Structured learning in time-dependent Cox models.

Cox models with time-dependent coefficients and covariates are widely used in survival analysis. In high-dimensional settings, sparse regularization techniques are employed for variable selection, but existing methods for time-dependent Cox models lack flexibility in enforcing specific sparsity patterns (ie, covariate structures). We propose a flexible framework for variable selection in time-dependent Cox models, accommodating complex selection rules. Our method can adapt to arbitrary grouping structures, including interaction selection, temporal, spatial, tree, and directed acyclic graph structures. It achieves accurate estimation with low false alarm rates. We develop the sox package, implementing a network flow algorithm for efficiently solving models with complex covariate structures. sox offers a user-friendly interface for specifying grouping structures and delivers fast computation. Through examples, including a case study on identifying predictors of time to all-cause death in atrial fibrillation patients, we demonstrate the practical application of our method with specific selection rules.

Machine learning for predicting neurodegenerative diseases in the general older population: a cohort study.

BACKGROUND: In the older general population, neurodegenerative diseases (NDs) are associated with increased disability, decreased physical and cognitive function. Detecting risk factors can help implement prevention measures. Using deep neural networks (DNNs), a machine-learning algorithm could be an alternative to Cox regression in tabular datasets with many predictive features. We aimed to compare the performance of different types of DNNs with regularized Cox proportional hazards models to predict NDs in the older general population. METHODS: We performed a longitudinal analysis with participants of the English Longitudinal Study of Ageing. We included men and women with no NDs at baseline, aged 60 years and older, assessed every 2 years from 2004 to 2005 (wave2) to 2016-2017 (wave 8). The features were a set of 91 epidemiological and clinical baseline variables. The outcome was new events of Parkinson's, Alzheimer or dementia. After applying multiple imputations, we trained three DNN algorithms: Feedforward, TabTransformer, and Dense Convolutional (Densenet). In addition, we trained two algorithms based on Cox models: Elastic Net regularization (CoxEn) and selected features (CoxSf). RESULTS: 5433 participants were included in wave 2. During follow-up, 12.7% participants developed NDs. Although the five models predicted NDs events, the discriminative ability was superior using TabTransformer (Uno's C-statistic (coefficient (95% confidence intervals)) 0.757 (0.702, 0.805). TabTransformer showed superior time-dependent balanced accuracy (0.834 (0.779, 0.889)) and specificity (0.855 (0.0.773, 0.909)) than the other models. With the CoxSf (hazard ratio (95% confidence intervals)), age (10.0 (6.9, 14.7)), poor hearing (1.3 (1.1, 1.5)) and weight loss 1.3 (1.1, 1.6)) were associated with a higher DNN risk. In contrast, executive function (0.3 (0.2, 0.6)), memory (0, 0, 0.1)), increased gait speed (0.2, (0.1, 0.4)), vigorous physical activity (0.7, 0.6, 0.9)) and higher BMI (0.4 (0.2, 0.8)) were associated with a lower DNN risk. CONCLUSION: TabTransformer is promising for prediction of NDs with heterogeneous tabular datasets with numerous features. Moreover, it can handle censored data. However, Cox models perform well and are easier to interpret than DNNs. Therefore, they are still a good choice for NDs.

Gene-based analysis of bi-variate survival traits via functional regressions with applications to eye diseases.

Genetic studies of two related survival outcomes of a pleiotropic gene are commonly encountered but statistical models to analyze them are rarely developed. To analyze sequencing data, we propose mixed effect Cox proportional hazard models by functional regressions to perform gene-based joint association analysis of two survival traits motivated by our ongoing real studies. These models extend fixed effect Cox models of univariate survival traits by incorporating variations and correlation of multivariate survival traits into the models. The associations between genetic variants and two survival traits are tested by likelihood ratio test statistics. Extensive simulation studies suggest that type I error rates are well controlled and power performances are stable. The proposed models are applied to analyze bivariate survival traits of left and right eyes in the age-related macular degeneration progression.

Longitudinal Methods for Modeling Exposures in Pharmacoepidemiologic Studies in Pregnancy.

In many perinatal pharmacoepidemiologic studies, exposure to a medication is classified as "ever exposed" versus "never exposed" within each trimester or even over the entire pregnancy. This approach is often far from real-world exposure patterns, may lead to exposure misclassification, and does not to incorporate important aspects such as dosage, timing of exposure, and treatment duration. Alternative exposure modeling methods can better summarize complex, individual-level medication use trajectories or time-varying exposures from information on medication dosage, gestational timing of use, and frequency of use. We provide an overview of commonly used methods for more refined definitions of real-world exposure to medication use during pregnancy, focusing on the major strengths and limitations of the techniques, including the potential for method-specific biases. Unsupervised clustering methods, including k-means clustering, group-based trajectory models, and hierarchical cluster analysis, are of interest because they enable visual examination of medication use trajectories over time in pregnancy and complex individual-level exposures, as well as providing insight into comedication and drug-switching patterns. Analytical techniques for time-varying exposure methods, such as extended Cox models and Robins' generalized methods, are useful tools when medication exposure is not static during pregnancy. We propose that where appropriate, combining unsupervised clustering techniques with causal modeling approaches may be a powerful approach to understanding medication safety in pregnancy, and this framework can also be applied in other areas of epidemiology.

Understanding Long-term Evolution and Predictors of Sequelae of Ebola Virus Disease Survivors in Guinea: A 48-Month Prospective, Longitudinal Cohort Study (PostEboGui).

BACKGROUND: Longitudinal analyses are needed to better understand long-term Ebola virus disease (EVD) sequelae. We aimed to estimate the prevalence, incidence, and duration of sequelae and to identify risk factors associated with symptom occurrence among EVD survivors in Guinea. METHODS: We followed 802 EVD survivors over 48 months and recorded clinical symptoms with their start/end dates. Prevalence, incidence, and duration of sequelae were calculated. Risk factors associated with symptom occurrence were assessed using an extended Cox model for recurrent events. RESULTS: Overall, the prevalence and incidence of all symptoms decreased significantly over time, but sequelae remained present 48 months after Ebola treatment center discharge with a prevalence of 30.68% (95% confidence interval [CI] 21.40-39.96) for abdominal, 30.55% (95% CI 20.68-40.41) for neurologic, 5.80% (95% CI 1.96-9.65) for musculoskeletal, and 4.24% (95% CI 2.26-6.23) for ocular sequelae. Half of all patients (50.70%; 95% CI 47.26-54.14) complained of general symptoms 2 years' postdischarge and 25.35% (95% CI 23.63-27.07) 4 years' post-discharge. Hemorrhage (hazard ratio [HR], 2.70; P = .007), neurologic (HR 2.63; P = .021), and general symptoms (HR 0.34; P = .003) in the EVD acute phase were significantly associated with the further occurrence of ocular sequelae, whereas hemorrhage (HR 1.91; P = .046) and abdominal (HR 2.21; P = .033) symptoms were significantly associated with musculoskeletal sequelae. CONCLUSIONS: Our findings provide new insight into the long-term clinical complications of EVD and their significant association with symptoms in the acute phase, thus reinforcing the importance of regular, long-term follow-up for EVD survivors.

Construction and optimization of gene expression signatures for prediction of survival in two-arm clinical trials.

BACKGROUND: Gene expression signatures for the prediction of differential survival of patients undergoing anti-cancer therapies are of great interest because they can be used to prospectively stratify patients entering new clinical trials, or to determine optimal treatment for patients in more routine clinical settings. Unlike prognostic signatures however, predictive signatures require training set data from clinical studies with at least two treatment arms. As two-arm studies with gene expression profiling have been rarer than similar one-arm studies, the methodology for constructing and optimizing predictive signatures has been less prominently explored than for prognostic signatures. RESULTS: Focusing on two "use cases" of two-arm clinical trials, one for metastatic colorectal cancer (CRC) patients treated with the anti-angiogenic molecule aflibercept, and the other for triple negative breast cancer (TNBC) patients treated with the small molecule iniparib, we present derivation steps and quantitative and graphical tools for the construction and optimization of signatures for the prediction of progression-free survival based on cross-validated multivariate Cox models. This general methodology is organized around two more specific approaches which we have called subtype correlation (subC) and mechanism-of-action (MOA) modeling, each of which leverage a priori knowledge of molecular subtypes of tumors or drug MOA for a given indication. The tools and concepts presented here include the so-called differential log-hazard ratio, the survival scatter plot, the hazard ratio receiver operating characteristic, the area between curves and the patient selection matrix. In the CRC use case for instance, the resulting signature stratifies the patient population into "sensitive" and "relatively-resistant" groups achieving a more than two-fold difference in the aflibercept-to-control hazard ratios across signature-defined patient groups. Through cross-validation and resampling the probability of generalization of the signature to similar CRC data sets is predicted to be high. CONCLUSIONS: The tools presented here should be of general use for building and using predictive multivariate signatures in oncology and in other therapeutic areas.

Gene-based association analysis of survival traits via functional regression-based mixed effect cox models for related samples.

The importance to integrate survival analysis into genetics and genomics is widely recognized, but only a small number of statisticians have produced relevant work toward this study direction. For unrelated population data, functional regression (FR) models have been developed to test for association between a quantitative/dichotomous/survival trait and genetic variants in a gene region. In major gene association analysis, these models have higher power than sequence kernel association tests. In this paper, we extend this approach to analyze censored traits for family data or related samples using FR based mixed effect Cox models (FamCoxME). The FamCoxME model effect of major gene as fixed mean via functional data analysis techniques, the local gene or polygene variations or both as random, and the correlation of pedigree members by kinship coefficients or genetic relationship matrix or both. The association between the censored trait and the major gene is tested by likelihood ratio tests (FamCoxME FR LRT). Simulation results indicate that the LRT control the type I error rates accurately/conservatively and have good power levels when both local gene or polygene variations are modeled. The proposed methods were applied to analyze a breast cancer data set from the Consortium of Investigators of Modifiers of BRCA1 and BRCA2 (CIMBA). The FamCoxME provides a new tool for gene-based analysis of family-based studies or related samples.

A novel Bayesian continuous piecewise linear log-hazard model, with estimation and inference via reversible jump Markov chain Monte Carlo.

We present a reversible jump Bayesian piecewise log-linear hazard model that extends the Bayesian piecewise exponential hazard to a continuous function of piecewise linear log hazards. A simulation study encompassing several different hazard shapes, accrual rates, censoring proportion, and sample sizes showed that the Bayesian piecewise linear log-hazard model estimated the true mean survival time and survival distributions better than the piecewsie exponential hazard. Survival data from Wake Forest Baptist Medical Center is analyzed by both methods and the posterior results are compared.

A continuous-time Markov model approach for modeling myelodysplastic syndromes progression from cross-sectional data.

The integration of both genomics and clinical data to model disease progression is now possible, thanks to the increasing availability of molecular patients' profiles. This may lead to the definition of novel decision support tools, able to tailor therapeutic interventions on the basis of a "precise" patients' risk stratification, given their health status evolution. However, longitudinal analysis requires long-term data collection and curation, which can be time demanding, expensive and sometimes unfeasible. Here we present a clinical decision support framework that combines the simulation of disease progression from cross-sectional data with a Markov model that exploits continuous-time transition probabilities derived from Cox regression. Trajectories between patients at different disease stages are stochastically built according to a measure of patient similarity, computed with a matrix tri-factorization technique. Such trajectories are seen as realizations drawn from the stochastic process driving the transitions between the disease stages. Eventually, Markov models applied to the resulting longitudinal dataset highlight potentially relevant clinical information. We applied our method to cross-sectional genomic and clinical data from a cohort of Myelodysplastic syndromes (MDS) patients. MDS are heterogeneous clonal hematopoietic disorders whose patients are characterized by different risks of Acute Myeloid Leukemia (AML) development, defined by an international score. We computed patients' trajectories across increasing and subsequent levels of risk of developing AML, and we applied a Cox model to the simulated longitudinal dataset to assess whether genomic characteristics could be associated with a higher or lower probability of disease progression. We then used the learned parameters of such Cox model to calculate the transition probabilities of a continuous-time Markov model that describes the patients' evolution across stages. Our results are in most cases confirmed by previous studies, thus demonstrating that simulated longitudinal data represent a valuable resource to investigate disease progression of MDS patients.

Comparison of statistical methods for recurrent event analysis using pediatrics asthma data.

When the same type of event is experienced by a subject more than once it is called recurrent event, which possess two important characteristics, namely "within-subject correlation" and "time-varying covariate." As a result, the traditional statistical methods do not work well on recurrent event data. Over the past few decades, many alternatives methods have been proposed for the analysis of recurrent event data. In this article, the six most prominent methods for recurrent event analysis have been compared on pediatric asthma data. Three variance corrected models (viz "Anderson and Gill [AG] model," "Prentice, William, and Peterson-Counting Process [PWP-CP] model," and "Prentice, William, and Peterson-Gap Time [PWP-GT] model") and three corresponding frailty variants (AG-frailty, PWP-CP-frailty, and PWP-GT-frailty) were compared using three mathematical criterion (AIC, BIC, and log-likelihood) and one graphical criteria (Cox-Snell goodness of fit, visual test). All model comparison indices showed the PWP-GT model as the most appropriate model on asthma data over other models. By using PWP-GT model, seven predictors of asthma exacerbation (viz "abdominal pain at previous visit," "Z5 (%) at previous visit," "diagnosis of asthma at previous visit," "calendar month of exacerbation," "history of maternal asthma," "monthly per capita income," and "emotional stress") were identified. The PWP-GT model was identified as the most appropriate model over other models on pediatrics asthma data.

Trophy hunting mediates sex-specific associations between early-life environmental conditions and adult mortality in bighorn sheep.

Environmental conditions during early development, from conception to sexual maturity, can have lasting consequences on fitness components. Although adult life span often accounts for much of the variation in fitness in long-lived animals, we know little about how early environment affects adult life span in the wild, and even less about whether these effects differ between the sexes. Using data collected over 45 years from wild bighorn sheep (Ovis canadensis), we investigated the effects of early environment on adult mortality in both sexes, distinguishing between natural and anthropogenic sources of mortality. We used the average body mass of yearlings (at about 15 months of age) as a yearly index of environmental quality. We first examined sex differences in natural mortality responses to early environment by censoring harvested males in the year they were shot. We then investigated sex differences in the effects of early environment on overall mortality (natural and hunting mortality combined). Finally, we used path analysis to separate the direct influence of early environment from indirect influences, mediated by age at first reproduction, adult mass and horn length. As early environmental conditions improved, natural adult mortality decreased in both sexes, although for males the effect was not statistically supported. Sex differences in the effects of early environment on adult mortality were detected only when natural and hunting mortality were pooled. Males that experienced favourable early environment had longer horns as adults and died earlier because of trophy hunting, which does not mimic natural mortality. Females that experienced favourable early environment started to reproduce earlier and early primiparity was associated with reduced mortality, suggesting a silver-spoon effect. Our results show that early conditions affect males and females differently because of trophy hunting. These findings highlight the importance of considering natural and anthropogenic environmental factors across different life stages to understand sex differences in mortality.

Determinants of survival of common childhood cancers in Iran.

BACKGROUND: Cancer is the second most common cause of morbidity and mortality in children. This study aimed to epidemiologically and demographically assess common cancers in children in Iran. MATERIALS AND METHODS: This cohort study was conducted on children registered in Mahak Hospital and Rehabilitation Complex (which is a non-governmental organizations (NGO)-related hospital for only malignant diseases). A total of 2232 questionnaires were filled out for cancer patients between 2007 and 2016. The factors including age, gender, race, family history, type of treatment, and type of cancer were entered into Cox regression model to examine their effect on mortality of children diagnosed with cancer. RESULTS: The Cox regression model showed that age, race, type of cancer, family history of cancer, and type of treatment had a significant effect on mortality of children diagnosed with cancer (P < 0.05). The hazard ratio (HR) of mortality in 10-15 years old was higher than that of 1-5 years old (P = 0.03, HR = 1.3). The HR of mortality in patients with brain tumor (P < 0.01, HR = 2.24), sarcoma (P < 0.01, HR = 2.32), and neuroblastoma (P < 0.01, HR = 2.56) was twice the value in patients with leukemia. The HR of mortality in patients who had a family history of cancer was higher than that of patients without it (P < 0.01, HR = 1.33). Patients who had undergone chemotherapy along with surgery and radiotherapy (P = 0.02, HR = 0.68) and patients who received chemotherapy along with surgery (P = 0.01, HR = 0.67) had a lower HR of mortality compared to the chemotherapy group. CONCLUSION: Young age, multidisciplinary approach, and absence of family history were associated with lower hazard of death in children diagnosed with cancer; brain tumor, leukemia, and sarcoma had higher hazard of mortality compared to leukemia. Children with a family history of cancer should be under regular follow-up. Treatment should be multidisciplinary and comprehensive.

Gene-Based Association Analysis for Censored Traits Via Fixed Effect Functional Regressions.

Genetic studies of survival outcomes have been proposed and conducted recently, but statistical methods for identifying genetic variants that affect disease progression are rarely developed. Motivated by our ongoing real studies, here we develop Cox proportional hazard models using functional regression (FR) to perform gene-based association analysis of survival traits while adjusting for covariates. The proposed Cox models are fixed effect models where the genetic effects of multiple genetic variants are assumed to be fixed. We introduce likelihood ratio test (LRT) statistics to test for associations between the survival traits and multiple genetic variants in a genetic region. Extensive simulation studies demonstrate that the proposed Cox RF LRT statistics have well-controlled type I error rates. To evaluate power, we compare the Cox FR LRT with the previously developed burden test (BT) in a Cox model and sequence kernel association test (SKAT), which is based on mixed effect Cox models. The Cox FR LRT statistics have higher power than or similar power as Cox SKAT LRT except when 50%/50% causal variants had negative/positive effects and all causal variants are rare. In addition, the Cox FR LRT statistics have higher power than Cox BT LRT. The models and related test statistics can be useful in the whole genome and whole exome association studies. An age-related macular degeneration dataset was analyzed as an example.

A flexible parametric approach for estimating continuous-time inverse probability of treatment and censoring weights.

Marginal structural Cox models are used for quantifying marginal treatment effects on outcome event hazard function. Such models are estimated using inverse probability of treatment and censoring (IPTC) weighting, which properly accounts for the impact of time-dependent confounders, avoiding conditioning on factors on the causal pathway. To estimate the IPTC weights, the treatment assignment mechanism is conventionally modeled in discrete time. While this is natural in situations where treatment information is recorded at scheduled follow-up visits, in other contexts, the events specifying the treatment history can be modeled in continuous time using the tools of event history analysis. This is particularly the case for treatment procedures, such as surgeries. In this paper, we propose a novel approach for flexible parametric estimation of continuous-time IPTC weights and illustrate it in assessing the relationship between metastasectomy and mortality in metastatic renal cell carcinoma patients. Copyright © 2016 John Wiley & Sons, Ltd.

Cardiovascular Risk Factors Predicting Cardiovascular and Cancer Deaths in a Middle-Aged Population Followed-Up for 61 Years until Extinction.

BACKGROUND AND AIM: To study the relationships of cardiovascular risk factors with cancer and cardiovascular mortality in a cohort of middle-aged men followed-up for 61 years. MATERIALS AND METHODS: A rural cohort of 1611 cancer- and cardiovascular disease-free men aged 40-59 years was examined in 1960 within the Italian Section of the Seven Countries Study, and 28 risk factors measured at baseline were used to predict cancer (n = 459) and cardiovascular deaths (n = 678) that occurred during 61 years of follow-up until the extinction of the cohort with Cox proportional hazard models. RESULTS: A model with 28 risk factors and cancer deaths as the end-point produced eight statistically significant coefficients for age, smoking habits, mother early death, corneal arcus, xanthelasma and diabetes directly related to events, and arm circumference and healthy diet inversely related. In the corresponding models for major cardiovascular diseases and their subgroups, only the coefficients of age and smoking habits were significant among those found for cancer deaths, to which healthy diet can be added if considering coronary heart disease alone. Following a competing risks analysis by the Fine-Gray method, risk factors significantly common to both conditions were only age, smoking, and xanthelasma. CONCLUSIONS: A sizeable number of traditional cardiovascular risk factors were not predictors of cancer death in a middle-aged male cohort followed-up until extinction.

Survival analysis of cancer patients in Portugal following the reference centre model implementation.

Cancer has affected around eighteen million people all over the world in 2018. In Portugal, cancer was diagnosed in sixty thousand individuals during 2018, being the second leading cause of death (one in every four deaths). Following the European Directive 2011/24/EU, the Portuguese Health System has been recognizing oncology Reference Centres (RCs), which are focused on delivering best-in-class treatment for cancer patients. This paper performs a survival analysis of cancer patients in Portugal, having hospital episodes with discharge date after the official recognition, in 2016, of the first RCs for hepatobiliary, pancreatic, sarcomas and oesophageal cancer. The aim is to assess the impact of RCs on the survival probability of these patients. For each cancer type, survival curves are estimated using the Kaplan-Meier methodology, and hazard ratios are estimated for different covariates, using multivariate Extended Cox models. The results obtained support the implementation and encourage the further extension of the RC model for oncology in Portugal, as cancer patients treated in an oncology RC, overall, have a better survival probability when compared to patients who had no episode in an RC. These results are clearer for hepatobiliary and pancreatic cancer, but also visible for sarcomas and oesophageal cancer.

Gene expression and copy number profiling of follicular lymphoma biopsies from patients treated with first-line rituximab without chemotherapy.

The Nordic Lymphoma Study Group has performed two randomized clinical trials with chemotherapy-free first-line treatment (rituximab +/- interferon) in follicular lymphoma (FL), with 73% of patients alive and 38% without any need of chemotherapy after 10.6 years median follow-up. In order to identify predictive markers, that may also serve as therapeutic targets, gene expression- and copy number profiles were obtained from 97 FL patients using whole genome microarrays. Copy number alterations (CNAs) were identified, e.g. by GISTIC. Cox Lasso Regression and Lasso logistic regression were used to determine molecular features predictive of time to next therapy (TTNT). A few molecular changes were associated with TTNT (e.g. increased expression of INPP5B, gains in 12q23/q24), but were not significant after adjusting for multiple testing. Our findings suggest that there are no strong determinants of patient outcome with respect to GE data and CNAs in FL patients treated with a chemotherapy-free regimen (i.e. rituximab +/- interferon).

Associations Between Late-Onset Preeclampsia and the Use of Calcium-Based Antacids and Proton Pump Inhibitors During Pregnancy: A Prospective Cohort Study.

Purpose: Preeclampsia is a leading cause of maternal morbidity and mortality. Calcium-based antacids and proton pump inhibitors (PPIs) are commonly used during pregnancy to treat symptoms of gastroesophageal reflux disease. Both have been hypothesized to reduce the risk of preeclampsia. We determined associations of calcium-based antacid and PPI use during pregnancy with late-onset preeclampsia (≥34 weeks of gestation), taking into account dosage and timing of use. Patients and Methods: We included 9058 pregnant women participating in the PRIDE Study (2012-2019) or The Dutch Pregnancy Drug Register (2014-2019), two prospective cohorts in The Netherlands. Data were collected through web-based questionnaires and obstetric records. We estimated risk ratios (RRs) for late-onset preeclampsia for any use and trajectories of calcium-based antacid and PPI use before gestational day 238, and hazard ratios (HRs) for time-varying exposures after gestational day 237. Results: Late-onset preeclampsia was diagnosed in 2.6% of pregnancies. Any use of calcium-based antacids (RR 1.2 [95% CI 0.9-1.6]) or PPIs (RR 1.4 [95% CI 0.8-2.4]) before gestational day 238 was not associated with late-onset preeclampsia. Use of low-dose calcium-based antacids in gestational weeks 0-16 (<1 g/day; RR 1.8 [95% CI 1.1-2.9]) and any use of PPIs in gestational weeks 17-33 (RR 1.6 [95% CI 1.0-2.8]) seemed to increase risks of late-onset preeclampsia. We did not observe associations between late-onset preeclampsia and use of calcium-based antacids (HR 1.0 [95% CI 0.6-1.5]) and PPIs (HR 1.4 [95% CI 0.7-2.9]) after gestational day 237. Conclusion: In this prospective cohort study, use of calcium-based antacids and PPIs during pregnancy was not found to reduce the risk of late-onset preeclampsia.

Review of Prognostic Expression Markers for Clear Cell Renal Cell Carcinoma.

Context: The number of prognostic markers for clear cell renal cell carcinoma (ccRCC) has been increasing regularly over the last 15 years, without being integrated and compared. Objective: Our goal was to perform a review of prognostic markers for ccRCC to lay the ground for their use in the clinics. Evidence Acquisition: PubMed database was searched to identify RNA and protein markers whose expression level was reported as associated with survival of ccRCC patients. Relevant studies were selected through cross-reading by two readers. Evidence Synthesis: We selected 249 studies reporting an association with prognostic of either single markers or multiple-marker models. Altogether, these studies were based on a total of 341 distinct markers and 13 multiple-marker models. Twenty percent of these markers were involved in four biological pathways altered in ccRCC: cell cycle, angiogenesis, hypoxia, and immune response. The main genes (VHL, PBRM1, BAP1, and SETD2) involved in ccRCC carcinogenesis are not the most relevant for assessing survival. Conclusion: Among single markers, the most validated markers were KI67, BIRC5, TP53, CXCR4, and CA9. Of the multiple-marker models, the most famous model, ClearCode34, has been highly validated on several independent datasets, but its clinical utility has not yet been investigated. Patient Summary: Over the years, the prognosis studies have evolved from single markers to multiple-marker models. Our review highlights the highly validated prognostic markers and multiple-marker models and discusses their clinical utility for better therapeutic care.

Is Hemoglobin Concentration a Linear Predictor of Mortality in Older Adults From Chinese Longevity Regions?

Introduction: The association patterns of hemoglobin (HB) concentrations with mortality among the longevity older adults are unclear. We aimed to evaluate the relationship among older adults form Chinese longevity regions. Methods: We included 1,785 older adults aged ≥65 years (mean age, 86.7 years; 1,002 women, 783 men) from the community-based Chinese Longitudinal Healthy Longevity Survey. We estimated the hazard ratios (HRs) and 95% confidence intervals (CIs) for all-cause mortality using multivariable Cox proportional hazards models and Cox models with restricted cubic spline. Results: In total, 999 deaths occurred during a median follow-up of 5.4 years from 2011 to 2017. Restricted cubic spline analysis found no non-linear association between HB concentrations and all-cause mortality after a full adjustment for covariates among the older adults form longevity regions (p > 0.05 for non-linearity). The risk for all-cause mortality was significantly higher in the groups with HB concentration of <11.0 g/dL (HR: 1.37, 95% CI: 1.10-1.70) and 11.0-12.0 g/dL (HR: 1.25, 95% CI: 1.01-1.54); the risk of all-cause mortality was significantly lower in the groups with HB concentration ≥14.0 g/dL (HR: 0.76, 95% CI: 0.60-0.97) compared with the reference group (13.0-13.9 g/dL). Conclusions: Among older adults form Chinese longevity regions, HB concentrations were found to be inversely and linearly associated with all-cause mortality. Further prospective intervention trials are needed to confirm whether higher HB concentrations had a lower risk of mortality in these older adults.