for surveys with complex sample designs.
Release date: 2004-09-13 - 5. A multivariate technique for multiply imputing missing values using a sequence of regression models ArchivedArticles and reports: 12-001-X20010015857Description:
This article describes and evaluates a procedure for imputing missing values for a relatively complex data structure when the data are missing at random. The imputations are obtained by fitting a sequence of regression models and drawing values from the corresponding predictive distributions. The types of regression models used are linear, logistic, Poisson, generalized logit or a mixture of these depending on the type of variable being imputed. Two additional common features in the imputation process are incorporated: restriction to a relevant subpopulation for some variables and logical bounds or constraints for the imputed values. The restrictions involve subsetting the sample individuals that satisfy certain criteria while fitting the regression models. The bounds involve drawing values from a truncated predictive distribution. The development of this method was partly motivated by the analysis of two data sets which are used as illustrations. The sequential regression procedure is applied to perform multiple imputation analysis for the two applied problems. The sampling properties of inferences from multiply imputed data sets created using the sequential regression method are evaluated through simulated data sets.
Release date: 2001-08-22
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Articles and reports (5)
Articles and reports (5) ((5 results))
- Articles and reports: 11-522-X200600110408Description:
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: 12-001-X20060029548Description:
The theory of multiple imputation for missing data requires that imputations be made conditional on the sampling design. However, most standard software packages for performing model-based multiple imputation assume simple random samples, leading many practitioners not to account for complex sample design features, such as stratification and clustering, in their imputations. Theory predicts that analyses of such multiply-imputed data sets can yield biased estimates from the design-based perspective. In this article, we illustrate through simulation that (i) the bias can be severe when the design features are related to the survey variables of interest, and (ii) the bias can be reduced by controlling for the design features in the imputation models. The simulations also illustrate that conditioning on irrelevant design features in the imputation models can yield conservative inferences, provided that the models include other relevant predictors. These results suggest a prescription for imputers: the safest course of action is to include design variables in the specification of imputation models. Using real data, we demonstrate a simple approach for incorporating complex design features that can be used with some of the standard software packages for creating multiple imputations.
Release date: 2006-12-21 - Articles and reports: 12-001-X20060029555Description:
Researchers and policy makers often use data from nationally representative probability sample surveys. The number of topics covered by such surveys, and hence the amount of interviewing time involved, have typically increased over the years, resulting in increased costs and respondent burden. A potential solution to this problem is to carefully form subsets of the items in a survey and administer one such subset to each respondent. Designs of this type are called "split-questionnaire" designs or "matrix sampling" designs. The administration of only a subset of the survey items to each respondent in a matrix sampling design creates what can be considered missing data. Multiple imputation (Rubin 1987), a general-purpose approach developed for handling data with missing values, is appealing for the analysis of data from a matrix sample, because once the multiple imputations are created, data analysts can apply standard methods for analyzing complete data from a sample survey. This paper develops and evaluates a method for creating matrix sampling forms, each form containing a subset of items to be administered to randomly selected respondents. The method can be applied in complex settings, including situations in which skip patterns are present. Forms are created in such a way that each form includes items that are predictive of the excluded items, so that subsequent analyses based on multiple imputation can recover some of the information about the excluded items that would have been collected had there been no matrix sampling. The matrix sampling and multiple-imputation methods are evaluated using data from the National Health and Nutrition Examination Survey, one of many nationally representative probability sample surveys conducted by the National Center for Health Statistics, Centers for Disease Control and Prevention. The study demonstrates the feasibility of the approach applied to a major national health survey with complex structure, and it provides practical advice about appropriate items to include in matrix sampling designs in future surveys.
Release date: 2006-12-21 - Articles and reports: 11-522-X20020016715Description:
This paper will describe the multiple imputation of income in the National Health Interview Survey and discuss the methodological issues involved. In addition, the paper will present empirical summaries of the imputations as well as results of a Monte Carlo evaluation of inferences based on multiply imputed income items.
Analysts of health data are often interested in studying relationships between income and health. The National Health Interview Survey, conducted by the National Center for Health Statistics of the U.S. Centers for Disease Control and Prevention, provides a rich source of data for studying such relationships. However, the nonresponse rates on two key income items, an individual's earned income and a family's total income, are over 20%. Moreover, these nonresponse rates appear to be increasing over time. A project is currently underway to multiply impute individual earnings and family income along with some other covariates for the National Health Interview Survey in 1997 and subsequent years.
There are many challenges in developing appropriate multiple imputations for such large-scale surveys. First, there are many variables of different types, with different skip patterns and logical relationships. Second, it is not known what types of associations will be investigated by the analysts of multiply imputed data. Finally, some variables, such as family income, are collected at the family level and others, such as earned income, are collected at the individual level. To make the imputations for both the family- and individual-level variables conditional on as many predictors as possible, and to simplify modelling, we are using a modified version of the sequential regression imputation method described in Raghunathan et al. ( Survey Methodology, 2001).
Besides issues related to the hierarchical nature of the imputations just described, there are other methodological issues of interest such as the use of transformations of the income variables, the imposition of restrictions on the values of variables, the general validity of sequential regression imputation and, even more generally, the validity of multiple-imputation inferences for surveys with complex sample designs.
Release date: 2004-09-13 - 5. A multivariate technique for multiply imputing missing values using a sequence of regression models ArchivedArticles and reports: 12-001-X20010015857Description:
This article describes and evaluates a procedure for imputing missing values for a relatively complex data structure when the data are missing at random. The imputations are obtained by fitting a sequence of regression models and drawing values from the corresponding predictive distributions. The types of regression models used are linear, logistic, Poisson, generalized logit or a mixture of these depending on the type of variable being imputed. Two additional common features in the imputation process are incorporated: restriction to a relevant subpopulation for some variables and logical bounds or constraints for the imputed values. The restrictions involve subsetting the sample individuals that satisfy certain criteria while fitting the regression models. The bounds involve drawing values from a truncated predictive distribution. The development of this method was partly motivated by the analysis of two data sets which are used as illustrations. The sequential regression procedure is applied to perform multiple imputation analysis for the two applied problems. The sampling properties of inferences from multiply imputed data sets created using the sequential regression method are evaluated through simulated data sets.
Release date: 2001-08-22
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