P-value tables

Publications Using SAMHSA DataComparison of the 2002-2004 and 2010-2012 Model-Based Substate Estimates

The focus of this report is to describe the statistical inference procedures used to produce design-based estimates as presented in the 2018 detailed tables and the 2018 FFR, which are based on restricted-use data.

These detailed tables present standard errors for totals and prevalence estimates of tobacco and alcohol use from the 2011 National Survey on Drug Use and Health (NSDUH). Tobacco products include cigarettes, smokeless tobacco, cigars, and pipe tobacco. Alcohol use includes binge and heavy alcohol use. Standard errors are provided for lifetime, past year, and past month use by age group, gender, race/ethnicity, education level, employment status, and geographic area. Standard errors are provided for both 2011 and 2010.

Behavioral Health Barometer: Indiana, Volume 6

Publications Using SAMHSA DataBehavioral Health Barometer: Idaho, Volume 5

These detailed tables present standard errors for totals and prevalence estimates of risk and protective factors regarding substance use from the 2012 National Survey on Drug Use and Health (NSDUH). The factors include perceptions of 1) risk of substance use, 2) availability of substances, 3) parental disapproval of youth substance use, 4) peer substance use. These factors include measures of delinquent behavior, religious involvement, exposure to prevention messages, and parental involvement. Standard errors are provided by age group, gender, race/ethnicity, geographic area, and youth substance use behavior. Standard errors are provided for both 2012 and 2011.

The data set includes source code that implements a PBPK template model applicable to PFAS. It includes data digitized from Kim et al. (2018), Kim et al. (2019), and Loccisano et al. (2012) used to show the capability of the template to replicate published PFAS PBPK models. The template model is described in a paper that is in review at the journal Toxicological Sciences.

Supporting datasets and algorithms (R-based) for the manuscript entitled "Development of a Tool to Determine the Variability of Consensus Mass Spectra", including an R Markdown script to reproduce the manuscript's figures.

Background Readers may question the interpretation of findings in clinical trials when multiple outcome measures are used without adjustment of the p-value. This question arises because of the increased risk of Type I errors (findings of false "significance") when multiple simultaneous hypotheses are tested at set p-values. The primary aim of this study was to estimate the need to make appropriate p-value adjustments in clinical trials to compensate for a possible increased risk in committing Type I errors when multiple outcome measures are used. Discussion The classicists believe that the chance of finding at least one test statistically significant due to chance and incorrectly declaring a difference increases as the number of comparisons increases. The rationalists have the following objections to that theory: 1) P-value adjustments are calculated based on how many tests are to be considered, and that number has been defined arbitrarily and variably; 2) P-value adjustments reduce the chance of making type I errors, but they increase the chance of making type II errors or needing to increase the sample size. Summary Readers should balance a study's statistical significance with the magnitude of effect, the quality of the study and with findings from other studies. Researchers facing multiple outcome measures might want to either select a primary outcome measure or use a global assessment measure, rather than adjusting the p-value.