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Background Meta-analyses are used to summarise the results of several studies on a specific research question. Standard methods for meta-analyses, namely inverse variance random effects models, have unfavourable properties if only very few (2 – 4) studies are available. Therefore, alternative meta-analytic methods are needed. In the case of binary data, the “common-rho” beta-binomial model has shown good results in situations with sparse data or few studies. The major concern of this model is that it ignores the fact that each treatment arm is paired with a respective control arm from the same study. Thus, the randomisation to a study arm of a specific study is disrespected, which may lead to compromised estimates of the treatment effect. The aim of this simulation study was to compare the “common-rho” beta-binomial model and several other beta-binomial models with standard meta-analyses models, including generalised linear mixed models and several inverse variance random effects models. Methods We conducted a simulation study comparing beta-binomial models and various standard meta-analysis methods. The design of the simulation aimed to consider meta-analytic situations occurring in practice. Results No method performed well in scenarios with only 2 studies in the random effects scenario. In this situation, a fixed effect model or a qualitative summary of the study results may be preferable. In scenarios with 3 or 4 studies, most methods satisfied the nominal coverage probability. The “common-rho” beta-binomial model showed the highest power under the alternative hypothesis. Conclusion The “common-rho” beta-binomial appears to be a good option for meta-analyses of very few studies. As residual concerns about the consequences of disrespecting randomisation may still exist, we recommend a sensitivity analysis with a standard meta-analysis method that respects randomisation.

Background Incidence density ratios (IDRs) are frequently used to account for varying follow-up times when comparing the risks of adverse events in two treatment groups. The validity of the IDR as approximation of the hazard ratio (HR) is unknown in the situation of differential average follow up by treatment group and non-constant hazard functions. Thus, the use of the IDR when individual patient data are not available might be questionable. Methods A simulation study was performed using various survival-time distributions with increasing and decreasing hazard functions and various situations of differential follow up by treatment group. HRs and IDRs were estimated from the simulated survival times and compared with the true HR. A rule of thumb was derived to decide in which data situations the IDR can be used as approximation of the HR. Results The results show that the validity of the IDR depends on the survival-time distribution, the difference between the average follow-up durations, the baseline risk, and the sample size. For non-constant hazard functions, the IDR is only an adequate approximation of the HR if the average follow-up durations of the groups are equal and the baseline risk is not larger than 25%. In the case of large differences in the average follow-up durations between the groups and non-constant hazard functions, the IDR represents no valid approximation of the HR. Conclusions The proposed rule of thumb allows the use of the IDR as approximation of the HR in specific data situations, when it is not possible to estimate the HR by means of adequate survival-time methods because the required individual patient data are not available. However, in general, adequate survival-time methods should be used to analyze adverse events rather than the simple IDR.

ObjectiveTo systematically assess the diagnostic accuracy of rapid point-of-care tests for diagnosis of current SARS-CoV-2 infections in children under real-life conditions.DesignSystematic review and meta-analysis.Data sourcesMEDLINE, Embase, Cochrane Database for Systematic Reviews, INAHTA HTA database, preprint servers (via Europe PMC), ClinicalTrials.gov, WHO ICTRP from 1 January 2020 to 7 May 2021; NICE Evidence Search, NICE Guidance, FIND Website from 1 January 2020 to 24 May 2021.Review methodsDiagnostic cross-sectional or cohort studies were eligible for inclusion if they had paediatric study participants and compared rapid point-of care tests for diagnosing current SARS-CoV-2 infections with reverse transcription polymerase chain reaction (RT-PCR) as the reference standard. The Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) tool was used to assess the risk of bias and the applicability of the included studies. Bivariate meta-analyses with random effects were performed. Variability was assessed by subgroup analyses.Results17 studies with a total of 6355 paediatric study participants were included. All studies compared antigen tests against RT-PCR. Overall, studies evaluated eight antigen tests from six different brands. Only one study was at low risk of bias. The pooled overall diagnostic sensitivity and specificity in paediatric populations was 64.2% (95% CI 57.4% to 70.5%) and 99.1% (95% CI 98.2% to 99.5%), respectively. In symptomatic children, the pooled diagnostic sensitivity was 71.8% (95% CI 63.6% to 78.8%) and the pooled diagnostic specificity was 98.7% (95% CI 96.6% to 99.5%). The pooled diagnostic sensitivity in asymptomatic children was 56.2% (95% CI 47.6% to 64.4%) and the pooled diagnostic specificity was 98.6% (95% CI 97.3% to 99.3%).ConclusionsThe performance of current antigen tests in paediatric populations under real-life conditions varies broadly. Relevant data were only identified for very few antigen tests on the market, and the risk of bias was mostly unclear due to poor reporting. Additionally, the most common uses of these tests in children (eg, self-testing in schools or parents testing their toddlers before kindergarten) have not been addressed in clinical performance studies yet. The observed low diagnostic sensitivity may impact the planned purpose of the broad implementation of testing programmes.PROSPERO registration numberCRD42021236313.

Recent empirical evidence indicates that bond excess returns can be predicted using machine learning models. However, although the predictive power of machine learning models is intriguing, they typically lack transparency. This paper introduces the state-of-the-art explainable artificial intelligence technique SHapley Additive exPlanations (SHAP) to open the black box of these models. Our analysis identifies the key determinants that drive the predictions of bond excess returns produced by machine learning models and recognizes how these determinants relate to bond excess returns. This approach facilitates an economic interpretation of the predictions of bond excess returns made by machine learning models and contributes to a thorough understanding of the determinants of bond excess returns, which is critical for the decisions of market participants and the evaluation of economic theories.

Genetic and environmental risk factors and their interactions contribute to the development of complex diseases. In this review, we discuss methodological issues involved in investigating gene–environment (G × E) interactions in genetic–epidemiological studies of complex diseases and their potential relevance for clinical application. Although there are some important examples of interactions and applications, the widespread use of the knowledge about G × E interaction for targeted intervention or personalized treatment (pharmacogenetics) is still beyond current means. This is due to the fact that convincing evidence and high predictive or discriminative power are necessary conditions for usefulness in clinical practice. We attempt to clarify conceptual differences of the term ‘interaction’ in the statistical and biological sciences, since precise definitions are important for the interpretation of results. We argue that the investigation of G × E interactions is more rewarding for the detailed characterization of identified disease genes (ie at advanced stages of genetic research) and the stratified analysis of environmental effects by genotype or vice versa . Advantages and disadvantages of different epidemiological study designs are given and sample size requirements are exemplified. These issues as well as a critical appraisal of common methodological concerns are finally discussed.

Two optical techniques were developed and combined with a CFD simulation to obtain spatio-temporally resolved information on air/fuel mixing in the cylinder of a methane-fueled, fired, optically accessible engine. Laser-induced fluorescence (LIF) of anisole (methoxybenzene), vaporized in trace amounts into the gaseous fuel upstream of the injector, was captured by a two-camera system, providing one instantaneous image of the air/fuel ratio per cycle. Broadband infrared (IR) absorption by the methane fuel itself was measured in a small probe volume via a spark-plug integrated sensor, yielding time-resolved quasi-point information at kHz-rates. The simulation was based on the Reynolds-averaged Navier-Stokes (RANS) approach with the two-equation k-epsilon turbulence model in a finite volume discretization scheme and included the port-fuel injection event. Commercial CFD software was used to perform engine simulations close to the experimental conditions. Experimentally, the local gas temperature influences both LIF and IR measurements through the photophysics of fluorescence and IR absorption, respectively. Thus, in advances over previous implementations, both techniques also measured temperature and used this information to improve the accuracy of the measured air/fuel ratio. In the vicinity of the IR sensor, the local temperature deviated significantly from the bulk-gas temperature due to heat transfer. This was consistent with results of LIF measurements and CFD simulation. The simultaneous application of the two different, but complementary optical techniques together with a simulation gave detailed insight into mixture formation in the port-fueled engine. It also allowed for a cross-check of the uncertainties associated with the experiments as well as the simulation.

Objective Limited evidence suggests the importance of inflammatory processes for the etiology of lymphomas. To further research in this area, we investigated the role of genetic variants in key inflammatory factors, non-steroidal anti-inflammatory drug [NSAID] use, and their joint effect in lymphomagenesis. Methods The study comprised 710 case-control pairs, matched for gender, age, and study region. We examined the association of regular NSAID use and polymorphisms in prostaglandin-endoperoxide synthase-2 (COX2), prostaglandin E synthase (PTGES), interleukin-1 alpha (IL1A), IL-1 beta (IL1B), and IL-1 receptor antagonist (IL1RA), and lymphoma risk by applying logistic regression to calculate odds ratios (OR) and 95% confidence intervals (95% CI). Results Regular NSAID use was associated with a slightly reduced risk of B-NHL (OR = 0.8, 95% CI = 0.6-1.1). For T-NHL, the COX2 rs2745557 A-allele conferred a 2.2-fold (95% CI = 1.1-4.5) and homozygosis for the IL1RN rs454078 T-allele was associated with a 4.5-fold (95% CI = 1.4-13.9) elevated risk, however, based on sparse data. IL1 haplotype 5 was associated with a statistically significant 43% increased risk for B-NHL among non-regular users of NSAIDs, but a 70% decreased risk for regular users (p-value for interaction < 0.001). Conclusions These results suggest the relevance of joint effects between NSAID use and IL1 haplotypes on the risk of B-NHL.

Objective There is increasing interest in adding common genetic variants identified through genome wide association studies (GWAS) to breast cancer risk prediction models. First results from such models showed modest benefits in terms of risk discrimination. Heterogeneity of breast cancer as defined by hormone-receptor status has not been considered in this context. In this study we investigated the predictive capacity of 32 GWAS-detected common variants for breast cancer risk, alone and in combination with classical risk factors, and for tumours with different hormone receptor status. Material and methods Within the Breast and Prostate Cancer Cohort Consortium, we analysed 6009 invasive breast cancer cases and 7827 matched controls of European ancestry, with data on classical breast cancer risk factors and 32 common gene variants identified through GWAS. Discriminatory ability with respect to breast cancer of specific hormone receptor-status was assessed with the age adjusted and cohort-adjusted concordance statistic (AUROCa). Absolute risk scores were calculated with external reference data. Integrated discrimination improvement was used to measure improvements in risk prediction. Results We found a small but steady increase in discriminatory ability with increasing numbers of genetic variants included in the model (difference in AUROCa going from 2.7% to 4%). Discriminatory ability for all models varied strongly by hormone receptor status. Discussion and conclusions Adding information on common polymorphisms provides small but statistically significant improvements in the quality of breast cancer risk prediction models. We consistently observed better performance for receptor-positive cases, but the gain in discriminatory quality is not sufficient for clinical application.

Menopausal hormone therapy (HT) is associated with an increased breast cancer risk among postmenopausal women. In this study, we investigated genetic effect modification of HT associated breast cancer risk in 3,149 postmenopausal breast cancer patients and 5,489 controls from the two German population-based case–control studies MARIE and GENICA. Twenty-eight polymorphisms of 14 candidate genes including two drug and hormone transporter genes ( ABCB1/MDR1 and SHBG ), four genes involved in cell cycle regulation ( BRCA1 , P21/CDKN1A , STK15/AURKA and TP53 ), six cytokine genes ( IGFBP3 , IL6, TGFB1 , TNF , LTA and IGF1 ), and two cytokine receptor genes ( EGFR and ERBB2 ) were genotyped using validated methods. Conditional logistic regression was used to assess multiplicative statistical interaction between polymorphisms and duration of estrogen–progestagen therapy and estrogen monotherapy use with regard to breast cancer risk assuming log-additive and co-dominant modes of inheritance. Women homozygous for the major ABCB1 _rs2214102_G allele were found to be at a significantly increased breast cancer risk associated with combined estrogen–progestagen therapy [odds ratio (OR) = 1.17, 95% confidence interval (CI) = 1.12–1.23, P interaction  = 0.022]. Additionally, risk associated with estrogen monotherapy was modified by BRCA1 _rs799917. We observed a trend with increasing minor T alleles leading to the highest risk in homozygous carriers of the minor allele [OR (95% CI) = 1.17 (0.98–1.39), 1.06 (0.98–1.14), and 1.02 (0.94–1.11) for homozygous minor, heterozygous, and homozygous major allele carriers, respectively; P interaction  = 0.032]. Our results suggest that genetic variants in ABCB1 and BRCA1 may modify the effect of HT on postmenopausal breast cancer risk.