<p>This thesis presents methods for multiple imputation that can be applied to missing data and data with confidential variables. Imputation is useful for missing data because it results in a data set that can be analyzed with complete data statistical methods. The missing data are filled in by values generated from a model fit to the observed data. The model specification will depend on the observed data pattern and the missing data mechanism. For example, when the reason why the data is missing is related to the outcome of interest, that is nonignorable missingness, we need to alter the model fit to the observed data to generate the imputed values from a different distribution. Imputation is also used for generating synthetic values for data sets with disclosure restrictions. Since the synthetic values are not actual observations, they can be released for statistical analysis. The interest is in fitting a model that approximates well the relationships in the original data, keeping the utility of the synthetic data, while preserving the confidentiality of the original data. We consider applications of these methods to data from social sciences and epidemiology.</p><p>The first method is for imputation of multivariate continuous data with nonignorable missingness. Regular imputation methods have been used to deal with nonresponse in several types of survey data. However, in some of these studies, the assumption of missing at random is not valid since the probability of missing depends on the response variable. We propose an imputation method for multivariate data sets when there is nonignorable missingness. We fit a truncated Dirichlet process mixture of multivariate normals to the observed data under a Bayesian framework to provide flexibility. With the posterior samples from the mixture model, an analyst can alter the estimated distribution to obtain imputed data under different scenarios. To facilitate that, I developed an R application that allows the user to alter the values of the mixture parameters and visualize the imputation results automatically. I demonstrate this process of sensitivity analysis with an application to the Colombian Annual Manufacturing Survey. I also include a simulation study to show that the correct complete data distribution can be recovered if the true missing data mechanism is known, thus validating that the method can be meaningfully interpreted to do sensitivity analysis.</p><p>The second method uses the imputation techniques for nonignorable missingness to implement a procedure for adaptive design in surveys. Specifically, I develop a procedure that agencies can use to evaluate whether or not it is effective to stop data collection. This decision is based on utility measures to compare the data collected so far with potential follow-up samples. The options are assessed by imputation of the nonrespondents under different missingness scenarios considered by the analyst. The variation in the utility measures is compared to the cost induced by the follow-up sample sizes. We apply the proposed method to the 2007 U.S. Census of Manufactures.</p><p>The third method is for imputation of confidential data sets with spatial locations using disease mapping models. We consider data that include fine geographic information, such as census tract or street block identifiers. This type of data can be difficult to release as public use files, since fine geography provides information that ill-intentioned data users can use to identify individuals. We propose to release data with simulated geographies, so as to enable spatial analyses while reducing disclosure risks. We fit disease mapping models that predict areal-level counts from attributes in the file, and sample new locations based on the estimated models. I illustrate this approach using data on causes of death in North Carolina, including evaluations of the disclosure risks and analytic validity that can result from releasing synthetic geographies.</p> / Dissertation

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