The transplantation of a single kidney, while its partner kidney is discarded despite both having been initially offered (dual-offered, single-transplanted; DOST), represents a missed opportunity to preserve nephron mass and optimize donor utilization. Dual kidney transplantation (DKT) preserves nephron mass by transplanting both kidneys from a higher-risk donor, but its benefits relative to DOST remain underexplored. In this nationwide multicenter cohort (2008–2021), we analyzed 36 DKT and 20 DOST recipients. Both groups were propensity-matched 1:2 to 112 regular single-kidney transplant recipients (RegT) based on donor and recipient characteristics. The primary endpoint was estimated glomerular filtration rate (eGFR) at 12 months. Secondary endpoints included eGFR through 5 years, death-censored graft and patient survival, perioperative metrics, and 12-month quality of life (EQ-5D). At 12 months, median eGFR was higher in DKT (50.8 ml/min/1.73 m2) than DOST (33.5 ml/min/1.73 m2) or RegT (38.0 ml/min/1.73 m2; p < 0.001), with differences sustained through 5 years. Graft and patient survival were similar. DKT involved longer surgery (270 vs. 163 min; p < 0.001), greater blood loss (550 vs. 300 ml; p = 0.084), and more transfusions (75

The integration of machine learning methodologies has become prevalent in the development of clinical prediction models, often suggesting superior performance compared to traditional statistical techniques. Within the scope of low-dimensional datasets, encompassing both classical and machine learning paradigms, we plan to undertake a comparison of variable selection methodologies through simulation-based analysis. The principal aim is the comparison of the variable selection strategies with respect to relative predictive accuracy and its variability, with a secondary aim the comparison of descriptive accuracy. We use six distinct statistical learning approaches across both data generation and model learning. The present manuscript is a protocol for the corresponding simulation study registration (Study registration Open Science Framework ID: k6c8f). We describe the planned steps through the Aims, Data, Estimands, Methods, and Performance framework for simulation study design and reporting.

Background The quality of COVID-19 preprints should be considered with great care, as their contents can influence public policy. Surprisingly little has been done to calibrate the public’s evaluation of preprints and their contents. The PRECHECK project aimed to generate a tool to teach and guide scientifically literate non-experts to critically evaluate preprints, on COVID-19 and beyond. Methods To create a checklist, we applied a four-step procedure consisting of an initial internal review, an external review by a pool of experts (methodologists, meta-researchers/experts on preprints, journal editors, and science journalists), a final internal review, and a Preliminary implementation stage. For the external review step, experts rated the relevance of each element of the checklist on five-point Likert scales, and provided written feedback. After each internal review round, we applied the checklist on a small set of high-quality preprints from an online list of milestone research works on COVID-19 and low-quality preprints, which were eventually retracted, to verify whether the checklist can discriminate between the two categories. Results At the external review step, 26 of the 54 contacted experts responded. The final checklist contained four elements (Research question, study type, transparency and integrity, and limitations), with ‘superficial’ and ‘deep’ evaluation levels. When using both levels, the checklist was effective at discriminating a small set of high- and low-quality preprints. Its usability for assessment and discussion of preprints was confirmed in workshops with Bachelors students in Psychology and Medicine, and science journalists. Conclusions We created a simple, easy-to-use tool for helping scientifically literate non-experts navigate preprints with a critical mind and facilitate discussions within, for example, a beginner-level lecture on research methods. We believe that our checklist has potential to help guide decisions about the quality of preprints on COVID-19 in our target audience and that this extends beyond COVID-19.

Background The quality of COVID-19 preprints should be considered with great care, as their contents can influence public policy. Surprisingly little has been done to calibrate the public’s evaluation of preprints and their contents. The PRECHECK project aimed to generate a tool to teach and guide scientifically literate non-experts to critically evaluate preprints, on COVID-19 and beyond. Methods To create a checklist, we applied a four-step procedure consisting of an initial internal review, an external review by a pool of experts (methodologists, meta-researchers/experts on preprints, journal editors, and science journalists), a final internal review, and a Preliminary implementation stage. For the external review step, experts rated the relevance of each element of the checklist on five-point Likert scales, and provided written feedback. After each internal review round, we applied the checklist on a small set of high-quality preprints from an online list of milestone research works on COVID-19 and low-quality preprints, which were eventually retracted, to verify whether the checklist can discriminate between the two categories. Results At the external review step, 26 of the 54 contacted experts responded. The final checklist contained four elements (Research question, study type, transparency and integrity, and limitations), with ‘superficial’ and ‘deep’ evaluation levels. When using both levels, the checklist was effective at discriminating a small set of high- and low-quality preprints. Its usability for assessment and discussion of preprints was confirmed in workshops with Bachelors students in Psychology and Medicine, and science journalists. Conclusions We created a simple, easy-to-use tool for helping scientifically literate non-experts navigate preprints with a critical mind and facilitate discussions within, for example, a beginner-level lecture on research methods. We believe that our checklist has potential to help guide decisions about the quality of preprints on COVID-19 in our target audience and that this extends beyond COVID-19.

Deceased-donor kidney allografts are exposed to ischemic injury during ex vivo transport due to the lack of blood oxygen supply. Hypothermic machine perfusion (HMP) effectively reduces the risk of delayed graft function in kidney transplant recipients compared to standard cold storage. However, no free software implementation is available to analyze HMP data for state-of-the-art visualization and quality control. We developed the tool EXAM (ex-vivo allograft monitoring) as an interactive analytics dashboard. We wrote functions in the R programming language to read, process, and analyze HMP data from the LifePort kidney transporter (Organ Recovery Systems, USA). Time series for pressure, flow rate, organ resistance, and temperature are visualized, and relevant statistical indicators have been developed. We explain how data were processed, and indicators were calculated, and we present summary statistics for N = 255 kidney allografts receiving machine perfusion in Switzerland between 2020 and 2023. Median (interdecile range, IDR) of the main indicators were as follows: perfusion duration 5.18 hours (2.29-11.2), flow rate 110 ml/min (52.9-167), ice temperature 1.97°C (1.53-3.07), and perfusate temperature 6.68°C (5.58-8.36). We implemented the dashboard to identify issues, such as atypical perfusion parameters, high ice, or high perfusate temperature to inform transplant centers for quality assurance. In conclusion, EXAM is a free tool that statisticians and data scientists can quickly deploy to enable quality control at transplant organizations that use LifePort kidney transporters. An online viewer is available at https://data.swisstransplant.org/exam/.

ImportanceMedian organ waiting times published by transplant organizations may be biased when not appropriately accounting for censoring, death, and competing events. This can lead to overly optimistic waiting times for all transplant programs and, consequently, may deceive patients on the waiting list, transplant physicians, and health care policymakers.ObjectiveTo apply competing-risk multistate models to calculate probabilities for transplantation and adverse outcomes on the Swiss national transplant waiting list.Design, Setting, and ParticipantsThe WAIT (Waitlist Analysis in Transplantation) study was a retrospective cohort study of all transplant candidates in Switzerland listed from January 1, 2018, or later and observed until December 31, 2023. Transplant candidates were listed in 1 of the 6 transplant centers (Basel, Bern, Geneva, Lausanne, St Gallen, and Zurich) for heart, liver, lungs, kidney, or pancreas and/or islet transplant. A total of 4352 candidates were listed during the study period, of whom 709 (16.3%) were excluded due to living-donor transplant (691 in the kidney program and 18 in the liver program).ExposureWaiting for organ transplant.Main Outcomes and MeasuresTime to transplantation, death, or delisting. Competing-risk multistate models were used to analyze time-to-event data from the national organ waiting list with the Aalen-Johansen estimator to compute probabilities for both transplant and adverse outcomes. Results were compared with the sample median among only those undergoing transplant and the Kaplan-Meier method with censoring of competing events.ResultsData from 3643 transplant candidates (2428 [66.6%] male; median age, 56 [range, 0-79] years) were included in the analysis. The median time to transplantation (MTT) was 0.91 (95% CI, 0.83-1.07) years for heart, 3.10 (95% CI, 2.57-3.77) years for kidney, 1.32 (95% CI, 0.76-1.55) years for liver, 0.80 (95% CI, 0.37-1.12) years for lung, and 1.62 (95% CI, 0.91-2.17) years for pancreas and/or islet programs. Alternative estimation methods introduced bias to varying degrees: the sample median among only persons undergoing transplantation underestimated the waiting time by 38% to 61% and the Kaplan-Meier method by 2% to 12% compared with the MTT.Conclusions and RelevanceIn this cohort study of transplant candidates in Switzerland, the MTT, the duration at which the transplant probability is 0.50, was used as a measure of average waiting time. Suboptimal methods led to biased and overly optimistic waiting time estimations; thus, applying appropriate competing-risk methods to address censoring and competing events is crucial.

BACKGROUND: We have examined the primary efficacy results of 23,551 randomized clinical trials from the Cochrane Database of Systematic Reviews. METHODS: We estimate that the great majority of trials have much lower statistical power for actual effects than the 80 or 90% for the stated effect sizes. Consequently, “statistically significant” estimates tend to seriously overestimate actual treatment effects, “nonsignificant” results often correspond to important effects, and efforts to replicate often fail to achieve “significance” and may even appear to contradict initial results. To address these issues, we reinterpret the P value in terms of a reference population of studies that are, or could have been, in the Cochrane Database. RESULTS: This leads to an empirical guide for the interpretation of an observed P value from a “typical” clinical trial in terms of the degree of overestimation of the reported effect, the probability of the effect’s sign being wrong, and the predictive power of the trial. CONCLUSIONS: Such an interpretation provides additional insight about the effect under study and can guard medical researchers against naive interpretations of the P value and overoptimistic effect sizes. Because many research fields suffer from low power, our results are also relevant outside the medical domain. (Funded by the U.S. Office of Naval Research.)

Many potential prognostic factors for predicting kidney transplantation outcomes have been identified. However, in Switzerland, no widely accepted prognostic model or risk score for transplantation outcomes is being routinely used in clinical practice yet. We aim to develop three prediction models for the prognosis of graft survival, quality of life, and graft function following transplantation in Switzerland. The clinical kidney prediction models (KIDMO) are developed with data from a national multi-center cohort study (Swiss Transplant Cohort Study; STCS) and the Swiss Organ Allocation System (SOAS). The primary outcome is the kidney graft survival (with death of recipient as competing risk); the secondary outcomes are the quality of life (patient-reported health status) at 12 months and estimated glomerular filtration rate (eGFR) slope. Organ donor, transplantation, and recipient-related clinical information will be used as predictors at the time of organ allocation. We will use a Fine Gray subdistribution model and linear mixed-effects models for the primary and the two secondary outcomes, respectively. Model optimism, calibration, discrimination, and heterogeneity between transplant centres will be assessed using bootstrapping, internal-external cross-validation, and methods from meta-analysis. Thorough evaluation of the existing risk scores for the kidney graft survival or patient-reported outcomes has been lacking in the Swiss transplant setting. In order to be useful in clinical practice, a prognostic score needs to be valid, reliable, clinically relevant, and preferably integrated into the decision-making process to improve long-term patient outcomes and support informed decisions for clinicians and their patients. The state-of-the-art methodology by taking into account competing risks and variable selection using expert knowledge is applied to data from a nationwide prospective multi-center cohort study. Ideally, healthcare providers together with patients can predetermine the risk they are willing to accept from a deceased-donor kidney, with graft survival, quality of life, and graft function estimates available for their consideration. Open Science Framework ID: z6mvj

Mental disorders often emerge during adolescence and have been associated with age-related differences in connection strengths of brain networks (static functional connectivity), manifesting in non-typical trajectories of brain development. However, little is known about the direction of information flow (directed functional connectivity) in this period of functional brain progression. We employed dynamic graphical models (DGM) to estimate directed functional connectivity from resting state functional magnetic resonance imaging data on 1143 participants, aged 6 to 17 years from the healthy brain network (HBN) sample. We tested for effects of age, sex, cognitive abilities and psychopathology on estimates of direction flow. Across participants, we show a pattern of reciprocal information flow between visual-medial and visual-lateral connections, in line with findings in adults. Investigating directed connectivity patterns between networks, we observed a positive association for age and direction flow from the cerebellar to the auditory network, and for the auditory to the sensorimotor network. Further, higher cognitive abilities were linked to lower information flow from the visual occipital to the default mode network. Additionally, examining the degree networks overall send and receive information to each other, we identified age-related effects implicating the right frontoparietal and sensorimotor network. However, we did not find any associations with psychopathology. Our results suggest that the directed functional connectivity of large-scale resting-state brain networks is sensitive to age and cognition during adolescence, warranting further studies that may explore directed relationships at rest and trajectories in more fine-grained network parcellations and in different populations.

Alcohol use disorder (AUD) is characterized by enhanced cue-reactivity and the opposing control processes being insufficient. The ability to inhibit reactions to alcohol-related cues, alcohol-specific inhibition, is thus crucial to AUD; and trainings strengthening this ability might increase treatment outcome. The present study investigated whether neurophysiological correlates of alcohol-specific inhibition (I) vary with craving, (II) predict drinking outcome in AUD and (III) are modulated by alcohol-specific inhibition training. A total of 45 recently abstinent patients with AUD and 25 controls participated in this study. All participants underwent functional magnetic resonance imaging (fMRI) during a Go-NoGo task with alcohol-related as well as neutral conditions. Patients with AUD additionally participated in a double-blind RCT, where they were randomized to either an alcohol-specific inhibition training or an active control condition (non-specific inhibition training). After the training, patients participated in a second fMRI measurement where the Go-NoGo task was repeated. Percentage of days abstinent was assessed as drinking outcome 3 months after discharge from residential treatment. Whole brain analyses indicated that in the right inferior frontal gyrus (rIFG), activation related to alcohol-specific inhibition varied with craving and predicted drinking outcome at 3-months follow-up. This neurophysiological correlate of alcohol-specific inhibition was however not modulated by the training version. Our results suggest that enhanced rIFG activation during alcohol-specific (compared to neutral) inhibition (I) is needed to inhibit responses when craving is high and (II) fosters sustained abstinence in patients with AUD. As alcoholspecific rIFG activation was not affected by the training, future research might investigate whether potential training effects on neurophysiology are better detectable with other methodological approaches.

Background With age, medical conditions impairing safe driving accumulate. Consequently, the risk of accidents increases. To mitigate this risk, Swiss law requires biannual assessments of the fitness to drive of elderly drivers. Drivers may prove their cognitive and physical capacity for safe driving in a medically supervised driving test (MSDT) when borderline cases, as indicated by low performance in a set of four cognitive tests, including e.g. the mini mental status test (MMST). Any prognostic, rather than indicative, relations for MSDT outcomes have neither been confirmed nor falsified so far. In order to avoid use of unsubstantiated rules of thumb, we here evaluate the predictive value for MSDT outcomes of the outcomes of the standard set of four cognitive tests, used in Swiss traffic medicine examinations. Methods We present descriptive information on age, gender and cognitive pretesting results of all MSDTs recorded in our case database from 2017 to 2019. Based on these retrospective cohort data, we used logistic regression to predict the binary outcome MSDT. An exploratory analysis used all available data (model 1). Based on the Akaike Information Criterion (AIC), we then established a model including variables age and MMST (model 2). To evaluate the predictive value of the four cognitive assessments, model 3 included cognitive test outcomes only. Receiver operating characteristics (ROC) and area under the curve (AUC) allowed evaluating discriminative performance of the three different models using independent validation data. Results Using N = 188 complete data sets of a total of 225 included cases, AIC identified age ( p < 0.0008) and MMST ( p = 0.024) as dominating predictors for MSDT outcomes with a median AUC of 0.71 (95%-CI 0.57–0.85) across different training and validation splits, while using the four cognitive test results exclusively yielded a median AUC of 0.55 (95%-CI 0.40–0.71). Conclusions Our analysis provided strong evidence for age as the single most dominant predictor of MSDT outcomes. Adding MMST provides only weak additional predictive value for MSDT outcomes. Combining the results of four cognitive test used as standard screen in Swiss traffic medicine alone, proved to be of poor predictive value. This highlights the importance of MSDTs for balancing between the mitigation of risks by and the right to drive for the elderly.

This paper aims to provide early-career researchers with a useful introduction to good research practices.

Alcohol use disorder (AUD) is characterized by enhanced cue-reactivity and the opposing control processes being insufficient. The ability to inhibit reactions to alcohol-related cues, alcohol-specific inhibition, is thus crucial to AUD; and trainings strengthening this ability might increase treatment outcome. The present study investigated whether neurophysiological correlates of alcohol-specific inhibition (I) vary with craving, (II) predict drinking outcome in AUD and (III) are modulated by alcohol-specific inhibition training. A total of 45 recently abstinent patients with AUD and 25 controls participated in this study. All participants underwent functional magnetic resonance imaging (fMRI) during a Go-NoGo task with alcohol-related as well as neutral conditions. Patients with AUD additionally participated in a double-blind RCT, where they were randomized to either an alcohol-specific inhibition training or an active control condition (non-specific inhibition training). After the training, patients participated in a second fMRI measurement where the Go-NoGo task was repeated. Percentage of days abstinent was assessed as drinking outcome 3 months after discharge from residential treatment. Whole brain analyses indicated that in the right inferior frontal gyrus (rIFG), activation related to alcohol-specific inhibition varied with craving and predicted drinking outcome at 3-months follow-up. This neurophysiological correlate of alcohol-specific inhibition was however not modulated by the training version. Our results suggest that enhanced rIFG activation during alcohol-specific (compared to neutral) inhibition (I) is needed to inhibit responses when craving is high and (II) fosters sustained abstinence in patients with AUD. As alcoholspecific rIFG activation was not affected by the training, future research might investigate whether potential training effects on neurophysiology are better detectable with other methodological approaches.

We abstract the concept of a randomized controlled trial (RCT) as a triple (beta,b,s), where beta is the primary efficacy parameter, b the estimate and s the standard error (s>0). The parameter beta is either a difference of means, a log odds ratio or a log hazard ratio. If we assume that b is unbiased and normally distributed, then we can estimate the full joint distribution of (beta,b,s) from a sample of pairs (b_i,s_i). We have collected 23,747 such pairs from the Cochrane database to do so. Here, we report the estimated distribution of the signal-to-noise ratio beta/s and the achieved power. We estimate the median achieved power to be 0.13. We also consider the exaggeration ratio which is the factor by which the magnitude of beta is overestimated. We find that if the estimate is just significant at the 5% level, we would expect it to overestimate the true effect by a factor of 1.7. This exaggeration is sometimes referred to as the winner's curse and it is undoubtedly to a considerable extent responsible for disappointing replication results. For this reason, we believe it is important to shrink the unbiased estimator, and we propose a method for doing so.

Coding mistakes can lead to false results. Statisticians and data scientists should exploit best practices and tools in statistical programming to enhance reproducible analyses.