Haemoglobin trajectories in chronic kidney disease and risk of major adverse cardiovascular events.

BACKGROUND: The trajectories of haemoglobin in patients with chronic kidney disease (CKD) have been poorly described. In such patients, we aimed to identify typical haemoglobin trajectory profiles and estimate their risks of major adverse cardiovascular events (MACE). METHODS: We used 5-year longitudinal data from the CKD-REIN cohort patients with moderate to severe CKD enrolled from 40 nationally representative nephrology clinics in France. A joint latent class model was used to estimate, in different classes of haemoglobin trajectory, the competing risks of (i) MACE + defined as the first event among cardiovascular death, non-fatal myocardial infarction, stroke or hospitalization for acute heart failure, (ii) initiation of kidney replacement therapy (KRT) and (iii) non-cardiovascular death. RESULTS: During the follow-up, we gathered 33 874 haemoglobin measurements from 3011 subjects (median, 10 per patient). We identified five distinct haemoglobin trajectory profiles. The predominant profile (n = 1885, 62.6%) showed an overall stable trajectory and low risks of events. The four other profiles had nonlinear declining trajectories: early strong decline (n = 257, 8.5%), late strong decline (n = 75, 2.5%), early moderate decline (n = 356, 11.8%) and late moderate decline (n = 438, 14.6%). The four profiles had different risks of MACE, while the risks of KRT and non-cardiovascular death consistently increased from the haemoglobin decline. CONCLUSION: In this study, we observed that two-thirds of patients had a stable haemoglobin trajectory and low risks of adverse events. The other third had a nonlinear trajectory declining at different rates, with increased risks of events. Better attention should be paid to dynamic changes of haemoglobin in CKD.

Describing complex disease progression using joint latent class models for multivariate longitudinal markers and clinical endpoints.

Neurodegenerative diseases are characterized by numerous markers of progression and clinical endpoints. For instance, multiple system atrophy (MSA), a rare neurodegenerative synucleinopathy, is characterized by various combinations of progressive autonomic failure and motor dysfunction, and a very poor prognosis. Describing the progression of such complex and multi-dimensional diseases is particularly difficult. One has to simultaneously account for the assessment of multivariate markers over time, the occurrence of clinical endpoints, and a highly suspected heterogeneity between patients. Yet, such description is crucial for understanding the natural history of the disease, staging patients diagnosed with the disease, unravelling subphenotypes, and predicting the prognosis. Through the example of MSA progression, we show how a latent class approach modeling multiple repeated markers and clinical endpoints can help describe complex disease progression and identify subphenotypes for exploring new pathological hypotheses. The proposed joint latent class model includes class-specific multivariate mixed models to handle multivariate repeated biomarkers possibly summarized into latent dimensions and class-and-cause-specific proportional hazard models to handle time-to-event data. Maximum likelihood estimation procedure, validated through simulations is available in the lcmm R package. In the French MSA cohort comprising data of 598 patients during up to 13 years, five subphenotypes of MSA were identified that differ by the sequence and shape of biomarkers degradation, and the associated risk of death. In posterior analyses, the five subphenotypes were used to explore the association between clinical progression and external imaging and fluid biomarkers, while properly accounting for the uncertainty in the subphenotypes membership.

Modeling repeated self-reported outcome data: A continuous-time longitudinal Item Response Theory model.

Item Response Theory (IRT) models have received growing interest in health science for analyzing latent constructs such as depression, anxiety, quality of life or cognitive functioning from the information provided by each individual's items responses. However, in the presence of repeated item measures, IRT methods usually assume that the measurement occasions are made at the exact same time for all patients. In this paper, we show how the IRT methodology can be combined with the mixed model theory to provide a longitudinal IRT model which exploits the information of a measurement scale provided at the item level while simultaneously handling observation times that may vary across individuals and items. The latent construct is a latent process defined in continuous time that is linked to the observed item responses through a measurement model at each individual- and occasion-specific observation time; we focus here on a Graded Response Model for binary and ordinal items. The Maximum Likelihood Estimation procedure of the model is available in the R package lcmm. The proposed approach is contextualized in a clinical example in end-stage renal disease, the PREDIALA study. The objective is to study the trajectories of depressive symptomatology (as measured by 7 items of the Hospital Anxiety and Depression scale) according to the time from registration on the renal transplant waiting list and the renal replacement therapy. We also illustrate how the method can be used to assess Differential Item Functioning and lack of measurement invariance over time.

Joint models for the longitudinal analysis of measurement scales in the presence of informative dropout.

In health cohort studies, repeated measures of markers are often used to describe the natural history of a disease. Joint models allow to study their evolution by taking into account the possible informative dropout usually due to clinical events. However, joint modeling developments mostly focused on continuous Gaussian markers while, in an increasing number of studies, the actual quantity of interest is non-directly measurable; it constitutes a latent variable evaluated by a set of observed indicators from questionnaires or measurement scales. Classical examples include anxiety, fatigue, cognition. In this work, we explain how joint models can be extended to the framework of a latent quantity measured over time by indicators of different nature (e.g. continuous, binary, ordinal). The longitudinal submodel describes the evolution over time of the quantity of interest defined as a latent process in a structural mixed model, and links the latent process to each observation of the indicators through appropriate measurement models. Simultaneously, the risk of multi-cause event is modelled via a proportional cause-specific hazard model that includes a function of the mixed model elements as linear predictor to take into account the association between the latent process and the risk of event. Estimation, carried out in the maximum likelihood framework and implemented in the R-package JLPM, has been validated by simulations. The methodology is illustrated in the French cohort on Multiple-System Atrophy (MSA), a rare and fatal neurodegenerative disease, with the study of dysphagia progression over time stopped by the occurrence of death.

Impact of interventions scenarios targeting three main vascular risk factors on the future burden of dementia in France.

The epidemiological and societal burden of dementia is expected to increase in the coming decades due to the world population aging. In this context, the evaluation of the potential impact of intervention scenarios aiming at reducing the prevalence of dementia risk factors is an active area of research. However, such studies must account for the associated changes in mortality and the dependence between the risk factors. Using micro-simulations, this study aims to estimate the changes in dementia burden in France in 2040 according to intervention scenarios targeting the prevention or treatment of hypertension, diabetes and physical inactivity. Accounting for their communality and their effects on mortality, the results show that the disappearance of hypertension, diabetes and physical inactivity in France in 2020 could decrease dementia prevalence by 33% among men and 26% among women in 2040 and increase the life expectancy without dementia at age 65 by 3.4 years (men) and 2.6 years (women). Among the three factors, the prevention of hypertension would be the most efficient. These projections rely on current estimates of the risk of dementia and death associated with risk factors. Thanks to the R package developed they could be refined for different countries or different interventions and updated with new estimates.

Long-term risk of hip or forearm fractures in older occasional users of benzodiazepines.

AIMS: This article sought to study the association between patterns of benzodiazepine (BZD) use and the risk of hip and forearm fractures in people aged 50 and 75 years or more. METHODS: In a representative cohort of the French National Health Insurance Fund of individuals aged 50 years or older (n = 106 437), we followed up BZD dispensing (reflecting their patterns of use) and the most frequent fall-related fractures (hip and forearm) for 8 years. We used joint latent class models to simultaneously identify BZD dispensing trajectories and the risk of fractures in the entire cohort and in those 75 years or older). We used a survival model to estimate the adjusted hazard ratios (aHRs) between these trajectories and the risk of fractures. RESULTS: In the entire cohort, we identified 5 BZD trajectories: non-users (76.7% of the cohort); occasional users (15.2%); decreasing users (2.6%); late increasing users (3.0%); and early increasing users (2.4%). Compared with non-users, fracture risk was not increased in either occasional users (aHR = 0.99, 95% confidence interval [CI] 0.99-1.00) or in decreasing users (aHR = 0.90, 95% CI 0.74-1.08). It was significantly higher in early increasing users (aHR = 1.86, 95% CI 1.62-2.14) and in late increasing users (aHR = 1.39, 95% CI 1.15-1.60). We observed similar trajectories and risk levels in the people older than 75 years. CONCLUSION: Occasional BZD use, which is compatible with current recommendations, was not associated with an excess risk of the most frequent fall-related fractures in people older than 50 or 75 years.

A joint model for multiple dynamic processes and clinical endpoints: Application to Alzheimer's disease.

As other neurodegenerative diseases, Alzheimer's disease, the most frequent dementia in the elderly, is characterized by multiple progressive impairments in the brain structure and in clinical functions such as cognitive functioning and functional disability. Until recently, these components were mostly studied independently because no joint model for multivariate longitudinal data and time to event was available in the statistical community. Yet, these components are fundamentally interrelated in the degradation process toward dementia and should be analyzed together. We thus propose a joint model to simultaneously describe the dynamics of multiple correlated components. Each component, defined as a latent process, is measured by one or several continuous markers (not necessarily Gaussian). Rather than considering the associated time to diagnosis as in standard joint models, we assume diagnosis corresponds to the passing above a covariate-specific threshold (to be estimated) of a pathological process that is modeled as a combination of the component-specific latent processes. This definition captures the clinical complexity of diagnoses such as dementia diagnosis but also benefits from simplifications for the computation of maximum likelihood estimates. We show that the model and estimation procedure can also handle competing clinical endpoints. The estimation procedure, implemented in an R package, is validated by simulations and the method is illustrated on a large French population-based cohort of cerebral aging in which we focused on the dynamics of three clinical manifestations and the associated risk of dementia and death before dementia.

Normalized Mini-Mental State Examination for assessing cognitive change in population-based brain aging studies.

BACKGROUND: The Mini-Mental State Examination (MMSE) is widely used in population-based longitudinal studies to quantify cognitive change. However, its poor metrological properties, mainly ceiling/floor effects and varying sensitivity to change, have largely restricted its usefulness. We propose a normalizing transformation that corrects these properties, and makes possible the use of standard statistical methods to analyze change in MMSE scores. METHODS: The normalizing transformation designed to correct at best the metrological properties of MMSE was estimated and validated on two population-based studies (n = 4,889, 20-year follow-up) by cross-validation. The transformation was also validated on two external studies with heterogeneous samples mixing normal and pathological aging, and samples including only demented subjects. RESULTS: The normalizing transformation provided correct inference in contrast with models analyzing the change in crude MMSE that most often lead to biased estimates of risk factors and incorrect conclusions. CONCLUSIONS: Cognitive change can be easily and properly assessed with the normalized MMSE using standard statistical methods such as linear (mixed) models.

Quantile regression for incomplete longitudinal data with selection by death.

Quantile regressions are increasingly used to provide population norms for quantitative variables. Indeed, they do not require any Gaussian assumption for the response and allow to characterize its entire distribution through different quantiles. Quantile regressions are especially useful to provide norms of cognitive scores in the elderly that may help general practitioners to identify subjects with unexpectedly low cognitive level in routine examinations. These norms may be estimated from cohorts of elderly using quantile regression for longitudinal data, but this requires to properly account for selection by death, dropout and intermittent missing data. In this work, we extend the weighted estimating equation approach to estimate conditional quantiles in the population currently alive from mortal cohorts with dropout and intermittent missing data. Suitable weight estimation procedures are provided for both monotone and intermittent missing data and under two missing-at-random assumptions, when the observation probability given that the subject is alive depends on the survival time (p-MAR assumption) or not (u-MAR assumption). Inference is performed through subject-level bootstrap. The method is validated in a simulation study and applied to the French cohort Paquid to estimate quantiles of a cognitive test in the elderly population currently alive. On one hand, the simulations show that the u-MAR analysis is quite robust when the true missingness mechanism is p-MAR. This is a useful result because computation of suitable weights for intermittent missing data under the p-MAR assumption is untractable. On the other hand, the simulations highlight, along with the real data analysis, the usefulness of suitable weights for intermittent missing data. This method is implemented in the R package weightQuant.

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.

Are latent variable models preferable to composite score approaches when assessing risk factors of change? Evaluation of type-I error and statistical power in longitudinal cognitive studies.

As with many health constructs, cognition is difficult to measure accurately; it is assessed by multiple psychometric tests. Two approaches are commonly adopted to address this multivariate aspect in longitudinal analyses: the composite score approach summarizes the tests into a single outcome and subsequently analyzes its change; the multivariate approach relates the tests to the underlying cognitive level and simultaneously analyzes its change. We compared the quality of inference of these approaches in a simulation study based on three combinations of tests inspired by two population-based cohorts. In the absence of missing data and with relatively Gaussian psychometric tests, the composite score approach provided similar type-I error rates and statistical power as the multivariate latent process approach. In the more plausible scenario with departures from normality, transformations of each constituent test or of the composite score were required to avoid excess type-I error rates. When missing tests were more likely in cognitively impaired subjects, inference with the composite was not correct. In conclusion, composite scores can be used to assess risk factors for cognitive change provided they are correctly normalized, constituent tests are reliable and the amount of uninformative missing tests remains small. Otherwise, latent variable models are recommended.