Joint Modeling Approach Research Articles

Joint Modelling of Longitudinal Measurements and Time-to-Event Outcomes With a Cure Fraction Using Functional Principal Component Analysis.

In studying the association between clinical measurements and time-to-event outcomes within a cure model, utilizing repeated observations rather than solely baseline values may lead to more accurate estimation. However, there are two main challenges in this context. First, longitudinal measurements are usually observed at discrete time points and second, for diseases that respond well to treatment, a high censoring proportion may occur by the end of the trial. In this article, we propose a joint modelling approach to simultaneously study the longitudinal observations and time-to-event outcome with an assumed cure fraction. We employ the functional principal components analysis (FPCA) to model the longitudinal data, offering flexibility by not assuming a specific form for the longitudinal curve. We used a Cox's proportional hazards mixture cure model to study the survival outcome. To investigate the longitudinal binary observations, we adopt a quasi-likelihood method which builds pseudo normal distribution for the binary data and use the E-M algorithm to estimate the parameters. The tuning parameters are selected using the Akaike information criterion. Our proposed method is evaluated through extensive simulation studies and applied to a clinical trial data to study the relationship between the longitudinal prostate specific antigen (PSA) measurements and overall survival in men with metastatic prostate cancer.

Joint Modeling of Longitudinal and Survival Data

In medical studies, time-to-event outcomes such as time to death or relapse of a disease are routinely recorded along with longitudinal data that are observed intermittently during the follow-up period. For various reasons, marginal approaches to model the event time, corresponding to separate approaches for survival data/longitudinal data, tend to induce bias and lose efficiency. Instead, a joint modeling approach that brings the two types of data together can reduce or eliminate the bias and yield a more efficient estimation procedure. A well-established avenue for joint modeling is the joint likelihood approach that often produces semiparametric efficient estimators for the finite-dimensional parameter vectors in both models. Through a transformation survival model with an unspecified baseline hazard function, this review introduces joint modeling that accommodates both baseline covariates and time-varying covariates. The focus is on the major challenges faced by joint modeling and how they can be overcome. A review of available software implementations and a brief discussion of future directions of the field are also included.

Using Joint Longitudinal and Time-to-Event Models to Improve the Parameterization of Chronic Disease Microsimulation Models: an Application to Cardiovascular Disease.

Chronic disease microsimulation models often simulate disease incidence as a function of risk factors that evolve over time (e.g., blood pressure increasing with age) in order to facilitate decision analyses of different disease screening and prevention strategies. Existing models typically rely on incidence rates estimated with standard survival analysis techniques (e.g., proportional hazards from baseline data) that are not designed to be continually updated each model cycle. We introduce the use of joint longitudinal and time-to-event to parameterize microsimulations to avoid potential issues from using these existing methods. These joint models include random effects regressions to estimate the risk factor trajectories and a survival model to predict disease risk based on those estimated trajectories. In a case study on cardiovascular disease (CVD), we compare the validity of microsimulation models parameterized with this joint model approach to those parameterized with the standard approaches. A CVD microsimulation model was constructed that modeled the trajectory of seven CVD risk factors/predictors as a function of age (smoking, diabetes, systolic blood pressure, antihypertensive medication use, total cholesterol, HDL, and statin use) and predicted yearly CVD incidence as a function of these predictors, plus age, sex, and race. We parameterized the model using data from the Atherosclerosis Risk in Communities study (ARIC). The risk of CVD in the microsimulation was parameterized with three approaches: (1) joint longitudinal and time-to-event model, (2) proportional hazards model estimated using baseline data, and (3) proportional hazards model estimated using time-varying data. We accounted for non-CVD mortality across all the parameterization approaches. We simulated risk factor trajectories and CVD incidence from age 70y to 85y for an external test set comprised of individuals from the Multi-Ethnic Study of Atherosclerosis (MESA). We compared the simulated to observed incidence using both average survival curves and the E50 and E90 calibration metrics (the median and 90th percentile absolute difference between observed and predicted incidence) to measure the validity of each parameterization approach. The average CVD survival curve estimated by the microsimulation model parameterized with the joint model approach matched the observed curve from the test set relatively closely. The other parameterization methods generally performed worse, especially the proportional hazards model estimated using baseline data. Similar results were observed for the calibration metrics, with the joint model performing particularly well on the E90 metric compared to the other models. Using a joint longitudinal and time-to-event model to parameterize a CVD simulation model produced incidence predictions that more accurately reflected observed data than a model parameterized with standard approaches. This parameterization approach could lead to more reliable microsimulation models, especially for models that evaluate policies which depend on tracking dynamic risk factors over time. Beyond this single case study, more work is needed to identify the specific circumstances where the joint model approach will outperform existing methods.

Heterogeneous Mediation Analysis for Cox Proportional Hazards Model With Multiple Mediators.

This study proposes a heterogeneous mediation analysis for survival data that accommodates multiple mediators and sparsity of the predictors. We introduce a joint modeling approach that links the mediation regression and proportional hazards models through Bayesian additive regression trees with shared typologies. The shared tree component is motivated by the fact that confounders and effect modifiers on the causal pathways linked by different mediators often overlap. A sparsity-inducing prior is incorporated to capture the most relevant confounders and effect modifiers on different causal pathways. The individual-specific interventional direct and indirect effects are derived on the scale of the logarithm of hazards and survival function. A Bayesian approach with an efficient Markov chain Monte Carlo algorithm is developed to estimate the conditional interventional effects through the Monte Carlo implementation of the mediation formula. Simulation studies are conducted to verify the empirical performance of the proposed method. An application to the ACTG175 study further demonstrates the method's utility in causal discovery and heterogeneity quantification.

Conditional modeling of recurrent event data with terminal event.

Recurrent event data with a terminal event arise in follow-up studies. The current literature has primarily focused on the effect of covariates on the recurrent event process using marginal estimating equation approaches or joint modeling approaches via frailties. In this article, we propose a conditional model for recurrent event data with a terminal event, which provides an intuitive interpretation of the effect of the terminal event: at an early time, the rate of recurrent events is nearly independent of the terminal event, but the dependence gets stronger as time goes close to the terminal event time. A two-stage likelihood-based approach is proposed to estimate parameters of interest. Asymptotic properties of the estimators are established. The finite-sample behavior of the proposed method is examined through simulation studies. A real data of colorectal cancer is analyzed by the proposed method for illustration.

TdCoxSNN: Time-dependent Cox survival neural network for continuous-time dynamic prediction

Abstract The aim of dynamic prediction is to provide individualized risk predictions over time, which are updated as new data become available. In pursuit of constructing a dynamic prediction model for a progressive eye disorder, age-related macular degeneration (AMD), we propose a time-dependent Cox survival neural network (tdCoxSNN) to predict its progression using longitudinal fundus images. tdCoxSNN builds upon the time-dependent Cox model by utilizing a neural network to capture the nonlinear effect of time-dependent covariates on the survival outcome. Moreover, by concurrently integrating a convolutional neural network with the survival network, tdCoxSNN can directly take longitudinal images as input. We evaluate and compare our proposed method with joint modelling and landmarking approaches through extensive simulations. We applied the proposed approach to two real datasets. One is a large AMD study, the Age-Related Eye Disease Study, in which more than 50,000 fundus images were captured over a period of 12 years for more than 4,000 participants. Another is a public dataset of the primary biliary cirrhosis disease, where multiple laboratory tests were longitudinally collected to predict the time-to-liver transplant. Our approach demonstrates commendable predictive performance in both simulation studies and the analysis of the two real datasets.

Joint modeling of zero-inflated longitudinal measurements and time-to-event outcomes with applications to dynamic prediction.

In this article, we present a joint modeling approach for zero-inflated longitudinal count measurements and time-to-event outcomes. For the longitudinal sub-model, a mixed effects Hurdle model is utilized, incorporating various distributional assumptions such as zero-inflated Poisson, zero-inflated negative binomial, or zero-inflated generalized Poisson. For the time-to-event sub-model, a Cox proportional hazard model is applied. For the functional form linking the longitudinal outcome history to the hazard of the event, a linear combination is used. This combination is derived from the current values of the linear predictors of Hurdle mixed effects. Some other forms are also considered, including a linear combination of the current slopes of the linear predictors of Hurdle mixed effects as well as the shared random effects. A Markov chain Monte Carlo method is implemented for Bayesian parameter estimation. Dynamic prediction using joint modeling is highly valuable in personalized medicine, as discussed here for joint modeling of zero-inflated longitudinal count measurements and time-to-event outcomes. We assess and demonstrate the effectiveness of the proposed joint models through extensive simulation studies, with a specific emphasis on parameter estimation and dynamic predictions for both over-dispersed and under-dispersed data. We finally apply the joint model to longitudinal microbiome pregnancy and HIV data sets.

Applying Trial Emulation Methods to Population-Scale Linked Electronic Health Records for Individuals with Multiple Long-Term Conditions Whilst Adjusting for Informative Observations

Background & ObjectivesTrial Emulation methods can be applied to Population-Scale Linked Electronic Health Records (EHR) to generalise the results of Randomised Controlled Trials (RCTs) to a broader patient population. However, the Data Generating Mechanisms (DGMs) associated with EHR data mean that there is a need to adjust for informative observations, i.e. worse prognosis patients may have more observations. ApproachUsing the Anglo-Scandinavian Cardiac Outcomes Trial-Blood Pressure Lowering Arm (ASCOT-BPLA) as a case study: a RCT of a calcium-channel blocker-based regimen versus a β-blocker-based regimen, an emulated trial was created from population-scale, individual-level, linked anonymized EHR data curated as part of the Wales Multimorbidity e-cohort (WMC) for individuals who would have been eligible for ASCOT-BPLA but who also had Multiple Long-Term Conditions (MLTCs). Outcomes were systolic and diastolic blood pressure estimated/predicted at treatment initiation, 6 weeks, 3 months, and 6 monthly until 4 years. Methods & Results Predictions were made using a fully probabilistic Bayesian joint model which allowed for dropout due to death and which either did or did not also adjust for the frequency of blood pressure observations available. In previous simulation studies such a joint modelling approach has been shown to out-perform other adjustment methods in terms of bias and coverage. Conclusions & ImplicationsWhilst EHR data offer an attractive platform in which to evaluate interventions in people living with MLTCs, the inherent DGMs associated with them need to be taken into account, and the use of a joint modelling approach is an attractive way to achieve this.

Understanding medicine related adverse events in people with multiple long-term conditions and polypharmacy

Background and ObjectivesPolypharmacy is increasingly common in people living with multiple long-term conditions (MLTC), but clinical evidence often does not consider medicine interactions beyond two or three treatments at a time. The aim of this research is to develop statistical models to better understand interactions between treatments leading to adverse events for people living with MLTC. ApproachWe will develop models of risk of adverse events in people with MLTC and polypharmacy using data in the Secure Anonymised Information Linkage (SAIL) Databank - a trusted research environment with linked electronic health record data at the individual-level for the population of Wales, UK. Taking a flexible Bayesian joint modelling approach, where the posterior risk is the product of several sub-models, will enable the characterisation of this complex system in a way that is interpretable for healthcare decision-makers, leading to improved understanding of risk factors for adverse events. ResultsThe Welsh Multimorbidity e-Cohort (WMC) includes 2.9 million people alive and living in Wales on 1st Jan 2000 with follow-up for 20 years. Individuals in the cohort have 932,552,061 medication items prescribed and 277,279 coded adverse events recorded in primary care data. Modelling approaches will focus on feasibility of fitting models to population-level datasets, and the interpretability of the model results for healthcare decision-making. Conclusions and ImplicationsResults have the potential to inform healthcare policy and practice for the management of people living with MLTC and polypharmacy, thus improving patient outcomes and making the best use of limited healthcare resources.

Modeling informative dropout in longitudinal data: A joint model approach

In medical studies, a disease’s progress is often monitored through indicators that signify the improved or worsened condition of the patient. These are known as longitudinal biomarkers, which are observed along with an event of importance. Together, they form the framework of joint modeling, which has a longitudinal process and an inherently associated time-to-event process. In clinical studies, the change in biomarkers is often monitored in the form of a change in the patient’s plasma level after a drug is administered to the patient. Again, in such studies, patients also withdraw from the trials prematurely or at a later phase, thus giving rise to dropouts. In most cases, this dropout is not random (Missing Not at Random). A joint model has been considered to incorporate this informative dropout in longitudinal response. To demonstrate this approach, a one-compartmental pharmacokinetic (PK) nonlinear mixed-effects model consisting of time-dependent parameters has been used in this work. The dropout mechanism has been introduced using a proportional hazard model. A Bayesian model framework is adopted to study the model’s performance through detailed simulation. A PK study on the drug Divalproex subject to an informative dropout model has been discussed. Journal of Statistical Research 2024, Vol. 58, No. 1, pp. 97-110.

Association between longitudinal biomarkers and major adverse liver outcomes in patients with non-cirrhotic metabolic dysfunction-associated steatotic liver disease.

Non-invasive biomarkers provide prognostic information for the development of major adverse liver outcomes (MALO) in patients with metabolic dysfunction-associated steatotic liver disease (MASLD), but the predictive value of longitudinal biomarker measurements has not been evaluated. We assessed whether changes in biomarkers could predict incident MALO in MASLD. We analyzed a cohort of 1,260 patients (71.7% on biopsy) with non-cirrhotic MASLD between 1974 and 2019. Data at baseline and follow-up visits were obtained from medical charts. MALO was determined through medical charts and linkage to national registers until the end of 2020. A joint modeling approach was used to quantify the associations between the trajectory of biomarkers with the risk of MALO. MASLD was diagnosed at median age of 52 years (IQR: 39-60), and 59% were male. During a median follow-up of 12.2 years, 111 (8.8%) patients developed MALO. The joint modeling showed that an elevated FIB-4 (HR 2.60, 95% CI 1.89-3.50), AST (HR 2.69, 95% CI 2.57-3.05), and lower platelet count (HR 0.93, 95% CI 0.90-0.97) at any time point were associated with an increased risk of MALO, whereas the rate of change in these biomarkers had no association with this risk. In addition to baseline measurements of non-invasive biomarkers such as FIB-4 and AST, and platelets taken at MASLD diagnosis, monitoring their values over time is important, as the latest value of these biomarkers is closely associated with the risk of future MALO. The rate of change may not be as important.

Predicting progression-free survival from measurable residual disease in chronic lymphocytic leukemia.

Association between measurable residual disease (MRD) and survival outcomes in chronic lymphocytic leukemia (CLL) has often been reported. However, limited quantitative analyses over large datasets have been undertaken to establish the predictive power of MRD. Here, we provide a comprehensive assessment of published MRD data to explore the utility of MRD in the prediction of progression-free survival (PFS). We undertook two independent analyses, which leveraged available published data to address two complimentary questions. In the first, data from eight clinical trials was modeled via a meta-regression approach, showing that median PFS can be predicted from undetectable MRD rates at 3-6 months of post-treatment. The resulting model can be used to predict the probability of technical success of a planned clinical trial in chemotherapy. In the second, we investigated the evidence for predicting PFS from competing MRD metrics, for example baseline value and instantaneous MRD value, via a joint modeling approach. Using data from four small studies, we found strong evidence that including MRD metrics in joint models improves predictions of PFS compared with not including them. This analysis suggests that incorporating MRD is likely to better inform individual progression predictions. It is therefore proposed that systematic MRD collection should be accompanied by modeling to generate algorithms that inform patients' progression.

Relationship between updated MELD and prognosis in alcohol-associated hepatitis: Opportunities for more efficient trial design.

Alcohol-associated hepatitis (AH) is associated with significant mortality. Model for End-Stage Liver Disease (MELD) score is used to predict short-term mortality and aid in treatment decisions. MELD is frequently updated in the course of AH. However, once the most updated MELD is known, it is uncertain if previous ones still have prognostic value, which might be relevant for transplant allocation and trial design. We aimed to investigate the predictive performance of updated MELDs in a prospectively collected cohort of patients with AH by the InTeam consortium. Three hundred seven patients (with 859 MELD values within 60 d of admission) fulfilled the inclusion criteria. The main endpoint was time to death or transplant up to 90 days. We used a joint model approach to assess the predictive value of updated MELDs. Updated MELD measurements had a strong prognostic value for death/transplant (HR: 1.20, 95% CI: 1.14-1.27) (p < 0.0001). Previous MELD values did not add predictive value to the most current MELD. We also showed that MELD at day 28 (MELD28) had a significant predictive value for subsequent mortality/transplant in a landmark analysis (HR: 1.18, 95% CI: 1.12-1.23). We show that the use of an ordinal scale including death, transplant, and MELD28 as a trial outcome could substantially reduce the sample size required to demonstrate short-term benefit of an intervention. We show that updated MELDs during the trajectory of AH predict subsequent mortality or the need for transplant. MELD28 inclusion in an ordinal outcome (together with death or transplant) could increase the efficiency of randomized controlled trials.

Studying the association Between longitudinal non-dense breast tissue and breast cancer risk: a joint modelling approach.

Conflicting results have appeared in the literature on whether the amount of non-dense, adipose tissue in the breast is a risk factor or a protective factor for breast cancer, and biological hypotheses supporting both have been proposed. We suggest here that limitations in study design and statistical methodology could potentially explain the inconsistent results. Specifically, we exploit recent advances in methodology and software developed for the joint analysis of multiple longitudinal outcomes and time-to-event data to jointly analyze dense and non-dense tissue trajectories and the risk of breast cancer in a large, Swedish, screening cohort. We also perform extensive sensitivity analyses by mimicking analyses/designs of previously published studies, e.g. ignoring available longitudinal data. Overall, we did not find strong evidence supporting an association between non-dense tissue and the risk of incident breast cancer. We hypothesize that (1) previous studies have not been able to isolate the effect of non-dense tissue from dense tissue or adipose tissue elsewhere in the body, that (2) estimates of the effect of non-dense tissue on risk are strongly sensitive to modeling assumptions, or that (3) the effect size of non-dense tissue on breast cancer risk is likely to be small/not clinically relevant.

Relationship between prostate-specific antigen, alkaline phosphatase levels, and time-to-tumor shrinkage: understanding the progression of prostate cancer in a longitudinal study

BackgroundThis study delves into the complex interplay among prostate-specific antigen, alkaline phosphatase, and the temporal dynamics of tumor shrinkage in prostate cancer. By investigating the longitudinal trajectories and time-to-prostate cancer tumor shrinkage, we aim to untangle the intricate patterns of these biomarkers. This understanding is pivotal for gaining profound insights into the multifaceted aspects of prostate cancer progression. The joint model approach serves as a comprehensive framework, facilitating the elucidation of intricate interactions among these pivotal elements within the context of prostate cancer .MethodsA new joint model under a shared parameters strategy is proposed for mixed bivariate longitudinal biomarkers and event time data, for obtaining accurate estimates in the presence of missing covariate data. The primary innovation of our model resides in its effective management of covariates with missing observations. Built upon established frameworks, our joint model extends its capabilities by integrating mixed longitudinal responses and accounting for missingness in covariates, thus confronting this particular challenge. We posit that these enhancements bolster the model’s utility and dependability in real-world contexts characterized by prevalent missing data. The main objective of this research is to provide a model-based approach to get full information from prostate cancer data collected with patients’ baseline characteristics (Age\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$Age$$\\end{document}, body mass index (BMI\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$BMI$$\\end{document}), GleasonScore\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$Gleason Score$$\\end{document}, Grade\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$Grade$$\\end{document}, and Drug\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$Drug$$\\end{document}) and two longitudinal endogenous covariates (Platelets\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$Platelets$$\\end{document} and Bilirubin\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$Bilirubin$$\\end{document}).ResultsThe results reveal a clear association between prostate-specific antigen and alkaline phosphatase biomarkers in the context of time-to-prostate cancer tumor shrinkage. This underscores the interconnected dynamics of these key indicators in gauging disease progression.ConclusionsThe analysis of the prostate cancer dataset, incorporating a joint evaluation of mixed longitudinal prostate-specific antigen and alkaline phosphatase biomarkers alongside tumor status, has provided valuable insights into disease progression. The results demonstrate the effectiveness of the proposed joint model, as evidenced by accurate estimates. The shared variables associated with both longitudinal biomarkers and event times consistently deviate from zero, highlighting the robustness and reliability of the model in capturing the complex dynamics of prostate cancer progression. This approach holds promise for enhancing our understanding and predictive capabilities in the clinical assessment of prostate cancer.

Determinants of longitudinal changes of CD4 cell count and survival time to death of HIV/AIDS patients treated at Yabelo General Hospital, the case of pastoralist area: Using joint modelling approach.

HIV/AIDS is a chronic disease that challenges public health worldwide and causes morbidity and mortality in humans. The main purpose of this study was to investigate the determinants of longitudinal changes in CD4 cell count and survival time to death among HIV/AIDS patients as adults from January 2016 to December 2019 at Yabelo General Hospital. The intellectual gap in this study was focused on the study area, which means that the study related to joint modeling doesn't exist in the pastoralist community of Borena. This study involved 293 adult HIV-infected adults that could be collected from the recorded patient chart data, and the study design is a retrospective cohort design. The study used a Cox proportional hazard model, a linear mixed effect model, and a joint model, which is the combination of both model processes. The joint model showed that longitudinal CD4 cell count is significantly associated with survival time (p-value = 0.0253). Covariates such as visiting time, age, weight, educational status, ART adherence, and functional status were statistically significant factors associated with mean changes in the CD4 cell count of HIV patients. WHO stage, educational status, place of residence, TB, family history, and opportunistic infection disease had a significant effect on the survival time of HIV patients. The estimated association parameter is a negative value, which indicates both outcomes are negatively associated, and higher values of the CD4 cell count are associated with better survival.

Equilibrium bifurcation and extreme risk in the EU carbon futures market

Considering the long-term memory and volatility clustering of the European Union (EU) Carbon Emission Allowances (EUA) futures returns, based on the economy–energy–environment system perspective and the assumption of investors' heterogeneity, this study proposes a joint modeling approach combining the fractionally integrated generalized autoregressive conditional heteroscedasticity model (FIGARCH) and the stochastic cusp catastrophe model (SCC) to examine the equilibrium bifurcations and extreme risks in the EU carbon futures market. The relevant results are threefold. (1) The SCC model has good fitting effect and interpretability, and is an effective method for investigating catastrophe reactions under time-varying volatility conditions. (2) In the EUA futures market, chartists are mainly affected by short-term price and trading volume changes, which leads to the emergence of equilibrium bifurcations, while fundamentalists make investment decisions based on the economy, the energy market, and market supply–demand, which affects the asymmetry of equilibrium bifurcations. (3) Using the catastrophe criterion (i.e., Cardan's discriminant of the equilibrium surface equation), we identify148 equilibrium bifurcation time points in the EUA futures market from December 3, 2009 to September 16, 2020, most of which are concentrated in two upward periods with an average scale of extreme risks is about 32.51 %. Our analysis provides theoretical support for regulatory authorities to stabilize the carbon futures market and build a collaborative extreme risk management framework covering energy and macroeconomics, also proposing suggestions for traders to effectively prevent extreme risks.

Joint modeling of monocyte HLA-DR expression trajectories predicts 28-day mortality in severe SARS-CoV-2 patients.

The recent SarsCov2 pandemic has disrupted healthcare system notably impacting intensive care units (ICU). In severe cases, the immune system is dysregulated, associating signs of hyperinflammation and immunosuppression. In the present work, we investigated, using a joint modeling approach, whether the trajectories of cellular immunological parameters were associated with survival of COVID-19 ICU patients. This study is based on the REA-IMMUNO-COVID cohort including 538 COVID-19 patients admitted to ICU between March 2020 and May 2022. Measurements of monocyte HLA-DR expression (mHLA-DR), counts of neutrophils, of total lymphocytes, and of CD4+ and CD8+ subsets were performed five times during the first month after ICU admission. Univariate joint models combining survival at day 28 (D28), hospital discharge and longitudinal analysis of those biomarkers' kinetics with mixed-effects models were performed prior to the building of a multivariate joint model. We showed that a higher mHLA-DR value was associated with a lower risk of death. Predicted mHLA-DR nadir cutoff value that maximized the Youden index was 5414 Ab/C and led to an AUC = 0.70 confidence interval (95%CI) = [0.65; 0.75] regarding association with D28 mortality while dynamic predictions using mHLA-DR kinetics until D7, D12 and D20 showed AUCs of 0.82 [0.77; 0.87], 0.81 [0.75; 0.87] and 0.84 [0.75; 0.93]. Therefore, the final joint model provided adequate discrimination performances at D28 after collection of biomarker samples until D7, which improved as more samples were collected. After severe COVID-19, decreased mHLA-DR expression is associated with a greater risk of death at D28 independently of usual clinical confounders.

Tumor growth and overall survival modeling to support decision making in phase Ib/II trials: A comparison of the joint and two-stage approaches.

Model-based tumor growth inhibition (TGI) metrics are increasingly used to predict overall survival (OS) data in Phase III immunotherapy clinical trials. However, there is still a lack of understanding regarding the differences between two-stage or joint modeling methods to leverage Phase I/II trial data and help early decision-making. A recent study showed that TGI metrics such as the tumor growth rate constant KG may have good operating characteristics as early endpoints. This previous study used a two-stage approach that is easy to implement and intuitive but prone to bias as it does not account for the relationship between the longitudinal and time-to-event processes. A relevant alternative is to use a joint modeling approach. In the present article, we evaluated the operating characteristics of TGI metrics using joint modeling, assuming an OS model previously developed using historical data. To that end, we used TGI and OS data from IMpower150-a study investigating atezolizumab in over 750 patients suffering from non-small cell lung cancer-to mimic randomized Phase Ib/II trials varying in terms of number of patients included (40 to 15 patients per arm) and follow-up duration (24 to 6 weeks after the last patient included). In this context, joint modeling did not outperform the two-stage approach and provided similar operating characteristics in all the investigated scenarios. Our results suggest that KG geometric mean ratio could be used to support early decision-making provided that 30 or more patients per arm are included and followed for at least 12 weeks.

#2502 The CONVINCE randomized trial found positive effects of hemodiafiltration on quality of life in patients with kidney disease on dialysis

Abstract Background and Aims Health-related quality of life of ESKD patients is negatively affected by disease-specific symptoms and can additionally be impaired by renal replacement therapy. Previous studies indicate that hemodiafiltration (HDF) may have a positive impact on the patients’ perceived health status compared to hemodialysis (HD), but overall evidence has been inconclusive. We investigated whether HDF is beneficial with respect to the patients’ perceived health status in the CONVINCE Trial. Method The CONVINCE Trial is an international multi-center, prospective, randomized, open label, controlled trial, comparing benefits and safety high-dose HDF versus high-flux HD. All-cause mortality was the primary outcome of the trial and a broad range of Patient-Reported Outcomes (PROs) were investigated as secondary outcomes. Eight generic health domains (Physical Function, Pain Intensity, Pain Interference, Fatigue, Sleep Disturbance, Depression, Anxiety, Ability to Participate in Social Roles and Activities) were assessed using the Patient-Reported Outcome Measurement Information System, PROMIS®-29 v2.0 profile, and an additional PROMIS short-form assessing Cognitive Function. PROs were collected every three months over the course of the study. As the perceived health status is likely related to mortality, we estimated the effect of HDF on PROs using a joint modelling approach to account for potential bias by selective dropout due to death. Results A total of n = 1 360 patients have been randomized to HD (n = 677) and HDF (n = 683). Both groups were comparable at baseline, with physical function (44.0 on the PROMIS T-score metric) considerably lower than the general population. Median follow-up was 30 months. We could obtain 84% of the planned PRO follow-up assessments on average. Overall, we found a steady decline in Health-Related Quality of Life (HRQoL) over the observation period; with statistically significant group differences for Physical Function (P = .047), Cognitive Function (P = .049), Pain Interference (P = .027) and the Ability to Participate in Social Roles and Activities (P = .023), all favouring HDF over HD. Conclusion Within the CONVINCE trial we have applied a comprehensive assessment of PROs comparing HDF with HD. Along with a beneficial effect on survival rates, we observed a slower decline in patients perceived health status when treated with HDF compared to HD. CONVINCE provides further evidence that HDF is beneficial in terms of quality of life.