A joint frailty model for recurrent and competing terminal events: Application to delirium in the ICU.

Joint models linking longitudinal biomarkers or recurrent event processes with a terminal event, for example, mortality, have been studied extensively. Motivated by studies of recurrent delirium events in patients receiving care in an intensive care unit (ICU), we devise a joint model for a recurrent event process and multiple terminal events. Being discharged alive from the ICU or experiencing mortality may be associated with a patient's hazard of delirium, violating the assumption of independent censoring. Moreover, the direction of the association between the hazards of delirium and mortality may be opposite of the direction of association between the hazards of delirium and ICU discharge. Hence treating either terminal event as independent censoring may bias inferences. We propose a competing joint model that uses a latent frailty to link a patient's recurrent and competing terminal event processes. We fit our model to data from a completed placebo-controlled clinical trial, which studied whether Haloperidol could prevent death and delirium among ICU patients. The clinical trial served as a foundation for a simulation study, in which we evaluate the properties, for example, bias and confidence interval coverage, of the competing joint model. As part of the simulation study, we demonstrate the shortcomings of using a joint model with a recurrent delirium process and a single terminal event to study delirium in the ICU. Lastly, we discuss limitations and possible extensions for the competing joint model. The competing joint model has been added to frailtypack, an R package for fitting an assortment of joint models.

Sample size estimation for recurrent event data using multifrailty and multilevel survival models.

In epidemiology and clinical research, recurrent events refer to individuals who are likely to experience transient clinical events repeatedly over an observation period. Examples include hospitalizations in patients with heart failure, fractures in osteoporosis studies and the occurrence of new lesions in oncology. We provided an in-depth analysis of the sample size required for the analysis of recurrent time-to-event data using multifrailty or multilevel survival models. We covered the topic from the simple shared frailty model to models with hierarchical or joint frailties. We relied on a Wald-type test statistic to estimate the sample size assuming either a single or multiple endpoints. Simulations revealed that the sample size increased as heterogeneity increased. We also observed that it was more attractive to include more patients and reduce the duration of follow-up than to include fewer patients and increase the duration of follow-up to obtain the number of events required. Each model investigated can address the question of the number of subjects for recurrent events. However, depending on the research question, one model will be more suitable than another. We illustrated our methodology with the AFFIRM-AHF trial investigating the effect of intravenous ferric carboxymaltose in patients hospitalised for acute heart failure.

A marginalized two-part joint model for a longitudinal biomarker and a terminal event with application to advanced head and neck cancers.

The sum of the longest diameter (SLD) of the target lesions is a longitudinal biomarker used to assess tumor response in cancer clinical trials, which can inform about early treatment effect. This biomarker is semicontinuous, often characterized by an excess of zeros and right skewness. Conditional two-part joint models were introduced to account for the excess of zeros in the longitudinal biomarker distribution and link it to a time-to-event outcome. A limitation of the conditional two-part model is that it only provides an effect of covariates, such as treatment, on the conditional mean of positive biomarker values, and not an overall effect on the biomarker, which is often of clinical relevance. As an alternative, we propose in this article, a marginalized two-part joint model (M-TPJM) for the repeated measurements of the SLD and a terminal event, where the covariates affect the overall mean of the biomarker. Our simulation studies assessed the good performance of the marginalized model in terms of estimation and coverage rates. Our application of the M-TPJM to a randomized clinical trial of advanced head and neck cancer shows that the combination of panitumumab in addition with chemotherapy increases the odds of observing a disappearance of all target lesions compared to chemotherapy alone, leading to a possible indirect effect of the combined treatment on time to death.

Trends in epidemiology and risk factors of opportunistic infections in kidney transplant recipients between 2004 and 2017.

BACKGROUND: While opportunistic infections are a frequent and challenging problem in kidney transplant recipients, their long-term epidemiology remains hardly known. METHODS: Opportunistic infections were recorded in 1144 recipients transplanted in our center between 2004 and 2015. Incidence rates and baseline risk factors were determined using joint frailty models. RESULTS: After a median follow-up of 5.6 years, 544 opportunistic infections occurred in 373/1144 (33%) patients, dominated by viral infections (396/544, 72%), especially cytomegalovirus (CMV) syndromes and diseases (213/544, 39%). One-third of the infected patients experienced at least two opportunistic infections. The incidence of opportunistic infections was 10 times higher during the first year post-transplantation than after that (34.7 infections for 100 patient-years vs 3.64). Opportunistic infections associated with the age of the donor (P = .032), the age of the recipient (P = .049), the CMV serostatus (P < 10-6), a higher class II HLA mismatch (P = .032) and an induction treatment including rabbit anti-thymocyte globulins (P = .026). Repeated opportunistic infections associated with each other (P < 10-6) and with renal death (P < 10-6). CONCLUSION: Opportunistic infections occur with a two-period incidence pattern and many susceptible patients suffer from repeated episodes. This knowledge may help tailor new prevention and follow-up strategies to reduce the burden of opportunistic infections and their impact on transplantation outcome.

Arteriovenous access creation and hazards of hospitalization and death in patients starting hemodialysis.

BACKGROUND: Recent evidence suggests overestimation of benefits associated with arteriovenous (AV) fistula versus graft in certain populations. We assessed hazards of all-cause and cause-specific hospitalization and death associated with AV access type in patients who started hemodialysis with a catheter in France, overall and by subgroups of age, sex, and comorbidities. METHODS: From the REIN Registry, we included patients who initiated hemodialysis with a catheter from 2010 through 2018, and identified first-created fistula or graft through the French national health-administrative database. We used joint frailty models to deal with recurrent hospitalizations and potential informative censoring by death, and inverse probability weighting to account for confounding. RESULTS: From the 18 800 patients included (mean age 68 ± 15 years, 35% women), 5% underwent AV graft creation first. Weighted hazard ratio (wHR) of all-cause hospitalization associated with graft was 1.08 (95% CI 1.02 to 1.15), that of vascular access-related hospitalization was 1.43 (95% CI 1.32 to 1.55), and those of cardiovascular- and infection-related hospitalizations were 1.14 (95% CI 1.03 to 1.26) and 1.11 (95% CI 0.97 to 1.28), respectively. Results were consistent for most subgroups, except that the highest hazard of all-cause, cardiovascular-, and infection- related hospitalizations with graft was blunted in patients with comorbidities (i.e. diabetes, wHR 1.01, 95% CI 0.93 -1.10; 1.10, 95% CI 0.96 to 1.26; and 0.94, 95% CI 0.78 to 1.12, respectively). CONCLUSIONS: In patients starting hemodialysis with a catheter, AV graft creation is associated with increased hazard of vascular access-related hospitalizations compared to fistula. This may not be the case for death or other causes of hospitalization.

Association between pre-diagnosis geriatric syndromes and overall survival in older adults with cancer (the INCAPAC study).

INTRODUCTION: Several population-based studies have reported disparities in overall survival (OS) among older patients with cancer. However, geriatric syndromes, known to be associated with OS in the geriatric population, were rarely studied. Thus, our aim was to identify the determinants of OS among French older adults with cancer, including geriatric syndromes before cancer diagnosis. MATERIALS AND METHODS: Using cancer registries, we identified older subjects (≥65 years) with cancer in three French prospective cohort studies on aging from the Gironde department. Survival time was calculated from the date of diagnosis to the date of all-cause death or to the date of last follow-up, whichever came first. Demographic and socioeconomic characteristics, smoking status, self-rated health, cancer-related factors (stage at diagnosis, treatment), as well as geriatric syndromes (polypharmacy, activity limitation, depressive symptomatology, and cognitive impairment or dementia) were studied. Analyses were performed using Cox proportional hazard models for the whole population, then by age group (65-84 and 85+). RESULTS: Among the 607 subjects included in the study, the median age at cancer diagnosis was 84 years. Smoking habits, activity limitations, cognitive impairment or dementia, advanced cancer stage and absence of treatment were significantly associated with lower OS in the analysis including the whole population. Women presented higher OS. Factors associated with OS differed by age group. Polypharmacy was inversely associated with OS in older adults aged 65-84 and 85 + . DISCUSSION: Our findings support that geriatric assessment is needed to identify patients at higher risk of death and that an evaluation of activity limitation in older adults is essential. Improving early detection could enable interventions to address factors (activity limitations, cognitive impairment) associated with OS, potentially reducing disparities and lead to earlier palliative care.

On the Choice of Longitudinal Models for the Analysis of Antitumor Efficacy in Mouse Clinical Trials of Patient-derived Xenograft Models.

In translational oncology research, the patient-derived xenograft (PDX) model and its use in mouse clinical trials (MCT) are increasingly described. This involves transplanting a human tumor into a mouse and studying its evolution during follow-up or until death. A MCT contains several PDXs in which several mice are randomized to different treatment arms. Our aim was to compare longitudinal modeling of tumor growth using mixed and joint models. Mixed and joint models were compared in a real MCT (N = 225 mice) to estimate the effect of a chemotherapy and a simulation study. Mixed models assume that death is predictable by observed tumor volumes (data missing at random, MAR) while the joint models assume that death depends on nonobserved tumor volumes (data missing not at random, MNAR). In the real dataset, of 103 deaths, 97 mice were sacrificed when reaching a predetermined tumor size (MAR data). Joint and mixed model estimates of tumor growth slopes differed significantly [0.24 (0.13;0.36)log(mm3)/week for mixed model vs. -0.02 [-0.16;0.11] for joint model]. By disrupting the MAR process of mice deaths (inducing MNAR process), the estimate of the joint model was 0.24 [0.04;0.45], close to mixed model estimation for the original dataset. The simulation results confirmed the bias in the slope estimate from the joint model. Using a MCT example, we show that joint model can provide biased estimates under MAR mechanisms of dropout. We thus recommend to carefully choose the statistical model according to nature of mice deaths. Significance: This work brings new arguments to a controversy on the correct choice of statistical modeling methods for the analysis of MCTs. We conclude that mixed models are more robust than joint models.

A flexible class of generalized joint frailty models for the analysis of survival endpoints.

This article focuses on shared frailty models for correlated failure times, as well as joint frailty models for the simultaneous analysis of recurrent events (eg, appearance of new cancerous lesions or hospital readmissions) and a major terminal event (typically, death). As extensions of the Cox model, these joint models usually assume a frailty proportional hazards model for each of the recurrent and terminal event processes. In order to extend these models beyond the proportional hazards assumption, our proposal is to replace these proportional hazards models with generalized survival models, for which the survival function is modeled as a linear predictor through a link function. Depending on the link function considered, these can be reduced to proportional hazards, proportional odds, additive hazards, or probit models. We first consider a fully parametric framework for the time and covariate effects. For proportional and additive hazards models, our approach also allows the use of smooth functions for baseline hazard functions and time-varying coefficients. The dependence between recurrent and terminal event processes is modeled by conditioning on a shared frailty acting differently on the two processes. Parameter estimates are provided using the maximum (penalized) likelihood method, implemented in the R package frailtypack (function GenfrailtyPenal). We perform simulation studies to assess the method, which is also illustrated on real datasets.

Bayesian estimation of two-part joint models for a longitudinal semicontinuous biomarker and a terminal event with INLA: Interests for cancer clinical trial evaluation.

Two-part joint models for a longitudinal semicontinuous biomarker and a terminal event have been recently introduced based on frequentist estimation. The biomarker distribution is decomposed into a probability of positive value and the expected value among positive values. Shared random effects can represent the association structure between the biomarker and the terminal event. The computational burden increases compared to standard joint models with a single regression model for the biomarker. In this context, the frequentist estimation implemented in the R package frailtypack can be challenging for complex models (i.e., a large number of parameters and dimension of the random effects). As an alternative, we propose a Bayesian estimation of two-part joint models based on the Integrated Nested Laplace Approximation (INLA) algorithm to alleviate the computational burden and fit more complex models. Our simulation studies confirm that INLA provides accurate approximation of posterior estimates and to reduced computation time and variability of estimates compared to frailtypack in the situations considered. We contrast the Bayesian and frequentist approaches in the analysis of two randomized cancer clinical trials (GERCOR and PRIME studies), where INLA has a reduced variability for the association between the biomarker and the risk of event. Moreover, the Bayesian approach was able to characterize subgroups of patients associated with different responses to treatment in the PRIME study. Our study suggests that the Bayesian approach using the INLA algorithm enables to fit complex joint models that might be of interest in a wide range of clinical applications.

Effects of air pollution on clinical pregnancy rates after in vitro fertilisation (IVF): a retrospective cohort study.

OBJECTIVE: To evaluate the effect of air pollution, from oocyte retrieval to embryo transfer, on the results of in vitro fertilisation (IVF) in terms of clinical pregnancy rates, at two fertility centres, from 2013 to 2019. DESIGN: Exploratory retrospective cohort study. SETTING: This retrospective cohort study was performed in the Reproductive Biology Department of Bordeaux University Hospital localised in Bordeaux, France and the Jean Villar Fertility Center localised in Bruges, France. PARTICIPANTS: This study included 10 763 IVF attempts occurring between January 2013 and December 2019, 2194 of which resulted in a clinical pregnancy. PRIMARY AND SECONDARY OUTCOME MEASURES: The outcome of the IVF attempt was recorded as the presence or absence of a clinical pregnancy; exposure to air pollution was assessed by calculating the cumulative exposure of suspended particulate matter, fine particulate matter, black carbon, nitrogen dioxide and ozone (O3), over the period from oocyte retrieval to embryo transfer, together with secondary exposure due to the presence of the biomass boiler room, which was installed in 2016, close to the Bordeaux University Hospital laboratory. The association between air pollution and IVF outcome was evaluated by a random-effects logistic regression analysis. RESULTS: We found negative associations between cumulative O3 exposure and clinical pregnancy rate (OR=0.92, 95% CI = (0.86 to 0.98)), and between biomass boiler room exposure and clinical pregnancy rate (OR=0.75, 95% CI = (0.61 to 0.91)), after adjustment for potential confounders. CONCLUSION: Air pollution could have a negative effect on assisted reproductive technology results and therefore precautions should be taken to minimise the impact of outdoor air on embryo culture.

Time-to-event surrogate endpoint validation using mediation analysis and meta-analytic data.

With the ongoing development of treatments and the resulting increase in survival in oncology, clinical trials based on endpoints such as overall survival may require long follow-up periods to observe sufficient events and ensure adequate statistical power. This increase in follow-up time may compromise the feasibility of the study. The use of surrogate endpoints instead of final endpoints may be attractive for these studies. However, before a surrogate can be used in a clinical trial, it must be statistically validated. In this article, we propose an approach to validate surrogates when both the surrogate and final endpoints are censored event times. This approach is developed for meta-analytic data and uses a mediation analysis to decompose the total effect of the treatment on the final endpoint as a direct effect and an indirect effect through the surrogate. The meta-analytic nature of the data is accounted for in a joint model with random effects at the trial level. The proportion of the indirect effect over the total effect of the treatment on the final endpoint can be computed from the parameters of the model and used as a measure of surrogacy. We applied this method to investigate time-to-relapse as a surrogate endpoint for overall survival in resectable gastric cancer.

Socioeconomic status and its relation with breast cancer recurrence and survival in young women in the Netherlands.

BACKGROUND: Associations between socioeconomic status (SES) and breast cancer survival are most pronounced in young patients. We further investigated the relation between SES, subsequent recurrent events and mortality in breast cancer patients < 40 years. Using detailed data on all recurrences that occur between date of diagnosis of the primary tumor and last observation, we provide a unique insight in the prognosis of young breast cancer patients according to SES. METHODS: All women < 40 years diagnosed with primary operated stage I-III breast cancer in 2005 were selected from the nationwide population-based Netherlands Cancer Registry. Data on all recurrences within 10 years from primary tumor diagnosis were collected directly from patient files. Recurrence patterns and absolute risks of recurrence, contralateral breast cancer (CBC) and mortality - accounting for competing risks - were analysed according to SES. Relationships between SES, recurrence patterns and excess mortality were estimated using a multivariable joint model, wherein the association between recurrent events and excess mortality (expected mortality derived from the general population) was included. RESULTS: We included 525 patients. The 10-year recurrence risk was lowest in high SES (18.1%), highest in low SES (29.8%). Death and CBC as first events were rare. In high, medium and low SES 13.2%, 15.3% and 19.1% died following a recurrence. Low SES patients had shorter median time intervals between diagnosis, first recurrence and 10-year mortality (2.6 and 2.7 years, respectively) compared to high SES (3.5 and 3.3 years, respectively). In multivariable joint modeling, high SES was significantly related to lower recurrence rates over 10-year follow-up, compared to low SES. A strong association between the recurrent event process and excess mortality was found. CONCLUSIONS: High SES is associated with lower recurrence risks, less subsequent events and better prognosis after recurrence over 10 years than low SES. Breast cancer risk factors, adjuvant treatment adherence and treatment of recurrence may possibly play a role in this association.

Two-part joint model for a longitudinal semicontinuous marker and a terminal event with application to metastatic colorectal cancer data.

Joint models for a longitudinal biomarker and a terminal event have gained interests for evaluating cancer clinical trials because the tumor evolution reflects directly the state of the disease. A biomarker characterizing the tumor size evolution over time can be highly informative for assessing treatment options and could be taken into account in addition to the survival time. The biomarker often has a semicontinuous distribution, i.e., it is zero inflated and right skewed. An appropriate model is needed for the longitudinal biomarker as well as an association structure with the survival outcome. In this article, we propose a joint model for a longitudinal semicontinuous biomarker and a survival time. The semicontinuous nature of the longitudinal biomarker is specified by a two-part model, which splits its distribution into a binary outcome (first part) represented by the positive versus zero values and a continuous outcome (second part) with the positive values only. Survival times are modeled with a proportional hazards model for which we propose three association structures with the biomarker. Our simulation studies show some bias can arise in the parameter estimates when the semicontinuous nature of the biomarker is ignored, assuming the true model is a two-part model. An application to advanced metastatic colorectal cancer data from the GERCOR study is performed where our two-part model is compared to one-part joint models. Our results show that treatment arm B (FOLFOX6/FOLFIRI) is associated to higher SLD values over time and its positive association with the terminal event leads to an increased risk of death compared to treatment arm A (FOLFIRI/FOLFOX6).

Sample size estimation for cancer randomized trials in the presence of heterogeneous populations.

A key issue when designing clinical trials is the estimation of the number of subjects required. Assuming for multicenter trials or biomarker-stratified designs that the effect size between treatment arms is the same among the whole study population might be inappropriate. Limited work is available for properly determining the sample size for such trials. However, we need to account for both, the heterogeneity of the baseline hazards over clusters or strata but also the heterogeneity of the treatment effects, otherwise sample size estimates might be biased. Most existing methods account for either heterogeneous baseline hazards or treatment effects but they dot not allow to simultaneously account for both sources of variations. This article proposes an approach to calculate sample size formula for clustered or stratified survival data relying on frailty models. Both theoretical derivations and simulation results show the proposed approach can guarantee the desired power in worst case scenarios and is often much more efficient than existing approaches. Application to a real clinical trial designs is also illustrated.

Conditional copula models for correlated survival endpoints: Individual patient data meta-analysis of randomized controlled trials.

Correlations among survival endpoints are important for exploring surrogate endpoints of the true endpoint. With a valid surrogate endpoint tightly correlated with the true endpoint, the efficacy of a new drug/treatment can be measurable on it. However, the existing methods for measuring correlation between two endpoints impose an invalid assumption: correlation structure is constant across different treatment arms. In this article, we reconsider the definition of Kendall's concordance measure (tau) in the context of individual patient data meta-analyses of randomized controlled trials. According to our new definition of Kendall's tau, its value depends on the treatment arms. We then suggest extending the existing copula (and frailty) models so that their Kendall's tau can vary across treatment arms. Our newly proposed model, a joint frailty-conditional copula model, is the implementation of the new definition of Kendall's tau in meta-analyses. In order to facilitate our approach, we develop an original R function condCox.reg(.) and make it available in the R package joint.Cox (https://CRAN.R-project.org/package=joint.Cox). We apply the proposed method to a gastric cancer dataset (3288 patients in 14 randomized trials from the GASTRIC group). This data analysis concludes that Kendall's tau has different values between the surgical treatment arm and the adjuvant chemotherapy arm (p-value<0.001), whereas disease-free survival remains a valid surrogate at individual level for overall survival in these trials.

A joint frailty-copula model for meta-analytic validation of failure time surrogate endpoints in clinical trials.

In a meta-analysis framework, the classical approach for the validation of time-to-event surrogate endpoint is based on a two-step analysis. This approach often raises estimation issues. Recently, we proposed a one-step validation approach based on a joint frailty model. This approach was quite time consuming, despite parallel computing, due to individual-level frailties used to take into account heterogeneity in the data at the individual level. We now propose an alternative one-step approach for evaluating surrogacy, using a joint frailty-copula model. The model includes two correlated random effects treatment-by-trial interaction and a shared random effect associated with the baseline risks. At the individual level, the joint survivor functions of time-to-event endpoints are linked using copula functions. We used splines for the baseline hazard functions. We estimated parameters and hazard function using a semiparametric penalized marginal likelihood method, considering various numerical integration methods. Both individual-level and trial-level surrogacy were evaluated using Kendall's tau and coefficient of determination. The performance of the estimators was evaluated using simulation studies. The model was applied to individual patient data meta-analyses in advanced ovarian cancer to assess progression-free survival as a surrogate for overall survival, as part of the evaluation of new therapy. The model showed good performance and was quite robust regarding the integration methods and data variation, regardless of the surrogacy evaluation criteria. Kendall's Tau was better estimated using the Clayton copula model compared to the joint frailty model. The proposed model reduces the convergence and model estimation issues encountered in the two-step approach.

Improving the evaluation of COPD exacerbation treatment effects by accounting for early treatment discontinuations: a post-hoc analysis of randomized clinical trials.

BACKGROUND: Chronic obstructive pulmonary disease (COPD) clinical trials aimed at evaluating treatment effects on exacerbations often suffer from early discontinuations of randomized treatment. Treatment discontinuations imply a loss of information and should ideally be considered in the statistical analysis of trial results, particularly if the discontinuations are related to the disease or treatment itself. Here, we explore this issue by investigating (1) whether there exists an association between the risks of exacerbation and treatment discontinuation in COPD clinical trials and (2) whether disregarding this association can cause bias in exacerbation treatment effect estimates. We focus on the hypothetical estimand, i.e. the treatment effect that would have been observed had all subjects completed the trial as planned. METHODS: The association between exacerbation and discontinuation risks was analysed by applying a joint frailty (random effect) model - allowing for the simultaneous analysis of multiple types of correlated events - to data from five Phase III-IV COPD clinical trials. Specifically, the impact of the association on exacerbation treatment effect estimates was assessed by comparing the treatment hazard ratios of the joint frailty model to the rate/hazard ratios of two related statistical models (the negative binomial and shared frailty models), which both assume discontinuations to be unrelated to the trial outcome. The models were also compared using simulated data. RESULTS: A statistically significant (p < 0.0001), positive association between exacerbation and discontinuation risks was found in all trials. Importantly, simulations confirmed that - with such an association - models disregarding the association risk producing biased results (> 5 percentage point difference in hazard/rate ratio). For some treatment comparisons in the clinical trials, the difference in treatment effect estimates between the joint frailty and the other models was as high as 10-15 percentage points. The difference was affected by the strength of the exacerbation-discontinuation association, the population heterogeneity in exacerbation risk, and the difference in discontinuation rates between treatment arms. CONCLUSIONS: We have identified an association between the risks of exacerbation and treatment discontinuation in five COPD clinical trials. We recommend using the joint frailty model to account for this association when estimating exacerbation treatment effects, particularly when targeting the hypothetical estimand.

How to use frailtypack for validating failure-time surrogate endpoints using individual patient data from meta-analyses of randomized controlled trials.

BACKGROUND AND OBJECTIVE: The use of valid surrogate endpoints can accelerate the development of phase III trials. Numerous validation methods have been proposed with the most popular used in a context of meta-analyses, based on a two-step analysis strategy. For two failure time endpoints, two association measures are usually considered, Kendall's τ at individual level and adjusted R2 ([Formula: see text]) at trial level. However, [Formula: see text] is not always available mainly due to model estimation constraints. More recently, we proposed a one-step validation method based on a joint frailty model, with the aim of reducing estimation issues and estimation bias on the surrogacy evaluation criteria. The model was quite robust with satisfactory results obtained in simulation studies. This study seeks to popularize this new surrogate endpoints validation approach by making the method available in a user-friendly R package. METHODS: We provide numerous tools in the frailtypack R package, including more flexible functions, for the validation of candidate surrogate endpoints using data from multiple randomized clinical trials. RESULTS: We implemented the surrogate threshold effect which is used in combination with [Formula: see text] to make decisions concerning the validity of the surrogate endpoints. It is also possible thanks to frailtypack to predict the treatment effect on the true endpoint in a new trial using the treatment effect observed on the surrogate endpoint. The leave-one-out cross-validation is available for assessing the accuracy of the prediction using the joint surrogate model. Other tools include data generation, simulation study and graphic representations. We illustrate the use of the new functions with both real data and simulated data. CONCLUSION: This article proposes new attractive and well developed tools for validating failure time surrogate endpoints.

Joint nested frailty models for clustered recurrent and terminal events: An application to colonoscopy screening visits and colorectal cancer risks in Lynch Syndrome families.

Joint models for recurrent and terminal events have not been yet developed for clustered data. The goals of our study are to develop a statistical framework for modelling clustered recurrent and terminal events and to perform dynamic predictions of the terminal event in family studies. We propose a joint nested frailty model for colonoscopy screening visits and colorectal cancer onset in Lynch Syndrome families. The screening and disease processes could each depend on individuals' screening history and other measured covariates and be correlated within families; our approach allows for familial correlations to affect both the visit process and the terminal event and the dependence between the two processes is specified through frailty distributions. We provide dynamic predictions of colorectal cancer risk for an individual conditional on his/her own screening history, his/her family history of screening and disease and other important clinical covariates. We apply our model to 18 Lynch Syndrome families from Newfoundland for individualized dynamic predictions of colorectal cancer risks. We demonstrate that the screening visits are non-ignorable for estimating the disease risks, and the joint nested frailty model improves dynamic prediction accuracies compared to existing joint frailty models after accounting for familial and individual screening and cancer histories.

Agricultural exposures to carbamate herbicides and fungicides and central nervous system tumour incidence in the cohort AGRICAN.

BACKGROUND: Pesticides exposures could be implicated in the excess of Central Nervous System (CNS) tumors observed in farmers, but evidence concerning individual pesticides remains limited. Carbamate derivative pesticides, including herbicides and fungicides (i.e. (thio/dithio)-carbamates), have shown evidence of carcinogenicity in experimental studies in animals. In the French AGRICAN cohort, we assessed the associations between potential exposures to carbamate herbicides and fungicides and the incidence of CNS tumors, overall and by histological subtype. METHODS: AGRICAN enrolled 181,842 participants involved in agriculture. Incident CNS tumors were identified by linkage with cancer registries from enrollment (2005-2007) until 2013. Individual exposures were assessed by combining information on lifetime periods of pesticide use on crops and the French crop-exposure matrix PESTIMAT, for each of the 14 carbamate and thiocarbamate herbicides and the 16 carbamate and dithiocarbamate fungicides registered in France since 1950. Associations were estimated using proportional hazard models with age as the underlying timescale, adjusting for gender, educational level and smoking. RESULTS: During an average follow-up of 6.9 years, 381 incident cases of CNS tumors occurred, including 164 gliomas and 134 meningiomas. Analyses showed increased risks of CNS tumors with overall exposure to carbamate fungicides (Hazard Ratio, HR = 1.88; 95% CI: 1.27-2.79) and, to a lesser extent, to carbamate herbicides (HR = 1.44; 95% CI: 0.94-2.22). Positive associations were observed with specific carbamates, including some fungicides (mancozeb, maneb, metiram) and herbicides (chlorpropham, propham, diallate) already suspected of being carcinogens in humans. CONCLUSIONS: Although some associations need to be corroborate in further studies and should be interpreted cautiously, these findings provide additional carcinogenicity evidence for several carbamate fungicides and herbicides.