Biomedical subjects

Nathaniel Schenker

Publications and source records attributed to Nathaniel Schenker.

6 recordsLinked to original sources

PubMed2003

Overlapping confidence intervals or standard error intervals: what do they mean in terms of statistical significance?

We investigate the procedure of checking for overlap between confidence intervals or standard error intervals to draw conclusions regarding hypotheses about differences between population parameters. Mathematical expressions and algebraic manipulations are given, and computer simulations are performed to assess the usefulness of confidence and standard error intervals in this manner. We make recommendations for their use in situations in which standard tests of hypotheses do not exist. An example is given that tests this methodology for comparing effective dose levels in independent probit regressions, an application that is also pertinent to derivations of LC50s for insect pathogens and of detectability half-lives for prey proteins or DNA sequences in predator gut analysis.

Animals↗

PubMed2003

From single-race reporting to multiple-race reporting: using imputation methods to bridge the transition.

In 1997, the Office of Management and Budget issued revised standards for the collection of race information within the Federal statistical system. One revision allows individuals to choose more than one race group when responding to Federal surveys and other Federal data collections. This paper explores methods that impute single-race categories for those who have given multiple-race responses. Such imputations would be useful when it is desired to conduct analyses involving only single-race categories, such as when trends over time are being examined by race group so that data collected under the old and new standards are being combined. The National Health Interview Survey has allowed multiple-race responses for several years, while also asking respondents to specify one race as their primary race. Exploratory analyses of data from the survey suggest that imputation methods that use demographic and contextual covariate information to predict primary race can have advantages with respect to lower bias and improved variance estimation compared to simpler methods discussed by the Office of Management and Budget. It also appears, however, that the relationships between primary race and covariates might be changing over time. Thus, caution should be exercised if an imputation model fitted to data from one time period is to be applied to data from another time period. Published in 2003 by John Wiley & Sons, Ltd.

Adolescent↗

PubMed2003

United States Census 2000 population with bridged race categories.

OBJECTIVES: The objectives of this report are to document the methods developed at the National Center for Health Statistics (NCHS) to bridge the Census 2000 multiple-race resident population to single-race categories and to describe the resulting bridged race resident population estimates. METHOD: Data from the pooled 1997-2000 National Health Interview Surveys (NHIS) were used to develop models for bridging the Census 2000 multiple-race population to single-race categories. The bridging models included demographic and contextual covariates, some at the person-level and some at the county-level. Allocation probabilities were obtained from the regression models and applied to the Census Bureau's April 1, 2000, Modified Race Data Summary File population counts to assign multiple-race persons to single-race categories. RESULTS: Bridging has the most impact on the American Indian and Alaska Native (AIAN) and Asian or Pacific Islander (API) populations, a small impact on the Black population and a negligible impact on the White population. For the United States as a whole, the AIAN, API, Black, and White bridged population counts are 12.0, 5.0, 2.5, and 0.5 percent higher than the corresponding Census 2000 single-race counts. At the sub-national level, there is considerably more variation than observed at the national level. The bridged single-race population counts have been used to calculate birth and death rates produced by NCHS for 2000 and 2001 and to revise previously published rates for the 1990s, 2000, and 2001. The bridging methodology will be used to bridge postcensal population estimates for later years. The bridged population counts presented here and in subsequent years may be updated as additional data become available for use in the bridging process.

Censuses↗

PubMed2002

Survival analysis using auxiliary variables via multiple imputation, with application to AIDS clinical trial data.

We develop an approach, based on multiple imputation, to using auxiliary variables to recover information from censored observations in survival analysis. We apply the approach to data from an AIDS clinical trial comparing ZDV and placebo, in which CD4 count is the time-dependent auxiliary variable. To facilitate imputation, a joint model is developed for the data, which includes a hierarchical change-point model for CD4 counts and a time-dependent proportional hazards model for the time to AIDS. Markov chain Monte Carlo methods are used to multiply impute event times for censored cases. The augmented data are then analyzed and the results combined using standard multiple-imputation techniques. A comparison of our multiple-imputation approach to simply analyzing the observed data indicates that multiple imputation leads to a small change in the estimated effect of ZDV and smaller estimated standard errors. A sensitivity analysis suggests that the qualitative findings are reproducible under a variety of imputation models. A simulation study indicates that improved efficiency over standard analyses and partial corrections for dependent censoring can result. An issue that arises with our approach, however, is whether the analysis of primary interest and the imputation model are compatible.

Acquired Immunodeficiency Syndrome↗

PubMedDate not supplied

Combining estimates from complementary surveys: a case study using prevalence estimates from national health surveys of households and nursing homes.

OBJECTIVES: When a single survey does not cover a domain of interest, estimates from two or more complementary surveys can be combined to extend coverage. The purposes of this article are to discuss and demonstrate the benefits of combining estimates from complementary surveys and to provide a catalog of the analytic issues involved. METHODS: The authors present a case study in which data from the National Health Interview Survey and the National Nursing Home Survey were combined to obtain prevalence estimates for several chronic health conditions for the years 1985, 1995, and 1997. The combined prevalences were estimated by ratio estimation, and the associated variances were estimated by Taylor linearization. The survey weights, stratification, and clustering were reflected in the estimation procedures. RESULTS: In the case study, for the age group of 65 and older, the combined prevalence estimates for households and nursing homes are close to those for households alone. For the age group of 85 and older, however, the combined estimates are sometimes substantially different from the household estimates. Such differences are seen both for estimates within a single year and for estimates of trends across years. CONCLUSIONS: Several general issues regarding comparability arise when there is a goal of combining complementary survey data. As illustrated by this case study, combining estimates can be very useful for improving coverage and avoiding misleading conclusions.

Aged↗

PubMedDate not supplied

Bridging between two standards for collecting information on race and ethnicity: an application to Census 2000 and vital rates.

OBJECTIVES: The 2000 Census, which provides denominators used in calculating vital statistics and other rates, allowed multiple-race responses. Many other data systems that provide numerators used in calculating rates collect only single-race data. Bridging is needed to make the numerators and denominators comparable. This report describes and evaluates the method used by the National Center for Health Statistics to bridge multiple-race responses obtained from Census 2000 to single-race categories, creating single-race population estimates that are available to the public. METHODS: The authors fitted logistic regression models to multiple-race data from the National Health Interview Survey (NHIS) for 1997-2000. These fitted models, and two bridging methods previously suggested by the Office of Management and Budget, were applied to the public-use Census Modified Race Data Summary file to create single-race population estimates for the U.S. The authors also compared death rates for single-race groups calculated using these three approaches. RESULTS: Parameter estimates differed between the NHIS models for the multiple-race groups. For example, as the percentage of multiple-race respondents in a county increased, the likelihood of stating black as a primary race increased among black/white respondents but decreased among American Indian or Alaska Native/black respondents. The inclusion of county-level contextual variables in the regression models as well as the underlying demographic differences across states led to variation in allocation percentages; for example, the allocation of black/white respondents to single-race white ranged from nearly zero to more than 50% across states. Death rates calculated using bridging via the NHIS models were similar to those calculated using other methods, except for the American Indian/Alaska Native group, which included a large proportion of multiple-race reporters. CONCLUSION: Many data systems do not currently allow multiple-race reporting. When such data systems are used with Census counts to produce race-specific rates, bridging methods that incorporate geographic and demographic factors may lead to better rates than methods that do not consider such factors.

Adolescent↗