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1.
Natl Health Stat Report ; (156): 1-15, 2021 06.
Article in English | MEDLINE | ID: mdl-34181517

ABSTRACT

Background-The National Cancer Institute (NCI) Joinpoint regression software is a widely used software program for evaluating trends. In addition to producing model estimates for trend models, this software can search for changes in slope along the trend line. One component of the software, which tests whether line segment slopes are zero, is different from the usual t-test of zero slope that is used in linear models. This report will demonstrate this Joinpoint software procedure through replication using the SAS Institute's statistical software (that is, SAS) and discuss the implications of the different assumptions used by Joinpoint and a typical SAS model for the test of zero slope. Methods-First, Joinpoint's procedure for testing a zero slope is compared with a typical test of zero slope using SAS, and the assumptions behind both approaches are evaluated. Second, the test from the Joinpoint software is replicated in SAS using its PROC REG procedure and additional SAS programming. Trend analyses of rates of drug overdose deaths involving fentanyl from the general population and among females are used as examples. Results-In the evaluation of the trend of drug overdose deaths for the total population, Joinpoint produces a similar result to the linear model test in SAS. For the female subgroup, however, Joinpoint and SAS produce differing results for the test of zero slope. The replication of the Joinpoint test of zero slope using SAS demonstrates that Joinpoint's procedure is based on fewer degrees of freedom, which results in a larger standard error estimate. Conclusion-The Joinpoint approach accounts for the fact that the joinpoints are estimated and thus leads to a more conservative hypothesis test, particularly when the number of points in a trend analysis is small.


Subject(s)
Drug Overdose , Fentanyl , Drug Overdose/epidemiology , Female , Humans , Linear Models , National Cancer Institute (U.S.) , Software , United States/epidemiology
2.
Vital Health Stat 2 ; (179): 1-71, 2018 Apr.
Article in English | MEDLINE | ID: mdl-29775435

ABSTRACT

Many reports present analyses of trends over time based on multiple years of data from National Center for Health Statistics (NCHS) surveys and the National Vital Statistics System (NVSS). Trend analyses of NCHS data involve analytic choices that can lead to different conclusions about the trends. This report discusses issues that should be considered when conducting a time trend analysis using NCHS data and presents guidelines for making trend analysis choices. Trend analysis issues discussed include: choosing the observed time points to include in the analysis, considerations for survey data and vital records data (record level and aggregated), a general approach for conducting trend analyses, assorted other analytic issues, and joinpoint regression. This report provides 12 guidelines for trend analyses, examples of analyses using NCHS survey and vital records data, statistical details for some analysis issues, and SAS and SUDAAN code for specification of joinpoint regression models. Several an lytic choices must be made during the course of a trend analysis, and the choices made can affect the results. This report highlights the strengths and limitations of different choices and presents guidelines for making some of these choices. While this report focuses on time trend analyses, the issues discussed and guidelines presented are applicable to trend analyses involving other ordinal and interval variables.


Subject(s)
Guidelines as Topic/standards , Health Surveys/methods , Health Surveys/standards , National Center for Health Statistics, U.S. , Vital Statistics , Humans , Research Design , United States
3.
Vital Health Stat 2 ; (175): 1-22, 2017 Aug.
Article in English | MEDLINE | ID: mdl-30248016

ABSTRACT

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.


Subject(s)
Health Surveys/standards , Research Design/standards , Statistics as Topic/standards , Confidence Intervals , Data Interpretation, Statistical , Female , Humans , Male , National Center for Health Statistics, U.S. , Reference Standards , Sample Size , United States
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