Gravity-Dependent Modulation of Downbeat Nystagmus and Subjective Visual Vertical in the Roll Plane.

Downbeat nystagmus (DBN) is the most common form of acquired central vestibular nystagmus. Gravity perception in patients with DBN has previously been investigated by means of subjective visual straight ahead (SVA) and subjective visual vertical (SVV) in the pitch and roll planes only during whole-body tilts. To our knowledge, the effect of head tilt in the roll plane on the SVV and on DBN has not yet been systematically studied in patients. In this study, we investigated static and dynamic graviceptive function in the roll-plane in patients with DBN (patients) and healthy-controls (controls) by assessment of the Subjective Visual Vertical (SVV) and the modulation of slow-phase-velocity (SPV) of DBN. SPV of DBN and SVV were tested at different head-on trunk-tilt positions in the roll-plane (0°,30° clockwise (cw) and 30° counterclockwise (ccw)) in 26 patients suffering from DBN and 13 controls. In patients, SPV of DBN did not show significant modulations at different head-tilt angles in the roll-plane. SVV ratings did not differ significantly between DBN patients vs. controls, however patients with DBN exhibited a higher variability in mean SVV estimates than controls. Our results show that the DBN does not exhibit any modulation in the roll-plane, in contrast to the pitch-plane. Furthermore, patients with DBN show a higher uncertainty in the perception of verticality in the roll-plane in form of a higher variability of responses.

Causal Model Building in the Context of Cardiac Rehabilitation: A Systematic Review.

Randomization is an effective design option to prevent bias from confounding in the evaluation of the causal effect of interventions on outcomes. However, in some cases, randomization is not possible, making subsequent adjustment for confounders essential to obtain valid results. Several methods exist to adjust for confounding, with multivariable modeling being among the most widely used. The main challenge is to determine which variables should be included in the causal model and to specify appropriate functional relations for continuous variables in the model. While the statistical literature gives a variety of recommendations on how to build multivariable regression models in practice, this guidance is often unknown to applied researchers. We set out to investigate the current practice of explanatory regression modeling to control confounding in the field of cardiac rehabilitation, for which mainly non-randomized observational studies are available. In particular, we conducted a systematic methods review to identify and compare statistical methodology with respect to statistical model building in the context of the existing recent systematic review CROS-II, which evaluated the prognostic effect of cardiac rehabilitation. CROS-II identified 28 observational studies, which were published between 2004 and 2018. Our methods review revealed that 24 (86%) of the included studies used methods to adjust for confounding. Of these, 11 (46%) mentioned how the variables were selected and two studies (8%) considered functional forms for continuous variables. The use of background knowledge for variable selection was barely reported and data-driven variable selection methods were applied frequently. We conclude that in the majority of studies, the methods used to develop models to investigate the effect of cardiac rehabilitation on outcomes do not meet common criteria for appropriate statistical model building and that reporting often lacks precision.

Primary Hyperparathyroidism and Intraoperative Parathyroid Hormone Monitoring: Application of a Modified Interpretation in Patients With "Parathyroid Hormone Spikes".

INTRODUCTION: Intraoperative parathyroid hormone (PTH) spikes occur in up to 30% of patients during surgery for primary hyperparathyroidism. This can lead to a prolonged PTH decline and cause difficulties in using current interpretation criteria of intraoperative PTH monitoring. The aim of this study aim was to evaluate an alternative interpretation model in patients with PTH spikes during exploration. METHODS: 1035 consecutive patients underwent surgery for primary hyperparathyroidism in a single center. A subgroup of patients with intraoperative PTH spikes of >50 pg/mL were selected (n = 277; 27.0%). The prediction of cure applying the Miami and Vienna criteria was compared with a decay of ≥50% 10 min after excision of the enlarged parathyroid gland using the "visualization value" (VV; =PTH level immediately after visualization of the gland) as basal value. Sensitivity, specificity, accuracy, positive predictive value, and negative predictive value were calculated. RESULTS: Using the VV, sensitivity was 99.2% (Vienna 71.0%; Miami 97.7%), specificity was 18.2 (Vienna 63.6%; Miami 36.4%), and accuracy was 92.8 (Vienna 70.4%; Miami 92.8%). Of 255 single-gland disease patients, 72 were identified correctly as cured by applying the VV (P < 0.001), yet 10 of 22 patients with multiple-gland disease were missed compared with the Vienna Criterion (P = 0.002). The comparison with the Miami Criterion showed that six more patients were correctly identified as cured (P = 0.219), whereas four patients with multiple-gland disease were missed (P = 0.125). CONCLUSIONS: Using the VV as a baseline in patients with intraoperative PTH spikes may prove to be an alternative and therefore can be recommended. However, if the VV is higher than the preexcision value, it should not be applied.

The value of intraoperative parathyroid hormone monitoring in patients with primary hyperparathyroidism and varying baseline parathyroid hormone levels.

BACKGROUND: When applying intraoperative parathyroid hormone monitoring (IOPTH) to patients with primary hyperparathyroidism (PHPT), there are established criteria predicting biochemical cure in patients with basal parathyroid hormone (PTH) levels in the medium range (100-400 pg/ml); however, there is a challenge concerning patients with low (less than 100 pg/ml) or high (more than 400 pg/ml) basal PTH levels. The aim of this study was to investigate the value of the 'Vienna criterion' applied during IOPTH in patients with PHPT and various basal PTH concentrations. METHODS: Consecutive patients between 1999-2009 with a biochemical diagnosis of PHPT who underwent surgical parathyroidectomy were included. Based on preoperative PTH levels they were divided into three groups: group 1 (low) (<100 pg/ml), group 2 (medium) (100-400 pg/ml) and group 3 (high) (>400 pg/ml) basal PTH. PTH was measured at the start of the operation, when the gland was excised and then at 5, 10 and 15 min after. Calcium and PTH levels were measured at 7 days and 12 months postoperatively. Sensitivity, specificity, positive and negative predictive value, as well as accuracy of IOPTH were calculated for the different groups postoperatively. RESULTS: 675 patients with PHPT were analysed. Sensitivity and specificity were 83.7 per cent and 66.7 per cent in group 1 (n = 187), 90.7 per cent and 69.2 per cent in group 2 (n = 433), and 94.4 per cent and 100 per cent in group 3 (n = 55) to predict cure. Preoperative creatinine (p = 0.002) showed significant statistical difference between the groups but was not related to intraoperative PTH decline. At 12 months follow-up normocalcaemia was documented in 98.9 per cent in group 1, 99.0 per cent group 2, and 98.0 per cent of group 3 patients. CONCLUSION: Normocalcaemia was predicted intraoperatively by applying the 'Vienna criterion' in 98 to 100 per cent and was confirmed after 12 months follow-up in up to 99.0 per cent of patients. Low specificity and a high false-negative rate in patients with low basal PTH show that other criteria might be better suited for this group.

Evaluating methods for Lasso selective inference in biomedical research: a comparative simulation study.

BACKGROUND: Variable selection for regression models plays a key role in the analysis of biomedical data. However, inference after selection is not covered by classical statistical frequentist theory, which assumes a fixed set of covariates in the model. This leads to over-optimistic selection and replicability issues. METHODS: We compared proposals for selective inference targeting the submodel parameters of the Lasso and its extension, the adaptive Lasso: sample splitting, selective inference conditional on the Lasso selection (SI), and universally valid post-selection inference (PoSI). We studied the properties of the proposed selective confidence intervals available via R software packages using a neutral simulation study inspired by real data commonly seen in biomedical studies. Furthermore, we present an exemplary application of these methods to a publicly available dataset to discuss their practical usability. RESULTS: Frequentist properties of selective confidence intervals by the SI method were generally acceptable, but the claimed selective coverage levels were not attained in all scenarios, in particular with the adaptive Lasso. The actual coverage of the extremely conservative PoSI method exceeded the nominal levels, and this method also required the greatest computational effort. Sample splitting achieved acceptable actual selective coverage levels, but the method is inefficient and leads to less accurate point estimates. The choice of inference method had a large impact on the resulting interval estimates, thereby necessitating that the user is acutely aware of the goal of inference in order to interpret and communicate the results. CONCLUSIONS: Despite violating nominal coverage levels in some scenarios, selective inference conditional on the Lasso selection is our recommended approach for most cases. If simplicity is strongly favoured over efficiency, then sample splitting is an alternative. If only few predictors undergo variable selection (i.e. up to 5) or the avoidance of false positive claims of significance is a concern, then the conservative approach of PoSI may be useful. For the adaptive Lasso, SI should be avoided and only PoSI and sample splitting are recommended. In summary, we find selective inference useful to assess the uncertainties in the importance of individual selected predictors for future applications.

Retrospective, Multicenter Analysis Comparing Conventional with Oncoplastic Breast Conserving Surgery: Oncological and Surgical Outcomes in Women with High-Risk Breast Cancer from the OPBC-01/iTOP2 Study.

INTRODUCTION: Recent data suggest that margins ≥2 mm after breast-conserving surgery may improve local control in invasive breast cancer (BC). By allowing large resection volumes, oncoplastic breast-conserving surgery (OBCII; Clough level II/Tübingen 5-6) may achieve better local control than conventional breast conserving surgery (BCS; Tübingen 1-2) or oncoplastic breast conservation with low resection volumes (OBCI; Clough level I/Tübingen 3-4). METHODS: Data from consecutive high-risk BC patients treated in 15 centers from the Oncoplastic Breast Consortium (OPBC) network, between January 2010 and December 2013, were retrospectively reviewed. RESULTS: A total of 3,177 women were included, 30% of whom were treated with OBC (OBCI n = 663; OBCII n = 297). The BCS/OBCI group had significantly smaller tumors and smaller resection margins compared with OBCII (pT1: 50% vs. 37%, p = 0.002; proportion with margin <1 mm: 17% vs. 6%, p < 0.001). There were significantly more re-excisions due to R1 ("ink on tumor") in the BCS/OBCI compared with the OBCII group (11% vs. 7%, p = 0.049). Univariate and multivariable regression analysis adjusted for tumor biology, tumor size, radiotherapy, and systemic treatment demonstrated no differences in local, regional, or distant recurrence-free or overall survival between the two groups. CONCLUSIONS: Large resection volumes in oncoplastic surgery increases the distance from cancer cells to the margin of the specimen and reduces reexcision rates significantly. With OBCII larger tumors are resected with similar local, regional and distant recurrence-free as well as overall survival rates as BCS/OBCI.

The roles of predictors in cardiovascular risk models - a question of modeling culture?

BACKGROUND: While machine learning (ML) algorithms may predict cardiovascular outcomes more accurately than statistical models, their result is usually not representable by a transparent formula. Hence, it is often unclear how specific values of predictors lead to the predictions. We aimed to demonstrate with graphical tools how predictor-risk relations in cardiovascular risk prediction models fitted by ML algorithms and by statistical approaches may differ, and how sample size affects the stability of the estimated relations. METHODS: We reanalyzed data from a large registry of 1.5 million participants in a national health screening program. Three data analysts developed analytical strategies to predict cardiovascular events within 1 year from health screening. This was done for the full data set and with gradually reduced sample sizes, and each data analyst followed their favorite modeling approach. Predictor-risk relations were visualized by partial dependence and individual conditional expectation plots. RESULTS: When comparing the modeling algorithms, we found some similarities between these visualizations but also occasional divergence. The smaller the sample size, the more the predictor-risk relation depended on the modeling algorithm used, and also sampling variability played an increased role. Predictive performance was similar if the models were derived on the full data set, whereas smaller sample sizes favored simpler models. CONCLUSION: Predictor-risk relations from ML models may differ from those obtained by statistical models, even with large sample sizes. Hence, predictors may assume different roles in risk prediction models. As long as sample size is sufficient, predictive accuracy is not largely affected by the choice of algorithm.

Regression with Highly Correlated Predictors: Variable Omission Is Not the Solution.

Regression models have been in use for decades to explore and quantify the association between a dependent response and several independent variables in environmental sciences, epidemiology and public health. However, researchers often encounter situations in which some independent variables exhibit high bivariate correlation, or may even be collinear. Improper statistical handling of this situation will most certainly generate models of little or no practical use and misleading interpretations. By means of two example studies, we demonstrate how diagnostic tools for collinearity or near-collinearity may fail in guiding the analyst. Instead, the most appropriate way of handling collinearity should be driven by the research question at hand and, in particular, by the distinction between predictive or explanatory aims.

The spectrum of skin diseases in four different types of organ-transplant recipients: a comparative single-centre cohort study.

BACKGROUND: Organ transplant recipients (OTR) are at marked increased risk of skin cancer and skin infections compared to the general population. OBJECTIVES: The purpose of this study was to acquire long-term incidence data on commonly occurring skin diseases in four different transplant groups. MATERIALS & METHODS: This retrospective single-centre cohort study included 621 OTR. By counting defined malignant, inflammatory, infectious or drug-related skin conditions per patient and visit, incidence rates (IR) for the different groups of OTR were calculated as cases per 1000-patient years and cumulative incidences of non-melanoma skin cancer (NMSC), respectively. RESULTS: Overall, 2,309 non-malignant skin conditions and 340 NMSC were registered. Skin infections were most common (51.4%), followed by inflammatory skin conditions (35.6%) and sun-induced skin damage (32.9%). Kidney transplant recipients (KTR) had a 4.7-fold (95% CI: 2.7-8.0; p < 0.0001), 2.6-fold (95% CI: 1.2-5.3; p = 0.0098) and 5.4-fold (95% CI: 2.8-10.3; - < 0.0001) higher IR for oral candidiasis, oral aphthosis and herpes simplex virus infections, respectively, compared to the other OTR. Pruritus was most commonly reported in liver transplant recipients (95% CI: 1.3-5.3; p = 0.0047). KTR and lung transplant recipients (LuTR) had a 10.7-fold (95% CI:3.6-43.2; p < 0.0001) higher IR of steroid induced acne. KTR had a 1.6-fold (95% CI: 1.1-2.3; p = 0.0096) higher IR of squamous cell carcinoma compared to the other groups. The incidence of basal cell carcinoma was 2.5-fold higher (95% CI: 1.7-3.6; p < 0.0001) in LuTR, compared to the other OTR. CONCLUSION: This study provides additional organ-specific incidence data on non-malignant skin diseases and skin cancer in OTR.

Ventilation parameters and early graft function in double lung transplantation.

BACKGROUND: Currently, the primary graft dysfunction (PGD) score is used to measure allograft function in the early post-lung transplant period. Although PGD grades at later time points (T48 hours and T72 hours) are useful to predict mid- and long-term outcomes, their predictive value is less relevant within the first 24 hours after transplantation. This study aimed to evaluate the capability of PGD grades to predict prolonged mechanical ventilation (MV) and compare it with a model derived from ventilation parameters measured on arrival at the intensive care unit (ICU). METHODS: A retrospective single-center analysis of 422 double lung transplantations (LTxs) was performed. PGD was assessed 2 hours after arrival at ICU, and grades were associated with length of MV (LMV). In addition, peak inspiratory pressure (PIP), ratio of the arterial partial pressure of oxygen to fraction of inspired oxygen (P/F ratio), and dynamic compliance (cDyn) were collected, and a logistic regression model was created. The predictive capability for prolonged MV was calculated for both (the PGD score and the model). In a second step, the created model was externally validated using a prospective, international multicenter cohort including 102 patients from the lung transplant centers of Vienna, Toronto, and Budapest. RESULTS: In the retrospective cohort, a high percentage of extubated patients was reported at 24 hours (35.1%), 48 hours (68.0%), and 72 hours (80.3%) after transplantation. At T0 (time point defined as 2 hours after arrival at the ICU), patients with PGD grade 0 had a shorter LMV with a median of 26 hours (interquartile range [IQR]: 16-47 hours) than those with PGD grade 1 (median: 42 hours, IQR: 27-50 hours), PGD grade 2 (median: 37.5 hours, IQR: 15.5-78.5 hours), and PGD grade 3 (median: 46 hours, IQR: 27-86 hours). However, IQRs largely overlapped for all grades, and the value of PGD to predict prolonged MV was poor. A total of 3 ventilation parameters (PIP, cDyn, and P/F ratio), determined at T0, were chosen on the basis of clinical reasoning. A logistic regression model including these parameters predicted prolonged MV (>72 hours) with an optimism-corrected area under the curve (AUC) of 0.727. In the prospective validation cohort, the model proved to be stable and achieved an AUC of 0.679. CONCLUSIONS: The prediction model reported in this study combines 3 easily obtainable variables. It can be employed immediately after LTx to quantify the risk of prolonged MV, an important early outcome parameter.

Selection of variables for multivariable models: Opportunities and limitations in quantifying model stability by resampling.

Statistical models are often fitted to obtain a concise description of the association of an outcome variable with some covariates. Even if background knowledge is available to guide preselection of covariates, stepwise variable selection is commonly applied to remove irrelevant ones. This practice may introduce additional variability and selection is rarely certain. However, these issues are often ignored and model stability is not questioned. Several resampling-based measures were proposed to describe model stability, including variable inclusion frequencies (VIFs), model selection frequencies, relative conditional bias (RCB), and root mean squared difference ratio (RMSDR). The latter two were recently proposed to assess bias and variance inflation induced by variable selection. Here, we study the consistency and accuracy of resampling estimates of these measures and the optimal choice of the resampling technique. In particular, we compare subsampling and bootstrapping for assessing stability of linear, logistic, and Cox models obtained by backward elimination in a simulation study. Moreover, we exemplify the estimation and interpretation of all suggested measures in a study on cardiovascular risk. The VIF and the model selection frequency are only consistently estimated in the subsampling approach. By contrast, the bootstrap is advantageous in terms of bias and precision for estimating the RCB as well as the RMSDR. Though, unbiased estimation of the latter quantity requires independence of covariates, which is rarely encountered in practice. Our study stresses the importance of addressing model stability after variable selection and shows how to cope with it.

Publisher Correction: Re-estimation improved the performance of two Framingham cardiovascular risk equations and the Pooled Cohort equations: A nationwide registry analysis.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Re-estimation improved the performance of two Framingham cardiovascular risk equations and the Pooled Cohort equations: A nationwide registry analysis.

Equations predicting the risk of occurrence of cardiovascular disease (CVD) are used in primary care to identify high-risk individuals among the general population. To improve the predictive performance of such equations, we updated the Framingham general CVD 1991 and 2008 equations and the Pooled Cohort equations for atherosclerotic CVD within five years in a contemporary cohort of individuals who participated in the Austrian health-screening program from 2009-2014. The cohort comprised 1.7 M individuals aged 30-79 without documented CVD history. CVD was defined by hospitalization or death from cardiovascular cause. Using baseline and follow-up data, we recalibrated and re-estimated the equations. We evaluated the gain in discrimination and calibration and assessed explained variation. A five-year general CVD risk of 4.61% was observed. As expected, discrimination c-statistics increased only slightly and ranged from 0.73-0.79. The two original Framingham equations overestimated the CVD risk, whereas the original Pooled Cohort equations underestimated it. Re-estimation improved calibration of all equations adequately, especially for high-risk individuals. Half of the individuals were reclassified into another risk category using the re-estimated equations. Predictors in the re-estimated Framingham equations explained 7.37% of the variation, whereas the Pooled Cohort equations explained 5.81%. Age was the most important predictor.

Epithelial stem cell marker LGR6 expression identifies a low-risk subgroup in human papillomavirus positive oropharyngeal squamous cell carcinoma.

OBJECTIVES: R-Spondins (RSPOs) and leucine-rich repeat-containing G-protein coupled receptors (LGRs) play a critical role in embryonic and cancer development through potentiation of WNT/ß-catenin signaling, but their prognostic significance in head and neck squamous cell carcinoma (HNSCC) is still unclear. HNSCC is a group of neoplasms that include, amongst others, oropharyngeal squamous cell carcinoma (OPSCC), some of which are induced by human papillomavirus (HPV). We aimed to investigate the potential prognostic value of RSPO2 and LGR4/5/6 on overall survival (OS) and disease-free survival (DFS) in HNSCC patients. METHODS: We examined RSPO and LGR expression by means of immunohistochemistry in 126 HNSCC patients. Furthermore, in order to validate our findings externally, we examined RSPO2 and LGR6 mRNA expression levels using independent secondary datasets. RESULTS: The five-year OS of our cohort was 59.6%. RSPO2 and LGR4/5/6 expression were not associated with OS or DFS in multivariable analyses. Within the HPV+ cases (n = 26, 33%), however, we observed a difference in OS by RSPO2 expression (5-year OS: RSPO+ 45.4% vs. RSPO2- 84.6%) and LGR6 expression (5-year OS: LGR6+ 52.9% vs. LGR6-100%). Evidence for an interaction of HPV status with RSPO2 and LGR6 was found for OS. Relative to HPV+/LGR6- patients, HPV+/LGR6+ patients were 12 times more likely to die. These results were replicated in the second dataset. CONCLUSION: Our results indicated that the expression status of LGR6 had an influence on the aggressiveness of HPV+ OPSCC, potentially making this receptor a useful marker for identifying patients with a high risk of death.

Comparative effectiveness of branded vs. generic versions of antihypertensive, lipid-lowering and hypoglycemic substances: a population-wide cohort study.

Generic medications offer substantial potential cost savings to health systems compared to their branded counterparts. In Europe and the US, they are only approved if they are bioequivalent to the respective originator product. Nevertheless, the lack of clinical outcomes is sometimes used as the reason for hesitancy in prescribing generics. We performed an observational retrospective study on 17 branded vs. generic pharmaceutical substances for the treatment of hypertension/heart failure, hyperlipidemia, and diabetes mellitus in a dataset of 9,413,620 insured persons, representing nearly the full population of Austria, from 2007 to 2012. We compared generic vs. branded medications using hazard ratios for all-cause death and major adverse cardiac and cardiovascular events (MACCE) as outcomes of interest. Using patient demographics, health characteristics from hospitalization records, and pharmacy records as covariates, we controlled for confounding in Cox models through inverse probability of treatment weighting (IPTW) using high-dimensional propensity scores. We observed that the unadjusted hazard ratios strongly favor generic drugs for all three pooled treatment indications (hypertension/heart failure, hyperlipidemia, diabetes mellitus), but were attenuated towards unity with increasingly larger covariate sets used for confounding control. We found that after IPTW adjustment the generic formulation was associated with significantly fewer deaths in 10 of 17 investigated drugs, and with fewer MACCE in 11 of 17 investigated drugs. This result favoring generic drugs was also present in a number of sub-analyses based on gender, prior disease status, and treatment discontinuation. E-value sensitivity analyses suggested that only strong unmeasured confounding could fully explain away the observed results. In conclusion, generic medications were at least similar, and in some cases superior, to their branded counterparts regarding mortality and major cardiovascular events.

State of the art in selection of variables and functional forms in multivariable analysis-outstanding issues.

BACKGROUND: How to select variables and identify functional forms for continuous variables is a key concern when creating a multivariable model. Ad hoc 'traditional' approaches to variable selection have been in use for at least 50 years. Similarly, methods for determining functional forms for continuous variables were first suggested many years ago. More recently, many alternative approaches to address these two challenges have been proposed, but knowledge of their properties and meaningful comparisons between them are scarce. To define a state of the art and to provide evidence-supported guidance to researchers who have only a basic level of statistical knowledge, many outstanding issues in multivariable modelling remain. Our main aims are to identify and illustrate such gaps in the literature and present them at a moderate technical level to the wide community of practitioners, researchers and students of statistics. METHODS: We briefly discuss general issues in building descriptive regression models, strategies for variable selection, different ways of choosing functional forms for continuous variables and methods for combining the selection of variables and functions. We discuss two examples, taken from the medical literature, to illustrate problems in the practice of modelling. RESULTS: Our overview revealed that there is not yet enough evidence on which to base recommendations for the selection of variables and functional forms in multivariable analysis. Such evidence may come from comparisons between alternative methods. In particular, we highlight seven important topics that require further investigation and make suggestions for the direction of further research. CONCLUSIONS: Selection of variables and of functional forms are important topics in multivariable analysis. To define a state of the art and to provide evidence-supported guidance to researchers who have only a basic level of statistical knowledge, further comparative research is required.

Predicting donor, recipient and graft survival in living donor kidney transplantation to inform pretransplant counselling: the donor and recipient linked iPREDICTLIVING tool - a retrospective study.

Although separate prediction models for donors and recipients were previously published, we identified a need to predict outcomes of donor/recipient simultaneously, as they are clearly not independent of each other. We used characteristics from transplantations performed at the Oslo University Hospital from 1854 live donors and from 837 recipients of a live donor kidney transplant to derive Cox models for predicting donor mortality up to 20 years, and recipient death, and graft loss up to 10 years. The models were developed using the multivariable fractional polynomials algorithm optimizing Akaike's information criterion, and optimism-corrected performance was assessed. Age, year of donation, smoking status, cholesterol and creatinine were selected to predict donor mortality (C-statistic of 0.81). Linear predictors for donor mortality served as summary of donor prognosis in recipient models. Age, sex, year of transplantation, dialysis vintage, primary renal disease, cerebrovascular disease, peripheral vascular disease and HLA mismatch were selected to predict recipient mortality (C-statistic of 0.77). Age, dialysis vintage, linear predictor of donor mortality, HLA mismatch, peripheral vascular disease and heart disease were selected to predict graft loss (C-statistic of 0.66). Our prediction models inform decision-making at the time of transplant counselling and are implemented as online calculators.

To test or to estimate? P-values versus effect sizes.

Most research in transplant medicine includes statistical analysis of observed data. Too often authors solely rely on P-values derived by statistical tests to answer their research questions. A P-value smaller than 0.05 is typically used to declare "statistical significance" and hence, "proves" that, for example, an intervention has an effect on the outcome of interest. Especially in observational studies, such an approach is highly problematic and can lead to false conclusions. Instead, adequate estimates of the observed size of the effect, for example, expressed as the risk difference, the relative risk or the hazard ratio, should be reported. These effect size measures have to be accompanied with an estimate of their precision, like a 95% confidence interval. Such a duo of effect size measure and confidence interval can then be used to answer the important question of clinical relevance.

Analysis of Perioperative Platelet Indices and Their Prognostic Value in Head and Neck Cancer Patients Treated with Surgery and Postoperative Radiotherapy: A Retrospective Cohort Study.

OBJECTIVES: Activated platelets might play an important role in tumor progression. Mean platelet volume (MPV) has been used as a surrogate marker for platelet activation, and therefore its value as a marker of tumor prognosis has attracted recent attention. In this study, we aimed to critically evaluate the prognostic significance of the perioperative platelet count (COP), MPV and the MPV/COP ratio in head and neck cancer patients. Additionally, we explored the individual postoperative trajectory of these indices and their association with overall survival (OS) and disease-free survival (DFS). METHODS: We retrospectively evaluated 122 head and neck squamous cell carcinoma patients receiving surgery with curative intent followed by postoperative radiotherapy. Platelet indices were measured preoperatively and on days 1 and 7 postoperatively. OS and DFS were analyzed using Kaplan-Meier estimators, the log-rank test and uni and multivariable Cox models. Cutoffs to dichotomize patients for Kaplan-Meier curves and log-rank tests were empirically chosen at the respective median. The median follow-up was 8.8 years. RESULTS: The adjusted preoperative COP, MPV and MPV/COP ratio were not associated with disease outcome. A low postoperative COP and a high MPV/COP ratio on the first postoperative day were independently associated with worse OS and DFS. In comparison to the preoperative measurements, patients whose COP increased by day 1 post-op showed a better OS (hazard ratio (HR) per 50 G/L increase: 0.73, 95% confidence interval (CI): 0.58-0.93, p = 0.013) and DFS (HR per 50 G/L increase: 0.74, 95% CI: 0.58-0.94, p = 0.018) in multivariable analysis. CONCLUSIONS: Our results suggest that a low postoperative COP and a high MPV/COP ratio represent a negative prognostic factor for OS and DFS. Notably, patients with an increase in COP by day 1 post-op when compared to their preoperative value showed a significantly better OS and DFS.