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Accounting for study participants who are ineligible for linkage: a multiple imputation approach to analyzing the linked National Health and Nutrition Examination Survey and Centers for Medicare and Medicaid Services’ Medicaid data

CITE Title : Accounting for study participants who are ineligible for linkage: a multiple imputation approach to analyzing the linked National Health and Nutrition Examination Survey and Centers for Medicare and Medicaid Services’ Medicaid data Personal Author(s) : Rammon, Jennifer;He, Yulei;Parker, Jennifer D.; Published Date : 8 16 2018;8-16-; Source : Health Serv Outcomes Res Methodol. 19(2-3):87-105 URL : https://stacks.cdc.gov/view/cdc/92689 Rammon, Jennifer et al. (2018). Accounting for study participants who are ineligible for linkage: a multiple imputation approach to analyzing the linked National Health and Nutrition Examination Survey and Centers for Medicare and Medicaid Services’ Medicaid data. 19 Rammon, Jennifer and He, Yulei and Parker, Jennifer D. "Accounting for study participants who are ineligible for linkage: a multiple imputation approach to analyzing the linked National Health and Nutrition Examination Survey and Centers for Medicare and Medicaid Services’ Medicaid data" 19 (2018) Rammon, Jennifer et al. "Accounting for study participants who are ineligible for linkage: a multiple imputation approach to analyzing the linked National Health and Nutrition Examination Survey and Centers for Medicare and Medicaid Services’ Medicaid data" vol. 19, 2018 Export RIS Citation Information.

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Rammon, Jennifer

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He, Yulei

8 16 2018 | Health Serv Outcomes Res Methodol. 19(2-3):87-105

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Data from the National Health and Nutrition Examination Survey have been linked to the Center for Medicare and Medicaid Services' Medicaid Enrollment ...
Multiple imputation of missing data with skip-pattern covariates: a comparison of alternative strategies

CITE Title : Multiple imputation of missing data with skip-pattern covariates: a comparison of alternative strategies Personal Author(s) : Zhang, Guangyu;He, Yulei;Cai, Bill;Moriarity, Chris;Shin, Hee-Choon;Parsons, Van;Irimata, Katherine E.; Published Date : 2023;2023; Source : J Stat Comput Simul. 94(7):1543-1570 URL : https://stacks.cdc.gov/view/cdc/157972 Zhang, Guangyu et al. (2023). Multiple imputation of missing data with skip-pattern covariates: a comparison of alternative strategies. 94(7). Zhang, Guangyu et al. "Multiple imputation of missing data with skip-pattern covariates: a comparison of alternative strategies" 94, no. 7 (2023) Zhang, Guangyu et al. "Multiple imputation of missing data with skip-pattern covariates: a comparison of alternative strategies" vol. 94, no. 7, 2023 Export RIS Citation Information.

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Zhang, Guangyu ;

He, Yulei

2023 | J Stat Comput Simul. 94(7):1543-1570

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Multiple imputation (MI) is a widely used approach to address missing data issues in surveys. Variables included in MI can have various distributional...
Multiple imputation of race and Hispanic ethnicity in national surveillance data for chlamydia, gonorrhea, and syphilis

CITE Title : Multiple imputation of race and Hispanic ethnicity in national surveillance data for chlamydia, gonorrhea, and syphilis Personal Author(s) : Pondo, Tracy;Torrone, Elizabeth;Pagaoa, Melissa; Published Date : 11 01 2024;11-01-; Source : Sex Transm Dis. 51(11):719-727 URL : https://stacks.cdc.gov/view/cdc/169988 Pondo, Tracy et al. (2024). Multiple imputation of race and Hispanic ethnicity in national surveillance data for chlamydia, gonorrhea, and syphilis. 51(11). Pondo, Tracy and Torrone, Elizabeth and Pagaoa, Melissa "Multiple imputation of race and Hispanic ethnicity in national surveillance data for chlamydia, gonorrhea, and syphilis" 51, no. 11 (2024) Pondo, Tracy et al. "Multiple imputation of race and Hispanic ethnicity in national surveillance data for chlamydia, gonorrhea, and syphilis" vol. 51, no. 11, 2024 Export RIS Citation Information.

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Pondo, Tracy ;

Torrone, Elizabeth

11 01 2024 | Sex Transm Dis. 51(11):719-727

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BackgroundDisease burden of sexually transmitted infections such as chlamydia, gonorrhea, and syphilis is often compared across age categories, sex ca...