Multivariate imputation by chained equations (MICE) has emerged as a principled method of dealing with missing data. Despite properties that make MICE particularly useful for large imputation procedures and advances in software development that now make it accessible to many researchers, many psychiatric researchers have not been trained in these methods and few practical resources exist to guide researchers in the implementation of this technique. This paper provides an introduction to the MICE method with a focus on practical aspects and challenges in using this method. A brief review of software programs available to implement MICE and then analyze multiply imputed data is also provided.

Multiple Imputation by Chained Equations: What is it and how does it work?

Statistical Analysis With Missing Data (2nd ed.) (Book)

Multiple imputation was first conceived as a tool that statistical agencies could use to handle nonresponse in large-sample public use surveys. In the last two decades, the multiple-imputation framework has been adapted for other statistical contexts. For example, individual researchers use multiple imputation to handle missing data in small samples, statistical agencies disseminate multiply-imputed data sets for purposes of protecting data confidentiality, and survey methodologists and epidemiologists use multiple imputation to correct for measurement errors. In some of these settings, Rubin's original rules for combining the point and variance estimates from the multiply-imputed data sets are not appropriate, because what is known—and thus the conditional expectations and variances used to derive inferential methods—differs from that in the missing-data context. These applications require new combining rules and methods of inference. In fact, more than 10 combining rules exist in the published literatur...

The Multiple Adaptations of Multiple Imputation

Missing data are a pervasive problem in health investigations. We describe some background of missing data analysis and criticize ad hoc methods that are prone to serious problems. We then focus on multiple imputation, in which missing cases are first filled in by several sets of plausible values to create multiple completed datasets, then standard complete-data procedures are applied to each completed dataset, and finally the multiple sets of results are combined to yield a single inference. We introduce the basic concepts and general methodology and provide some guidance for application. For illustration, we use a study assessing the effect of cardiovascular diseases on hospice discussion for late stage lung cancer patients.

Missing Data Analysis Using Multiple Imputation Getting to the Heart of the Matter

https://www.nber.org/system/files/working_papers/w14173/w14173.pdf

Has public health insurance for older children reduced disparities in access to care and health outcomes

The National Health Interview Survey (NHIS) provides a rich source of data for studying relationships between income and health and for monitoring health and health care for persons at different income levels. However, the nonresponse rates are high for two key items, total family income in the previous calendar year and personal earnings from employment in the previous calendar year. To handle the missing data on family income and personal earnings in the NHIS, multiple imputation of these items, along with employment status and ratio of family income to the federal poverty threshold (derived from the imputed values of family income), has been performed for the survey years 1997–2004. (There are plans to continue this work for years beyond 2004 as well.) Files of the imputed values, as well as documentation, are available at the NHIS website (http://www.cdc.gov/nchs/nhis.htm). This article describes the approach used in the multiple-imputation project and evaluates the methods through analyses of the mul...

/pdf/multiple-imputation-of-missing-income-data-in-the-national-ydv8t5c37d.pdf

Pei Lu Chiu

Papers

Multiple Imputation of Missing Income Data in the National Health Interview Survey