PREVALENCE RATE DIFFERENCES BASED ON HERDMATE COMPARISONS

A non-random survey of ovine progressive pneumonia (OPP) seropositive prevalence rates among 16,827 sheep in 29 states in the United states revealed large breed differences, a higher prevalence rate among older sheep and an unexplainable female rate that was more that three times the male rate. The herdmate comparison procedure, successfully used in evaluating dairy bulls, was adapted to compare the prevalence of a breed to the rate of its herdmates within herds. Likewise, sex and age differences in OPP prevalence were compared within herds that contained animals of both sexes and several ages. Using herdmate comparisons, breed and age differences in OPP prevalence remained but the sex difference disappeared

Impact of participant and interventionist race concordance on weight loss outcomes

We have previously shown that racial composition of behavioral intervention groups does not affect achieved weight loss. However, it is unclear if the race of the interventionist affects intervention outcomes. The objective of this analysis is to estimate the impact of race concordance between participant and interventionist on weight change in the initial weight loss phase (phase 1) of the Weight Loss Maintenance trial (WLM)

Evaluation of appendicitis risk prediction models in adults with suspected appendicitis

Background Appendicitis is the most common general surgical emergency worldwide, but its diagnosis remains challenging. The aim of this study was to determine whether existing risk prediction models can reliably identify patients presenting to hospital in the UK with acute right iliac fossa (RIF) pain who are at low risk of appendicitis. Methods A systematic search was completed to identify all existing appendicitis risk prediction models. Models were validated using UK data from an international prospective cohort study that captured consecutive patients aged 16–45 years presenting to hospital with acute RIF in March to June 2017. The main outcome was best achievable model specificity (proportion of patients who did not have appendicitis correctly classified as low risk) whilst maintaining a failure rate below 5 per cent (proportion of patients identified as low risk who actually had appendicitis). Results Some 5345 patients across 154 UK hospitals were identified, of which two‐thirds (3613 of 5345, 67·6 per cent) were women. Women were more than twice as likely to undergo surgery with removal of a histologically normal appendix (272 of 964, 28·2 per cent) than men (120 of 993, 12·1 per cent) (relative risk 2·33, 95 per cent c.i. 1·92 to 2·84; P < 0·001). Of 15 validated risk prediction models, the Adult Appendicitis Score performed best (cut‐off score 8 or less, specificity 63·1 per cent, failure rate 3·7 per cent). The Appendicitis Inflammatory Response Score performed best for men (cut‐off score 2 or less, specificity 24·7 per cent, failure rate 2·4 per cent). Conclusion Women in the UK had a disproportionate risk of admission without surgical intervention and had high rates of normal appendicectomy. Risk prediction models to support shared decision‐making by identifying adults in the UK at low risk of appendicitis were identified

www.niss.org Choosing the Sample Size of a Computer Experiment: A Practical Guide

We produce reasons and evidence supporting the informal rule that the number of runs for an effective initial computer experiment should be about 10 times the input dimension. Our arguments quantify two key characteristics of computer codes that affect the sample size required for a desired level of accuracy when approximating the code via a Gaussian process (GP). The first characteristic is the total sensitivity of a code output variable to all input variables. The second corresponds to the way this total sensitivity is distributed across the input variables, specifically the possible presence of a few prominent input factors and many impotent ones (effect sparsity). Both measures relate directly to the correlation structure in the GP approximation of the code. In this way, the article moves towards a more formal treatment of sample size for a computer experiment. The evidence supporting these arguments stems primarily from a simulation study and via specific codes modeling climate and ligand activation of G-protein