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Abstract Aims In the EMPA‐REG OUTCOME trial, empagliflozin reduced risk of death from heart failure (HF) or hospitalization for heart failure (HHF) versus placebo in patients with type 2 diabetes mellitus (T2DM) and established cardiovascular (CV) disease. We evaluated post hoc the degree to which covariates mediated the effects of empagliflozin on HHF or HF death. Methods and results A mediator had to fulfil the following criteria: (i) affected by active treatment, (ii) associated with the outcome, and finally (iii) adjustment for it results in a reduced treatment effect compared with unadjusted analysis. Potential mediators were calculated as change from baseline or updated mean and evaluated in univariable analyses as time‐dependent covariates in Cox regression of time to HHF or HF death; those with the largest mediating effects were then included in a multivariable analysis. Increases in heart rate, log urine albumin‐to‐creatinine ratio (UACR), waist circumference, and uric acid were associated with increased risk of HHF or HF death; increases in high‐density lipoprotein cholesterol, estimated glomerular filtration rate, haematocrit, haemoglobin, and albumin were associated with reduced risk of HHF or HF death. In univariable analyses, change from baseline in haematocrit, haemoglobin, albumin, uric acid, and logUACR mediated 51%, 54%, 23%, 24%, and 27% of the risk reduction with empagliflozin versus placebo, respectively. Multivariable analysis including haemoglobin, logUACR, and uric acid mediated 85% of risk reduction with similar results when updated means were evaluated. Conclusions Changes in haematocrit and haemoglobin were the most important mediators of the reduction in HHF and death from HF in patients with T2DM and established CV disease treated with empagliflozin. Albumin, uric acid, and logUACR had smaller mediating effects in this population.

Abstract Background An essential aspect of preventing further COVID-19 outbreaks and to learn for future pandemics is the evaluation of different political strategies, which aim at reducing transmission of and mortality due to COVID-19. One important aspect in this context is the comparison of attributable mortality. Methods We give a comprehensive overview of six epidemiological measures that are used to quantify COVID-19 attributable mortality (p-score, standardized mortality ratio, absolute number of excess deaths, per capita rate, z-score and the population attributable fraction). Results By defining the six measures based on observed and expected deaths, we explain their relationship. Moreover, three publicly available data examples serve to illustrate the interpretational strengths and weaknesses of the various measures. Finally, we give recommendation which measures are suitable for an evaluation of public health strategies against COVID-19. The R code to reproduce the results is available as online supplementary material. Conclusion The number of excess deaths should be always reported together with the population attributable fraction, the p-score or the standardized mortality ratio instead of a per capita rate. For a complete picture of COVID-19 attributable mortality, quantifying and communicating its relative burden also to a lay audience is of major importance.

Abstract Background Healthcare decisions are ideally based on clinical trial results, published in study registries, as journal articles or summarized in secondary research articles. In this research project, we investigated the impact of academically and commercially sponsored clinical trials on medical practice by measuring the proportion of trials published and cited by systematic reviews and clinical guidelines. Methods We examined 691 multicenter, randomized controlled trials that started in 2005 or later and were completed by the end of 2016. To determine whether sponsorship/funding and place of conduct influence a trial’s impact, we created four sub-cohorts of investigator initiated trials (IITs) and industry sponsored trials (ISTs): 120 IITs and 171 ISTs with German contribution compared to 200 IITs and 200 ISTs without German contribution. We balanced the groups for study phase and place of conduct. German IITs were funded by the German Research Foundation (DFG), the Federal Ministry of Education and Research (BMBF), or by another non-commercial research organization. All other trials were drawn from the German Clinical Trials Register or ClinicalTrials.gov. We investigated, to what extent study characteristics were associated with publication and impact using multivariable logistic regressions. Results For 80% of the 691 trials, results were published as result articles in a medical journal and/or study registry, 52% were cited by a systematic review, and 26% reached impact in a clinical guideline. Drug trials and larger trials were associated with a higher probability to be published and to have an impact than non-drug trials and smaller trials. Results of IITs were more often published as a journal article while results of ISTs were more often published in study registries. International ISTs less often gained impact by inclusion in systematic reviews or guidelines than IITs. Conclusion An encouraging high proportion of the clinical trials were published, and a considerable proportion gained impact on clinical practice. However, there is still room for improvement. For publishing study results, study registries have become an alternative or complement to journal articles, especially for ISTs. IITs funded by governmental bodies in Germany reached an impact that is comparable to international IITs and ISTs.

Abstract Background The standard to ensure utmost cancer treatment is a prerequisite in national cancer plans for comprehensive cancer centers (CCCs) and ensured through multidisciplinary tumor boards (MTBs). Despite these being compulsory for CCCs, various analyses on MTBs have been performed, since MTBs are resource-intensive. Outcome measures in these prior analyses had been survival (OS), MTB-adherence and -satisfaction, inclusion of patients into clinical trials and better cancer care. Main body A publication from Freytag et al. performed an analysis in multiple tumor entities and assessed the effect of number of MTBs. By matched-pair analysis, they compared response and OS of patients, whose cases were discussed in MTBs vs. those that were not. The analysis included 454 patients and 66 different tumor types. Only patients with > 3 MTBs showed a significantly better OS than patients with no MTB meeting. Response to treatment, relapse free survival and time to progression were not found to be better, nor was there any difference for a specific tumor entity with vs. without MTB discussions. An in-depth discussion of these results, with respect to the literature (PubMed search: “MTBs AND cancer”) and within the author group, including statisticians specialized in data analysis of cancer patients and questions addressed in MTBs, was performed to interpret these findings. We conclude that the results by Freytag et al. are deceiving due to an “immortal time bias” that requires more careful data interpretation. Conclusions The result of Freytag et al. of a seemingly positive impact of higher number of MTBs needs to be interpreted cautiously: their presumed better OS in patients with > 3 MTB discussions is misleading, due to an immortal time bias. Here patients need to survive long enough to be discussed more often. Therefore, these results should not lead to the conclusion that more MTBs will “automatically” increase cancer patients’ OS, rather than that the insightful discussion, at best in MTBs and with statisticians, will generate meaningful advice, that is important for cancer patients.

Abstract Purpose We consider an existing clinical cohort with events but limited resources for the investigation of a further potentially expensive marker. Biological material of the patients is stored in a biobank, but only a limited number of samples can be analyzed with respect to the marker. The question arises as to which patients to sample, if the number of events preclude standard sampling designs. Methods Modifications of the nested case‐control and the case‐cohort design for the proportional hazards model are applied, that allow efficient sampling in situations where standard nested case‐control and case‐cohort are not feasible. These sampling designs are compared to simple random sampling and extreme group sampling, the latter including only patients with extreme outcomes, ie either with an event early in time or without an event until at least a point later in time. Results The modified nested case‐control design and the modified case‐cohort design provide powerful methods for sampling in a clinical cohort with many events. The simple random sampling usually is less efficient. If focus is on precise estimation of a potential effect in terms of a hazard ratio, extreme group sampling is not competitive. If focus is on screening for important biomarkers, extreme group sampling markedly outperforms the other sampling designs. Conclusions When it is not feasible to sample all events, a modified nested case‐control design or case‐cohort design leads to efficient effect estimates in the proportional hazards model. If screening for important biomarkers is the primary objective, extreme group sampling is preferable.

Abstract Background This analysis aims at evaluating the impact of multidisciplinary tumor boards on clinical outcome of multiple tumor entities, the effect of the specific number of multidisciplinary tumor boards and potential differences between the tumor entities. Methods By a matched-pair analysis we compared the response to treatment, overall survival, relapse or disease free survival and progression free survival of patients whose cases were discussed in a tumor board meeting with patients whose cases were not. It was performed with patients registered in the cancer registry of the University of Bonn and diagnosed between 2010 and 2016. After the matching process with a pool of 7262 patients a total of 454 patients with 66 different tumor types were included in this study. Results First, patients with three or more multidisciplinary tumor board meetings in their history show a significantly better overall survival than patients with no tumor board meeting. Second, response to treatment, relapse free survival and time to progression were not found to be significantly different. Third, there was no significant difference for a specific tumor entity. Conclusion This study revealed a positive impact of a higher number of multidisciplinary tumor boards on the clinical outcome. Also, our analysis hints towards a positive effect of multidisciplinary tumor boards on overall survival.

Abstract Background Increasingly, molecular measurements from multiple studies are pooled to identify risk scores, with only partial overlap of measurements available from different studies. Univariate analyses of such markers have routinely been performed in such settings using meta-analysis techniques in genome-wide association studies for identifying genetic risk scores. In contrast, multivariable techniques such as regularized regression, which might potentially be more powerful, are hampered by only partial overlap of available markers even when the pooling of individual level data is feasible for analysis. This cannot easily be addressed at a preprocessing level, as quality criteria in the different studies may result in differential availability of markers – even after imputation. Methods Motivated by data from the InterLymph Consortium on risk factors for non-Hodgkin lymphoma, which exhibits these challenges, we adapted a regularized regression approach, componentwise boosting, for dealing with partial overlap in SNPs. This synthesis regression approach is combined with resampling to determine stable sets of single nucleotide polymorphisms, which could feed into a genetic risk score. The proposed approach is contrasted with univariate analyses, an application of the lasso, and with an analysis that discards studies causing the partial overlap. The question of statistical significance is faced with an approach called stability selection. Results Using an excerpt of the data from the InterLymph Consortium on two specific subtypes of non-Hodgkin lymphoma, it is shown that componentwise boosting can take into account all applicable information from different SNPs, irrespective of whether they are covered by all investigated studies and for all individuals in the single studies. The results indicate increased power, even when studies that would be discarded in a complete case analysis only comprise a small proportion of individuals. Conclusions Given the observed gains in power, the proposed approach can be recommended more generally whenever there is only partial overlap of molecular measurements obtained from pooled studies and/or missing data in single studies. A corresponding software implementation is available upon request. Trial registration All involved studies have provided signed GWAS data submission certifications to the U.S. National Institute of Health and have been retrospectively registered.

Abstract Background Length of stay evaluations are very common to determine the burden of nosocomial infections. However, there exist fundamentally different methods to quantify the prolonged length of stay associated with nosocomial infections. Previous methodological studies emphasized the need to account for the timing of infection in order to differentiate the length of stay before and after the infection. Methods We derive four different approaches in a simple multi-state framework, display their mathematical relationships in a multiplicative as well as additive way and apply them to a real cohort study (n=756 German intensive-care unit patients of whom 124 patients acquired a nosocomial infection). Results The first approach ignores the timing of infection and quantifies the difference of eventually infected and eventually uninfected; it is 12.31 days in the real data. The second approach compares the average sojourn time with infection with the average sojourn time of being hypothetically uninfected; it is 2.12 days. The third one compares the average length of stay of a population in a world with nosocomial infections with a population in a hypothetical world without nosocomial infections; it is 0.35 days. Finally, approach four compares the mean residual length of stay between currently infected and uninfected patients on a daily basis; the difference is 1.77 days per infected patient. Conclusions The first approach should be avoided because it compares the eventually infected with the eventually uninfected, but has no prospective interpretation. The other approaches differ in their interpretation but are suitable because they explicitly distinguish between the pre- and post-time of the nosocomial infection.

BackgroundResults of medical research should be made publicly available in a timely manner to enable patients and health professionals to make informed decisions about health issues. We aimed to apply a multi-state model to analyze the overall time needed to publish study results, and to examine predictors of the timing of transitions within the research process from study initiation through completion/discontinuation to eventual publication.MethodsUsing a newly developed multi-state model approach, we analysed the effect of different study-related factors on each of the transitions from study approval to eventual publication, using a data set of clinical studies approved by a German research ethics committee between 2000 and 2002.ResultsOf 917 approved studies, 806 were included in our analyses. About half of the clinical studies which began were subsequently published as full articles, and the median time from study approval to publication was 10 years. Differences across model states were apparent; several factors were predictive of the transition from study approval to completion, while funding source and collaboration were predictive of the transition from completion to publication.ConclusionsThe proposed multi-state model approach permits a more comprehensive analysis of time to publication than a simple examination of the transition from approval to publication, and thus the findings represent an advance on previous studies of this aspect of the research process.

Abstract Background We evaluate three methods for competing risks analysis with time-dependent covariates in comparison with the corresponding methods with time-independent covariates. Methods We used cause-specific hazard analysis and two summary approaches for in-hospital death: logistic regression and regression of the subdistribution hazard. We analysed real hospital data (n=1864) and considered pneumonia on admission / hospital-acquired pneumonia as time-independent / time-dependent covariates for the competing events ’discharge alive’ and ’in-hospital death’. Several simulation studies with time-constant hazards were conducted. Results All approaches capture the effect of time-independent covariates, whereas the approaches perform differently with time-dependent covariates. The subdistribution approach for time-dependent covariates detected effects in a simulated no-effects setting and provided counter-intuitive effects in other settings. Conclusions The extension of the Fine and Gray model to time-dependent covariates is in general not a helpful synthesis of the cause-specific hazards. Cause-specific hazard analysis and, for uncensored data, the odds ratio are capable of handling competing risks data with time-dependent covariates but the use of the subdistribution approach should be neglected until the problems can be resolved. For general right-censored data, cause-specific hazard analysis is the method of choice.

Abstract Background In many studies the information of patients who are dying in the hospital is censored when examining the change in length of hospital stay (cLOS) due to hospital-acquired infections (HIs). While appropriate estimators of cLOS are available in literature, the existence of the bias due to censoring of deaths was neither mentioned nor discussed by the according authors. Methods Using multi-state models, we systematically evaluate the bias when estimating cLOS in such a way. We first evaluate the bias in a mathematically closed form assuming a setting with constant hazards. To estimate the cLOS due to HIs non-parametrically, we relax the assumption of constant hazards and consider a time-inhomogeneous Markov model. Results In our analytical evaluation we are able to discuss challenging effects of the bias on cLOS. These are in regard to direct and indirect differential mortality. Moreover, we can make statements about the magnitude and direction of the bias. For real-world relevance, we illustrate the bias on a publicly available prospective cohort study on hospital-acquired pneumonia in intensive-care. Conclusion Based on our findings, we can conclude that censoring the death cases in the hospital and considering only patients discharged alive should be avoided when estimating cLOS. Moreover, we found that the closed mathematical form can be used to describe the bias for settings with constant hazards.

Abstract Background When many (up to millions) of statistical tests are conducted in discovery set analyses such as genome-wide association studies (GWAS), approaches controlling family-wise error rate (FWER) or false discovery rate (FDR) are required to reduce the number of false positive decisions. Some methods were specifically developed in the context of high-dimensional settings and partially rely on the estimation of the proportion of true null hypotheses. However, these approaches are also applied in low-dimensional settings such as replication set analyses that might be restricted to a small number of specific hypotheses. The aim of this study was to compare different approaches in low-dimensional settings using (a) real data from the CKDGen Consortium and (b) a simulation study. Results In both application and simulation FWER approaches were less powerful compared to FDR control methods, whether a larger number of hypotheses were tested or not. Most powerful was the q-value method. However, the specificity of this method to maintain true null hypotheses was especially decreased when the number of tested hypotheses was small. In this low-dimensional situation, estimation of the proportion of true null hypotheses was biased. Conclusions The results highlight the importance of a sizeable data set for a reliable estimation of the proportion of true null hypotheses. Consequently, methods relying on this estimation should only be applied in high-dimensional settings. Furthermore, if the focus lies on testing of a small number of hypotheses such as in replication settings, FWER methods rather than FDR methods should be preferred to maintain high specificity.

Abstract Background The extended illness-death model is a useful tool to study the risks and consequences of hospital-acquired infections (HAIs). The statistical quantities of interest are the transition-specific hazard rates and the transition probabilities as well as attributable mortality (AM) and the population-attributable fraction (PAF). In the most general case calculation of these expressions is mathematically complex. Methods When assuming time-constant hazards calculation of the quantities of interest is facilitated. In this situation the transition probabilities can be expressed in closed mathematical forms. The estimators for AM and PAF can be easily derived from these forms. Results In this paper, we show how to explicitly calculate all the transition probabilities of an extended-illness model with constant hazards. Using a parametric model to estimate the time-constant transition specific hazard rates of a data example, the transition probabilities, AM and PAF can be directly calculated. With a publicly available data example, we show how the approach provides first insights into principle time-dynamics and data structure. Conclusion Assuming constant hazards facilitates the understanding of multi-state processes. Even in a non-constant hazards setting, the approach is a helpful first step for a comprehensive investigation of complex data.

The status of the experimental and theoretical investigations of the nucleon is presented. This includes a confirmation of the validity of the previously introduced recommended polarizabilities. It is shown that the only meaningful approach to a prediction of the polarizabilities is obtained from nonsubtracted dispersion theory, where the appropriate degrees of freedon taken from other precise experimental data are taken into account. The present values of the recommended polarizabilities are \(\alpha_p=12.0\pm 0.5\), \(\beta_p=1.9\mp 0.5\), \(\alpha_n=12.6\pm 1.2\), \(\beta_n=2.6\mp 1.2\) in units of \(10^{-4}\)fm\(^3\) and \(\gamma^{(p)}_\pi =-36.4 \pm 1.5\), \(\gamma^{(n)}_\pi=+58.6\pm 4.0\), \(\gamma^{(p)}_0= -0.58\pm 0.20\), \(\gamma^{(n)}_0=+0.38\pm 0.22\) in units of \(10^{-4}\)fm\(^4\).

Estimating and selecting risk factors with extremely low prevalences of exposure for a binary outcome is a challenge because classical standard techniques, markedly logistic regression, often fail to provide meaningful results in such settings. While penalized regression methods are widely used in high-dimensional settings, we were able to show their usefulness in low-dimensional settings as well. Specifically, we demonstrate that Firth correction, ridge, the lasso and boosting all improve the estimation for low-prevalence risk factors. While the methods themselves are well-established, comparison studies are needed to assess their potential benefits in this context. This is done here using the dataset of a large unmatched case-control study from France (2005-2008) about the relationship between prescription medicines and road traffic accidents and an accompanying simulation study. Results show that the estimation of risk factors with prevalences below 0.1% can be drastically improved by using Firth correction and boosting in particular, especially for ultra-low prevalences. When a moderate number of low prevalence exposures is available, we recommend the use of penalized techniques.

Abstract Background When patients are admitted to an intensive care unit (ICU) their risk of getting an infection will be highly depend on the length of stay at-risk in the ICU. In addition, risk of infection is likely to vary over calendar time as a result of fluctuations in the prevalence of the pathogen on the ward. Hence risk of infection is expected to depend on two time scales (time in ICU and calendar time) as well as competing events (discharge or death) and their spatial location. The purpose of this paper is to develop and apply appropriate statistical models for the risk of ICU-acquired infection accounting for multiple time scales, competing risks and the spatial clustering of the data. Methods A multi-center data base from a Spanish surveillance network was used to study the occurrence of an infection due to Methicillin-resistant Staphylococcus aureus (MRSA). The analysis included 84,843 patient admissions between January 2006 and December 2011 from 81 ICUs. Stratified Cox models were used to study multiple time scales while accounting for spatial clustering of the data (patients within ICUs) and for death or discharge as competing events for MRSA infection. Results Both time scales, time in ICU and calendar time, are highly associated with the MRSA hazard rate and cumulative risk. When using only one basic time scale, the interpretation and magnitude of several patient-individual risk factors differed. Risk factors concerning the severity of illness were more pronounced when using only calendar time. These differences disappeared when using both time scales simultaneously. Conclusions The time-dependent dynamics of infections is complex and should be studied with models allowing for multiple time scales. For patient individual risk-factors we recommend stratified Cox regression models for competing events with ICU time as the basic time scale and calendar time as a covariate. The inclusion of calendar time and stratification by ICU allow to indirectly account for ICU-level effects such as local outbreaks or prevention interventions.

Abstract Background The case-crossover design is an attractive alternative to the classical case–control design which can be used to study the onset of acute events if the risk factors of interest vary in time. By comparing exposures within cases at different time periods, the case-crossover design does not rely on control subjects which can be difficult to acquire. However, using the standard method of maximum likelihood, resulting risk estimates can be heavily biased when the prevalence to risk factors is very low (or very high). Methods To overcome the problem of low risk factor prevalences, penalized conditional logistic regression via the lasso (least absolute shrinkage and selection operator) has been proposed in the literature as well as related methods such as the Firth correction. We apply and compare several penalized regression approaches in the context of a case-crossover analysis of the European Study of Severe Cutaneous Adverse Reactions (EuroSCAR; 1997–2001). Results Out of 30 drugs, standard methods only correctly classified 17 drugs (including some highly implausible risk estimates), while penalized methods correctly classified 22 drugs. Conclusion Penalized methods generally yield better risk classifications and much more plausible risk estimates for the EuroSCAR study than standard methods. As these novel techniques can be easily implemented using available R packages, we encourage routine use of penalized conditional logistic regression for case-crossover data.

Abstract Background In meta-analyses of diagnostic test accuracy, routinely only one pair of sensitivity and specificity per study is used. However, for tests based on a biomarker or a questionnaire often more than one threshold and the corresponding values of true positives, true negatives, false positives and false negatives are known. Methods We present a new meta-analysis approach using this additional information. It is based on the idea of estimating the distribution functions of the underlying biomarker or questionnaire within the non-diseased and diseased individuals. Assuming a normal or logistic distribution, we estimate the distribution parameters in both groups applying a linear mixed effects model to the transformed data. The model accounts for across-study heterogeneity and dependence of sensitivity and specificity. In addition, a simulation study is presented. Results We obtain a summary receiver operating characteristic (SROC) curve as well as the pooled sensitivity and specificity at every specific threshold. Furthermore, the determination of an optimal threshold across studies is possible through maximization of the Youden index. We demonstrate our approach using two meta-analyses of B type natriuretic peptide in heart failure and procalcitonin as a marker for sepsis. Conclusions Our approach uses all the available information and results in an estimation not only of the performance of the biomarker but also of the threshold at which the optimal performance can be expected.

With growing numbers of elderly multiple myeloma patients, reliable tools to assess their vulnerability are required. The objective of the analysis herein was to develop and validate an easy to use myeloma risk score (revised Myeloma Comorbidity Index) that allows for risk prediction of overall survival and progression-free survival differences in a large patient cohort. We conducted a comprehensive comorbidity, frailty and disability evaluation in 801 consecutive myeloma patients, including comorbidity risks obtained at diagnosis. The cohort was examined within a training and validation set. Multivariate analysis determined renal, lung and Karnofsky Performance Status impairment, frailty and age as significant risks for overall survival. These were combined in a weighted revised Myeloma Comorbidity Index, allowing for the identification of fit (revised Myeloma Comorbidity Index ≤3 [n=247, 30.8%]), intermediate-fit (revised Myeloma Comorbidity Index 4–6 [n=446, 55.7%]) and frail patients (revised Myeloma Comorbidity Index >6 [n=108, 13.5%]): these subgroups, confirmed via validation analysis, showed median overall survival rates of 10.1, 4.4 and 1.2 years, respectively. The revised Myeloma Comorbidity Index was compared to other commonly used comorbidity indices (Charlson Comorbidity Index, Hematopoietic Cell Transplantation-Specific Comorbidity Index, Kaplan-Feinstein Index): if each were divided in risk groups based on 25% and 75% quartiles, highest hazard ratios, best prediction and Brier scores were achieved with the revised Myeloma Comorbidity Index. The advantages of the revised Myeloma Comorbidity Index include its accurate assessment of patients’ physical conditions and simple clinical applicability. We propose the revised Myeloma Comorbidity Index to be tested with the “reference” International Myeloma Working Group frailty score in multicenter analyses and future clinical trials. The study was registered at the German Clinical Trials Register (DRKS-00003868).

[This corrects the article DOI: 10.1371/journal.pone.0160430.].

This first validation of the International Myeloma Working Group geriatric assessment in 125 newly diagnosed multiple myeloma patients was performed using the International Myeloma Working Group score based on age, the Charlson Comorbidity Index and cognitive and physical conditions (Activities of Daily Living / Instrumental Activities of Daily Living) to classify patients as fit, intermediate-fit or frail. We verified the International Myeloma Working Group score’s impact on outcome, and whether additional tools complement it. Since our prior analyses determined renal, lung and Karnofsky performance impairment as multivariate risks, and the inclusion of frailty, age and cytogenetics complements this, we included the revised myeloma comorbidity index, the Charlson Comorbidity Index, the Hematopoietic Cell Transplantation-Comorbidity Index and the Kaplan-Feinstein Index in this assessment. Multivariate analysis confirmed cytogenetics, Activities of Daily Living, Instrumental Activities of Daily Living and the Charlson Comorbidity Index as risks: 3-year overall survival for fit, intermediate-fit and frail patients was 91%, 77% and 47%, respectively. Using the Charlson Comorbidity Index, the Hematopoietic Cell Transplantation-Comorbidity Index, the Kaplan-Feinstein Index and the revised Myeloma Comorbidity Index allowed us to define fit and frail patients with distinct progression-free and overall survival rates, with the most pronounced differences evidenced via the International Myeloma Working Group score, the Charlson Comorbidity Index and the revised Myeloma Comorbidity Index. Since the Charlson Comorbidity Index is included in the International Myeloma Working Group score, we propose the latter and the revised Myeloma Comorbidity Index for future frailty measurements. Both are useful instruments for identifying myeloma patients with a geriatric risk profile and have a strong prognostic value for functional decline and overall survival. The study was registered as: (clinicaltrials.gov Identifier: 00003686).

Clinical cohorts with time-to-event endpoints are increasingly characterized by measurements of a number of single nucleotide polymorphisms that is by a magnitude larger than the number of measurements typically considered at the gene level. At the same time, the size of clinical cohorts often is still limited, calling for novel analysis strategies for identifying potentially prognostic SNPs that can help to better characterize disease processes. We propose such a strategy, drawing on univariate testing ideas from epidemiological case-controls studies on the one hand, and multivariable regression techniques as developed for gene expression data on the other hand. In particular, we focus on stable selection of a small set of SNPs and corresponding genes for subsequent validation. For univariate analysis, a permutation-based approach is proposed to test at the gene level. We use regularized multivariable regression models for considering all SNPs simultaneously and selecting a small set of potentially important prognostic SNPs. Stability is judged according to resampling inclusion frequencies for both the univariate and the multivariable approach. The overall strategy is illustrated with data from a cohort of acute myeloid leukemia patients and explored in a simulation study. The multivariable approach is seen to automatically focus on a smaller set of SNPs compared to the univariate approach, roughly in line with blocks of correlated SNPs. This more targeted extraction of SNPs results in more stable selection at the SNP as well as at the gene level. Thus, the multivariable regression approach with resampling provides a perspective in the proposed analysis strategy for SNP data in clinical cohorts highlighting what can be added by regularized regression techniques compared to univariate analyses.

BACKGROUND:Observational studies claimed reducing effects of neuraminidase inhibitors (NI) on hospital mortality in patients with H1N1 influenza A. It has been criticized that such findings are prone to common and serious survival biases. METHODS:With observational data from the FLU-CIN study group, multi-state and dynamic prediction models have been used to avoid such biases. The data included 1391 patients with confirmed pandemic influenza A/H1N1 infection collected during 2009-2010 in the UK. Due to their close relationship, the main outcome measures were hospital death and length of hospital stay. FINDINGS:There is no direct effect of NI on the hospital death rate; the hazard ratio (HR) of NI was 1.03 (95%-CI: 0.64-1.66). The discharge rate is increased for NI patients (HR = 1.89 (95%-CI: 1.65-2.16)) indicating that NI-treated patients stay shorter in hospital than NI-untreated patients, on average 3.10 days (95%-CI: 2.07-4.14). We also showed that the initiation timing of NI treatment (≤ 2 days versus > 2 days after onset) made no difference on the effects on the hospital death and discharge hazards. The hazard ratios remain stable after adjusting for potential confounders measured at admission (such as comorbidities and influenza-related clinical symptoms). CONCLUSIONS:The potential beneficial effect of NI on hospitalized patients in the UK is rather a reduction of the length of hospital stay than a reduction of the mortality rate. There seems to be no confounding by indication and no differences if NI is given early or late. Different effects could be present in other populations (such as non-hospitalized individuals) or countries. Careful interpretation of the effect on length of hospital stay is needed due to potentially different discharge policies of NI-treated and NI-untreated patients.

BACKGROUND:Prognostic factors and prognostic models play a key role in medical research and patient management. The Nottingham Prognostic Index (NPI) is a well-established prognostic classification scheme for patients with breast cancer. In a very simple way, it combines the information from tumor size, lymph node stage and tumor grade. For the resulting index cutpoints are proposed to classify it into three to six groups with different prognosis. As not all prognostic information from the three and other standard factors is used, we will consider improvement of the prognostic ability using suitable analysis approaches. METHODS AND FINDINGS:Reanalyzing overall survival data of 1560 patients from a clinical database by using multivariable fractional polynomials and further modern statistical methods we illustrate suitable multivariable modelling and methods to derive and assess the prognostic ability of an index. Using a REMARK type profile we summarize relevant steps of the analysis. Adding the information from hormonal receptor status and using the full information from the three NPI components, specifically concerning the number of positive lymph nodes, an extended NPI with improved prognostic ability is derived. CONCLUSIONS:The prognostic ability of even one of the best established prognostic index in medicine can be improved by using suitable statistical methodology to extract the full information from standard clinical data. This extended version of the NPI can serve as a benchmark to assess the added value of new information, ranging from a new single clinical marker to a derived index from omics data. An established benchmark would also help to harmonize the statistical analyses of such studies and protect against the propagation of many false promises concerning the prognostic value of new measurements. Statistical methods used are generally available and can be used for similar analyses in other diseases.

Additional malignancies in multiple myeloma patients after first-line and maintenance treatment have been observed, questioning whether specific risks exist. Second primary malignancies have also gained attention since randomized data showed associations to newer drugs. We have conducted this large registry analysis in 744 consecutive patients and analyzed: 1) frequency and onset of additional malignancies; and 2) second primary malignancy- and myeloma-specific risks. We assessed the frequency of additional malignancies in terms of host-, myeloma- and treatment-specific characteristics. To compare these risks, we estimated cumulative incidence rates for second malignancies and myeloma with Fine and Gray regression models taking into account competing risks. Additional malignancies were found in 118 patients: prior or synchronous malignancies in 63% and subsequent in 37%. Cumulative incidence rates for second malignancies were increased in IgG-myeloma and decreased in bortezomib-treated patients (P

We recently found that rat hepatocyte transplantation was efficient (liver repopulation: 2.4%) in a sublethal nude mouse model (less than 33% mortality) of repeated liver injury generated using Jo2, a mouse-specific anti-Fas antibody, at sublethal dose of 250 μg/kg for 3 weeks. Genomic analysis of the livers revealed cell cycle blockade and an antiproliferative status of circadian genes, suggesting a selective advantage. By contrast, in the present study, freshly isolated human hepatocyte transplantation performed in the same mouse model resulted in implantation of less than 6,000 cells per liver (about 0.006% repopulation) in all animals. Genomic analysis of nude mouse livers revealed a lack of P21 upregulation, while a signature of stimulation of liver regeneration was observed, including upregulation of early response genes and upregulation of circadian genes. When we translated this sublethal model to a lethal model (65% mortality) by increasing the Jo2 repeated doses to 375 μg/kg, human hepatocyte engraftment was still very low; however, animal mortality was corrected by transplantation (only 20% mortality). Genomic findings in livers from the mice of the lethal Jo2 transplanted group were similar to those of the sublethal Jo2 transplanted group, that is, no selective advantage genomic signature and signature of mouse liver regeneration. In conclusion, transplanted human hepatocytes acted as if they modified nude mouse liver responses to Jo2 by stimulating liver regeneration, leading to an increased survival rate.

BackgroundMany clinical studies are ultimately not fully published in peer-reviewed journals. Underreporting of clinical research is wasteful and can result in biased estimates of treatment effect or harm, leading to recommendations that are inappropriate or even dangerous.MethodsWe assembled a cohort of clinical studies approved 2000-2002 by the Research Ethics Committee of the University of Freiburg, Germany. Published full articles were searched in electronic databases and investigators contacted. Data on study characteristics were extracted from protocols and corresponding publications. We characterized the cohort, quantified its publication outcome and compared protocols and publications for selected aspects.ResultsOf 917 approved studies, 807 were started and 110 were not, either locally or as a whole. Of the started studies, 576 (71%) were completed according to protocol, 128 (16%) discontinued and 42 (5%) are still ongoing; for 61 (8%) there was no information about their course. We identified 782 full publications corresponding to 419 of the 807 initiated studies; the publication proportion was 52% (95% CI: 0.48-0.55). Study design was not significantly associated with subsequent publication. Multicentre status, international collaboration, large sample size and commercial or non-commercial funding were positively associated with subsequent publication. Commercial funding was mentioned in 203 (48%) protocols and in 205 (49%) of the publications. In most published studies (339; 81%) this information corresponded between protocol and publication. Most studies were published in English (367; 88%); some in German (25; 6%) or both languages (27; 6%). The local investigators were listed as (co-)authors in the publications corresponding to 259 (62%) studies.ConclusionHalf of the clinical research conducted at a large German university medical centre remains unpublished; future research is built on an incomplete database. Research resources are likely wasted as neither health care professionals nor patients nor policy makers can use the results when making decisions.

Multi-State models provide a relevant framework for modelling complex event histories. Quantities of interest are the transition probabilities that can be estimated by the empirical transition matrix, that is also referred to as the Aalen-Johansen estimator. In this paper, we present the R package etm that computes and displays the transition probabilities. etm also features a Greenwood-type estimator of the covariance matrix. The use of the package is illustrated through a prominent example in bone marrow transplant for leukaemia patients.

Hepatocyte transplantation is a promising therapy for acute liver failure in humans. Recently, we succeeded in inducing various acute and chronic liver failures in nude mice. Engraftment of transplanted xenogeneic rat hepatocytes, visualized in the host liver by anti-MHC class I immunohistochemistry, revealed that liver repopulation was limited, and equivalent in nude mice with and without acute liver failure. In the present study, acute liver failure was induced in nude mice by a single injection of sublethal anti-Fas antibody Jo2, followed 24 h later by rat hepatocyte transplantation and than by a weekly repeated injection of Jo2. Rat hepatocyte engraftment into the recipient liver parenchyma 3 weeks following hepatocyte transplantation was about sevenfold increased when nude mice were subsequently subjected to weekly repeated Jo2 injection. Genomic analysis of these mice showed an overall transcriptome profile of upregulation of cellular cycle blocking transcripts, activation of liver injury inducing IFN-γ/STAT1 pathway, and circadian transcript signature of antiproliferative cell status compared to mice submitted to hepatocyte transplantation only. The findings of the present study suggest that the induction of cell proliferation blockade in recipient livers could promote sufficient engraftment of transplanted hepatocytes to allow transient or definitive treatment of liver failure in humans.