Reliable Single Chip Genotyping with Semi-Parametric Log-Concave Mixtures

The common approach to SNP genotyping is to use (model-based) clustering per individual SNP, on a set of arrays. Genotyping all SNPs on a single array is much more attractive, in terms of flexibility, stability and applicability, when developing new chips. A new semi-parametric method, named SCALA, is proposed. It is based on a mixture model using semi-parametric log-concave densities. Instead of using the raw data, the mixture is fitted on a two-dimensional histogram, thereby making computation time almost independent of the number of SNPs. Furthermore, the algorithm is effective in low-MAF situations. Comparisons between SCALA and CRLMM on HapMap genotypes show very reliable calling of single arrays. Some heterozygous genotypes from HapMap are called homozygous by SCALA and to lesser extent by CRLMM too. Furthermore, HapMap's NoCalls (NN) could be genotyped by SCALA, mostly with high probability. The software is available as R scripts from the website www.math.leidenuniv.nl/~rrippe

A flexible approach to identify interaction effects between moderators in meta-analysis.

In meta‐analytic studies, there are often multiple moderators available (eg, study characteristics). In such cases, traditional meta‐analysis methods often lack sufficient power to investigate interaction effects between moderators, especially high‐order interactions. To overcome this problem, meta‐CART was proposed: an approach that applies classification and regression trees (CART) to identify interactions, and then subgroup meta‐analysis to test the significance of moderator effects. The aim of this study is to improve meta‐CART upon two aspects: 1) to integrate the two steps of the approach into one and 2) to consistently take into account the fixed‐effect or random‐effects assumption in both the the interaction identification and testing process. For fixed effect meta‐CART, weights are applied, and subgroup analysis is adapted. For random effects meta‐CART, a new algorithm has been developed. The performance of the improved meta‐CART was investigated via an extensive simulation study on different types of moderator variables (ie, dichotomous, nominal, ordinal, and continuous variables). The simulation results revealed that the new method can achieve satisfactory performance (power greater than 0.80 and Type I error less than 0.05) if appropriate pruning rule is applied and the number of studies is large enough. The required minimum number of studies ranges from 40 to 120 depending on the complexity and strength of the interaction effects, the within‐study sample size, the type of moderators, and the residual heterogeneity.Multivariate analysis of psychological dat

Multiple moderator meta-analysis using the R-package Meta-CART

In meta-analysis, heterogeneity often exists between studies. Knowledge about study features (i.e., moderators) that can explain the heterogeneity in effect sizes can be useful for researchers to assess the effectiveness of existing interventions and design new potentially effective interventions. When there are multiple moderators, they may amplify or attenuate each other's effect on treatment effectiveness. However, in most meta-analysis studies, interaction effects are neglected due to the lack of appropriate methods. The method meta-CART was recently proposed to identify interactions between multiple moderators. The analysis result is a tree model in which the studies are partitioned into more homogeneous subgroups by combinations of moderators. This paper describes the R-packagemetacart, which provides user-friendly functions to conduct meta-CART analyses in R. This package can fit both fixed- and random-effects meta-CART, and can handle dichotomous, categorical, ordinal and continuous moderators. In addition, a new look ahead procedure is presented. The application of the package is illustrated step-by-step using diverse examples.Analysis and Stochastic

Identification of antibiotic collateral sensitivity and resistance interactions in population surveillance data

Background\Objectives\Methods\Results\Conclusions\Collateral effects of antibiotic resistance occur when resistance to one antibiotic agent leads to increased resistance or increased sensitivity to a second agent, known respectively as collateral resistance (CR) and collateral sensitivity (CS). Collateral effects are relevant to limit impact of antibiotic resistance in design of antibiotic treatments. However, methods to detect antibiotic collateral effects in clinical population surveillance data of antibiotic resistance are lacking.\nTo develop a methodology to quantify collateral effect directionality and effect size from large-scale antimicrobial resistance population surveillance data. We propose a methodology to quantify and test collateral effects in clinical surveillance data based on a conditional t-test. Our methodology was evaluated using MIC data for 419 Escherichia coli strains, containing MIC data for 20 antibiotics, which were obtained from the Pathosystems Resource Integration Center (PATRIC) database. We demonstrate that the proposed approach identifies several antibiotic combinations that show symmetrical or non-symmetrical CR and CS. For several of these combinations, collateral effects were previously confirmed in experimental studies. We furthermore provide insight into the power of our method for multiple collateral effect sizes and MIC distributions. Our proposed approach is of relevance as a tool for analysis of large-scale population surveillance studies to provide broad systematic identification of collateral effects related to antibiotic resistance, and is made available to the community as an R package. This method can help mapping CS and CR, which could guide combination therapy and prescribing in the future.Pharmacolog

Identification of high‐dimensional omics‐derived predictors for tumor growth dynamics using machine learning and pharmacometric modeling

Analytical BioScience

Factors influencing speech perception in adults with a cochlear implant

Objectives: The primary objective of this study is to identify the biographic, audiologic, and electrode position factors that influence speech perception performance in adult cochlear implant (CI) recipients implanted with a device from a single manufacturer. The secondary objective is to investigate the independent association of the type of electrode (precurved or straight) with speech perception. Design: In a cross-sectional study design, speech perception measures and ultrahigh-resolution computed tomography scans were performed in 129 experienced CI recipients with a postlingual onset of hearing loss. Data were collected between December 2016 and January 2018 in the Radboud University Medical Center, Nijmegen, the Netherlands. The participants received either a precurved electrode (N = 85) or a straight electrode (N = 44), all from the same manufacturer. The biographic variables evaluated were age at implantation, level of education, and years of hearing loss. The audiometric factors explored were preoperative and postoperative pure-tone average residual hearing and preoperative speech perception score. The electrode position factors analyzed, as measured from images obtained with the ultrahigh-resolution computed tomography scan, were the scalar location, angular insertion depth of the basal and apical electrode contacts, and the wrapping factor (i.e., electrode-to-modiolus distance), as well as the type of electrode used. These 11 variables were tested for their effect on three speech perception outcomes: consonant-vowel-consonant words in quiet tests at 50 dB SPL (CVC50) and 65 dB SPL (CVC65), and the digits-in-noise test. Results: A lower age at implantation was correlated with a higher CVC50 phoneme score in the straight electrode group. Other biographic variables did not correlate with speech perception. Furthermore, participants implanted with a precurved electrode and who had poor preoperative hearing thresholds performed better in all speech perception outcomes than the participants implanted with a straight electrode and relatively better preoperative hearing thresholds. After correcting for biographic factors, audiometric variables, and scalar location, we showed that the precurved electrode led to an 11.8 percentage points (95% confidence interval: 1.4-20.4%; p = 0.03) higher perception score for the CVC50 phonemes compared with the straight electrode. Furthermore, contrary to our initial expectations, the preservation of residual hearing with the straight electrode was poor, as the median preoperative and the postoperative residual hearing thresholds for the straight electrode were 88 and 122 dB, respectively. Conclusions: Cochlear implantation with a precurved electrode results in a significantly higher speech perception outcome, independent of biographic factors, audiometric factors, and scalar location.Neuro Imaging Researc

Meta-CART: A tool to identify interactions between moderators in meta-analysis

Analysis and Stochastic

Optimal scaling for survival analysis with ordinal data

Development and application of statistical models for medical scientific researc