The fraternal birth-order effect as a statistical artefact: convergent evidence from probability calculus, simulated data, and multiverse meta-analysis.

The fraternal-birth order effect (FBOE) is a research claim which states that each older brother increases the odds of homosexual orientation in men via an immunoreactivity process known as the maternal immune hypothesis. Importantly, older sisters supposedly either do not affect these odds, or affect them to a lesser extent. Consequently, the fraternal birth-order effect predicts that the association between the number of older brothers and homosexual orientation in men is greater in magnitude than any association between the number of older sisters and homosexual orientation. This difference in magnitude represents the main theoretical estimand of the FBOE. In addition, no comparable effects should be observable among homosexual vs heterosexual women. Here, we triangulate the empirical foundations of the FBOE from three distinct, informative perspectives, complementing each other: first, drawing on basic probability calculus, we deduce mathematically that the body of statistical evidence used to make inferences about the main theoretical estimand of the FBOE rests on incorrect statistical reasoning. In particular, we show that throughout the literature researchers ascribe to the false assumptions that effects of family size should be adjusted for and that this could be achieved through the use of ratio variables. Second, using a data-simulation approach, we demonstrate that by using currently recommended statistical practices, researchers are bound to frequently draw incorrect conclusions. And third, we re-examine the empirical evidence of the fraternal birth-order effect in men and women by using a novel specification-curve and multiverse approach to meta-analysis (64 male and 17 female samples, N = 2,778,998). When analyzed correctly, the specific association between the number of older brothers and homosexual orientation is small, heterogenous in magnitude, and apparently not specific to men. In addition, existing research evidence seems to be exaggerated by small-study effects.

Patient adherence to fully reimbursed proprotein convertase subtilisin/kexin type 9 inhibitor (PCSK9i) treatment.

AIMS: Proprotein convertase subtilisin/kexin-type 9 inhibitor (PCSK9i) treatment reduces cardiovascular events when taken over a long time for secondary prevention. Data on treatment adherence are scarce and maybe affected by co-payment of patients. The aim of this study was to elucidate PCSK9i treatment adherence in a setting of full cost coverage as it is the case in a number of European countries. METHODS AND RESULTS: Baseline data and prescription patterns of all 7302 patients with PCSK9i prescriptions dispensed on the account of Austrian Social Insurances between September 2015 and December 2020 were retrieved and analyzed. A gap of ≥ 60 days between prescriptions was defined as treatment discontinuation. Patient adherence was evaluated as the proportion of days covered (PDC) over the observation period and treatment discontinuation rates were investigated by the Kaplan-Meier method. The mean PDC was 81.8% and was significantly lower in female patients. A PDC of ≥ 80% indicating adequate adherence was found in 73.8%. Of the study population 27.4% discontinued PCSK9i treatment and 49.2% thereof re-initiated treatment during the observation period. Most of the patients who discontinued treatment did so within the first year. Male patients and patients under 64 years showed significantly lower discontinuation and higher re-initiation rates. CONCLUSION: Considering the high PDC and low discontinuation rates, the majority of patients adhere to PCSK9i treatment. Hence, in a system where PCSK9i treatment is made available at virtually no costs for patients this highly effective treatment is well-accepted as a long-term treatment.

Orphan drugs' clinical uncertainty and prices: Addressing allocative and technical inefficiencies in orphan drug reimbursement.

Legislations incentivising orphan drug development and scientific advances have made orphan drugs pharma's high-end favourite for the past two decades. Currently, around 50% of new marketing authorizations are for orphan drugs. For third-party healthcare payers ("payers") the rise of orphan drugs presents new challenges, including a high degree of uncertainty around clinical benefits and harms, a moderate effect size (for many orphan drugs), and a high price tag. The association of high clinical uncertainty and moderate effect sizes is not surprising in small target populations but in combination with high prices creates the risk of allocative and technical inefficiencies for payers. We here discuss and illustrate these risks. A combination of policies is needed for mitigation of allocative inefficiency: while there may be a rationale for higher prices for orphan than non-orphan drugs, a focus of pricing and reimbursement negotiations should include considerations of product profitability and of the consequences of orphan drug costs on the distribution inequality of medication costs for individual insured persons, coupled to knowledge generation from reimbursement contracts covering high-price orphan drugs that would benefit the wider patient community. Performance-based managed entry agreements could help to de-risk the economic consequences of clinical uncertainty and to mitigate technical inefficiency.

Cardiovascular risk and mortality in patients with hyperuricemia treated with febuxostat or allopurinol: a retrospective nation-wide cohort study in Austria 2014-2017.

Patients with hyperuricemia and gout are at an increased risk for cardiovascular (CV) disease. Inhibition of the xanthine oxidase with allopurinol or febuxostat have become the mainstay for urate lowering therapy. However, it has been suggested that febuxostat increases the risk for CV mortality as compared to allopurinol. The aim of this retrospective cohort study was to assess the CV risk among patients with febuxostat or allopurinol therapy. Patients who initiated urate lowering therapy with febuxostat or allopurinol between 2014 and 2017 were selected from the drug reimbursement database of the Austrian health insurances funds. The primary CV endpoint was a composite of angina pectoris, nonfatal myocardial infarction, nonfatal subarachnoid or cerebral hemorrhage, nonfatal ischemic stroke, or death from any cause. In total, 28.068 patients (62.1% male) with a mean age of 71 years were included. 7.767 initiated febuxostat treatment and 20.301 received allopurinol. The incidence rate per 100 patient-years of the composite primary endpoint was 448 (febuxostat) and 356 (allopurinol) with a corresponding adjusted hazard ratio (HR) of 0.58 (95% CI 0.53-0.63) for allopurinol vs. febuxostat initiators. Similar HR were found for secondary endpoints including all-cause mortality [0.61 (95% CI 0.55-0.68)] and separate analyses of cardiac events [0.48 (95% CI 0.38-0.61)] and ischemic stroke [0.47 (95% CI 0.36-0.61)]. Data from this Austrian population-based study suggests that febuxostat initiators are at an increased risk for nonfatal CV events or death from any cause as compared to those with allopurinol. This is consistent with CV concerns of other trials, which limited the broad therapeutic use of febuxostat.

Assessing the Accuracy of Sales Forecasts Submitted by Pharmaceutical Companies Applying for Reimbursement in Austria.

Objectives: Reimbursement decisions on new medicines require an assessment of their value. In Austria, when applying for reimbursement of new medicines, pharmaceutical companies are also obliged to submit forecasts of future sales. We systematically examined the accuracy of these pharmaceutical sales forecasts and hence the usefulness of these forecasts for reimbursement evaluations. Methods: We retrospectively analyzed reimbursement applications of 102 new drugs submitted between 2005 and 2014, which were accepted for reimbursement outside of hospitals, and for which actual reimbursed sales were available for at least 3 years. The main outcome variable was the accuracy ratio, defined as the ratio of forecasted sales submitted by pharmaceutical companies when applying for reimbursement to actual sales from reimbursement data. Results: The median accuracy ratio [95% confidence interval] was 1.33 [1.03; 1.74, range 0.15-37.5], corresponding to a median overestimation of actual sales by 33%. Forecasts of actual sales for 55.9% of all examined products either overestimated actual sales by more than 100% or underestimated them by more than 50%. The accuracy of sales forecasts did not show systematic change over the analyzed decade nor was it discernibly influenced by reimbursement status (restricted or unrestricted), the degree of therapeutic benefit, or the therapeutic area of the pharmaceutical product. Sales forecasts of drugs with a higher degree of innovation and those within a dynamic market tended to be slightly more accurate. Conclusions: The majority of sales forecasts provided by applicants for reimbursement evaluations in Austria were highly inaccurate and were on average too optimistic. This is in line with published results for other jurisdictions and highlights the need for caution when using such forecasts for reimbursement procedures.

Nomogram Predicting Overall Survival in Patients With Locally Advanced Cervical Cancer Treated With Radiochemotherapy Including Image-Guided Brachytherapy: A Retro-EMBRACE Study.

PURPOSE: To present a nomogram for prediction of overall survival (OS) in patients with locally advanced cervical cancer (LACC) undergoing definitive radiochemotherapy including image-guided adaptive brachytherapy (IGABT). METHODS AND MATERIALS: Seven hundred twenty patients with LACC treated with radiochemotherapy including IGABT in 12 institutions (median follow-up 56 months) were analyzed; 248 deaths occurred. Thirteen candidate predictors for OS were a priori chosen on the basis of the literature and expert knowledge. Missing data (7.2%) were imputed using multiple imputation and predictive mean matching. Univariate analysis with a multivariable Cox regression model for OS stratified by center was performed. Stepwise selection of predictive factors with the Akaike Information Criterion was used to obtain a predictive model and construct a nomogram for OS predictions 60 months from diagnosis; this was internally validated by concordance probability as a measure of discrimination and a calibration plot. RESULTS: Thirteen potential predictive factors were evaluated; 10 factors reached statistical significance in univariate analysis (age, Hemoglobin, FIGO Stage2009, tumor width, corpus involvement, lymph node involvement, concurrent chemotherapy, dose to 90% of the high-risk clinical target volume, volume of CTV at the first brachytherapy [CTVHRVolumeBT], overall treatment time [OTT]). Four factors were confirmed significant within the multivariable Cox regression model (FIGO Stage2009, lymph node involvement, concurrent chemotherapy, CTVHRVolumeBT). The predictive model and corresponding nomogram were based on 7 Akaike Information Criterion-selected factors (age, corpus involvement, FIGO Stage2009, lymph node involvement, concurrent chemotherapy, CTVHRVolumeBT, OTT) and showed promising calibration and discrimination (cross-validated concordance probability c = 0.73). CONCLUSIONS: This is the first nomogram to predict OS in patients with LACC treated with IGABT. In addition to previously reported factors (age, FIGO2009 stage, corpus involvement, chemotherapy delivery, OTT, lymph node involvement), status of primary tumor at the time of brachytherapy seems to be an essential outcome predictor. These results can facilitate individualized tailoring of treatment and patient counseling during the treatment.

Charting the landscape of graphical displays for meta-analysis and systematic reviews: a comprehensive review, taxonomy, and feature analysis.

BACKGROUND: Data-visualization methods are essential to explore and communicate meta-analytic data and results. With a large number of novel graphs proposed quite recently, a comprehensive, up-to-date overview of available graphing options for meta-analysis is unavailable. METHODS: We applied a multi-tiered search strategy to find the meta-analytic graphs proposed and introduced so far. We checked more than 150 retrievable textbooks on research synthesis methodology cover to cover, six different software programs regularly used for meta-analysis, and the entire content of two leading journals on research synthesis. In addition, we conducted Google Scholar and Google image searches and cited-reference searches of prior reviews of the topic. Retrieved graphs were categorized into a taxonomy encompassing 11 main classes, evaluated according to 24 graph-functionality features, and individually presented and described with explanatory vignettes. RESULTS: We ascertained more than 200 different graphs and graph variants used to visualize meta-analytic data. One half of these have accrued within the past 10 years alone. The most prevalent classes were graphs for network meta-analysis (45 displays), graphs showing combined effect(s) only (26), funnel plot-like displays (24), displays showing more than one outcome per study (19), robustness, outlier and influence diagnostics (15), study selection and p-value based displays (15), and forest plot-like displays (14). The majority of graphs (130, 62.5%) possessed a unique combination of graph features. CONCLUSIONS: The rich and diverse set of available meta-analytic graphs offers a variety of options to display many different aspects of meta-analyses. This comprehensive overview of available graphs allows researchers to make better-informed decisions on which graphs suit their needs and therefore facilitates using the meta-analytic tool kit of graphs to its full potential. It also constitutes a roadmap for a goal-driven development of further graphical displays for research synthesis.

Predicting future citation counts of scientific manuscripts submitted for publication: a cohort study in transplantology.

Citations are widely used for measuring scientific impact. The goal of the present study was to predict citation counts of manuscripts submitted to Transplant International (TI) in the two calendar years following publication. We considered a comprehensive set of 21 manuscript, author, and peer-review-related predictor variables available early in the peer-review process. We also evaluated how successfully the peer-review process at TI identified and accepted the most promising manuscripts for publication. A developed predictive model with nine selected variables showed acceptable test performance to identify often cited articles (AUROC = 0.685). Particularly important predictors were the number of pages, month of publication, publication type (review versus other), and study on humans (yes versus no). Accepted manuscripts at TI were cited more often than rejected but elsewhere published manuscripts (median 4 vs. 2 citations). The predictive model did not outperform the actual editorial decision. Both findings suggest that the peer-review process at TI, in its current form, was successful in selecting submitted manuscripts with a high scientific impact in the future. Predictive models might have the potential to support the review process when decisions are made under great uncertainty.

Associations of Bisexuality and Homosexuality with Handedness and Footedness: A Latent Variable Analysis Approach.

Non-right-handedness appears to be more common among bisexuals and homosexuals than among heterosexuals, which might be indirect evidence of effects of prenatal androgen exposure. Current data suggest higher prenatal testosterone levels among bisexual and homosexual women, but are inconclusive for men. This study examined the association between sexual orientation and non-right-handedness for sex differences and whether higher rates of mixed-handedness, rather than left-handedness, might be the driving factor. This allowed for more specific tests regarding the predictions of two competing theories of prenatal androgen exposure, the Geschwind-Galaburda theory and the callosal hypothesis, than in previous research. Being a potentially better indicator of cerebral lateralization than handedness, associations with footedness were also explored. To counter inconsistencies and shortcomings of previous research, we utilized two large discovery and replication datasets (ns = 2368 and 1565) and applied latent variable analysis to reliably classify lateral preferences (i.e., handedness, footedness). This maximized the statistical conclusion validity and allowed for direct tests of replicability. Sexual orientation was differentially associated with lateral preferences among men and women. Associations among women were consistent with predictions of the Geschwind-Galaburda theory, whereas among men they were consistent with predictions of the callosal hypothesis. The results were further consistent with models of homosexuality that suggest a role of parental epigenetic marks on sexually dimorphic fetal development. Research efforts should be increased with regard to footedness and epigenetic theories of homosexuality.

META-ANALYSIS SHOWS ASSOCIATIONS OF DIGIT RATIO (2D:4D) AND TRANSGENDER IDENTITY ARE SMALL AT BEST.

ABBREVIATIONS: 2D:4D = digit ratio; CI = confidence interval; F = female; FtM = female-to-male transgender; M = male; MtF = male-to-female transgender; TGI = transgender identity.

Rainforest plots for the presentation of patient-subgroup analysis in clinical trials.

While the conventional forest plot is useful to present results within subgroups of patients in clinical studies, it has been criticized for several reasons. First, small subgroups are visually overemphasized by long confidence interval lines, which is misleading. Second, the point estimates of large subgroups are difficult to discern because of the large box representing the precision of the estimate within subgroups. Third, confidence intervals depicted by lines might incorrectly convey the impression that all points within the interval are equally likely. Rainforest plots have been proposed to overcome these potentially misleading aspects of conventional forest plots. The metaviz package enables to generate rainforest plots for meta-analysis within the statistical computing environment R. We suggest the application of rainforest plots for the depiction of subgroup analysis in clinical trials. In this tutorial, detailed step-by-step guidance on the generation of rainforest plot for this purpose is provided.