MetaNorm: incorporating meta-analytic priors into normalization of NanoString nCounter data.

MOTIVATION: Non-informative or diffuse prior distributions are widely employed in Bayesian data analysis to maintain objectivity. However, when meaningful prior information exists and can be identified, using an informative prior distribution to accurately reflect current knowledge may lead to superior outcomes and great efficiency. RESULTS: We propose MetaNorm, a Bayesian algorithm for normalizing NanoString nCounter gene expression data. MetaNorm is based on RCRnorm, a powerful method designed under an integrated series of hierarchical models that allow various sources of error to be explained by different types of probes in the nCounter system. However, a lack of accurate prior information, weak computational efficiency, and instability of estimates that sometimes occur weakens the approach despite its impressive performance. MetaNorm employs priors carefully constructed from a rigorous meta-analysis to leverage information from large public data. Combined with additional algorithmic enhancements, MetaNorm improves RCRnorm by yielding more stable estimation of normalized values, better convergence diagnostics and superior computational efficiency. AVAILABILITY AND IMPLEMENTATION: R Code for replicating the meta-analysis and the normalization function can be found at github.com/jbarth216/MetaNorm.

External Validation of Predictors of Mortality in Polytrauma Patients.

INTRODUCTION: The Parkland Trauma Index of Mortality (PTIM) is an integrated, machine learning 72-h mortality prediction model that automatically extracts and analyzes demographic, laboratory, and physiological data in polytrauma patients. We hypothesized that this validated model would perform equally as well at another level 1 trauma center. METHODS: A retrospective cohort study was performed including ∼5000 adult level 1 trauma activation patients from January 2022 to September 2023. Demographics, physiologic and laboratory values were collected. First, a test set of models using PTIM clinical variables (CVs) was used as external validation, named PTIM+. Then, multiple novel mortality prediction models were developed considering all CVs designated as the Cincinnati Trauma Index of Mortality (CTIM). The statistical performance of the models was then compared. RESULTS: PTIM CVs were found to have similar predictive performance within the PTIM + external validation model. The highest correlating CVs used in CTIM overlapped considerably with those of the PTIM, and performance was comparable between models. Specifically, for prediction of mortality within 48 h (CTIM versus PTIM): positive prediction value was 35.6% versus 32.5%, negative prediction value was 99.6% versus 99.3%, sensitivity was 81.0% versus 82.5%, specificity was 97.3% versus 93.6%, and area under the curve was 0.98 versus 0.94. CONCLUSIONS: This external cohort study suggests that the variables initially identified via PTIM retain their predictive ability and are accessible in a different level 1 trauma center. This work shows that a trauma center may be able to operationalize an effective predictive model without undertaking a repeated time and resource intensive process of full variable selection.

Bayesian estimation and testing in random-effects meta-analysis of rare binary events allowing for flexible group variability.

Rare binary events data arise frequently in medical research. Due to lack of statistical power in individual studies involving such data, meta-analysis has become an increasingly important tool for combining results from multiple independent studies. However, traditional meta-analysis methods often report severely biased estimates in such rare-event settings. Moreover, many rely on models assuming a pre-specified direction for variability between control and treatment groups for mathematical convenience, which may be violated in practice. Based on a flexible random-effects model that removes the assumption about the direction, we propose new Bayesian procedures for estimating and testing the overall treatment effect and inter-study heterogeneity. Our Markov chain Monte Carlo algorithm employs Pólya-Gamma augmentation so that all conditionals are known distributions, greatly facilitating computational efficiency. Our simulation shows that the proposed approach generally reports less biased and more stable estimates compared to existing methods. We further illustrate our approach using two real examples, one using rosiglitazone data from 56 studies and the other using stomach ulcers data from 41 studies.

Neurocognitive Latent Factors Associate With Early Tobacco and Alcohol Use Among Adolescent Brain Cognitive Development Study Youth.

PURPOSE: Prospective associations between preadolescent neurocognitive structure and onset of substance use in adolescence have not been examined. This study investigated associations between cognitive structure among youth aged 9 - 10 years and the likelihood of experimentation with tobacco and alcohol by ages 13-14 years. METHODS: A principal component (PC) analysis of nine neurocognitive assessments was used to identify the cognitive structure of unrelated adolescent brain cognitive development study participants (n = 9,655). We modeled associations between neurocognitive PCs and odds of tobacco or alcohol use by ages 13-14 years using generalized linear mixed models with a logit link and random intercept for recruitment sites. Demographics, family conflict, neighborhood safety, and externalizing and internalizing behavior were considered covariates. RESULTS: Four neurocognitive PCs were identified and labeled general ability, executive function, learning and memory, and mental rotation. Mental rotation [odds ratio [OR] = 0.88, p-value = .013] was associated with lower odds of youth tobacco use; the association was stronger among female youth. General ability [OR = 1.20, p-value < .0001] among both males and females, and learning and memory [OR = 1.11, p-value = .024] among females, were associated with increased odds of youth alcohol use. DISCUSSION: Among youth, higher neurocognitive performance was protective for tobacco use but increased the likelihood of alcohol use. Potential sex differences were identified. The role of cognition in processing the social contexts surrounding tobacco and alcohol use in the United States may contribute to the formation of disparate youth expectancies for tobacco and alcohol use.