Fast and flexible inference for joint models of multivariate longitudinal and survival data using integrated nested Laplace approximations.

Modeling longitudinal and survival data jointly offers many advantages such as addressing measurement error and missing data in the longitudinal processes, understanding and quantifying the association between the longitudinal markers and the survival events, and predicting the risk of events based on the longitudinal markers. A joint model involves multiple submodels (one for each longitudinal/survival outcome) usually linked together through correlated or shared random effects. Their estimation is computationally expensive (particularly due to a multidimensional integration of the likelihood over the random effects distribution) so that inference methods become rapidly intractable, and restricts applications of joint models to a small number of longitudinal markers and/or random effects. We introduce a Bayesian approximation based on the integrated nested Laplace approximation algorithm implemented in the R package R-INLA to alleviate the computational burden and allow the estimation of multivariate joint models with fewer restrictions. Our simulation studies show that R-INLA substantially reduces the computation time and the variability of the parameter estimates compared with alternative estimation strategies. We further apply the methodology to analyze five longitudinal markers (3 continuous, 1 count, 1 binary, and 16 random effects) and competing risks of death and transplantation in a clinical trial on primary biliary cholangitis. R-INLA provides a fast and reliable inference technique for applying joint models to the complex multivariate data encountered in health research.

Bayesian survival analysis with INLA.

This tutorial shows how various Bayesian survival models can be fitted using the integrated nested Laplace approximation in a clear, legible, and comprehensible manner using the INLA and INLAjoint R-packages. Such models include accelerated failure time, proportional hazards, mixture cure, competing risks, multi-state, frailty, and joint models of longitudinal and survival data, originally presented in the article "Bayesian survival analysis with BUGS." In addition, we illustrate the implementation of a new joint model for a longitudinal semicontinuous marker, recurrent events, and a terminal event. Our proposal aims to provide the reader with syntax examples for implementing survival models using a fast and accurate approximate Bayesian inferential approach.

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.

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).

Spatial and seasonal determinants of Lyme borreliosis incidence in France, 2016 to 2021.

BackgroundLyme borreliosis (LB) is the most widespread hard tick-borne zoonosis in the northern hemisphere. Existing studies in Europe have focused mainly on acarological risk assessment, with few investigations exploring human LB occurrence.AimWe explored the determinants of spatial and seasonal LB variations in France from 2016 to 2021 by integrating environmental, animal, meteorological and anthropogenic factors, and then mapped seasonal LB risk predictions.MethodsWe fitted 2016-19 LB national surveillance data to a two-part spatio-temporal statistical model. Spatial and temporal random effects were specified using a Besag-York-Mollie model and a seasonal model, respectively. Coefficients were estimated in a Bayesian framework using integrated nested Laplace approximation. Data from 2020-21 were used for model validation.ResultsA high vegetation index (≥ 0.6) was positively associated with seasonal LB presence, while the index of deer presence (> 60%), mild soil temperature (15-22 °C), moderate air saturation deficit (1.5-5 mmHg) and higher tick bite frequency were associated with increased incidence. Prediction maps show a higher risk of LB in spring and summer (April-September), with higher incidence in parts of eastern, midwestern and south-western France.ConclusionWe present a national level spatial assessment of seasonal LB occurrence in Europe, disentangling factors associated with the presence and increased incidence of LB. Our findings yield quantitative evidence for national public health agencies to plan targeted prevention campaigns to reduce LB burden, enhance surveillance and identify further data needs. This approach can be tested in other LB endemic areas.

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.

DNA methylation mediates the association between breastfeeding and early-life growth trajectories.

BACKGROUND: The role of breastfeeding in modulating epigenetic factors has been suggested as a possible mechanism conferring its benefits on child development but it lacks evidence. Using extensive DNA methylation data from the ALSPAC child cohort, we characterized the genome-wide landscape of DNA methylation variations associated with the duration of exclusive breastfeeding and assessed whether these variations mediate the association between exclusive breastfeeding and BMI over different epochs of child growth. RESULTS: Exclusive breastfeeding elicits more substantial DNA methylation variations during infancy than at other periods of child growth. At the genome-wide level, 13 CpG sites in girls (miR-21, SNAPC3, ATP6V0A1, DHX15/PPARGC1A, LINC00398/ALOX5AP, FAM238C, NATP/NAT2, CUX1, TRAPPC9, OSBPL1A, ZNF185, FAM84A, PDPK1) and 2 CpG sites in boys (IL16 and NREP), mediate the association between exclusive breastfeeding and longitudinal BMI. We found enrichment of CpG sites located within miRNAs and key pathways (AMPK signaling pathway, insulin signaling pathway, endocytosis). Overall DNA methylation variation corresponding to 3 to 5 months of exclusive breastfeeding was associated with slower BMI growth the first 6 years of life compared to no breastfeeding and in a dose-response manner with exclusive breastfeeding duration. CONCLUSIONS: Our study confirmed the early postnatal period as a critical developmental period associated with substantial DNA methylation variations, which in turn could mitigate the development of overweight and obesity from infancy to early childhood. Since an accelerated growth during these developmental periods has been linked to the development of sustained obesity later in life, exclusive breastfeeding could have a major role in preventing the risks of overweight/obesity and children and adults through DNA methylation mechanisms occurring early in life.

Immune-checkpoint inhibitors and candidate surrogate endpoints for overall survival across tumour types: A systematic literature review.

BACKGROUND: Surrogate endpoints (SEs) for overall survival (OS) are specific to therapeutic class. The objective of this review was to document all alternative endpoints studied for their association with OS in Immune-Checkpoint Inhibitors (ICI)-treated patients. METHODS: We searched PubMed and Embase for publications reporting the association between a clinical endpoint and OS in ICI-treated populations from 01/01/2003 to 03/31/2018. RESULTS: Out of 6,335 references identified, 24 were selected. Only 3 studies assessed surrogacy at both the patient and trial levels. The main traditional alternative endpoints included progression-free survival (N = 10) and objective response rate (N = 8). New alternative endpoints, such as durable response rate (N = 1) and intermediate response endpoint (N = 1) statistically better correlate with OS in the cancer types analysed. CONCLUSION: Based on the published evidence, there is insufficient data to support validated SE for OS. Adequate surrogacy assessment of promising composite endpoints which consider a duration component is encouraged.