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This report presents operating characteristics of the NHIS 2016-2025 sample design. The general sampling structure is presented, along with a discussion of weighting and variance estimation techniques primarily for 2016-2018. This report is organized into four major sections. The first section presents a general overview of NHIS and its sample design. The second section describes the redesign process, updates for 2016-2025, and includes general frame and sample design considerations. The third section provides a more detailed description of the sample design and how the sample was selected. The last two sections present a description of the estimators used in NHIS for analyzing and summarizing survey results. Documentation for subsequent changes to the sampling and weighting procedures is available on the NCHS website as separate reports and through each year's survey description document. This report is intended for general users of NHIS data.

While web surveys have become increasingly popular as a method of data collection, there is concern that estimates obtained from web surveys may not reflect the target population of interest. Web survey estimates can be calibrated to existing national surveys using a propensity score adjustment, although requirements for the size and collection timeline of the reference data set have not been investigated. We evaluate health outcomes estimates from the National Center for Health Statistics’ Research and Development web survey. In our study, the 2016 National Health Interview Survey as well as its quarterly subsets are considered as reference datasets for the web data. It is demonstrated that the calibrated health estimates overall vary little when using the quarterly or yearly data, suggesting that there is flexibility in selecting the reference dataset. This finding has many practical implications for constructing reference data, including the reduced cost and burden of a smaller sample size and a more flexible timeline.

This report presents an overview, a detailed description of the sample design features, and estimation structures for the 2006-2015 National Health Interview Survey NHIS). It fulfills the same role for the current 2006-2015 NHIS design as NCHS Series 2, No. 130, "Design and Estimation for the National Health Interview Survey, 1995-2004" provided for the previous design, which was extended through 2005.The 2006-2015 NHIS sample design uses cost-effective complex sampling techniques including stratification, clustering, and differential sampling rates to achieve several objectives, among them improved reliability of racial, ethnic, and geographical domains. This report describes these methods.This report presents operating characteristics of NHIS 2006-2015. The general sampling structure is presented, along with a discussion of weighting and variance estimation techniques. This report is intended for general users of NHIS data systems.

Combinations of survival regression trees called survival forests (SF) have been proposed by Breiman to estimate survival functions and assess variable importance. We examine some operating characteristics of the method on simulated data, and we apply it to breast cancer data for prognosis.

Back pain is a significant health problem, especially in industry. To gain a better understanding of back pain in light of its diverse causality, data for 29,322 working adults interviewed in the Occupational Health Supplement to the 1988 National Health Interview Survey were analyzed by self-reported cause of back pain. The 12-month prevalences of back pain for “injury or repeated activities at work,” “injury or repeated activities not at work,” and “other reasons,” were 9.1 %, 4.4%, and 4.0%, respectively. The worker attributes and work impacts associated with these self-reported causes were compared for this nationally representative sample. Results from multivariate logistic regressions indicated different profiles of worker attributes among the three cause-specific subgroups of back pain sufferers. Work impact was substantial in all cases, but was highest for workers with work-related back pain.

Bias and mean squared error for linear combinations of the isotonic regression estimators are computed. The case of sampling three distinct populations and the case of sampling seven or fewer populations having common mean are studied in detail. Numerical results are given, and comparisons between isotonic and unbiased estimation procedures are made. Le biais et l'erreur quadratique moyenne des estimateurs isotoniques sont calcules. On etudie en detail le cas de trois populations distinctes, et celui de sept populations ou moins de meme moyenne. On enonce des resultats numeriques et on etablit une comparaison entre l'estimation isotonique et l'estimation sans biais.

The exact distribution of a nonparametric test statistic for ordered alternatives, the so-called $\bar{\chi}^2_{\operatorname{rank}}$ statistic, is discussed. Two tables are given.