Trajectories of long-term exposure to PCB153 and Benzo[a]pyrene (BaP) air pollution and risk of breast cancer.

BACKGROUND: While genetic, hormonal, and lifestyle factors partially elucidate the incidence of breast cancer, emerging research has underscored the potential contribution of air pollution. Polychlorinated biphenyls (PCBs) and benzo[a]pyrene (BaP) are of particular concern due to endocrine-disrupting properties and their carcinogenetic effect. OBJECTIVE: To identify distinct long term trajectories of exposure to PCB153 and BaP, and estimate their associations with breast cancer risk. METHODS: We used data from the XENAIR case-control study, nested within the ongoing prospective French E3N cohort which enrolled 98,995 women aged 40-65 years in 1990-1991. Cases were incident cases of primary invasive breast cancer diagnosed from cohort entry to 2011. Controls were randomly selected by incidence density sampling, and individually matched to cases on delay since cohort entry, and date, age, department of residence, and menopausal status at cohort entry. Annual mean outdoor PCB153 and BaP concentrations at residential addresses from 1990 to 2011 were estimated using the CHIMERE chemistry-transport model. Latent class mixed models were used to identify profiles of exposure trajectories from cohort entry to the index date, and conditional logistic regression to estimate their association with the odds of breast cancer. RESULTS: 5058 cases and 5059 controls contributed to the analysis. Five profiles of trajectories of PCB153 exposure were identified. The class with the highest PCB153 concentrations had a 69% increased odds of breast cancer compared to the class with the lowest concentrations (95% CI 1.08, 2.64), after adjustment for education and matching factors. The association between identified BaP trajectories and breast cancer was weaker and suffered from large CI. CONCLUSIONS: Our results support an association between long term exposure to PCB153 and the risk of breast cancer, and encourage further studies to account for lifetime exposure to persistent organic pollutants.

Association of dietary and nutritional factors with cognitive decline, dementia, and depressive symptomatology in older individuals according to a neurogenesis-centred biological susceptibility to brain ageing.

Hippocampal neurogenesis (HN) occurs throughout the life course and is important for memory and mood. Declining with age, HN plays a pivotal role in cognitive decline (CD), dementia, and late-life depression, such that altered HN could represent a neurobiological susceptibility to these conditions. Pertinently, dietary patterns (e.g., Mediterranean diet) and/or individual nutrients (e.g., vitamin D, omega 3) can modify HN, but also modify risk for CD, dementia, and depression. Therefore, the interaction between diet/nutrition and HN may alter risk trajectories for these ageing-related brain conditions. Using a subsample (n = 371) of the Three-City cohort-where older adults provided information on diet and blood biobanking at baseline and were assessed for CD, dementia, and depressive symptomatology across 12 years-we tested for interactions between food consumption, nutrient intake, and nutritional biomarker concentrations and neurogenesis-centred susceptibility status (defined by baseline readouts of hippocampal progenitor cell integrity, cell death, and differentiation) on CD, Alzheimer's disease (AD), vascular and other dementias (VoD), and depressive symptomatology, using multivariable-adjusted logistic regression models. Increased plasma lycopene concentrations (OR [95% CI] = 1.07 [1.01, 1.14]), higher red meat (OR [95% CI] = 1.10 [1.03, 1.19]), and lower poultry consumption (OR [95% CI] = 0.93 [0.87, 0.99]) were associated with an increased risk for AD in individuals with a neurogenesis-centred susceptibility. Increased vitamin D consumption (OR [95% CI] = 1.05 [1.01, 1.11]) and plasma γ-tocopherol concentrations (OR [95% CI] = 1.08 [1.01, 1.18]) were associated with increased risk for VoD and depressive symptomatology, respectively, but only in susceptible individuals. This research highlights an important role for diet/nutrition in modifying dementia and depression risk in individuals with a neurogenesis-centred susceptibility.

Statistical classification of treatment responses in mouse clinical trials for stratified medicine in oncology drug discovery.

Translational oncology research strives to explore a new aspect: identifying subgroups that exhibit treatment response even during pre-clinical phases. In this study, we focus on PDX models and their implementation in mouse clinical trials (MCT). Our primary objective was to identify subgroups with different treatment responses using Latent Class Mixed Model (LCMM).We used a public dataset and focused on one treatment, encorafenib, and two indications, melanoma and colorectal cancer, for which efficacy depends on a specific mutation BRAF V600E. One LCMM per indication was implemented to classify treatment responses at the PDX level, analyzing the growth kinetics of treated tumors and matched controls within the PDX models. A simulation study was carried out to explore the performance of LCMM in this context. For both applications, LCMM identified classes for which the higher the proportion of mutated BRAF V600E PDX models the greater the treatment effect, which is aligned with encorafenib use recommendations. The simulation study showed that LCMM could identify classes with large differences in treatment effects. LCMM is a suitable tool for MCT to explore treatment response subgroups of PDX. Once these subgroups are defined, characterization of their phenotypes/genotypes could be performed to explore treatment response predictors.

A multistate approach for the study of interventions on an intermediate time-to-event in health disparities research.

We propose a novel methodology to quantify the effect of stochastic interventions for a non-terminal intermediate time-to-event on a terminal time-to-event outcome. Investigating these effects is particularly important in health disparities research when we seek to quantify inequities in the timely delivery of treatment and its impact on patients' survival time. Current approaches fail to account for time-to-event intermediates and semi-competing risks arising in this setting. Under the potential outcome framework, we define causal contrasts relevant in health disparities research and provide identifiability conditions when stochastic interventions on an intermediate non-terminal time-to-event are of interest. Causal contrasts are estimated in continuous time within a multistate modeling framework and analytic formulae for the estimators of the causal contrasts are developed. We show via simulations that ignoring censoring in intermediate and/or terminal time-to-event processes or ignoring semi-competing risks may give misleading results. This work demonstrates that a rigorous definition of the causal effects and joint estimation of the terminal outcome and intermediate non-terminal time-to-event distributions are crucial for valid investigation of interventions and mechanisms in continuous time. We employ this novel methodology to investigate the role of delaying treatment uptake in explaining racial disparities in cancer survival in a cohort study of colon cancer patients.

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.

CA-125 Early Dynamics to Predict Overall Survival in Women with Newly Diagnosed Advanced Ovarian Cancer Based on Meta-Analysis Data.

(1) Background: Cancer antigen 125 (CA-125) is a protein produced by ovarian cancer cells that is used for patients' monitoring. However, the best ways to analyze its decline and prognostic role are poorly quantified. (2) Methods: We leveraged individual patient data from the Gynecologic Cancer Intergroup (GCIG) meta-analysis (N = 5573) to compare different approaches summarizing the early trajectory of CA-125 before the prediction time (called the landmark time) at 3 or 6 months after treatment initiation in order to predict overall survival. These summaries included observed and estimated measures obtained by a linear mixed model (LMM). Their performances were evaluated by 10-fold cross-validation with the Brier score and the area under the ROC (AUC). (3) Results: The estimated value and the last observed value at 3 months were the best measures used to predict overall survival, with an AUC of 0.75 CI 95% [0.70; 0.80] at 24 and 36 months and 0.74 [0.69; 0.80] and 0.75 [0.69; 0.80] at 48 months, respectively, considering that CA-125 over 6 months did not improve the AUC, with 0.74 [0.68; 0.78] at 24 months and 0.71 [0.65; 0.76] at 36 and 48 months. (4) Conclusions: A 3-month surveillance provided reliable individual information on overall survival until 48 months for patients receiving first-line chemotherapy.

A longitudinal evaluation of alcohol intake throughout adulthood and colorectal cancer risk.

BACKGROUND: Alcohol intake is an established risk factor for colorectal cancer (CRC); however, there is limited knowledge on whether changing alcohol drinking habits during adulthood modifies CRC risk. OBJECTIVE: Leveraging longitudinal exposure assessments on alcohol intake at different ages, we examined the relationship between change in alcohol intake and subsequent CRC risk. METHODS: Within the European Prospective Investigation into Cancer and Nutrition, changes in alcohol intake comparing follow-up with baseline assessments were investigated in relation to CRC risk. The analysis included 191,180, participants and 1530 incident CRC cases, with exclusion of the first three years of follow-up to minimize reverse causation. Trajectory profiles of alcohol intake, assessed at ages 20, 30, 40, 50 years, at baseline and during follow-up, were estimated using latent class mixed models and related to CRC risk, including 407,605 participants and 5,008 incident CRC cases. RESULTS: Mean age at baseline was 50.2 years and the follow-up assessment occurred on average 7.1 years later. Compared to stable intake, a 12 g/day increase in alcohol intake during follow-up was positively associated with CRC risk (HR = 1.15, 95%CI 1.04, 1.25), while a 12 g/day reduction was inversely associated with CRC risk (HR = 0.86, 95%CI 0.78, 0.95). Trajectory analysis showed that compared to low alcohol intake, men who increased their alcohol intake from early- to mid- and late-adulthood by up to 30 g/day on average had significantly increased CRC risk (HR = 1.24; 95%CI 1.08, 1.42), while no associations were observed in women. Results were consistent by anatomical subsite. CONCLUSIONS: Increasing alcohol intake during mid-to-late adulthood raised CRC risk, while reduction lowered risk.

Determinants of Distinct Trajectories of Fatigue in Patients Undergoing Chemotherapy for a Metastatic Colorectal Cancer: 6-Month Follow-up Using Growth Mixture Modeling.

OBJECTIVES: This longitudinal prospective and observational study was designed to identify fatigue trajectories during a 6-month period of chemotherapy in patients with metastatic colorectal cancer, and examine the psychosocial factors predicting these trajectories. Associations between fatigue and survival were also investigated. METHODS: A total of 169 patients (Mage = 64.36 years, SD = 10.5) reported their fatigue levels every 2 weeks for 6 months. Psychological variables (anxiety, depression, internal control, and coping) were assessed at baseline. A Growth Mixture Model was used to identify latent trajectories of fatigue, and a multinomial logistic regression tested covariate predictors of patients' trajectories. RESULTS: Four clinically distinct fatigue trajectories were identified: intense fatigue (6.51%), moderate fatigue (48.52%), no fatigue (33%), and increasing fatigue (11.83%). Fatigue severity was directly associated with overall survival. High depression levels were associated with fatigue severity over time for intense (OR = 1.80 [1.32-2.47]) and for moderate (OR = 1.58 [1.25-2.00]) fatigue, compared to patients reporting no fatigue. Patients who did not report fatigue were better adjusted, and had more resources, such as better internal control over the disease and less emotion-focused coping (guilt and avoidance), than those who reported intense (ORcontrol = 0.77 [0.65-0.92]) or moderate (ORcontrol = 0.89 [0.79-0.99] and ORcoping = 1.13 [1.02-1.24]) fatigue. CONCLUSION: Fatigue trajectories differed considerably across patients with metastatic colorectal cancer. This first longitudinal study on colorectal cancer patients involving transactional variables suggests that psychosocial interventions should target these specific outcomes, in order to help patients manage their fatigue.

A new trajectory approach for investigating the association between an environmental or occupational exposure over lifetime and the risk of chronic disease: Application to smoking, asbestos, and lung cancer.

Quantifying the association between lifetime exposures and the risk of developing a chronic disease is a recurrent challenge in epidemiology. Individual exposure trajectories are often heterogeneous and studying their associations with the risk of disease is not straightforward. We propose to use a latent class mixed model (LCMM) to identify profiles (latent classes) of exposure trajectories and estimate their association with the risk of disease. The methodology is applied to study the association between lifetime trajectories of smoking or occupational exposure to asbestos and the risk of lung cancer in males of the ICARE population-based case-control study. Asbestos exposure was assessed using a job exposure matrix. The classes of exposure trajectories were identified using two separate LCMM for smoking and asbestos, and the association between the identified classes and the risk of lung cancer was estimated in a second stage using weighted logistic regression and all subjects. A total of 2026/2610 cases/controls had complete information on both smoking and asbestos exposure, including 1938/1837 cases/controls ever smokers, and 1417/1520 cases/controls ever exposed to asbestos. The LCMM identified four latent classes of smoking trajectories which had different risks of lung cancer, all much stronger than never smokers. The most frequent class had moderate constant intensity over lifetime while the three others had either long-term, distant or recent high intensity. The latter had the strongest risk of lung cancer. We identified five classes of asbestos exposure trajectories which all had higher risk of lung cancer compared to men never occupationally exposed to asbestos, whatever the dose and the timing of exposure. The proposed approach opens new perspectives for the analyses of dose-time-response relationships between protracted exposures and the risk of developing a chronic disease, by providing a complete picture of exposure history in terms of intensity, duration, and timing of exposure.

The prognostic value of volumetric changes of the primary tumor measured on Cone Beam-CT during radiotherapy for concurrent chemoradiation in NSCLC patients.

INTRODUCTION: The aim of this study was to identify subgroups of locally advanced NSCLC patients with a distinct treatment response during concurrent chemoradiotherapy (CCRT). Subsequently, we investigated the association of subgroup membership with treatment outcomes. METHODS: 394 NSCLC-patients treated with CCRT between 2007 and 2013 were included. Gross Tumor Volume (GTV) during treatment was determined and relative GTV-volume change from the planning-CT was subsequently calculated. Latent Class Mixed Modeling (LCMM) was used to identify subgroups with distinct volume changes during CCRT. The association of subgroup membership with overall survival (OS), progression free survival (PFS) and local regional control (LRC) was assessed using cox regression analyses. RESULTS: Three subgroups of GTV-volume change during treatment were identified, with each subsequent subgroup showing a more profound reduction of GTV during treatment. No associations between subgroup membership and OS, PFS nor LRC were observed. Nonetheless, baseline GTV (HR1.42; 95%CI 1.06-1.91) was significantly associated with OS. CONCLUSIONS: Three different subgroups of GTV-volume change during treatment were identified. Surprisingly, these subgroups did not differ in their risk of treatment outcomes. Only patients with a larger GTV at baseline had a significantly worse OS. Therefore, risk stratification at baseline might already be accurate in identifying the best treatment strategy for most patients.

Long-Term Trajectories of Body Weight, Diet, and Physical Activity From Midlife Through Late Life and Subsequent Cognitive Decline in Women.

Healthy lifestyles are promising targets for prevention of cognitive aging, yet the optimal time windows for interventions remain unclear. We selected a case-control sample nested within the Nurses' Health Study (starting year 1976, mean age = 51 years), including 14,956 women aged ≥70 years who were free of both stroke and cognitive impairment at enrollment in a cognitive substudy (1995-2001). Cases (n = 1,496) were women with the 10% worst slopes of cognitive decline, and controls (n = 7,478) were those with slopes better than the median. We compared the trajectories of body mass index (weight (kg)/height (m)2), alternate Mediterranean diet (A-MeDi) score, and physical activity between groups, from midlife through 1 year preceding the cognitive substudy. In midlife, cases had higher body mass index than controls (mean difference (MD) = 0.59 units, 95% confidence interval (CI): 0.39, 0.80), lower physical activity (MD = -1.41 metabolic equivalent of task-hours/week, 95% CI: -2.07, -0.71), and worse A-MeDi scores (MD = -0.16 points, 95% CI: -0.26, -0.06). From midlife through later life, compared with controls, cases had consistently lower A-MeDi scores but a deceleration of weight gain and a faster decrease in physical activity. In conclusion, maintaining a healthy lifestyle since midlife may help reduce cognitive decline in aging. At older ages, both deceleration of weight gain and a decrease in physical activity may reflect early signs of cognitive impairment.

Individual dynamic predictions using landmarking and joint modelling: Validation of estimators and robustness assessment.

After the diagnosis of a disease, one major objective is to predict cumulative probabilities of events such as clinical relapse or death from the individual information collected up to a prediction time, usually including biomarker repeated measurements. Several competing estimators have been proposed, mainly from two approaches: joint modelling and landmarking. These approaches differ by the information used, the model assumptions and the complexity of the computational procedures. This paper aims to review the two approaches, precisely define the derived estimators of dynamic predictions and compare their performances notably in case of misspecification. The ultimate goal is to provide key elements for the use of individual dynamic predictions in clinical practice. Prediction of two competing causes of prostate cancer progression from the history of prostate-specific antigen is used as a motivated example. We formally define the quantity to estimate and its estimators, propose techniques to assess the uncertainty around predictions and validate them. We then conduct an in-depth simulation study compare the estimators in terms of prediction error, discriminatory power, efficiency and robustness to model assumptions. We show that prediction tools should be handled with care, in particular by properly specifying models and estimators.

A two-stage model in a Bayesian framework to estimate a survival endpoint in the presence of confounding by indication.

Background Biomarker series can indicate disease progression and predict clinical endpoints. When a treatment is prescribed depending on the biomarker, confounding by indication might be introduced if the treatment modifies the marker profile and risk of failure. Objective Our aim was to highlight the flexibility of a two-stage model fitted within a Bayesian Markov Chain Monte Carlo framework. For this purpose, we monitored the prostate-specific antigens in prostate cancer patients treated with external beam radiation therapy. In the presence of rising prostate-specific antigens after external beam radiation therapy, salvage hormone therapy can be prescribed to reduce both the prostate-specific antigens concentration and the risk of clinical failure, an illustration of confounding by indication. We focused on the assessment of the prognostic value of hormone therapy and prostate-specific antigens trajectory on the risk of failure. Methods We used a two-stage model within a Bayesian framework to assess the role of the prostate-specific antigens profile on clinical failure while accounting for a secondary treatment prescribed by indication. We modeled prostate-specific antigens using a hierarchical piecewise linear trajectory with a random changepoint. Residual prostate-specific antigens variability was expressed as a function of prostate-specific antigens concentration. Covariates in the survival model included hormone therapy, baseline characteristics, and individual predictions of the prostate-specific antigens nadir and timing and prostate-specific antigens slopes before and after the nadir as provided by the longitudinal process. Results We showed positive associations between an increased prostate-specific antigens nadir, an earlier changepoint and a steeper post-nadir slope with an increased risk of failure. Importantly, we highlighted a significant benefit of hormone therapy, an effect that was not observed when the prostate-specific antigens trajectory was not accounted for in the survival model. Conclusion Our modeling strategy was particularly flexible and accounted for multiple complex features of longitudinal and survival data, including the presence of a random changepoint and a time-dependent covariate.

Integrative Analysis of Immunological Data to Explore Chronic Immune T-Cell Activation in Successfully Treated HIV Patients.

OBJECTIVES: To unravel the complex relationships between cytomegalovirus-induced-, autoimmune-induced responses, microbial translocation and chronic immune activation (CIA) in successfully treated HIV-infected patients and to explore the mediating role of alpha-interferon in these processes. DESIGN: Cross-sectional study nested in the ANRS CO3 Aquitaine Cohort, a prospective hospital-based cohort of HIV-1-infected patients in South-Western France. METHODS: Patients initiated antiretroviral therapy between 2005 and 2008 and were treated with sustained virological suppression for at least two years. CIA was defined by the percentage of HLA-DR+/CD38+ among CD8+T-cells. Integrative analyses were performed using structural equation modelling (SEM). RESULTS: The main analysis was performed in 57 HLA-A*0201 positive patients, due to availability of percentages of actin-, vimentin-, lamin-specific CD8+T-cells (HLA-A2-restricted tests) to further characterize autoimmune response. Cytomegalovirus-induced response was assessed by Quantiferon and pp-65 ELISPOT. SEM revealed a direct effect of cytomegalovirus-induced response on CIA (standardized estimate ßstd = 0.56, p-value = 0.0004). The effect of autoimmune-induced response on CIA was indirect through alpha-interferon pathway, assessed by expression levels of 5 alpha-interferon-stimulated genes ADAR, ISG15, IFIT1, Mx1 and OAS1 (effect of autoimmune response on alpha-interferon: ßstd = 0.36, p-value = 0.0401; effect of alpha-interferon on CIA: ßstd = 0.39, p-value = 0.0044). There was no direct effect of autoimmune-induced response on CIA (p-value = 0.3169). Microbial translocation as measured by 16SrDNA and sCD14 in plasma was not associated with CIA. Results were consistent in 142 patients in whom cytomegalovirus and auto-immunity responses were measured by Quantiferon and anti-nuclear antibodies, respectively. All analyses performed in HLA-A*0201 positive patients and in the overall population revealed a significant effect of IFN-α latent variable on CIA. CONCLUSION: The role of cytomegalovirus-induced response on CIA was confirmed as well as the involvement of alpha-interferon on CIA. The indirect effect of auto-immunity response on CIA revealed through the alpha-interferon pathway requires further investigation to confirm the potential role of auto-immunity for CIA in HIV-infected patients.

Joint modelling of longitudinal and multi-state processes: application to clinical progressions in prostate cancer.

Joint modelling of longitudinal and survival data is increasingly used in clinical trials on cancer. In prostate cancer for example, these models permit to account for the link between longitudinal measures of prostate-specific antigen (PSA) and time of clinical recurrence when studying the risk of relapse. In practice, multiple types of relapse may occur successively. Distinguishing these transitions between health states would allow to evaluate, for example, how PSA trajectory and classical covariates impact the risk of dying after a distant recurrence post-radiotherapy, or to predict the risk of one specific type of clinical recurrence post-radiotherapy, from the PSA history. In this context, we present a joint model for a longitudinal process and a multi-state process, which is divided into two sub-models: a linear mixed sub-model for longitudinal data and a multi-state sub-model with proportional hazards for transition times, both linked by a function of shared random effects. Parameters of this joint multi-state model are estimated within the maximum likelihood framework using an EM algorithm coupled with a quasi-Newton algorithm in case of slow convergence. It is implemented under R, by combining and extending mstate and JM packages. The estimation program is validated by simulations and applied on pooled data from two cohorts of men with localized prostate cancer. Thanks to the classical covariates available at baseline and the repeated PSA measurements, we are able to assess the biomarker's trajectory, define the risks of transitions between health states and quantify the impact of the PSA dynamics on each transition intensity. Copyright © 2016 John Wiley & Sons, Ltd.

Joint model for left-censored longitudinal data, recurrent events and terminal event: Predictive abilities of tumor burden for cancer evolution with application to the FFCD 2000-05 trial.

In oncology, the international WHO and RECIST criteria have allowed the standardization of tumor response evaluation in order to identify the time of disease progression. These semi-quantitative measurements are often used as endpoints in phase II and phase III trials to study the efficacy of new therapies. However, through categorization of the continuous tumor size, information can be lost and they can be challenged by recently developed methods of modeling biomarkers in a longitudinal way. Thus, it is of interest to compare the predictive ability of cancer progressions based on categorical criteria and quantitative measures of tumor size (left-censored due to detection limit problems) and/or appearance of new lesions on overall survival. We propose a joint model for a simultaneous analysis of three types of data: a longitudinal marker, recurrent events, and a terminal event. The model allows to determine in a randomized clinical trial on which particular component treatment acts mostly. A simulation study is performed and shows that the proposed trivariate model is appropriate for practical use. We propose statistical tools that evaluate predictive accuracy for joint models to compare our model to models based on categorical criteria and their components. We apply the model to a randomized phase III clinical trial of metastatic colorectal cancer, conducted by the Fédération Francophone de Cancérologie Digestive (FFCD 2000-05 trial), which assigned 410 patients to two therapeutic strategies with multiple successive chemotherapy regimens.

Individualized dynamic prediction of prostate cancer recurrence with and without the initiation of a second treatment: Development and validation.

With the emergence of rich information on biomarkers after treatments, new types of prognostic tools are being developed: dynamic prognostic tools that can be updated at each new biomarker measurement. Such predictions are of interest in oncology where after an initial treatment, patients are monitored with repeated biomarker data. However, in such setting, patients may receive second treatments to slow down the progression of the disease. This paper aims to develop and validate dynamic individual predictions that allow the possibility of a new treatment in order to help understand the benefit of initiating new treatments during the monitoring period. The prediction of the event in the next x years is done under two scenarios: (1) the patient initiates immediately a second treatment, (2) the patient does not initiate any treatment in the next x years. Predictions are derived from shared random-effect models. Applied to prostate cancer data, different specifications for the dependence between the prostate-specific antigen repeated measures, the initiation of a second treatment (hormonal therapy), and the risk of clinical recurrence are investigated and compared. The predictive accuracy of the dynamic predictions is evaluated with two measures (Brier score and prognostic cross-entropy) for which approximated cross-validated estimators are proposed.

Joint latent class models for longitudinal and time-to-event data: a review.

Most statistical developments in the joint modelling area have focused on the shared random-effect models that include characteristics of the longitudinal marker as predictors in the model for the time-to-event. A less well-known approach is the joint latent class model which consists in assuming that a latent class structure entirely captures the correlation between the longitudinal marker trajectory and the risk of the event. Owing to its flexibility in modelling the dependency between the longitudinal marker and the event time, as well as its ability to include covariates, the joint latent class model may be particularly suited for prediction problems. This article aims at giving an overview of joint latent class modelling, especially in the prediction context. The authors introduce the model, discuss estimation and goodness-of-fit, and compare it with the shared random-effect model. Then, dynamic predictive tools derived from joint latent class models, as well as measures to evaluate their dynamic predictive accuracy, are presented. A detailed illustration of the methods is given in the context of the prediction of prostate cancer recurrence after radiation therapy based on repeated measures of Prostate Specific Antigen.

Real-time individual predictions of prostate cancer recurrence using joint models.

Patients who were previously treated for prostate cancer with radiation therapy are monitored at regular intervals using a laboratory test called Prostate Specific Antigen (PSA). If the value of the PSA test starts to rise, this is an indication that the prostate cancer is more likely to recur, and the patient may wish to initiate new treatments. Such patients could be helped in making medical decisions by an accurate estimate of the probability of recurrence of the cancer in the next few years. In this article, we describe the methodology for giving the probability of recurrence for a new patient, as implemented on a web-based calculator. The methods use a joint longitudinal survival model. The model is developed on a training dataset of 2386 patients and tested on a dataset of 846 patients. Bayesian estimation methods are used with one Markov chain Monte Carlo (MCMC) algorithm developed for estimation of the parameters from the training dataset and a second quick MCMC developed for prediction of the risk of recurrence that uses the longitudinal PSA measures from a new patient.

Chronic use of benzodiazepines and latent cognitive decline in the elderly: results from the Three-city study.

We aimed to examine whether long-term use of benzodiazepines is associated with an accelerated decline of cognitive performances by using a statistical model specifically adapted to multivariate longitudinal bounded quantitative outcomes. The data came from the "Three-city" study, a French population based study. All the subjects were 65 years old or older at inclusion and had been followed-up for 7 years. The use of benzodiazepines and cognitive functioning were assessed at each examination phase (baseline, 2, 4 and 7 years). Cognitive decline was analyzed using a nonlinear multivariate mixed model with a latent process. This model makes it possible to assess change over time of the latent cognitive process underlying several neuropsychological tests: Mini Mental Status Examination, Isaacs Set test, Benton Visual Retention Test, and Trail Making Test (A and B), and to describe and account for their metrological properties. Analyses were adjusted for age, center, gender, education, socio-professional status, depression, insomnia, high blood pressure, hypercholesterolemia, alcohol, tobacco consumption and physical activity. Nine hundred and sixty nine subjects who reported taking benzodiazepines for 2, 4 or 7 consecutive years were compared to 4226 subjects who were non-benzodiazepine users. Chronic use of benzodiazepine was significantly associated with a lower latent cognitive level (ß=-1.79 SE=0.25 p=<0.001), but no association was found between chronic use and an acceleration of cognitive decline, neither on the latent cognitive process (ß × time=0.010 SE=0.04 p=0.81), nor on specific neuropsychological tests. Our results suggest that chronic benzodiazepine use is associated with poorer cognitive performance but not with accelerated cognitive decline with age.