Last data update: May 06, 2024. (Total: 46732 publications since 2009)
Records 1-3 (of 3 Records) |
Query Trace: Moriarity C[original query] |
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Multiple imputation of missing data with skip-pattern covariates: a comparison of alternative strategies
Zhang G , He Y , Cai B , Moriarity C , Shin HC , Parsons V , Irimata KE . J Stat Comput Simul 2023 Multiple imputation (MI) is a widely used approach to address missing data issues in surveys. Variables included in MI can have various distributional forms with different degrees of missingness. However, when variables with missing data contain skip patterns (i.e. questions not applicable to some survey participants are thus skipped), implementation of MI may not be straightforward. In this research, we compare two approaches for MI when skip-pattern covariates with missing values exist. One approach imputes missing values in the skip-pattern variables only among applicable subjects (denoted as imputation among applicable cases (IAAC)). The second approach imputes skip-pattern covariates among all subjects while using different recoding methods on the skip-pattern variables (denoted as imputation with recoded non-applicable cases (IWRNC)). A simulation study is conducted to compare these methods. Both approaches are applied to the 2015 and 2016 Research and Development Survey data from the National Center for Health Statistics. © 2023 Informa UK Limited, trading as Taylor & Francis Group. |
National Center for Health Statistics Data presentation standards for proportions
Parker JD , Talih M , Malec DJ , Beresovsky V , Carroll M , Gonzalez JF , Hamilton BE , Ingram DD , Kochanek K , McCarty F , Moriarity C , Shimizu I , Strashny A , Ward BW . Vital Health Stat 2 2017 (175) 1-22 The National Center for Health Statistics (NCHS) disseminates information on a broad range of health topics through diverse publications. These publications must rely on clear and transparent presentation standards that can be broadly and efficiently applied. Standards are particularly important for large, cross-cutting reports where estimates cannot be individually evaluated and indicators of precision cannot be included alongside the estimates. This report describes the NCHS Data Presentation Standards for Proportions. The multistep NCHS Data Presentation Standards for Proportions are based on a minimum denominator sample size and on the absolute and relative widths of a confidence interval calculated using the Clopper-Pearson method. Proportions (usually multiplied by 100 and expressed as percentages) are the most commonly reported estimates in NCHS reports. |
Changing methods of NCHS surveys: 1960-2010 and beyond
Sirken MG , Hirsch R , Mosher W , Moriarity C , Sonnenfeld N . MMWR Suppl 2011 60 (4) 42-8 The year 2011 marks the 50th anniversary of CDC's publication of MMWR. It also marks the 24th anniversary of the National Center for Health Statistics (NCHS) joining CDC in 1987. One of NCHS's greatest contributions to public health has been in surveys and survey methodology. Today, more than 50 years after NCHS was formed in 1960, NCHS continues to conduct some of the leading health surveys of the United States. This report describes some of the many innovations and changes in NCHS survey methods during the past 50 years and briefly previews how the methods might change in the future. |
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