Search

Meta-analysis is a practical and powerful analytic tool that enables a unified statistical inference across the results from multiple studies. Notably, researchers often report the results on multiple related markers in each study (eg, various α-diversity indices in microbiome studies). However, univariate meta-analyses are limited to combining the results on a single common marker at a time, whereas existing multivariate meta-analyses are limited to the situations where marker-by-marker correlations are given in each study. Thus, here we introduce two meta-analysis methods, multi-marker meta-analysis (mMeta) and adaptive multi-marker meta-analysis (aMeta), to combine multiple studies throughout multiple related markers with no priori results on marker-by-marker correlations. mMeta is a statistical estimator for a pooled estimate and its SE across all the studies and markers, whereas aMeta is a statistical test based on the test statistic of the minimum P-value among marker-specific meta-analyses. mMeta conducts both effect estimation and hypothesis testing based on a weighted average of marker-specific pooled estimates while estimating marker-by-marker correlations non-parametrically via permutations, yet its power is only moderate. In contrast, aMeta closely approaches the highest power among marker-specific meta-analyses, yet it is limited to hypothesis testing. While their applications can be broader, we illustrate the use of mMeta and aMeta to combine microbiome studies throughout multiple α-diversity indices. We evaluate mMeta and aMeta in silico and apply them to real microbiome studies on the disparity in α-diversity by the status of human immunodeficiency virus (HIV) infection. The R package for mMeta and aMeta is freely available at https://github.com/hk1785/mMeta. [ABSTRACT FROM AUTHOR]

ABSTRACT BACKGROUND Given ever-reducing budgets of community and school substance use prevention programs, there is a call for identifying the first substance in the sequence leading to polydrug use. METHODS Examining data from a nationally representative sample of 2835 United States 12th graders, we sought to determine (1) the first substance adolescents use; (2) order in which adolescents progress through alcohol, tobacco, and marijuana use; and (3) impact of age of initial substance use on lifetime and frequency of illicit substance use. RESULTS Alcohol is the most commonly used substance, and the majority of polysubstance using respondents consumed alcohol prior to tobacco or marijuana initiation. Respondents initiating alcohol use in sixth grade reported significantly greater lifetime illicit substance use (M = 1.9, standard deviation [ SD] = 1.7, p < .001) and more frequent illicit substance use (M = 6.0, SD = 6.5, p < .001) than those initiating alcohol use in ninth grade or later. Overall, effect sizes for these differences were large (eta squared = 0.30 and 0.28, respectively). CONCLUSIONS Findings underscore the importance of screening for substance use, even among youth enrolled in elementary/middle school. In addition, school prevention programs should begin in elementary school (third grade) and target alcohol use. [ABSTRACT FROM AUTHOR]