Joint Survival Model Research Articles

Valuing Equity-Linked Death Benefits on Multiple Life with Time until Death following a K n Distribution

The purpose of this paper is to investigate the valuation of equity-linked death benefit contracts and the multiple life insurance on two heads based on a joint survival model. Using the exponential Wiener process assumption for the stock price process and a K n distribution for the time until death, we provide explicit formulas for the expectation of the discounted payment of the guaranteed minimum death benefit products, and we derive closed expressions for some options and numerical illustrations. We investigate multiple life insurance based on a joint survival using the bivariate Sarmanov distribution with K n (i.e., the Laplace transform of their density function is a ratio of two polynomials of degree at most) marginal distributions. We present analytical results of the joint-life status.

Optimizing care coordination to address social determinants of health needs for dual-use veterans

BackgroundVeterans increasingly utilize both the Veteran’s Health Administration (VA) and non-VA hospitals (dual-users). Dual-users are at increased risk of fragmented care and adverse outcomes and often do not receive necessary follow-up care addressing social determinants of health (SDOH). We developed a Veteran-informed social worker-led Advanced Care Coordination (ACC) program to decrease fragmented care and provide longitudinal care coordination addressing SDOH for dual-users accessing non-VA emergency departments (EDs) in two communities.Methods ACC had four core components: 1. Notification from non-VA ED providers of Veterans’ ED visit; 2. ACC social worker completed a comprehensive assessment with the Veteran to identify SDOH needs; 3. Clinical intervention addressing SDOH up to 90 days post-ED discharge; and 4. Warm hand-off to Veteran’s VA primary care team. Data was documented in our program database. We performed propensity matching between a control group and ACC participants between 4/10/2018 – 4/1/2020 (N- = 161). A joint survival model using Markov Chain Monte Carlo technique was employed for 30-day outcomes. We performed Difference-In-Difference analyses on number of ED visits, admissions, and primary care physician (PCP) visits 120-day pre/post discharge.ResultsWhen compared to a matched control group ACC had significantly lower risk of 30-day ED visits (Hazard Ratio (HR) = 0.61, 95% Confidence Interval (CI) = (0.42, 0.92)) and a higher probability of PCP visits at 13–30 days post-ED visit (HR = 1.5, 95% CI = (1.01, 2.22)). Veterans enrolled in ACC were connected to VA PCP visits (50%), VA benefits (19%), home health care (10%), mental health and substance use treatment (7%), transportation (7%), financial assistance (5%), and homeless resources (2%).ConclusionWe developed and implemented a program addressing dual-users’ SDOH needs post non-VA ED discharge.Social workers connected dual-users to needed follow-up care and resources which reduced fragmentation and adverse outcomes.

Associations of longitudinal D-Dimer and Factor II on early trauma survival risk

BackgroundTrauma-induced coagulopathy (TIC) is a disorder that occurs in one-third of severely injured trauma patients, manifesting as increased bleeding and a 4X risk of mortality. Understanding the mechanisms driving TIC, clinical risk factors are essential to mitigating this coagulopathic bleeding and is therefore essential for saving lives. In this retrospective, single hospital study of 891 trauma patients, we investigate and quantify how two prominently described phenotypes of TIC, consumptive coagulopathy and hyperfibrinolysis, affect survival odds in the first 25 h, when deaths from TIC are most prevalent.MethodsWe employ a joint survival model to estimate the longitudinal trajectories of the protein Factor II (% activity) and the log of the protein fragment D-Dimer (upmug/ml), representative biomarkers of consumptive coagulopathy and hyperfibrinolysis respectively, and tie them together with patient outcomes. Joint models have recently gained popularity in medical studies due to the necessity to simultaneously track continuously measured biomarkers as a disease evolves, as well as to associate them with patient outcomes. In this work, we estimate and analyze our joint model using Bayesian methods to obtain uncertainties and distributions over associations and trajectories.ResultsWe find that a unit increase in log D-Dimer increases the risk of mortality by 2.22 [1.57, 3.28] fold while a unit increase in Factor II only marginally decreases the risk of mortality by 0.94 [0.91,0.96] fold. This suggests that, while managing consumptive coagulopathy and hyperfibrinolysis both seem to affect survival odds, the effect of hyperfibrinolysis is much greater and more sensitive. Furthermore, we find that the longitudinal trajectories, controlling for many fixed covariates, trend differently for different patients. Thus, a more personalized approach is necessary when considering treatment and risk prediction under these phenotypes.ConclusionThis study reinforces the finding that hyperfibrinolysis is linked with poor patient outcomes regardless of factor consumption levels. Furthermore, it quantifies the degree to which measured D-Dimer levels correlate with increased risk. The single hospital, retrospective nature can be understood to specify the results to this particular hospital’s patients and protocol in treating trauma patients. Expanding to a multi-hospital setting would result in better estimates about the underlying nature of consumptive coagulopathy and hyperfibrinolysis with survival, regardless of protocol. Individual trajectories obtained with these estimates can be used to provide personalized dynamic risk prediction when making decisions regarding management of blood factors.

Joint Survival Model of CD4 Outcome for HIV/TB Coinfected: Data from Kenya AIDS Indicator Survey

HIV infection leads to immune deficiency, increasing the risk of TB in people with HIV. HIV/TB co-infection increases the risk of death from TB or other opportunist infections. CD4 cell counts (cells/mm3) along with viral load are measures of treatment failure. This study purposed to apply shared frailty model in analyzing the survival and hazard rates of the TB/HIV co-infected persons. This work is very important because co-morbidity with TB and HIV is a rambling cause of death in Africa. The research employed a bivariate Gamma Frailty model to get the correlation amongst the HIV/TB outcomes to necessitate valid and reliable statistical inferencing. A survival frailty model on the CD4 counts is developed and fitted to factor in the unobserved heterogeneity that might occur in some observations. Ignoring some unobserved or unmeasured effects gives misguided estimates of survival. Thus, correcting these overdispersion or under-dispersion helps adjust these frailties. Frailty model provided a solid statistical analysis to CD4 data accounting for TB/HIV co-infection. The study also carried out some simulations along with the standard errors to compare the true values of the parameters. From the simulation findings, it is evident that precision and coverage improves with increase in sample size. Data used in this paper is from Kenya AIDS Indicator Survey (2012) which comprised of 648 HIV-positive patients, 10978 HIV-negative, and 2094 whose status was unknown. From the results, it is evident that the survival rate for the HIV positive individuals who are TB negative, with CD4 ≤ 310 is higher, at 0.9963 than that of the TB positive persons, at 0.975. The research finding points TB/HIV co-infection as a key factor for predicting immunological failure as measured by CD4 counts. The Kenyan government, and in particular the ministry of health should develop policies that mandate TB diagnosis among the PLHIV and linkage to TB treatment for the positive cases.

Estimation of delay time in survival data with delayed treatment effect

ABSTRACT In randomized controlled trials with delayed treatment effect, there is a delay period before the experimental therapy starts to exhibit a beneficial effect. The phenomenon of delayed treatment effect is often observed in the emerging and important field of immuno-oncology. It is important to estimate the duration of delay as this information helps in characterizing the pattern of comparative treatment effect, understanding the mechanism of action of the experimental therapy, and forming optimal treatment strategies. For a fixed delay time, we propose a maximum likelihood estimator and evaluate its asymptotic properties via simulation. We further evaluate two functions that link the pre- and postdelay hazard ratios to the average hazard ratio given a fixed delay time. For the case of random delay time, where the delay time may vary from patient to patient, we propose a semiparametric joint survival model for delay time and event time to estimate the mean delay time and the postdelay hazard ratio, assuming a Beta distribution for the delay time. We describe an extension of the model to estimate subgroup-specific mean delay times. Simulation study and application to data from a clinical trial in colon cancer patients demonstrate the robustness of the proposed model.

Variation of renal function over time is associated with major bleeding in patients treated with direct oral anticoagulants for atrial fibrillation

Background Chronic kidney disease is a risk factor for major bleeding in patients with atrial fibrillation (AF) treated with warfarin. Objective To assess the effect of variations in renal function over time on the risk of major bleeding during treatment with direct oral anticoagulants (DOACs) in patients with non-valvular AF. Methods Consecutive AF patients were prospectively followed after they had received the first DOAC prescription. Estimated glomerular filtration rate (eGFR) was periodically assessed, and the incidence of major bleeding was recorded. A joint survival model was used to estimate the association between variation in eGFR and the risk of major bleeding. Results During a mean follow-up of 575days, 44 major bleeds occurred in 449 patients (6.1% per patient-year). eGFR over time was inversely and independently associated with the risk of major bleeding; every 1mLmin-1 absolute decrease in eGFR was associated with a 2% increase in the risk of major bleeding (hazard ratio [HR]1.02, 95% confidence interval [CI]1.01-1.04). A similar effect of the variation in eGFR over time was observed on the risk of clinically relevant non-major bleeding (HR1.02, 95%CI1.01-1.03). Deterioration of renal function leading to a change in eGFR staging was associated with an increase in the risk of major bleeding (HR2.43, 95%CI1.33-4.45). Conclusions Variation in renal function over time is associated with the risk of major bleeding in AF patients treated with DOACs in real life. Identification of intervening clinical conditions associated with variation in renal function is essential to reduce the risk of major bleeding and to make DOAC treatment more safe.

A joint frailty-copula model between tumour progression and death for meta-analysis.

Dependent censoring often arises in biomedical studies when time to tumour progression (e.g., relapse of cancer) is censored by an informative terminal event (e.g., death). For meta-analysis combining existing studies, a joint survival model between tumour progression and death has been considered under semicompeting risks, which induces dependence through the study-specific frailty. Our paper here utilizes copulas to generalize the joint frailty model by introducing additional source of dependence arising from intra-subject association between tumour progression and death. The practical value of the new model is particularly evident for meta-analyses in which only a few covariates are consistently measured across studies and hence there exist residual dependence. The covariate effects are formulated through the Cox proportional hazards model, and the baseline hazards are nonparametrically modeled on a basis of splines. The estimator is then obtained by maximizing a penalized log-likelihood function. We also show that the present methodologies are easily modified for the competing risks or recurrent event data, and are generalized to accommodate left-truncation. Simulations are performed to examine the performance of the proposed estimator. The method is applied to a meta-analysis for assessing a recently suggested biomarker CXCL12 for survival in ovarian cancer patients. We implement our proposed methods in R joint.Cox package.

Lipid peroxidation, antioxidant status and survival in institutionalised elderly: A five-year longitudinal study

Oxidative stress has been related to ageing and risk of death. To determine whether oxidative status was associated with all-cause risk of death we carried out a prospective study in 154 non-smoking Spanish elderly without major illness. Baseline glutathione peroxidase (GPx) and superoxide dismutase (SOD) were analysed in plasma and erythrocytes. α-tocopherol, β-carotene, lycopene and retinol were determined in serum samples and malondialdehyde (MDA), as a lipid peroxidation marker, in plasma. Mean survival time was 4.3 years. A total of 31 death cases (20.1%) occurred during the follow-up. Plasma-MDA predicted mortality independently of all other variables, while erythrocyte-SOD (e-SOD), β-carotene and α-tocopherol were positively associated with survival. α-tocopherol and MDA were revealed as independent predictors in a joint survival model, being the group with low MDA and high α-tocopherol that with the lowest mortality. In conclusion, a higher risk of death was associated with increased lipid peroxidation and lower antioxidant defenses.