Joint modeling of survival and longitudinal non-survival data: current methods and issues. Report of the DIA Bayesian joint modeling working group

Joint modeling of longitudinal CD4 count data and time to first occurrence of composite outcome

In this study, we jointly modeled longitudinal CD4 count data and survival outcome (time-to-first occurrence of composite outcome of death, cardiac tamponade or constriction) in other to investigate the effects of Mycobacterium indicus pranii immunotherapy and the CD4 count measurements on the hazard of the composite outcome among patients with HIV and tuberculous (TB) pericarditis. In this joint modeling framework, the models for longitudinal and the survival data are linked by an association structure. The association structure represents the hazard of the event for 1-unit increase in the longitudinal measurement. Models fitting and parameter estimation were carried out using R version 4.2.3. The association structure that represents the strength of the association between the hazard for an event at time point j and the area under the longitudinal trajectory up to the same time j provides the best fit. We found that 1-unit increase in CD4 count results in 2 % significant reduction in the hazard of the composite outcome. Among HIV and TB pericarditis individuals, the hazard of the composite outcome does not differ between of M.indicus pranii versus placebo. Application of joint models to investigate the effect of M.indicus pranii on the hazard of the composite outcome is limited. Hence, this study provides information on the effect of M.indicus pranii on the hazard of the composite outcome among HIV and TB pericarditis patients.

Clinical Predictive Modeling of Heart Failure: Domain Description, Models' Characteristics and Literature Review

This study attempts to identify and briefly describe the current directions in applied and theoretical clinical prediction research. Context-rich chronic heart failure syndrome (CHFS) telemedicine provides the medical foundation for this effort. In the chronic stage of heart failure, there are sudden exacerbations of syndromes with subsequent hospitalizations, which are called acute decompensation of heart failure (ADHF). These decompensations are the subject of diagnostic and prognostic predictions. The primary purpose of ADHF predictions is to clarify the current and future health status of patients and subsequently optimize therapeutic responses. We proposed a simplified discrete-state disease model as an attempt at a typical summarization of a medical subject before starting predictive modeling. The study tries also to structure the essential common characteristics of quantitative models in order to understand the issue in an application context. The last part provides an overview of prediction works in the field of CHFS. These three parts provide the reader with a comprehensive view of quantitative clinical predictive modeling in heart failure telemedicine with an emphasis on several key general aspects. The target community is medical researchers seeking to align their clinical studies with prognostic or diagnostic predictive modeling, as well as other predictive researchers. The study was written by a non-medical expert.

Technology transfer for green investments: exploring how technology transfer through foreign direct investments can contribute to sustainable practices and reduced environmental impact in OIC economies

Estimating the asymmetrical influence of foreign direct investment is the primary goal of the current study. In addition, further controlled variables affect environmental degradation in OIC nations. Due to this, current research employs the asymmetric (NPARDL) approach and the data period from 1980 to 2021 to estimate about viability of the EKC (environmental Kuznets curve) theory. The study utilized greenhouse gas (GHG) including emissions of carbon dioxide (CO2), nitrous oxide (N2O), methane (CH4), and ecological footprint as substantial parameters of environmental quality. A nonlinear link between foreign direct investments, trade openness, economic growth, urbanization, energy consumption, and environmental pollution with CO2, N2O, CH4, and ecological footprint in the OIC nations is confirmed by the study's outcomes, which however reveals inconsistent results. Furthermore, the results also show that wrong conclusions might result from disregarding intrinsic nonlinearities. The study's conclusions provide the most important recommendations for decision-makers.

Choice of baseline hazards in joint modeling of longitudinal and time-to-event cancer survival data

Longitudinal time-to-event analysis is a statistical method to analyze data where covariates are measured repeatedly. In survival studies, the risk for an event is estimated using Cox-proportional hazard model or extended Cox-model for exogenous time-dependent covariates. However, these models are inappropriate for endogenous time-dependent covariates like longitudinally measured biomarkers, Carcinoembryonic Antigen (CEA). Joint models that can simultaneously model the longitudinal covariates and time-to-event data have been proposed as an alternative. The present study highlights the importance of choosing the baseline hazards to get more accurate risk estimation. The study used colon cancer patient data to illustrate and compare four different joint models which differs based on the choice of baseline hazards [piecewise-constant Gauss-Hermite (GH), piecewise-constant pseudo-adaptive GH, Weibull Accelerated Failure time model with GH & B-spline GH]. We conducted simulation study to assess the model consistency with varying sample size (N = 100, 250, 500) and censoring (20 %, 50 %, 70 %) proportions. In colon cancer patient data, based on Akaike information criteria (AIC) and Bayesian information criteria (BIC), piecewise-constant pseudo-adaptive GH was found to be the best fitted model. Despite differences in model fit, the hazards obtained from the four models were similar. The study identified composite stage as a prognostic factor for time-to-event and the longitudinal outcome, CEA as a dynamic predictor for overall survival in colon cancer patients. Based on the simulation study Piecewise-PH-aGH was found to be the best model with least AIC and BIC values, and highest coverage probability(CP). While the Bias, and RMSE for all the models showed a competitive performance. However, Piecewise-PH-aGH has shown least bias and RMSE in most of the combinations and has taken the shortest computation time, which shows its computational efficiency. This study is the first of its kind to discuss on the choice of baseline hazards.

Predictors for CD4 cell count and hemoglobin level with survival time to default for HIV positive adults under ART treatment at University of Gondar Comprehensive and Specialized Hospital, Ethiopia

BACKGROUND

HIV/AIDS is the most known powerful risk factor for morbidity and mortality in the world. The greatest biological markers in HIV patients are CD4 cell count and hemoglobin level, as they are independent predictors of survival of HIV patients. The objective of this study was to investigate the common socio-demographic, clinical, and behavioral Predictor's affecting the CD4 cell count, and hemoglobin level with survival time to default from ART treatment among HIV positive adults under ART treatment at university of Gondar comprehensive and specialized hospital, North-west Ethiopia.

METHOD

This study was conducted at University of Gondar comprehensive specialized hospital by using a retrospective cohort follow up study design. The source of data in this study was secondary data obtained from patients chart. Bayesian joint models were employed to get wide-ranging information about HIV/AIDS progression.

RESULT

From a total of 403 HIV positive adults, about 44.2% were defaulted from therapy and the rest were actively followed ART treatment. The estimate of the association parameter for the current true value of CD4 cell count ([Formula: see text]), and hemoglobin level ([Formula: see text]), trend of CD4 cell count ([Formula: see text]) and hemoglobin level ([Formula: see text]) is positive. Positive values indicating that the higher CD4 cell count and hemoglobin level is related with the higher time of defaulting from ART. Predictor's hematocrit, weight, platelet cell count, lymphocyte count, sex, adherence, and WHO clinical stage were joint determinate risk factors affecting CD4 cell count, hemoglobin level and time to default at 5% level of significance.

CONCLUSION

Current study results revealed that hematocrit, weight, BMI, platelet cell count, lymphocyte count, sex (female), and good treatment adherence were significantly associated with higher CD4 cell count, hemoglobin level and time to default while having advanced WHO clinical stage-IV had significantly decreased CD4 cell, hemoglobin level, and time to default from treatment. Patients with HIV should be given special attention based on these important factors to improve their health and prolong their lives.

Joint modeling of longitudinal changes of pulse rate and body temperature with time to recovery of pneumonia patients under treatment: a prospective cohort study

BACKGROUND

Pneumonia is the leading infectious cause of mortality worldwide and one of the most common lower respiratory tract infections that is contributing significantly to the burden of antibiotic consumption. The study aims to identify the determinants of the progress of pulse rate, body temperature and time to recovery of pneumonia patients.

METHOD

A prospective cohort study design was used from Felege Hiwot referral hospital on 214 sampled pneumonia patients from March 01, 2022 up to May 31, 2022. The Kaplan-Meier survival estimate and Log-Rank test was used to compare the survival time. Joint model of bivariate longitudinal and time to event model was used to identify factors of longitudinal change of pulse rate and body temperature with time to recovery jointly.

RESULT

As the follow up time of pneumonia patient's increase by one hour the average longitudinal change of pulse rate and body temperature were decreased by 0.4236 bpm and 0.0119 [Formula: see text]. The average longitudinal change of pulse rate and body temperature of patients who lived in rural was 1.4602 bpm and 0.1550 [Formula: see text] times less as compared to urban residence. Patients who had dangerous signs are significantly increased the average longitudinal change of pulse rate and body temperature by 2.042 bpm and 0.6031 [Formula: see text] as compared to patients who had no dangerous signs. A patient from rural residence was 1.1336 times more likely to experience the event of recovery as compared to urban residence. The estimated values of the association parameter for pulse rate and body temperature were -0.4236 bpm and -0.0119 respectively, which means pulse rate and body temperature were negatively related with patients recovery time.

CONCLUSION

Pulse rate and body temperature significantly affect the time to the first recovery of pneumonia patients who are receiving treatment. Age, residence, danger sign, comorbidity, baseline symptom and visiting time were the joint determinant factors for the longitudinal change of pulse rate, body temperature and time to recovery of pneumonia patients. The joint model approach provides precise dynamic predictions, widespread information about the disease transitions, and better knowledge of disease etiology.

Bayesian joint models for multi-regional clinical trials

In recent years, multi-regional clinical trials (MRCTs) have increased in popularity in the pharmaceutical industry due to their ability to accelerate the global drug development process. To address potential challenges with MRCTs, the International Council for Harmonisation released the E17 guidance document which suggests the use of statistical methods that utilize information borrowing across regions if regional sample sizes are small. We develop an approach that allows for information borrowing via Bayesian model averaging in the context of a joint analysis of survival and longitudinal data from MRCTs. In this novel application of joint models to MRCTs, we use Laplace's method to integrate over subject-specific random effects and to approximate posterior distributions for region-specific treatment effects on the time-to-event outcome. Through simulation studies, we demonstrate that the joint modeling approach can result in an increased rejection rate when testing the global treatment effect compared with methods that analyze survival data alone. We then apply the proposed approach to data from a cardiovascular outcomes MRCT.

Variable selection for joint models of multivariate skew-normal longitudinal and survival data

Many joint models of multivariate skew-normal longitudinal and survival data have been presented to accommodate for the non-normality of longitudinal outcomes in recent years. But existing work did not consider variable selection. This article investigates simultaneous parameter estimation and variable selection in joint modeling of longitudinal and survival data. The penalized splines technique is used to estimate unknown log baseline hazard function, the rectangle integral method is adopted to approximate conditional survival function. Monte Carlo expectation-maximization algorithm is developed to estimate model parameters. Based on local linear approximations to conditional expectation of likelihood function and penalty function, a one-step sparse estimation procedure is proposed to circumvent the computationally challenge in optimizing the penalized conditional expectation of likelihood function, which is utilized to select significant covariates and trajectory functions, and identify the departure from normality of longitudinal data. The conditional expectation of likelihood function-based Bayesian information criterion is developed to select the optimal tuning parameter. Simulation studies and a real example from the clinical trial are used to illustrate the proposed methodologies.

Characterization of direct and/or indirect genetic associations for multiple traits in longitudinal studies of disease progression

When quantitative longitudinal traits are risk factors for disease progression and subject to random biological variation, joint model analysis of time-to-event and longitudinal traits can effectively identify direct and/or indirect genetic association of single nucleotide polymorphisms (SNPs) with time-to-event. We present a joint model that integrates: (1) a multivariate linear mixed model describing trajectories of multiple longitudinal traits as a function of time, SNP effects, and subject-specific random effects and (2) a frailty Cox survival model that depends on SNPs, longitudinal trajectory effects, and subject-specific frailty accounting for dependence among multiple time-to-event traits. Motivated by complex genetic architecture of type 1 diabetes complications (T1DC) observed in the Diabetes Control and Complications Trial (DCCT), we implement a 2-stage approach to inference with bootstrap joint covariance estimation and develop a hypothesis testing procedure to classify direct and/or indirect SNP association with each time-to-event trait. By realistic simulation study, we show that joint modeling of 2 time-to-T1DC (retinopathy and nephropathy) and 2 longitudinal risk factors (HbA1c and systolic blood pressure) reduces estimation bias in genetic effects and improves classification accuracy of direct and/or indirect SNP associations, compared to methods that ignore within-subject risk factor variability and dependence among longitudinal and time-to-event traits. Through DCCT data analysis, we demonstrate feasibility for candidate SNP modeling and quantify effects of sample size and Winner's curse bias on classification for 2 SNPs identified as having indirect associations with time-to-T1DC traits. Joint analysis of multiple longitudinal and multiple time-to-event traits provides insight into complex traits architecture.

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.

Joint modeling of longitudinal changes of blood pressure and time to remission of hypertensive patients receiving treatment: Bayesian approach

INTRODUCTION

Hypertension is a widespread condition when the blood's force on the artery walls is extremely high to develop adverse health effects. This paper aimed to jointly model the longitudinal change of blood pressures (systolic and diastolic) and time to the first remission of hypertensive outpatients receiving treatment.

METHODS

A retrospective study design was used to collect appropriate data on longitudinal changes in blood pressure and time-to-event from the medical charts of 301 hypertensive outpatients under follow-up at Felege Hiwot referral hospital, Ethiopia. The data exploration was done using summary statistics measures, individual profile plots, Kaplan-Meier plots, and log-rank tests. To get wide-ranging information about the progression, joint multivariate models were employed.

RESULTS

A total of 301 hypertensive patients who take treatment was taken from Felege Hiwot referral hospital recorded between Sep. 2018 to Feb. 2021. Of this 153 (50.8%) were male, and 124 (49.2%) were residents from rural areas. About 83(27.6%), 58 (19.3%), 82 (27.2%), and 25 (8.3%) have a history of diabetes mellitus, cardiovascular disease, stroke, and HIV respectively. The median time of hypertensive patients to have first remission time was 11 months. The hazard of the patient's first remission time for males was 0.63 times less likely than the hazard for females. The time to attain the first remission for patients who had a history of diabetes mellitus was 46% lower than for those who had no history of diabetes mellitus.

CONCLUSION

Blood pressure dynamics significantly affect the time to the first remission of hypertensive outpatients receiving treatment. The patients who had a good follow-up, lower BUN, lower serum calcium, lower serum sodium, lower hemoglobin, and take the treatment enalapril showed an opportunity in decreasing their blood pressure. This compels patients to experience the first remission early. Besides, age, patient's history of diabetes, patient's history of cardiovascular disease, and treatment type were the joint determinant factors for the longitudinal change of BP and the first remission time. The Bayesian joint model approach provides specific dynamic predictions, wide-ranging information about the disease transitions, and better knowledge of disease etiology.

Defining measures of kidney function in observational studies using routine health care data: methodological and reporting considerations

The availability of electronic health records and access to a large number of routine measurements of serum creatinine and urinary albumin enhance the possibilities for epidemiologic research in kidney disease. However, the frequency of health care use and laboratory testing is determined by health status and indication, imposing certain challenges when identifying patients with kidney injury or disease, when using markers of kidney function as covariates, or when evaluating kidney outcomes. Depending on the specific research question, this may influence the interpretation, generalizability, and/or validity of study results. This review illustrates the heterogeneity of working definitions of kidney disease in the scientific literature and discusses advantages and limitations of the most commonly used approaches using 3 examples. We summarize ways to identify and overcome possible biases and conclude by proposing a framework for reporting definitions of exposures and outcomes in studies of kidney disease using routinely collected health care data.

Sample size determination for the association between longitudinal and time-to-event outcomes using the joint modeling time-dependent slopes parameterization

Given their improvements in bias reduction and efficiency, joint models (JMs) for longitudinal and time-to-event data offer great potential to clinical trials. However, for JM to become more widely used, there is a need for additional development of design considerations. To this end, Chen et al previously developed two closed-form sample size formulas in the JM setting. In this current work, we expand upon this framework by utilizing the time-dependent slopes parameterization, where the change in the longitudinal outcome influences the hazard, in addition to the current value of the longitudinal process. Our extended formula for the required number of events can be used when testing significance of the association between the longitudinal and time-to-event outcomes. We find that if the data indeed are generated such that not only the current value, but also the slope of the longitudinal outcome influence the hazard of the time-to-event process, it is advisable to use the current formula developed utilizing the time-dependent slopes parameterization. In this setting, our proposed formula will provide a more accurate estimate of power compared to the method by Chen et al. To illustrate our proposed method, we present power calculations of a biomarker qualification study for Hutchinson-Gilford progeria syndrome, an ultra-rare premature aging disease.

Trends in inequalities in the prevalence of dementia in the United States

This paper presents estimates of the prevalence of dementia in the United States from 2000 to 2016 by age, sex, race and ethnicity, education, and a measure of lifetime earnings, using data on 21,442 individuals aged 65 y and older and 97,629 person-year observations from a nationally representative survey, the Health and Retirement Study (HRS). The survey includes a range of cognitive tests, and a subsample underwent clinical assessment for dementia. We developed a longitudinal, latent-variable model of cognitive status, which we estimated using the Markov Chain Monte Carlo method. This model provides more accurate estimates of dementia prevalence in population subgroups than do previously used methods on the HRS. The age-adjusted prevalence of dementia decreased from 12.2% in 2000 (95% CI, 11.7 to 12.7%) to 8.5% in 2016 (7.9 to 9.1%) in the 65+ population, a statistically significant decline of 3.7 percentage points or 30.1%. Females are more likely to live with dementia, but the sex difference has narrowed. In the male subsample, we found a reduction in inequalities across education, earnings, and racial and ethnic groups; among females, those inequalities also declined, but less strongly. We observed a substantial increase in the level of education between 2000 and 2016 in the sample. This compositional change can explain, in a statistical sense, about 40% of the reduction in dementia prevalence among men and 20% among women, whereas compositional changes in the older population by age, race and ethnicity, and cardiovascular risk factors mattered less.

Joint models for dynamic prediction in localised prostate cancer: a literature review

BACKGROUND

Prostate cancer is a very prevalent disease in men. Patients are monitored regularly during and after treatment with repeated assessment of prostate-specific antigen (PSA) levels. Prognosis of localised prostate cancer is generally good after treatment, and the risk of having a recurrence is usually estimated based on factors measured at diagnosis. Incorporating PSA measurements over time in a dynamic prediction joint model enables updates of patients' risk as new information becomes available. We review joint model strategies that have been applied to model time-dependent PSA trajectories to predict time-to-event outcomes in localised prostate cancer.

METHODS

We identify articles that developed joint models for prediction of localised prostate cancer recurrence over the last two decades. We report, compare, and summarise the methodological approaches and applications that use joint modelling accounting for two processes: the longitudinal model (PSA), and the time-to-event process (clinical failure). The methods explored differ in how they specify the association between these two processes.

RESULTS

Twelve relevant articles were identified. A range of methodological frameworks were found, and we describe in detail shared-parameter joint models (9 of 12, 75%) and joint latent class models (3 of 12, 25%). Within each framework, these articles presented model development, estimation of dynamic predictions and model validations.

CONCLUSIONS

Each framework has its unique principles with corresponding advantages and differing interpretations. Regardless of the framework used, dynamic prediction models enable real-time prediction of individual patient prognosis. They utilise all available longitudinal information, in addition to baseline prognostic risk factors, and are superior to traditional baseline-only prediction models.

Analysis of Recurrent Times-to-Clinical Malaria Episodes and Plasmodium falciparum Parasitemia: A Joint Modeling Approach Applied to a Cohort Data

Background

Recurrent clinical malaria episodes due to Plasmodium falciparum parasite infection are common in endemic regions. With each infection, acquired immunity develops, making subsequent disease episodes less likely. To capture the effect of acquired immunity to malaria, it may be necessary to model recurrent clinical disease episodes jointly with P. falciparum parasitemia data. A joint model of longitudinal parasitemia and time-to-first clinical malaria episode (single-event joint model) may be inaccurate because acquired immunity is lost when subsequent episodes are excluded. This study's informativeness assessed whether joint modeling of recurrent clinical malaria episodes and parasitemia is more accurate than a single-event joint model where the subsequent episodes are ignored.

Methods

The single event joint model comprised Cox Proportional Hazards (PH) sub-model for time-to-first clinical malaria episode and Negative Binomial (NB) mixed-effects sub-model for the longitudinal parasitemia. The recurrent events joint model extends the survival sub-model to a Gamma shared frailty model to include all recurrent clinical episodes. The models were applied to cohort data from Malawi. Simulations were also conducted to assess the performance of the model under different conditions.

Results

The recurrent events joint model, which yielded higher hazard ratios of clinical malaria, was more precise and in most cases produced smaller standard errors than the single-event joint model; hazard ratio (HR) = 1.42, [95% confidence interval [CI]: 1.22, 2.03] vs. HR = 1.29, [95% CI:1.60, 2.45] among participants who reported not to use LLINs every night compared to those who used the nets every night; HR = 0.96, [ 95% CI: 0.94, 0.98] vs. HR = 0.81, [95% CI: 0.75, 0.88] for each 1-year increase in participants' age; and HR = 1.36, [95% CI: 1.05, 1.75] vs. HR = 1.10, [95% CI: 0.83, 4.11] for observations during the rainy season compared to the dry season.

Conclusion

The recurrent events joint model in this study provides a way of estimating the risk of recurrent clinical malaria in a cohort where the effect of immunity on malaria disease acquired due to P. falciparum parasitemia with aging is captured. The simulation study has shown that if correctly specified, the recurrent events joint model can give risk estimates with low bias.

Modelling time-varying covariates effect on survival via functional data analysis: application to the MRC BO06 trial in osteosarcoma

Time-varying covariates are of great interest in clinical research since they represent dynamic patterns which reflect disease progression. In cancer studies biomarkers values change as functions of time and chemotherapy treatment is modified by delaying a course or reducing the dose intensity, according to patient’s toxicity levels. In this work, a Functional covariate Cox Model (FunCM) to study the association between time-varying processes and a time-to-event outcome is proposed. FunCM first exploits functional data analysis techniques to represent time-varying processes in terms of functional data. Then, information related to the evolution of the functions over time is incorporated into functional regression models for survival data through functional principal component analysis. FunCM is compared to a standard time-varying covariate Cox model, commonly used despite its limiting assumptions that covariate values are constant in time and measured without errors. Data from MRC BO06/EORTC 80931 randomised controlled trial for treatment of osteosarcoma are analysed. Time-varying covariates related to alkaline phosphatase levels, white blood cell counts and chemotherapy dose during treatment are investigated. The proposed method allows to detect differences between patients with different biomarkers and treatment evolutions, and to include this information in the survival model. These aspects are seldom addressed in the literature and could provide new insights into the clinical research.

A Joint Model for Unbalanced Nested Repeated Measures with Informative Drop-Out Applied to Ambulatory Blood Pressure Monitoring Data

This study proposes a Bayesian joint model with extended random effects structure that incorporates nested repeated measures and provides simultaneous inference on treatment effects over time and drop-out patterns. The proposed model includes flexible splines to characterize the circadian variation inherent in blood pressure sequences, and we assess the effectiveness of an intervention to resolve pediatric obstructive sleep apnea. We demonstrate that the proposed model and its conventional two-stage counterpart provide similar estimates of nighttime blood pressure but estimates on the mean evolution of daytime blood pressure are discrepant. Our simulation studies tailored to the motivating data suggest reasonable estimation and coverage probabilities for both fixed and random effects. Computational challenges of model implementation are discussed.

Treatment Trials in Young Patients with Chronic Obstructive Pulmonary Disease and Pre-Chronic Obstructive Pulmonary Disease Patients: Time to Move Forward

Chronic obstructive pulmonary disease (COPD) is the end result of a series of dynamic and cumulative gene-environment interactions over a lifetime. The evolving understanding of COPD biology provides novel opportunities for prevention, early diagnosis, and intervention. To advance these concepts, we propose therapeutic trials in two major groups of subjects: "young" individuals with COPD and those with pre-COPD. Given that lungs grow to about 20 years of age and begin to age at approximately 50 years, we consider "young" patients with COPD those patients in the age range of 20-50 years. Pre-COPD relates to individuals of any age who have respiratory symptoms with or without structural and/or functional abnormalities, in the absence of airflow limitation, and who may develop persistent airflow limitation over time. We exclude from the current discussion infants and adolescents because of their unique physiological context and COPD in older adults given their representation in prior randomized controlled trials (RCTs). We highlight the need of RCTs focused on COPD in young patients or pre-COPD to reduce disease progression, providing innovative approaches to identifying and engaging potential study subjects. We detail approaches to RCT design, including potential outcomes such as lung function, patient-reported outcomes, exacerbations, lung imaging, mortality, and composite endpoints. We critically review study design components such as statistical powering and analysis, duration of study treatment, and formats to trial structure, including platform, basket, and umbrella trials. We provide a call to action for treatment RCTs in 1) young adults with COPD and 2) those with pre-COPD at any age.

Joint analysis of longitudinal measurements and spatially clustered competing risks HIV/AIDS data

The joint modeling of repeated measurements and time-to-event provides a general framework to describe better the link between the progression of disease through longitudinal measurements and time-to-event outcome. In the survival data, a sample of individuals is frequently grouped into clusters. In some applications, these clusters could be arranged spatially, for example, based on geographical regions. There are two benefits of considering spatial variation in these data, enhancing the efficiency and accuracy of the parameters estimations, and investigating the survivorship spatial pattern. On the other hand, in survival data, there is a situation that subjects are supposed to experience more than one type of event potentially, but the occurrence of one type of event prevents the occurrence of the others. In this article, we considered a Bayesian joint model of longitudinal and competing risks outcomes for spatially clustered HIV/AIDS data. The data were from a registry-based study carried in Hamadan Province, Iran, from December 1997 to June 2020. In this joint model, a linear mixed effects model was used for the longitudinal submodel and a cause-specific hazard model with spatial and spatial-risk random effects was used for the survival submodel. Also, a latent structure was defined by random effects to link both event times and longitudinal processes. We used a univariate intrinsic conditional autoregressive (ICAR) distribution and a multivariate ICAR distribution for modeling the areal spatial and spatial-risk random effects, respectively. The performance of our proposed model using simulation studies and analysis of HIV/AIDS data were assessed.

A two-stage approach for joint modeling of longitudinal measurements and competing risks data

Joint modeling of longitudinal measurements and time-to-event data is used in many practical studies of medical sciences. Most of the time, particularly in clinical studies and health inquiry, there are more than one event and they compete for failing an individual. In this situation, assessing the competing risk failure time is important. In most cases, implementation of joint modeling involves complex calculations. Therefore, we propose a two-stage method for joint modeling of longitudinal measurements and competing risks (JMLC) data based on the full likelihood approach via the conditional EM (CEM) algorithm. In the first stage, a linear mixed effect model is used to estimate the parameters of the longitudinal sub-model. In the second stage, we consider a cause-specific sub-model to construct competing risks data and describe an approximation for the joint log-likelihood that uses the estimated parameters of the first stage. We express the results of a simulation study and perform this method on the "standard and new anti-epileptic drugs" trial to check the effect of drug assaying on the treatment effects of lamotrigine and carbamazepine through treatment failure.

Glucocorticoid exposure predicts survival in female baboons

Are differences in hypothalamic-pituitary-adrenal (HPA) axis activation across the adult life span linked to differences in survival? This question has been the subject of considerable debate. We analyze the link between survival and fecal glucocorticoid (GC) measures in a wild primate population, leveraging an unusually extensive longitudinal dataset of 14,173 GC measurements from 242 adult female baboons over 1634 female years. We document a powerful link between GCs and survival: Females with relatively high current GCs or high lifelong cumulative GCs face an elevated risk of death. A hypothetical female who maintained GCs in the top 90% for her age across adulthood would be expected to lose 5.4 years of life relative to a female who maintained GCs in the bottom 10% for her age. Hence, differences among individuals in HPA axis activity provide valuable prognostic information about disparities in life span.

Multivariate prediction of mixed, multilevel, sequential outcomes arising from in vitro fertilisation

In vitro fertilisation (IVF) comprises a sequence of interventions concerned with the creation and culture of embryos which are then transferred to the patient's uterus. While the clinically important endpoint is birth, the responses to each stage of treatment contain additional information about the reasons for success or failure. As such, the ability to predict not only the overall outcome of the cycle, but also the stage-specific responses, can be useful. This could be done by developing separate models for each response variable, but recent work has suggested that it may be advantageous to use a multivariate approach to model all outcomes simultaneously. Here, joint analysis of the sequential responses is complicated by mixed outcome types defined at two levels (patient and embryo). A further consideration is whether and how to incorporate information about the response at each stage in models for subsequent stages. We develop a case study using routinely collected data from a large reproductive medicine unit in order to investigate the feasibility and potential utility of multivariate prediction in IVF. We consider two possible scenarios. In the first, stage-specific responses are to be predicted prior to treatment commencement. In the second, responses are predicted dynamically, using the outcomes of previous stages as predictors. In both scenarios, we fail to observe benefits of joint modelling approaches compared to fitting separate regression models for each response variable.

Impact of Abstinence and of Reducing Illicit Drug Use Without Abstinence on Human Immunodeficiency Virus Viral Load

BACKGROUND

Substance use is common among people living with human immunodeficiency virus (PLWH) and a barrier to achieving viral suppression. Among PLWH who report illicit drug use, we evaluated associations between HIV viral load (VL) and reduced use of illicit opioids, methamphetamine/crystal, cocaine/crack, and marijuana, regardless of whether or not abstinence was achieved.

METHODS

This was a longitudinal cohort study of PLWH from 7 HIV clinics or 4 clinical studies. We used joint longitudinal and survival models to examine the impact of decreasing drug use and of abstinence for each drug on viral suppression. We repeated analyses using linear mixed models to examine associations between change in frequency of drug use and VL.

RESULTS

The number of PLWH who were using each drug at baseline ranged from n = 568 (illicit opioids) to n = 4272 (marijuana). Abstinence was associated with higher odds of viral suppression (odds ratio [OR], 1.4-2.2) and lower relative VL (ranging from 21% to 42% by drug) for all 4 drug categories. Reducing frequency of illicit opioid or methamphetamine/crystal use without abstinence was associated with VL suppression (OR, 2.2, 1.6, respectively). Reducing frequency of illicit opioid or methamphetamine/crystal use without abstinence was associated with lower relative VL (47%, 38%, respectively).

CONCLUSIONS

Abstinence was associated with viral suppression. In addition, reducing use of illicit opioids or methamphetamine/crystal, even without abstinence, was also associated with viral suppression. Our findings highlight the impact of reducing substance use, even when abstinence is not achieved, and the potential benefits of medications, behavioral interventions, and harm-reduction interventions.

Longitudinal Tumor Size and Neutrophil-to-Lymphocyte Ratio Are Prognostic Biomarkers for Overall Survival in Patients With Advanced Non-Small Cell Lung Cancer Treated With Durvalumab

Therapy optimization remains an important challenge in the treatment of advanced non-small cell lung cancer (NSCLC). We investigated tumor size (sum of the longest diameters (SLD) of target lesions) and neutrophil-to-lymphocyte ratio (NLR) as longitudinal biomarkers for survival prediction. Data sets from 335 patients with NSCLC from study NCT02087423 and 202 patients with NSCLC from study NCT01693562 of durvalumab were used for model qualification and validation, respectively. Nonlinear Bayesian joint models were designed to assess the impact of longitudinal measurements of SLD and NLR on patient subgrouping (by Response Evaluation Criteria in Solid Tumors 1.1 criteria at 3 months after therapy start), long-term survival, and precision of survival predictions. Various validation scenarios were investigated. We determined a more distinct patient subgrouping and a substantial increase in the precision of survival estimates after the incorporation of longitudinal measurements. The highest performance was achieved using a multivariate SLD and NLR model, which enabled predictions of NSCLC clinical outcomes.

A Two-Stage Approach for Bayesian Joint Models of Longitudinal and Survival Data: Correcting Bias with Informative Prior

Joint models of longitudinal and survival outcomes have gained much popularity in recent years, both in applications and in methodological development. This type of modelling is usually characterised by two submodels, one longitudinal (e.g., mixed-effects model) and one survival (e.g., Cox model), which are connected by some common term. Naturally, sharing information makes the inferential process highly time-consuming. In particular, the Bayesian framework requires even more time for Markov chains to reach stationarity. Hence, in order to reduce the modelling complexity while maintaining the accuracy of the estimates, we propose a two-stage strategy that first fits the longitudinal submodel and then plug the shared information into the survival submodel. Unlike a standard two-stage approach, we apply a correction by incorporating an individual and multiplicative fixed-effect with informative prior into the survival submodel. Based on simulation studies and sensitivity analyses, we empirically compare our proposal with joint specification and standard two-stage approaches. The results show that our methodology is very promising, since it reduces the estimation bias compared to the other two-stage method and requires less processing time than the joint specification approach.

C-Reactive Protein (CRP) Levels in Immune Checkpoint Inhibitor Response and Progression in Advanced Non-Small Cell Lung Cancer: A Bi-Center Study

Background: Biomarkers for predicting response to immune checkpoint inhibitors (ICI) are scarce and often lack external validation. This study provides a comprehensive investigation of pretreatment C-reactive protein (CRP) levels as well as its longitudinal trajectories as a marker of treatment response and disease outcome in patients with advanced non-small cell lung cancer (NSCLC) undergoing immunotherapy with anti PD-1 or anti PD-L1 agents. Methods: We performed a retrospective bi-center study to assess the association between baseline CRP levels and anti PD-(L)1 treatment outcomes in the discovery cohort (n = 90), confirm these findings in an external validation cohort (n = 101) and explore the longitudinal evolution of CRP during anti PD-(L)1 treatment and the potential impact of dynamic CRP changes on treatment response and disease outcome in the discovery cohort. Joint models were implemented to evaluate the association of longitudinal CRP trajectories and progression risk. Primary treatment outcomes were progression-free survival (PFS) and overall survival (OS), while the objective response rate (ORR) was a secondary outcome, respectively. Results: In the discovery cohort, elevated pretreatment CRP levels emerged as independent predictors of worse PFS (HR per doubling of baseline CRP = 1.37, 95% CI: 1.16–1.63, p < 0.0001), worse OS (HR per doubling of baseline CRP = 1.42, 95% CI: 1.18–1.71, p < 0.0001) and a lower ORR ((odds ratio (OR) of ORR per doubling of baseline CRP = 0.68, 95% CI: 0.51–0.92, p = 0.013)). In the validation cohort, pretreatment CRP could be fully confirmed as a predictor of PFS and OS, but not ORR. Elevated trajectories of CRP during anti PD-(L)1 treatment (adjusted HR per 10 mg/L increase in CRP = 1.22, 95% CI: 1.15–1.30, p < 0.0001), as well as a faster increases of CRP over time (HR per 10 mg/L/month faster increase in CRP levels = 13.26, 95% CI: 1.14–154.54, p = 0.039) were strong predictors of an elevated progression risk, whereas an early decline of CRP was significantly associated with a reduction in PFS risk (HR = 0.91, 95% CI: 0.83–0.99, p = 0.036), respectively. Conclusion: These findings support the concept that CRP should be further explored by future prospective studies as a simple non-invasive biomarker for assessing treatment benefit during anti PD-(L)1 treatment in advanced NSCLC.

Association of immigration background with kidney graft function in a publicly funded health system: a nationwide retrospective cohort study in Italy

The impact of immigration background on kidney graft function (eGFR) is unknown. Italy has a publicly funded health system with universal coverage. Since immigration from non-European Union (EU) countries beyond Eastern Europe is a recent and extensive phenomenon, Italy is a rather unique setting for studying the effect of immigration status as a socioeconomic and cultural condition. We retrospectively identified all adult deceased donor kidney transplant recipients (KTRs) in Italy (2010-2015) and followed them until death, dialysis or 5-years post-transplantation; 6346 were EU-born, 161 Eastern European-born, and 490 non-European-born. We examined changes in eGFR after 1-year post-transplant using multivariable-adjusted joint longitudinal survival random-intercept Cox regression. Compared to EU-born KTRs, in non-European-born KTRs the adjusted average yearly eGFR decline was -0.96 ml/min/year (95% confidence interval: -1.48 to -0.45; P < 0.001), whereas it was similar in Eastern European-born KTRs [+0.02 ml/min/year (-0.77 to +0.81; P = 0.96)]. Adjusted 5-year transplant survival did not statistically differ between non-European-born, Eastern European-born, and EU-born. In those surviving beyond 1-year, it was 91.8% in EU-born (87.1-96.8), 92.5% in Eastern European-born (86.1-99.4), and 89.3% in non-European-born KTRs (83.0-96.0). This study provides evidence that among EU KTRs, non-European immigration background is associated with eGFR decline.

Time-varying Hazards Model for Incorporating Irregularly Measured, High-Dimensional Biomarkers

Clinical studies with time-to-event outcomes often collect measurements of a large number of time-varying covariates over time (e.g., clinical assessments or neuroimaging biomarkers) to build time-sensitive prognostic model. An emerging challenge is that due to resource-intensive or invasive (e.g., lumbar puncture) data collection process, biomarkers may be measured infrequently and thus not available at every observed event time point. Lever-aging all available, infrequently measured time-varying biomarkers to improve prognostic model of event occurrence is an important and challenging problem. In this paper, we propose a kernel-smoothing based approach to borrow information across subjects to remedy infrequent and unbalanced biomarker measurements under a time-varying hazards model. A penalized pseudo-likelihood function is proposed for estimation, and an efficient augmented penalization minimization algorithm related to the alternating direction method of multipliers (ADMM) is adopted for computation. Under some regularity conditions to carefully control approximation bias and stochastic variability, we show that even in the presence of ultra-high dimensionality, the proposed method selects important biomarkers with high probability. Through extensive simulation studies, we demonstrate superior performance in terms of estimation and selection performance compared to alternative methods. Finally, we apply the proposed method to analyze a recently completed real world study to model time to disease conversion using longitudinal, whole brain structural magnetic resonance imaging (MRI) biomarkers, and show a substantial improvement in performance over current standards including using baseline measures only.

Family caregiving in the community up to 8-years after onset of dementia

BACKGROUND

Persons with Alzheimer's disease and related dementias (ADRD) receive care from family/friends, but how care changes from the onset of dementia remains less understood.

METHODS

We used the Health and Retirement Study (2002-2012) to identify community-dwelling individuals predicted to have incident ADRD. We investigated the amount of caregiving received for activities of daily living in the 8-years after disease onset.

RESULTS

At incidence (n = 1158), persons with ADRD received 151 h (SD = 231) of caregiving a month, 25 (SD = 26) caregiving days a month and had 1.3 (SD = 1.4) caregivers a month. By 8-years post incidence, 187 (16%) individuals transitioned to a nursing home and 662 (57%) died in the community. Community-dwelling persons with ADRD at 8-years post incidence (n = 30) received 283 h (SD = 257) of caregiving, 38 (SD = 24) caregiving days, and had 2.2 (SD = 1.3) caregivers.

CONCLUSIONS

Community-dwelling persons with ADRD receive a substantial amount of caregiving over the first 8-years after disease onset.

Dynamic assessment of venous thromboembolism risk in patients with cancer by longitudinal D-Dimer analysis: A prospective study

BACKGROUND

Venous thromboembolism (VTE) is a frequent complication of cancer. Elevated D-dimer is associated with an increased risk of cancer-associated VTE. Whether changes in D-dimer over time harbor additional prognostic information that may be exploited clinically for dynamic prediction of VTE is unclear.

OBJECTIVES

To explore the potential role of longitudinal D-dimer trajectories for personalized prediction of cancer-associated VTE.

PATIENTS/METHODS

A total of 167 patients with active malignancy were prospectively enrolled (gastrointestinal: n = 59 [35%], lung: n = 56 [34%], brain: n = 50 [30%], others: n = 2 [1%]; metastatic disease: n = 74 [44%]). D-dimer (median = 0.8 µg/mL [25th-75th percentile: 0.4-2.0]) was measured at baseline and during 602 monthly follow-up visits. Joint models of longitudinal and time-to-event data were implemented to quantify the association between D-dimer trajectories and prospective risk of VTE.

RESULTS

VTE occurred in 20 patients (250-day VTE risk = 12.1%, 95% confidence interval [CI], 7.8-18.5). D-dimer increased by 34%/month (0.47 µg/mL/month, 95% CI, 0.22-0.72, P < .0001) in patients who developed VTE, but remained constant in patients who did not develop VTE (change/month = -0.06 µg/mL, 95% CI, -0.15 to 0.02, P = .121). In joint modeling, a doubling of the D-dimer trajectory was associated with a 2.8-fold increase in the risk of VTE (hazard ratio = 2.78, 95% CI, 1.69-4.58, P < .0001). This finding was independent of established VTE risk factors. Highly personalized, dynamic predictions of VTE conditional on individual patients' D-dimer trajectories could be obtained.

CONCLUSIONS

D-dimer increases before the onset of cancer-associated VTE, but remains constant over time in patients without VTE. This study represents proof-of-concept that longitudinal trajectories of D-Dimer may advance the personalized assessment of VTE risk in the oncologic setting.

Genetics of physiological dysregulation: findings from the long life family study using joint models

Recently, Mahalanobis distance (D_M) was suggested as a statistical measure of physiological dysregulation in aging individuals. We constructed D_M variants using sets of biomarkers collected at the two visits of the Long Life Family Study (LLFS) and performed joint analyses of longitudinal observations of D_M and follow-up mortality in LLFS using joint models. We found that D_M is significantly associated with mortality (hazard ratio per standard deviation: 1.31 [1.16, 1.48] to 2.22 [1.84, 2.67]) after controlling for age and other covariates. GWAS of random intercepts and slopes of D_M estimated from joint models found a genome-wide significant SNP (rs12652543, p=7.2×10^-9) in the TRIO gene associated with the slope of D_M constructed from biomarkers declining in late life. Review of biological effects of genes corresponding to top SNPs from GWAS of D_M slopes revealed that these genes are broadly involved in cancer prognosis and axon guidance/synapse function. Although axon growth is mainly observed during early development, the axon guidance genes can function in adults and contribute to maintenance of neural circuits and synaptic plasticity. Our results indicate that decline in axons' ability to maintain complex regulatory networks may potentially play an important role in the increase in physiological dysregulation during aging.

An introduction to joint models-applications in nephrology

In nephrology, a great deal of information is measured repeatedly in patients over time, often alongside data on events of clinical interest. In this introductory article we discuss how these two types of data can be simultaneously analysed using the joint model (JM) framework, illustrated by clinical examples from nephrology. As classical survival analysis and linear mixed models form the two main components of the JM framework, we will also briefly revisit these techniques.

Nonlinear and time-dependent effects of sparsely measured continuous time-varying covariates in time-to-event analysis

Many flexible extensions of the Cox proportional hazards model incorporate time-dependent (TD) and/or nonlinear (NL) effects of time-invariant covariates. In contrast, little attention has been given to the assessment of such effects for continuous time-varying covariates (TVCs). We propose a flexible regression B-spline-based model for TD and NL effects of a TVC. To account for sparse TVC measurements, we added to this model the effect of time elapsed since last observation (TEL), which acts as an effect modifier. TD, NL, and TEL effects are estimated with the iterative alternative conditional estimation algorithm. Furthermore, a simulation extrapolation (SIMEX)-like procedure was adapted to correct the estimated effects for random measurement errors in the observed TVC values. In simulations, TD and NL estimates were unbiased if the TVC was measured with a high frequency. With sparse measurements, the strength of the effects was underestimated but the TEL estimate helped reduce the bias, whereas SIMEX helped further to correct for bias toward the null due to "white noise" measurement errors. We reassessed the effects of systolic blood pressure (SBP) and total cholesterol, measured at two-year intervals, on cardiovascular risks in women participating in the Framingham Heart Study. Accounting for TD effects of SBP, cholesterol and age, the NL effect of cholesterol, and the TEL effect of SBP improved substantially the model's fit to data. Flexible estimates yielded clinically important insights regarding the role of these risk factors. These results illustrate the advantages of flexible modeling of TVC effects.

A Birnbaum-Saunders Model for Joint Survival and Longitudinal Analysis of Congestive Heart Failure Data

We consider a parametric joint modelling of longitudinal measurements and survival times, motivated by a study conducted at the Heart Institute (Incor), São Paulo, Brazil, with the objective of evaluating the impact of B-type Natriuretic Peptide (BNP) collected at different instants on the survival of patients with Congestive Heart Failure (CHF). We employ a linear mixed model for the longitudinal response and a Birnbaum-Saunders model for the survival times, allowing the inclusion of subjects without longitudinal observations. We derive maximum likelihood estimators of the joint model parameters and conduct a simulation study to compare the true survival probabilities with dynamic predictions obtained from the fit of the proposed joint model and to evaluate the performance of the method for estimating the model parameters.The proposed joint model is applied to the cohort of 1609 patients with CHF, of which 1080 have no BNP measurements. The parameter estimates and their standard errors obtained via: i) the traditional approach, where only individuals with at least one measurement of the longitudinal response are included and ii) the proposed approach, which includes survival information from all individuals, are compared with those obtained via marginal (longitudinal and survival) models.

Association of Lipid Profile with Type 2 Diabetes in First-Degree Relatives: A 14-Year Follow-Up Study in Iran

PURPOSE

Dyslipidemia is claimed to be associated with an increased risk of type 2 diabetes mellitus (T2DM). However, first-degree relatives (FDRs) of patients with T2DM are reported to be at higher risk. The aim of this study was to evaluate the association between serum lipid profile and T2DM incidence in FDRs.

PATIENTS AND METHODS

Information on 1222 T2DM FDRs during 14 years of follow-up was retrieved. All individuals were examined for diabetes status and dyslipidemia once a year. We used a Bayesian joint longitudinal-survival model to assess the association.

RESULTS

Our data showed that a 10 mg/dL increase in triglycerides (TG), very-low-density lipoprotein (VLDL), and non-high-density lipoprotein (non-HDL) cholesterol levels during the follow-up period was associated with an increased risk of diabetes by 5%, 29%, and 6.6%, respectively. Moreover, for every one-unit increase in the TG to HDL ratio, the T2DM incidence increased by 35%. Subgroup analysis also showed that the increased risk of diabetes was significant only in female FDRs, so that a 10 mg/dL increase in TG and VLDL cholesterol level and a one-unit increase in TG to HDL ratio in female FDRs resulted in an increased risk of diabetes by 7.8%, 46%, and 64%, respectively. However, analysis of HDL, low-density lipoprotein (LDL), total cholesterol (TC), TC to HDL, and LDL to HDL cholesterol levels/ratios did not find any statistically significant associations.

CONCLUSION

Increases in TG, VLDL, non-HDL cholesterol level, and TG to HDL ratio are associated with an increased risk of T2DM in FDRs, especially in female FDRs.

Investigating the association between anxiety symptoms and mood disorder in high-risk offspring of bipolar parents: a comparison of Joint and Cox models

BACKGROUND

Anxiety is associated with mood disorders including bipolar disorder. Two statistical modelling frameworks were compared to investigate the longitudinal relationship between repeatedly measured anxiety symptoms and the onset of depression and bipolar disorder in youth at confirmed familial risk.

METHODS

Prospectively collected data on 156 offspring of a parent with confirmed bipolar disorder participating in the Canadian Flourish high-risk offspring longitudinal cohort study were used for this analysis. As part of the research protocol at approximately yearly visits, a research psychiatrist completed the HAM-A and a semi-structured diagnostic research interview following KSADS-PL format. Diagnoses using DSM-IV criteria were made on blind consensus review of all available clinical information. We investigated two statistical approaches, Cox model and Joint model, to evaluate the relationship between repeated HAM-A scores and the onset of major depressive or bipolar disorder. The Joint model estimates the trajectory of the longitudinal variable using a longitudinal sub-model and incorporates this estimated trajectory into a Cox sub-model.

RESULTS

There was evidence of an increased hazard of major mood disorder for high-risk individuals with higher HAM-A scores under both modelling frameworks. After adjusting for other covariates, a one-unit increase in log-transformed HAM-A score was associated with a hazard ratio of 1.74 (95% CI (1.12, 2.72)) in the Cox model compared to 2.91(95% CI (1.29, 6.52)) in the Joint model. In an exploratory analysis there was no evidence that family clustering substantially affected the conclusions.

CONCLUSIONS

Estimated effects from the conventional Cox model, which is often the model of choice, were dramatically lower in this dataset, compared to the Joint model. While the Cox model is often considered the approach of choice for analysis, research has shown that the Joint model may be more efficient and less biased. Our analysis based on a Joint model suggests that the magnitude of association between anxiety and mood disorder in individuals at familial risk of developing bipolar disorder may be stronger than previously reported.

Brief Report: Reduced Use of Illicit Substances, Even Without Abstinence, Is Associated With Improved Depressive Symptoms Among People Living With HIV

PURPOSE

Substance use is linked with poor outcomes among people living with HIV (PLWH) and is associated with mental health disorders. This analysis examines the impact of decreasing substance use, even without abstinence, on depressive symptoms among PLWH.

METHODS

Data are from PLWH enrolled in the Centers for AIDS Research Network of Integrated Clinical Sites cohort. Participants completed longitudinal assessments of substance use (modified ASSIST) and depressive symptoms (PHQ-9). Changes in substance use frequency were categorized as abstinence, reduced use, and nondecreasing use. Adjusted linear mixed models with time-updated change in substance use frequency and depressive symptom scores were used to examine associations between changes in the use of individual substances and depressive symptoms. Analyses were repeated using joint longitudinal survival models to examine associations with a high (PHQ-9 ≥10) score.

RESULTS

Among 9905 PLWH, 728 used cocaine/crack, 1016 used amphetamine-type substances (ATS), 290 used illicit opiates, and 3277 used marijuana at baseline. Changes in ATS use were associated with the greatest improvements in depressive symptoms: stopping ATS led to a mean decrease of PHQ-9 by 2.2 points (95% CI: 1.8 to 2.7) and a 61% lower odds of PHQ-9 score ≥10 (95% CI: 0.30 to 0.52), and decreasing ATS use led to a mean decrease of 1.7 points (95% CI: 1.2 to 2.3) and a 62% lower odds of PHQ-9 score ≥10 (95% CI: 0.25 to 0.56). Stopping and reducing marijuana and stopping cocaine/crack use were also associated with improvement in depressive symptoms.

CONCLUSIONS

We demonstrated that both substance use reduction and abstinence are associated with improvements in depressive symptoms over time.

Survival analysis with time-dependent covariates subject to missing data or measurement error: Multiple Imputation for Joint Modeling (MIJM)

Modern epidemiological studies collect data on time-varying individual-specific characteristics, such as body mass index and blood pressure. Incorporation of such time-dependent covariates in time-to-event models is of great interest, but raises some challenges. Of specific concern are measurement error, and the non-synchronous updating of covariates across individuals, due for example to missing data. It is well known that in the presence of either of these issues the last observation carried forward (LOCF) approach traditionally used leads to bias. Joint models of longitudinal and time-to-event outcomes, developed recently, address these complexities by specifying a model for the joint distribution of all processes and are commonly fitted by maximum likelihood or Bayesian approaches. However, the adequate specification of the full joint distribution can be a challenging modeling task, especially with multiple longitudinal markers. In fact, most available software packages are unable to handle more than one marker and offer a restricted choice of survival models. We propose a two-stage approach, Multiple Imputation for Joint Modeling (MIJM), to incorporate multiple time-dependent continuous covariates in the semi-parametric Cox and additive hazard models. Assuming a primary focus on the time-to-event model, the MIJM approach handles the joint distribution of the markers using multiple imputation by chained equations, a computationally convenient procedure that is widely available in mainstream statistical software. We developed an R package "survtd" that allows MIJM and other approaches in this manuscript to be applied easily, with just one call to its main function. A simulation study showed that MIJM performs well across a wide range of scenarios in terms of bias and coverage probability, particularly compared with LOCF, simpler two-stage approaches, and a Bayesian joint model. The Framingham Heart Study is used to illustrate the approach.

Review and Comparison of Computational Approaches for Joint Longitudinal and Time-to-Event Models

Joint models for longitudinal and time-to-event data are useful in situations where an association exists between a longitudinal marker and an event time. These models are typically complicated due to the presence of shared random effects and multiple submodels. As a consequence, software implementation is warranted that is not prohibitively time consuming. While methodological research in this area continues, several statistical software procedures exist to assist in the fitting of some joint models. We review the available implementation for frequentist and Bayesian models in the statistical programming languages R, SAS, and Stata. A description of each procedure is given including estimation techniques, input and data requirements, available options for customization, and some available extensions, such as competing risks models. The software implementations are compared and contrasted through extensive simulation, highlighting their strengths and weaknesses. Data from an ongoing trial on adrenal cancer patients is used to study different nuances of software fitting on a practical example.

Comparison of a time-varying covariate model and a joint model of time-to-event outcomes in the presence of measurement error and interval censoring: application to kidney transplantation

Background

Tacrolimus (TAC) is an immunosuppressant drug given to kidney transplant recipients post-transplant to prevent antibody formation and kidney rejection. The optimal therapeutic dose for TAC is poorly defined and therapy requires frequent monitoring of drug trough levels. Analyzing the association between TAC levels over time and the development of potentially harmful de novo donor specific antibodies (dnDSA) is complex because TAC levels are subject to measurement error and dnDSA is assessed at discrete times, so it is an interval censored time-to-event outcome.

Methods

Using data from the University of Colorado Transplant Center, we investigated the association between TAC and dnDSA using a shared random effects (intercept and slope) model with longitudinal and interval censored survival sub-models (JM) and compared it with the more traditional interval censored survival model with a time-varying covariate (TVC). We carried out simulations to compare bias, level and power for the association parameter in the TVC and JM under varying conditions of measurement error and interval censoring. In addition, using Markov Chain Monte Carlo (MCMC) methods allowed us to calculate clinically relevant quantities along with credible intervals (CrI).

Results

The shared random effects model was a better fit and showed both the average TAC and the slope of TAC were associated with risk of dnDSA. The simulation studies demonstrated that, in the presence of heavy interval censoring and high measurement error, the TVC survival model underestimates the association between the survival and longitudinal measurement and has inflated type I error and considerably less power to detect associations.

Conclusions

To avoid underestimating associations, shared random effects models should be used in analyses of data with interval censoring and measurement error.

Electronic supplementary material

The online version of this article (10.1186/s12874-019-0773-1) contains supplementary material, which is available to authorized users.

Integrating Quality of Life and Survival Outcomes in Cardiovascular Clinical Trials

Background Survival and health status (eg, symptoms and quality of life) are key outcomes in clinical trials of heart failure treatment. However, health status can only be recorded on survivors, potentially biasing treatment effect estimates when there is differential survival across treatment groups. Joint modeling of survival and health status can address this bias. Methods and Results We analyzed patient-level data from the PARTNER 1B trial (Placement of Aortic Transcatheter Valves) of transcatheter aortic valve replacement versus standard care. Health status was quantified with the Kansas City Cardiomyopathy Questionnaire (KCCQ) at randomization, 1, 6, and 12 months. We compared hazard ratios for survival and mean differences in KCCQ scores at 12 months using several models: the original growth curve model for KCCQ scores (ignoring death), separate Bayesian models for survival and KCCQ scores, and a Bayesian joint longitudinal-survival model fit to either 12 or 30 months of survival follow-up. The benefit of transcatheter aortic valve replacement on 12-month KCCQ scores was greatest in the joint-model fit to all survival data (mean difference, 33.7 points; 95% credible intervals [CrI], 24.2-42.4), followed by the joint-model fit to 12 months of survival follow-up (32.3 points; 95% CrI, 22.5-41.5), a Bayesian model without integrating death (30.4 points; 95% CrI, 21.4-39.3), and the original growth curve model (26.0 points; 95% CI, 18.7-33.3). At 12 months, the survival benefit of transcatheter aortic valve replacement was also greater in the joint model (hazard ratio, 0.50; 95% CrI, 0.32-0.73) than in the nonjoint Bayesian model (0.54; 95% CrI, 0.37-0.75) or the original Kaplan-Meier estimate (0.55; 95% CI, 0.40-0.74). Conclusions In patients with severe symptomatic aortic stenosis and prohibitive surgical risk, the estimated benefits of transcatheter aortic valve replacement on survival and health status compared with standard care were greater in joint Bayesian models than other approaches.

A joint model for mixed and truncated longitudinal data and survival data, with application to HIV vaccine studies

In HIV vaccine studies, a major research objective is to identify immune response biomarkers measured longitudinally that may be associated with risk of HIV infection. This objective can be assessed via joint modeling of longitudinal and survival data. Joint models for HIV vaccine data are complicated by the following issues: (i) left truncations of some longitudinal data due to lower limits of quantification; (ii) mixed types of longitudinal variables; (iii) measurement errors and missing values in longitudinal measurements; (iv) computational challenges associated with likelihood inference. In this article, we propose a joint model of complex longitudinal and survival data and a computationally efficient method for approximate likelihood inference to address the foregoing issues simultaneously. In particular, our model does not make unverifiable distributional assumptions for truncated values, which is different from methods commonly used in the literature. The parameters are estimated based on the h-likelihood method, which is computationally efficient and offers approximate likelihood inference. Moreover, we propose a new approach to estimate the standard errors of the h-likelihood based parameter estimates by using an adaptive Gauss-Hermite method. Simulation studies show that our methods perform well and are computationally efficient. A comprehensive data analysis is also presented.

Assessment of breath volatile organic compounds in acute cardiorespiratory breathlessness: a protocol describing a prospective real-world observational study

INTRODUCTION

Patients presenting with acute undifferentiated breathlessness are commonly encountered in admissions units across the UK. Existing blood biomarkers have clinical utility in distinguishing patients with single organ pathologies but have poor discriminatory power in multifactorial presentations. Evaluation of volatile organic compounds (VOCs) in exhaled breath offers the potential to develop biomarkers of disease states that underpin acute cardiorespiratory breathlessness, owing to their proximity to the cardiorespiratory system. To date, there has been no systematic evaluation of VOC in acute cardiorespiratory breathlessness. The proposed study will seek to use both offline and online VOC technologies to evaluate the predictive value of VOC in identifying common conditions that present with acute cardiorespiratory breathlessness.

METHODS AND ANALYSIS

A prospective real-world observational study carried out across three acute admissions units within Leicestershire. Participants with self-reported acute breathlessness, with a confirmed primary diagnosis of either acute heart failure, community-acquired pneumonia and acute exacerbation of asthma or chronic obstructive pulmonary disease will be recruited within 24 hours of admission. Additionally, school-age children admitted with severe asthma will be evaluated. All participants will undergo breath sampling on admission and on recovery following discharge. A range of online technologies including: proton transfer reaction mass spectrometry, gas chromatography ion mobility spectrometry, atmospheric pressure chemical ionisation-mass spectrometry and offline technologies including gas chromatography mass spectroscopy and comprehensive two-dimensional gas chromatography-mass spectrometry will be used for VOC discovery and replication. For offline technologies, a standardised CE-marked breath sampling device (ReCIVA) will be used. All recruited participants will be characterised using existing blood biomarkers including C reactive protein, brain-derived natriuretic peptide, troponin-I and blood eosinophil levels and further evaluated using a range of standardised questionnaires, lung function testing, sputum cell counts and other diagnostic tests pertinent to acute disease.

ETHICS AND DISSEMINATION

The National Research Ethics Service Committee East Midlands has approved the study protocol (REC number: 16/LO/1747). Integrated Research Approval System (IRAS) 198921. Findings will be presented at academic conferences and published in peer-reviewed scientific journals. Dissemination will be facilitated via a partnership with the East Midlands Academic Health Sciences Network and via interaction with all UK-funded Medical Research Council and Engineering and Physical Sciences Research Council molecular pathology nodes.

TRIAL REGISTRATION NUMBER

NCT03672994.