Multiple Imputation With Large Data Sets: A Case Study of the Children's Mental Health Initiative
| Autor: | Elizabeth A. Stuart, Philip J. Leaf, Constantine Frangakis, Melissa Azur |
|---|---|
| Rok vydání: | 2009 |
| Předmět: |
medicine.medical_specialty
Biometry Practice of Epidemiology Epidemiology Population Salud mental Child Behavior Disorders Health services Bias Health care Computer Graphics Humans Medicine Longitudinal Studies Imputation (statistics) Child education education.field_of_study Models Statistical business.industry Public health Missing data Mental health Data science Community Mental Health Services United States Data Interpretation Statistical Epidemiologic Methods business |
| Zdroj: | American Journal of Epidemiology. 169:1133-1139 |
| ISSN: | 1476-6256 0002-9262 |
| DOI: | 10.1093/aje/kwp026 |
| Popis: | Multiple imputation is an effective method for dealing with missing data, and it is becoming increasingly common in many fields. However, the method is still relatively rarely used in epidemiology, perhaps in part because relatively few studies have looked at practical questions about how to implement multiple imputation in large data sets used for diverse purposes. This paper addresses this gap by focusing on the practicalities and diagnostics for multiple imputation in large data sets. It primarily discusses the method of multiple imputation by chained equations, which iterates through the data, imputing one variable at a time conditional on the others. Illustrative data were derived from 9,186 youths participating in the national evaluation of the Community Mental Health Services for Children and Their Families Program, a US federally funded program designed to develop and enhance community-based systems of care to meet the needs of children with serious emotional disturbances and their families. Multiple imputation was used to ensure that data analysis samples reflect the full population of youth participating in this program. This case study provides an illustration to assist researchers in implementing multiple imputation in their own data. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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