Accurate diagnostic tests are essential for effective screening and treatment. However, individual biomarkers often fail to provide sufficient diagnostic accuracy, as they typically capture only one aspect of the complex disease process. Combining multiple biomarkers, each capturing a distinct mechanism, can help constructing more informative diagnostic tests. In practice, logistic regression is used as the default to combine biomarkers, but it can perform poorly when biomarker distributions exhibit skewness or differ across disease groups. Nonparametric methods provide more flexibility but generally require large sample sizes that are infrequently available in biomedical research. We propose a novel framework called transformation discriminant analysis which combines biomarkers through the likelihood ratio function to construct theoretically optimal diagnostic scores. Transformation discriminant analysis balances between flexibility and efficiency. It can accommodate a wide range of distributional shapes and disease-specific dependence structures while remaining fully parametric. This allows for likelihood inference and strong performance even in small-sample settings. We evaluate TDA through simulations and benchmark its performance against commonly used methods. Finally, we illustrate its utility in constructing an optimal diagnostic test for hepatocellular carcinoma, a disease with no single ideal biomarker. An open-source R implementation is provided for reproducibility and broader application.

Over the last five decades, we have seen strong methodological advances in survival analysis, using parametric methods and, more prominently, methods based on non-/semi-parametric estimation. As the methodological landscape continues to evolve, the task of navigating through the multitude of methods and identifying available software resources is becoming increasingly challenging-especially in more complex scenarios, such as when dealing with interval-censored or clustered survival data, non-proportional hazards, or dependent censoring. This tutorial explores the potential of using the framework of smooth transformation models for survival analysis in the R system for statistical computing. This framework provides a unified maximum-likelihood approach that covers a wide range of survival models, including well-established ones such as the Weibull model and a fully parametric version of the famous Cox proportional hazards model, and various extensions for more complex scenarios. We explore models for non-proportional/crossing hazards, dependent censoring, clustered observations and extensions towards personalized medicine within this framework. Using survival data from a two-arm randomized controlled trial on rectal cancer therapy, we demonstrate how survival analysis tasks can be seamlessly navigated in R within this framework using the implementation provided by the tram package, and few related packages.

According to the Federal Statistical Office of Switzerland 0, 8% of births in 2021 were born below 32 gestational weeks (1). This number is comparable to other European countries (2). According to the WHO preterm-born infants are at risk for serious sickness and death (3). Subsequently, the birth of a very preterm infant and the following stay in the hospital leads to emotional distress of the parents (4). Very preterm-born infants have a higher risk of adverse outcomes including respiratory problems, hearing and visual impairment, gastrointestinal morbidities, neurodevelopmental disabilities, and cognitive impairment (5). During neonatal hospital care, infants are monitored by medical staff. Interventions aimed at improving parent-infant bonding and family centered care is implemented in many neonatal units. While coming home is long awaited it is also associated with an interruption of highly specialized professional care. Aydon et al. showed that parents of preterm-born infants felt anxious and not prepared regarding the transition to home (6). Parents of very preterm-born infants often experience substantial stress leading to symptoms of posttraumatic stress disorder (7). In the published protocol by Bedwell (8) there is some overlap to our study. However, our systematic review will focus on interventions that target the transition from hospital to home. In the published protocol by Aagaard (9) there is also some overlap. But, contrary to our review, their focus is the parental experience regarding the transition to home. Both protocols reported on a narrower time frame.

INTRODUCTION:Medical progress has significantly improved the survival rates of very preterm-born infants in recent decades. Nevertheless, these infants are still at increased risk for long-term impairments as compared with term-born infants. While the homecoming of a preterm-born infant is long-awaited and brings relief to families, it also marks the end of intensive monitoring and highly specialised professional care. This situation often leaves parents coping with anxiety and a sense of unpreparedness, as they navigate the transition to home. Correspondingly, the WHO advocates for additional support for parents of preterm infants following the transition to home. According to the WHO, preparation for this transition yields positive effects on crucial aspects such as nutrition, parent-child interaction and parental well-being. However, there is significant heterogeneity as to which interventions are in place and regarding their level of efficacy. Consequently, we aimed to provide an overview of existing transition-to-home interventions and their efficacy by conducting a systematic review of the literature. METHODS AND ANALYSIS:We will perform a systematic review of interventions aiming at improving the transition to home process for very preterm-born infants and their parents and will search the following databases: Cochrane, Medline, CINAHL, EMBASE and PsycINFO. Our main aim is to provide an overview of existing transition-to-home interventions and their efficacy in categories of intervention types. We will not predefine specific outcomes, but we will describe, assess and summarise the outcomes of the included studies. All reported outcomes will be recalculated to the scale of standardised mean differences. Meta-analysis will be performed with a random-effects model to account for expected large between-study heterogeneity. ETHICS AND DISSEMINATION:No ethics approval was necessary for this study. The aim of our systematic review is to provide a comprehensive overview of current interventions designed to improve the transition from hospital to home. Subsequently, it will show the individual effect of these interventions and pool effect sizes if possible. The most promising interventions will then be combined and used as the basis of a subsequent randomised controlled trial of a new transition-to-home intervention. The results of the study will be published in a peer-reviewed journal. PROSPERO REGISTRATION NUMBER:The systematic review protocol was registered with the International Prospective Register of Systematic Reviews (PROSPERO) on 30 August 2023. PROSPERO CRD42023455401.

We introduce a generalized additive model for location, scale, and shape (GAMLSS) next of kin aiming at distribution-free and parsimonious regression modelling for arbitrary outcomes. We replace the strict parametric distribution formulating such a model by a transformation function, which in turn is estimated from data. Doing so not only makes the model distribution-free but also allows to limit the number of linear or smooth model terms to a pair of location-scale predictor functions. We derive the likelihood for continuous, discrete, and randomly censored observations, along with corresponding score functions. A plethora of existing algorithms is leveraged for model estimation, including constrained maximum-likelihood, the original GAMLSS algorithm, and transformation trees. Parameter interpretability in the resulting models is closely connected to model selection. We propose the application of a novel best subset selection procedure to achieve especially simple ways of interpretation. All techniques are motivated and illustrated by a collection of applications from different domains, including crossing and partial proportional hazards, complex count regression, non-linear ordinal regression, and growth curves. All analyses are reproducible with the help of the "tram" add-on package to the R system for statistical computing and graphics.

The question of how individual patient data from cohort studies or historical clinical trials can be leveraged for designing more powerful, or smaller yet equally powerful, clinical trials becomes increasingly important in the era of digitalization. Today, the traditional statistical analyses approaches may seem questionable to practitioners in light of ubiquitous historical prognostic information. Several methodological developments aim at incorporating historical information in the design and analysis of future clinical trials, most importantly Bayesian information borrowing, propensity score methods, stratification, and covariate adjustment. Adjusting the analysis with respect to a prognostic score, which was obtained from some model applied to historical data, received renewed interest from a machine learning perspective, and we study the potential of this approach for randomized clinical trials. In an idealized situation of a normal outcome in a two‐arm trial with 1:1 allocation, we derive a simple sample size reduction formula as a function of two criteria characterizing the prognostic score: (1) the coefficient of determination R2 on historical data and (2) the correlation ρ between the estimated and the true unknown prognostic scores. While maintaining the same power, the original total sample size n planned for the unadjusted analysis reduces to (1−R2ρ2)×n$(1 - R^2 \rho ^2) \times n$ in an adjusted analysis. Robustness in less ideal situations was assessed empirically. We conclude that there is potential for substantially more powerful or smaller trials, but only when prognostic scores can be accurately estimated.

Background: In 2018, tafenoquine was approved for malaria chemoprophylaxis. We evaluated all available data on the safety and efficacy of tafenoquine chemoprophylaxis. Methods: This systematic review followed the PRISMA guidelines and was registered on PROSPERO (CRD42019123839). We searched PubMed, Embase, Scopus, CINAHL and Cochrane databases. Two authors (JDM, PS) screened all papers. Results: We included 44 papers in the qualitative and 9 in the quantitative analyses. These 9 randomized, controlled trials included 2495 participants, aged 12-60 years with 27.3% women. Six studies were conducted in Plasmodium spp.-endemic regions; two were human infection studies. 200 mg weekly tafenoquine and higher dosages lead to a significant reduction of Plasmodium spp. infection compared to placebo and were comparable to 250 mg mefloquine weekly with a protective efficacy between 77.9 and 100% or a total risk ratio of 0.22 (95%-CI: 0.07-0.73; p = 0.013) in favour of tafenoquine. Adverse events (AE) were comparable in frequency and severity between tafenoquine and comparator arms. One study reported significantly more gastrointestinal events in tafenoquine users (p <= 0.001). Evidence of increased, reversible, asymptomatic vortex keratopathy in subjects with prolonged tafenoquine exposures was found. A single, serious event of decreased macular sensitivity occurred. Conclusion: This systematic review and meta-analysis of trials of G6PD-normal adults show that weekly tafenoquine 200 mg is well tolerated and effective as malaria chemoprophylaxis focusing primarily on Plasmodium falciparum but also on Plasmodium vivax. Our safety analysis is limited by heterogenous methods of adverse events reporting. Further research is indicated on the use of tafenoquine in diverse traveller populations.

OBJECTIVE:During the first COVID-19 wave in Switzerland, relative mortality was at least eight times higher compared with the uninfected general population. We aimed to assess sex-specific and age-specific relative mortality associated with a SARS-CoV-2 diagnosis during the second wave.DESIGN:Prospective population-based study.SETTING:Individuals testing positive for SARS-CoV-2 after the start of the second wave on 1 October 2020 were followed up until death or administrative censoring on 31 December 2020.PARTICIPANTS:5 179 740 inhabitants of Switzerland in fall 2018 aged 35-95 years (without COVID-19) and 257 288 persons tested positive for SARS-CoV-2 by PCR or antigen testing during the second wave.PRIMARY AND SECONDARY OUTCOME MEASURES:The planned outcome measure was time to death from any cause, measured from the date of a SARS-CoV-2 diagnosis or 1 October in the general population. Information on confirmed SARS-CoV-2 diagnoses and deaths was matched by calendar time with the all-cause mortality of the general Swiss population of 2018. Proportional hazards models were used to estimate sex-specific and age-specific mortality rates and probabilities of death within 60 days.RESULTS:The risk of death for individuals tested positive for SARS-CoV-2 in the second wave in Switzerland increased at least sixfold compared with the general population. HRs, reflecting the risk attributable to a SARS-CoV-2 infection, were higher for men (1.40, 95% CI 1.29 to 1.52) and increased for each additional year of age (1.01, 95% CI 1.01 to 1.02). COVID-19 mortality was reduced by at least 20% compared with the first wave in spring 2020.CONCLUSION:General mortality patterns, increased for men and older persons, were similar in spring and in fall. Absolute and relative COVID-19 mortality was smaller in fall.TRIAL REGISTRATION:The protocol for this study was registered on 3 December 2020 at https://osf.io/gbd6r.

BackgroundObstructive sleep apnoea (OSA) is associated with an increased prevalence of aortic aneurysms and it has also been suggested that severe OSA furthers aneurysm expansion in the abdomen. We evaluated whether OSA is a risk factor for the progression of ascending thoracic aortic aneurysm (TAA).MethodsPatients with TAA underwent yearly standardised echocardiographic measurements of the ascending aorta over 3 years and two level III sleep studies. The primary outcome was the expansion rate of TAA in relation to the apnoea–hypopnoea index (AHI). Secondary outcomes included surveillance for aortic events (composite end-points of rupture/dissection, elective surgery or death).ResultsBetween July 2014 and March 2020, 230 patients (median age 70 years, 83.5% male) participated in the cohort. At baseline, 34.8% of patients had AHI ≥15 events·h−1. There was no association between TAA diameter and AHI at baseline. After 3 years, mean±sdexpansion rates were 0.55±1.25 mm at the aortic sinus and 0.60±1.12 mm at the ascending aorta. In the regression analysis, after controlling for baseline diameter and cardiovascular risk factors, there was strong evidence for a positive association of TAA expansion with AHI (aortic sinus estimate 0.025 mm, 95% CI 0.009–0.040 mm; p<0.001 and ascending aorta estimate 0.026 mm, 95% CI 0.011–0.041 mm; p=0.001). 20 participants (8%) experienced an aortic event; however, there was no association with OSA severity.ConclusionOSA may be a modest but independent risk factor for faster TAA expansion and thus potentially contributes to life-threatening complications in aortic disease.

BACKGROUND:Strong evidence exists for clinically relevant night-to-night variability of respiratory events in patients with suspected OSA. RESEARCH QUESTION:How many sleep study nights are required to diagnose OSA accurately? STUDY DESIGN AND METHODS:Patients with suspected OSA underwent up to 14 nights of pulse oximetry (PO) at home and one night of in-hospital respiratory polygraphy (RP). The accuracy of each of the 13 sleep study nights was analyzed using the mean oxygen desaturation index 3% (ODI3%) of all 14 nights as a reference. Multiple regression analyses assessed possible predictors for night-to-night variability. RESULTS:One hundred three patients underwent in-hospital RP. Using only the results of the RP, 19.7% were misdiagnosed using an ODI3% cutoff of 15/h. One hundred eight patients underwent properly performed PO studies at home with a coefficient of variation (CV) of 31.5% (SD, 14.7%) across all nights. The first PO night demonstrated a sensitivity of 71.4% (95% CI, 55.4%-84.3%) and a specificity of 89.4% (95% CI, 79.4%-95.6%) to diagnose moderate OSA. Using only the first PO night, the negative predictive value was 83.1%. Adding a second recording night increased sensitivity up to 88.1% (95% CI, 74.4%-96.0%) with a slightly lower specificity of 85.9% (95% CI, 74.9%-93.4%). The ODI3% of the in-hospital RP showed an independent negative association to the log-transformed CV (exponentiated coefficient, 0.989; 95% CI, 0.984-0.995). INTERPRETATION:One single night of in-hospital RP may miss relevant OSA. Multiple study nights, for example, using ambulatory oxygen saturation monitoring, increase accuracy for diagnosing moderate OSA. TRIAL REGISTRY:ClinicalTrials.gov; No.: NCT03819361; URL: www.clinicaltrials.gov.

The cotram package offers a ready-to-use R implementation of count transformation models, providing a simple but flexible approach for the regression analysis of count responses arising from various, and possibly complex, data-generating processes. In this unified maximum-likelihood framework count models can be formulated, estimated, and evaluated easily. Specific models in the class can be flexibly customised by the choice of the link function and the parameterisation of the transformation function. Interpretation of explanatory variables in the linear predictor is possible at the scales of the discrete odds ratio, hazard ratio, or reverse time hazard ratio, or as conditional mean of transformed counts. The implemented methods for the model class further provide simple tools for model evaluation. The package simplifies the use of transformation models for modelling counts, while ensuring appropriate settings for count data specifically. Extension to the formulated models can be made by the inclusion of response-varying effects, strata-specific transformation functions, or offsets, based on the underlying infrastructure of the tram and mlt R add-on packages, which further ensure the correct handling of the likelihood for censored or truncated observations.

Background Strong evidence exists for clinically relevant night-to-night variability of respiratory events in patients with suspected OSA. Research Question How many sleep study nights are required to diagnose OSA accurately? Study Design and Methods Patients with suspected OSA underwent up to 14 nights of pulse oximetry (PO) at home and one night of in-hospital respiratory polygraphy (RP). The accuracy of each of the 13 sleep study nights was analyzed using the mean oxygen desaturation index 3% (ODI3%) of all 14 nights as a reference. Multiple regression analyses assessed possible predictors for night-to-night variability. Results One hundred three patients underwent in-hospital RP. Using only the results of the RP, 19.7% were misdiagnosed using an ODI3% cutoff of 15/h. One hundred eight patients underwent properly performed PO studies at home with a coefficient of variation (CV) of 31.5% (SD, 14.7%) across all nights. The first PO night demonstrated a sensitivity of 71.4% (95% CI, 55.4%-84.3%) and a specificity of 89.4% (95% CI, 79.4%-95.6%) to diagnose moderate OSA. Using only the first PO night, the negative predictive value was 83.1%. Adding a second recording night increased sensitivity up to 88.1% (95% CI, 74.4%-96.0%) with a slightly lower specificity of 85.9% (95% CI, 74.9%-93.4%). The ODI3% of the in-hospital RP showed an independent negative association to the log-transformed CV (exponentiated coefficient, 0.989; 95% CI, 0.984-0.995). Interpretation One single night of in-hospital RP may miss relevant OSA. Multiple study nights, for example, using ambulatory oxygen saturation monitoring, increase accuracy for diagnosing moderate OSA. Trial Registry ClinicalTrials.gov; No.: NCT03819361; URL: www.clinicaltrials.gov Strong evidence exists for clinically relevant night-to-night variability of respiratory events in patients with suspected OSA. How many sleep study nights are required to diagnose OSA accurately? Patients with suspected OSA underwent up to 14 nights of pulse oximetry (PO) at home and one night of in-hospital respiratory polygraphy (RP). The accuracy of each of the 13 sleep study nights was analyzed using the mean oxygen desaturation index 3% (ODI3%) of all 14 nights as a reference. Multiple regression analyses assessed possible predictors for night-to-night variability. One hundred three patients underwent in-hospital RP. Using only the results of the RP, 19.7% were misdiagnosed using an ODI3% cutoff of 15/h. One hundred eight patients underwent properly performed PO studies at home with a coefficient of variation (CV) of 31.5% (SD, 14.7%) across all nights. The first PO night demonstrated a sensitivity of 71.4% (95% CI, 55.4%-84.3%) and a specificity of 89.4% (95% CI, 79.4%-95.6%) to diagnose moderate OSA. Using only the first PO night, the negative predictive value was 83.1%. Adding a second recording night increased sensitivity up to 88.1% (95% CI, 74.4%-96.0%) with a slightly lower specificity of 85.9% (95% CI, 74.9%-93.4%). The ODI3% of the in-hospital RP showed an independent negative association to the log-transformed CV (exponentiated coefficient, 0.989; 95% CI, 0.984-0.995). One single night of in-hospital RP may miss relevant OSA. Multiple study nights, for example, using ambulatory oxygen saturation monitoring, increase accuracy for diagnosing moderate OSA. ClinicalTrials.gov; No.: NCT03819361; URL: www.clinicaltrials.gov

The effect of explanatory environmental variables on a species' distribution is often assessed using a count regression model. Poisson generalized linear models or negative binomial models are common, but the traditional approach of modelling the mean after log or square root transformation remains popular and in some cases is even advocated. We propose a novel framework of linear models for count data. Similar to the traditional approach, the new models apply a transformation to count responses; however, this transformation is estimated from the data and not defined a priori. In contrast to simple least‐squares fitting and in line with Poisson or negative binomial models, the exact discrete likelihood is optimized for parameter estimation and inference. Simple interpretation of effects in the linear predictors is possible. Count transformation models provide a new approach to regressing count data in a distribution‐free yet fully parametric fashion, obviating the need to a priori commit to a specific parametric family of distributions or to a specific transformation. The models are a generalization of discrete Weibull models for counts and are thus able to handle over‐ and underdispersion. We demonstrate empirically that the models are more flexible than Poisson or negative binomial models but still maintain interpretability of multiplicative effects. A re‐analysis of deer–vehicle collisions and the results of artificial simulation experiments provide evidence of the practical applicability of the model framework. In ecology studies, uncertainties regarding whether and how to transform count data can be resolved in the framework of count transformation models, which were designed to simultaneously estimate an appropriate transformation and the linear effects of environmental variables by maximizing the exact count log‐likelihood. The application of data‐driven transformations allows over‐ and underdispersion to be addressed in a model‐based approach. Models in this class can be compared to Poisson or negative binomial models using the in‐ or out‐of‐sample log‐likelihood. Extensions to nonlinear additive or interaction effects, correlated observations, hurdle‐type models and other more complex situations are possible. A free software implementation is available in the cotram add‐on package to the R system for statistical computing.

Obstructive sleep apnea (OSA) is associated with aortic aneurysms and it has been suggested that OSA furthers its expansion in the abdomen (Gaisl et al. ERJ 2015 46: 532-544). The aim of this ongoing study is to evaluate whether OSA is a risk factor for the progression of thoracic aortic aneurysms (TAA). In this ongoing study, patients with TAA will undergo 4 annual, standardized, echocardiographic measurements of the TAA in addition to a level-3 sleep study at baseline and on the last follow-up. The primary outcome is the yearly expansion rate of TAA in relation to OSA-severity; the secondary outcomes encompass aortic events i.e. ruptures, dissections, endovascular repairs or rapid progressions of TAA. Currently, 230 patients were included at baseline, 39% of participants completed the study and 4% experienced an aortic event. OSA (defined as apnoea-hypopnoea-index ≥5/h) was present in 61% of all subjects at baseline. In total, 14 patients were started on continuous positive air pressure therapy during follow-up. The progression rates were 0.25±0.34 mm at the level of the aorta ascendens, and 0.46±0.6 mm at the level of the sinus vasalvae after the first year. OSA severity was not associated with TAA-progression after the first year of the study. OSA is highly prevalent in patients with TAA. Whether OSA is associated with accelerated TAA expansion rate or aortic events over a follow-up of 3 years, remains to be established at the end of the study.

Particular groups of plant-beneficial fluorescent pseudomonads are not only root colonizers that provide plant disease suppression, but in addition are able to infect and kill insect larvae. The mechanisms by which the bacteria manage to infest this alternative host, to overcome its immune system, and to ultimately kill the insect are still largely unknown. However, the investigation of the few virulence factors discovered so far, points to a highly multifactorial nature of insecticidal activity. Antimicrobial compounds produced by fluorescent pseudomonads are effective weapons against a vast diversity of organisms such as fungi, oomycetes, nematodes and protozoa. Here, we investigated whether these compounds also contribute to insecticidal activity. We tested mutants of the highly insecticidal strains Pseudomonas protegens CHA0, Pseudomonas chlororaphis PCL1391, and Pseudomonas sp. CMR12a, defective for individual or multiple antimicrobial compounds, for injectable and oral activity against lepidopteran insect larvae. Moreover, we studied expression of biosynthesis genes for these antimicrobial compounds for the first time in insects. Our survey revealed that hydrogen cyanide and different types of cyclic lipopeptides contribute to insecticidal activity. Hydrogen cyanide was essential to full virulence of CHA0 and PCL1391 directly injected into the hemolymph. The cyclic lipopeptide orfamide produced by CHA0 and CMR12a was mainly important in oral infections. Mutants of CMR12a and PCL1391 impaired in the production of the cyclic lipopeptides sessilin and clp1391, respectively, showed reduced virulence in injection and feeding experiments. Although virulence of mutants lacking one or several of the other antimicrobial compounds, i.e. 2,4-diacetylphloroglucinol, phenazines, pyrrolnitrin, or pyoluteorin, was not reduced, these metabolites might still play a role in an insect background since all investigated biosynthetic genes for antimicrobial compounds of strain CHA0 were expressed at some point during insect infection. In summary, our study identified new factors contributing to insecticidal activity and extends the diverse functions of antimicrobial compounds produced by fluorescent pseudomonads from the plant environment to the insect host.