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.

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.

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.

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.

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.

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.

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.

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.

Variable selection - A review and recommendations for the practicing statistician.

Statistical models support medical research by facilitating individualized outcome prognostication conditional on independent variables or by estimating effects of risk factors adjusted for covariates. Theory of statistical models is well-established if the set of independent variables to consider is fixed and small. Hence, we can assume that effect estimates are unbiased and the usual methods for confidence interval estimation are valid. In routine work, however, it is not known a priori which covariates should be included in a model, and often we are confronted with the number of candidate variables in the range 10-30. This number is often too large to be considered in a statistical model. We provide an overview of various available variable selection methods that are based on significance or information criteria, penalized likelihood, the change-in-estimate criterion, background knowledge, or combinations thereof. These methods were usually developed in the context of a linear regression model and then transferred to more generalized linear models or models for censored survival data. Variable selection, in particular if used in explanatory modeling where effect estimates are of central interest, can compromise stability of a final model, unbiasedness of regression coefficients, and validity of p-values or confidence intervals. Therefore, we give pragmatic recommendations for the practicing statistician on application of variable selection methods in general (low-dimensional) modeling problems and on performing stability investigations and inference. We also propose some quantities based on resampling the entire variable selection process to be routinely reported by software packages offering automated variable selection algorithms.

Five myths about variable selection.

Multivariable regression models are often used in transplantation research to identify or to confirm baseline variables which have an independent association, causally or only evidenced by statistical correlation, with transplantation outcome. Although sound theory is lacking, variable selection is a popular statistical method which seemingly reduces the complexity of such models. However, in fact, variable selection often complicates analysis as it invalidates common tools of statistical inference such as P-values and confidence intervals. This is a particular problem in transplantation research where sample sizes are often only small to moderate. Furthermore, variable selection requires computer-intensive stability investigations and a particularly cautious interpretation of results. We discuss how five common misconceptions often lead to inappropriate application of variable selection. We emphasize that variable selection and all problems related with it can often be avoided by the use of expert knowledge.

Population-Attributable Fractions of Modifiable Lifestyle Factors for CKD and Mortality in Individuals With Type 2 Diabetes: A Cohort Study.

BACKGROUND: We quantified the impact of lifestyle and dietary modifications on chronic kidney disease (CKD) by estimating population-attributable fractions (PAFs). STUDY DESIGN: Observational cohort study. SETTING & PARTICIPANTS: Middle-aged adults with type 2 diabetes but without severe albuminuria from the Ongoing Telmisartan Alone and in Combination With Ramipril Global Endpoint Trial (ONTARGET; n=6,916). FACTORS: Modifiable lifestyle/dietary risk factors, such as physical activity, size of social network, alcohol intake, tobacco use, diet, and intake of various food items. OUTCOMES: The primary outcome was CKD, ascertained as moderate to severe albuminuria or ≥5% annual decline in estimated glomerular filtration rate (eGFR) after 5.5 years. The competing risk for death was considered. PAF was defined as the proportional reduction in CKD or mortality (within 5.5 years) that would occur if exposure to a risk factor was changed to an optimal level. RESULTS: At baseline, median urinary albumin-creatinine ratio and eGFR were 6.6 (IQR, 2.9-25.0) mg/mmol and 71.5 (IQR, 58.1-85.9) mL/min/1.73m(2), respectively. After 5.5 years, 704 (32.5%) participants developed albuminuria, 1,194 (55.2%) had a ≥5% annual eGFR decline, 267 (12.3%) had both, and 1,022 (14.8%) had died. Being physically active every day has PAFs of 5.1% (95% CI, 0.5%-9.6%) for CKD and 12.3% (95% CI, 4.9%-19.1%) for death. Among food items, increasing vegetable intake would have the largest impact on population health. Considering diet, weight, physical activity, tobacco use, and size of social network, exposure to less than optimum levels gives PAFs of 13.3% (95% CI, 5.5%-20.9%) for CKD and 37.5% (95% CI, 27.8%-46.7%) for death. For the 17.8 million middle-aged Americans with diabetes, improving 1 of these lifestyle behaviors to the optimal range could reduce the incidence or progression of CKD after 5.5 years by 274,000 and the number of deaths within 5.5 years by 405,000. LIMITATIONS: Ascertainment of changes in kidney measures does not precisely match the definitions for incidence or progression of CKD. CONCLUSIONS: Healthy lifestyle and diet are associated with less CKD and mortality and may have a substantial impact on population kidney health.

Modifiable lifestyle and social factors affect chronic kidney disease in high-risk individuals with type 2 diabetes mellitus.

This observational study examined the association between modifiable lifestyle and social factors on the incidence and progression of early chronic kidney disease (CKD) among those with type 2 diabetes. All 6972 people from the Ongoing Telmisartan Alone and in Combination with Ramipril Global Endpoint Trial (ONTARGET) with diabetes but without macroalbuminuria were studied. CKD progression was defined as decline in GFR of more than 5% per year, progression to end-stage renal disease, microalbuminuria, or macroalbuminuria at 5.5 years. Lifestyle/social factors included tobacco and alcohol use, physical activity, stress, financial worries, the size of the social network and education. Adjustments were made for known risks such as age, diabetes duration, GFR, albuminuria, gender, body mass index, blood pressure, fasting plasma glucose, and angiotensin-converting enzyme inhibitors/angiotensin-receptor blockers use. Competing risk of death was considered. At study end, 31% developed CKD and 15% had died. The social network score (SNS) was a significant independent risk factor of CKD and death, reducing the risk by 11 and 22% when comparing the third to the first tertile of the SNS (odds ratios of CKD 0.89 and death 0.78). Education showed a significant association with CKD but stress and financial worries did not. Those with moderate alcohol consumption had a significantly decreased CKD risk compared with nonusers. Regular physical activity significantly decreased the risk of CKD. Thus, lifestyle is a determinant of kidney health in people at high cardiovascular risk with diabetes.

Analysis of risk factors associated with renal function trajectory over time: a comparison of different statistical approaches.

BACKGROUND: The most commonly used methods to investigate risk factors associated with renal function trajectory over time include linear regression on individual glomerular filtration rate (GFR) slopes, linear mixed models and generalized estimating equations (GEEs). The objective of this study was to explain the principles of these three methods and to discuss their advantages and limitations in particular when renal function trajectories are not completely observable due to dropout. METHODS: We generated data from a hypothetical cohort of 200 patients with chronic kidney disease at inclusion and seven subsequent annual measurements of GFR. The data were generated such that both baseline level and slope of GFR over time were associated with baseline albuminuria status. In a second version of the dataset, we assumed that patients systematically dropped out after a GFR measurement of <15 mL/min/1.73 m(2). Each dataset was analysed with the three methods. RESULTS: The estimated effects of baseline albuminuria status on GFR slope were similar among the three methods when no patient dropped out. When 32.7% dropped out, standard GEE provided biased estimates of the mean GFR slope in normo-, micro- and macroalbuminuric patients. Linear regression on individual slopes and linear mixed models provided slope estimates of the same magnitude, likely because most patients had at least three GFR measurements. However, the linear mixed model was the only method to provide effect estimates on both slope and baseline level of GFR unaffected by dropout. CONCLUSION: This study illustrates that the linear mixed model is the preferred method to investigate risk factors associated with renal function trajectories in studies, where patients may dropout during the study period because of initiation of renal replacement therapy.

Prediction of prevalence of chronic kidney disease in diabetic patients in countries of the European Union up to 2025.

BACKGROUND: Diabetes and chronic kidney disease (CKD) are a growing burden for health-care systems. The prevalence of diabetes has increased constantly during the last decade, although a slight flattening of end-stage renal disease as a result of diabetes has been observed recently in some European countries. In this study, we project the prevalence of CKD in patients with diabetes in European countries up to the year 2025. METHODS: We analysed the population with diabetes and development of nephropathy in 12 European countries, which we computed from models published previously and on data from the annual reports of the European Renal Association (1998-2011). The prevalence of CKD stage 5 in patients with diabetes up to the year 2025 was projected by the Lee-Carter algorithm. Those for stage 3 and 4 were then estimated by applying the same ratios of CKD prevalences as estimated in the Austrian population with diabetic nephropathy. RESULTS: The estimated prevalence of CKD in patients with diabetes is expected to increase in all 12 countries up to the year 2025. For CKD stage 3, we estimate for Austria in 2025 a prevalence of 215 000 per million diabetic population (p.m.p.) (95% confidence interval 169 000, 275 000), for CKD4 18 600 p.m.p. (14 500, 23 700) and for CKD5 6900 p.m.p. (5400, 8900). The median prevalence in the considered countries is 132 900 p.m.p. (IQR: 118 500, 195 800), 11 500 (10 200, 16 900) and 4300 (3800, 6300) for CKD stages 3, 4 and 5, respectively. Altogether, these data predict in the years 2012-25 an annual increase of 3.2% in the prevalence of diabetic CKD stage 5. CONCLUSIONS: Due to the increase in prevalence of diabetes and CKD5, the costs of renal therapy are expected to rise. We believe that these data may help health-care policy makers to make informed decisions.

Dietary risk factors for incidence or progression of chronic kidney disease in individuals with type 2 diabetes in the European Union.

BACKGROUND: Although the prevalence of chronic kidney disease (CKD) is ∼ 30% in the group of people with diabetes, data on interventions in the very early stage of the disease are still missing. Furthermore, the effects of modifiable lifestyle factors such as nutrition on incidence and progression of CKD in patients with diabetes in Europe remain elusive. METHODS: We analyzed whether diet quality and adherence to dietary guidelines using the modified Alternate Healthy Eating Index (mAHEI) score was associated with CKD incidence or progression after 5.5 years in 3088 European participants of the ONgoing Telmisartan Alone and in combination with Ramipril Global Endpoint Trial (ONTARGET) with type 2 diabetes and baseline normo- or micro-albuminuria. Death was considered as a competing risk in the multinomial logit regression models, which were adjusted for age, gender, duration of diabetes, ONTARGET randomization, baseline albuminuria and glomerular filtration rate (GFR). We also estimated the potential impact on population health of improvement in diet quality. RESULTS: At study end, 450 (14.6%) participants had died and 926 (30%) had experienced the renal endpoint of incidence or progression of CKD, of which 422 (13.7%) participants had progressed to micro- or macro-albuminuria, 596 (19.3%) had a GFR-decline of >5% per year and 18 (0.6%) had developed end-stage renal disease. Participants in the healthiest tertile of the mAHEI score had a decreased risk of incidence or progression of CKD (odds ratio 0.8, 95% confidence interval 0.68-0.94) and death (0.65, 0.52-0.81) compared with participants in the least healthy tertile. If individuals with a suboptimal dietary quality (e.g. mAHEI < 28) were able to improve their diet to an mAHEI of 28, 3.2% of CKD incidence or progression and 10.0% of deaths might be avoided in 5.5 years. CONCLUSIONS: If the association between diet and these endpoints is causal, then optimizing diet quality in individuals with diabetes who have no CKD or very early CKD would have substantial population benefits in terms of prevention of CKD incidence or progression and mortality in this high-risk population.

The relationship between estimated sodium and potassium excretion and subsequent renal outcomes.

Patients are often advised to reduce sodium and potassium intake, but supporting evidence is limited. To help provide such evidence we estimated 24 h urinary sodium and potassium excretion in 28,879 participants at high cardiovascular risk who were followed for a mean of 4.5 years in the ONTARGET and TRANSCEND trials. The primary outcome was eGFR decline of 30% or more or chronic dialysis. Secondary outcomes were eGFR decline of 40% or more or chronic dialysis, doubling of serum creatinine or chronic dialysis, an over 5%/year loss of eGFR, progression of albuminuria, and hyperkalemia. Multinomial logit regression with multivariable fractional polynomials, adjusted for confounders, determined the association between urinary sodium and potassium excretion and renal outcomes, with death as a competing risk. The primary outcome occurred in 2,052 (7.6%) patients. There was no significant association between sodium and any renal outcome (primary outcome odds ratio 0.99; 95% CI 0.89-1.09 for highest [median 6.2 g/day] vs. lowest third [median 3.3 g/day]). Higher potassium was associated with lower odds of all renal outcomes (primary outcome odds ratio 0.74; 95% CI 0.67-0.82 for highest [median 2.7 g/day] vs. lowest third [median 1.7 g/day], except hyperkalemia nonsignificant. Thus, urinary potassium, but not sodium, excretion predicted clinically important renal outcomes. Our findings do not support routine low sodium and potassium diets for prevention of renal outcomes in people with vascular disease with or without chronic kidney disease.

Evaluating variable selection methods for multivariable regression models: A simulation study protocol.

Researchers often perform data-driven variable selection when modeling the associations between an outcome and multiple independent variables in regression analysis. Variable selection may improve the interpretability, parsimony and/or predictive accuracy of a model. Yet variable selection can also have negative consequences, such as false exclusion of important variables or inclusion of noise variables, biased estimation of regression coefficients, underestimated standard errors and invalid confidence intervals, as well as model instability. While the potential advantages and disadvantages of variable selection have been discussed in the literature for decades, few large-scale simulation studies have neutrally compared data-driven variable selection methods with respect to their consequences for the resulting models. We present the protocol for a simulation study that will evaluate different variable selection methods: forward selection, stepwise forward selection, backward elimination, augmented backward elimination, univariable selection, univariable selection followed by backward elimination, and penalized likelihood approaches (Lasso, relaxed Lasso, adaptive Lasso). These methods will be compared with respect to false inclusion and/or exclusion of variables, consequences on bias and variance of the estimated regression coefficients, the validity of the confidence intervals for the coefficients, the accuracy of the estimated variable importance ranking, and the predictive performance of the selected models. We consider both linear and logistic regression in a low-dimensional setting (20 independent variables with 10 true predictors and 10 noise variables). The simulation will be based on real-world data from the National Health and Nutrition Examination Survey (NHANES). Publishing this study protocol ahead of performing the simulation increases transparency and allows integrating the perspective of other experts into the study design.

Regional lymph node metastases are a strong risk factor for venous thromboembolism: results from the Vienna Cancer and Thrombosis Study.

Advanced cancer is a risk factor for venous thromboembolism. However, lymph node metastases are usually not considered an established risk factor. In the framework of the prospective, observational Vienna Cancer and Thrombosis Study we investigated the association between local (N0), regional (N1-3), and distant (M1) cancer stages and the occurrence of venous thromboembolism. Furthermore, we were specifically interested in the relationship between stage and biomarkers that have been reported to be associated with venous thromboembolism. We followed 832 patients with solid tumors for a median of 527 days. The study end-point was symptomatic venous thromboembolism. At study inclusion, 241 patients had local, 138 regional, and 453 distant stage cancer. The cumulative probability of venous thromboembolism after 6 months in patients with local, regional and distant stage cancer was 2.1%, 6.5% and 6.0%, respectively (P=0.002). Compared to patients with local stage disease, patients with regional and distant stage disease had a significantly higher risk of venous thromboembolism in multivariable Cox-regression analysis including age, newly diagnosed cancer (versus progression of disease), surgery, radiotherapy, and chemotherapy (regional: HR=3.7, 95% CI: 1.5-9.6; distant: HR=5.4, 95% CI: 2.3-12.9). Furthermore, patients with regional or distant stage disease had significantly higher levels of D-dimer, factor VIII, and platelets, and lower hemoglobin levels than those with local stage disease. These results demonstrate an increased risk of venous thromboembolism in patients with regional disease. Elevated levels of predictive biomarkers in patients with regional disease underpin the results and are in line with the activation of the hemostatic system in the early phase of metastatic dissemination.

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.