A Bayesian analysis of the Conway-Maxwell-Poisson cure rate model

The purpose of this paper is to develop a Bayesian analysis for the right-censored survival data when immune or cured individuals may be present in the population from which the data is taken. In our approach the number of competing causes of the event of interest follows the Conway-Maxwell-Poisson distribution which generalizes the Poisson distribution. Markov chain Monte Carlo (MCMC) methods are used to develop a Bayesian procedure for the proposed model. Also, some discussions on the model selection and an illustration with a real data set are considered.

The polysurvival model with long-term survivors

Long-term survival models have historically been considered for analyzing time-to-event data with long-term survivors fraction. However, situations in which a fraction (1 - p) of systems is subject to failure from independent competing causes of failure, while the remaining proportion p is cured or has not presented the event of interest during the time period of the study, have not been fully considered in the literature. In order to accommodate such situations, we present in this paper a new long-term survival model. Maximum likelihood estimation procedure is discussed as well as interval estimation and hypothesis tests. A real dataset illustrates the methodology.

A Bayesian destructive weighted Poisson cure rate model and an application to a cutaneous melanoma data

In this article, we propose a new Bayesian flexible cure rate survival model, which generalises the stochastic model of Klebanov et al. [Klebanov LB, Rachev ST and Yakovlev AY. A stochastic-model of radiation carcinogenesis - latent time distributions and their properties. Math Biosci 1993; 113: 51-75], and has much in common with the destructive model formulated by Rodrigues et al. [Rodrigues J, de Castro M, Balakrishnan N and Cancho VG. Destructive weighted Poisson cure rate models. Technical Report, Universidade Federal de Sao Carlos, Sao Carlos-SP. Brazil, 2009 (accepted in Lifetime Data Analysis)]. In our approach, the accumulated number of lesions or altered cells follows a compound weighted Poisson distribution. This model is more flexible than the promotion time cure model in terms of dispersion. Moreover, it possesses an interesting and realistic interpretation of the biological mechanism of the occurrence of the event of interest as it includes a destructive process of tumour cells after an initial treatment or the capacity of an individual exposed to irradiation to repair altered cells that results in cancer induction. In other words, what is recorded is only the damaged portion of the original number of altered cells not eliminated by the treatment or repaired by the repair system of an individual. Markov Chain Monte Carlo (MCMC) methods are then used to develop Bayesian inference for the proposed model. Also, some discussions on the model selection and an illustration with a cutaneous melanoma data set analysed by Rodrigues et al. [Rodrigues J, de Castro M, Balakrishnan N and Cancho VG. Destructive weighted Poisson cure rate models. Technical Report, Universidade Federal de Sao Carlos, Sao Carlos-SP. Brazil, 2009 (accepted in Lifetime Data Analysis)] are presented.

THE LOG-BURR XII REGRESSION MODEL FOR GROUPED SURVIVAL DATA

The log-Burr XII regression model for grouped survival data is evaluated in the presence of many ties. The methodology for grouped survival data is based on life tables, where the times are grouped in k intervals, and we fit discrete lifetime regression models to the data. The model parameters are estimated by maximum likelihood and jackknife methods. To detect influential observations in the proposed model, diagnostic measures based on case deletion, so-called global influence, and influence measures based on small perturbations in the data or in the model, referred to as local influence, are used. In addition to these measures, the total local influence and influential estimates are also used. We conduct Monte Carlo simulation studies to assess the finite sample behavior of the maximum likelihood estimators of the proposed model for grouped survival. A real data set is analyzed using a regression model for grouped data.

A multiple time scale survival model with a cure fraction

Many recent survival studies propose modeling data with a cure fraction, i.e., data in which part of the population is not susceptible to the event of interest. This event may occur more than once for the same individual (recurrent event). We then have a scenario of recurrent event data in the presence of a cure fraction, which may appear in various areas such as oncology, finance, industries, among others. This paper proposes a multiple time scale survival model to analyze recurrent events using a cure fraction. The objective is analyzing the efficiency of certain interventions so that the studied event will not happen again in terms of covariates and censoring. All estimates were obtained using a sampling-based approach, which allows information to be input beforehand with lower computational effort. Simulations were done based on a clinical scenario in order to observe some frequentist properties of the estimation procedure in the presence of small and moderate sample sizes. An application of a well-known set of real mammary tumor data is provided.

Overall Survival Improvement in Patients with Lung Cancer and Bone Metastases Treated with Denosumab Versus Zoledronic Acid Subgroup Analysis from a Randomized Phase 3 Study

Introduction: Denosumab, a fully human anti-RANKL monoclonal antibody, reduces the incidence of skeletal-related events in patients with bone metastases from solid tumors. We present survival data for the subset of patients with lung cancer, participating in the phase 3 trial of denosumab versus zoledronic acid (ZA) in the treatment of bone metastases from solid tumors (except breast or prostate) or multiple myeloma. Methods: Patients were randomized 1:1 to receive monthly subcutaneous denosumab 120 mg or intravenous ZA 4 mg. An exploratory analysis, using Kaplan-Meier estimates and proportional hazards models, was performed for overall survival among patients with non-small-cell lung cancer (NSCLC) and SCLC. Results: Denosumab was associated with improved median overall survival versus ZA in 811 patients with any lung cancer (8.9 versus 7.7 months; hazard ratio [HR] 0.80) and in 702 patients with NSCLC (9.5 versus 8.0 months; HR 0.78) (p = 0.01, each comparison). Further analysis of NSCLC by histological type showed a median survival of 8.6 months for denosumab versus 6.4 months for ZA in patients with squamous cell carcinoma (HR 0.68; p = 0.035). Incidence of overall adverse events was balanced between treatment groups; serious adverse events occurred in 66.0% of denosumab-treated patients and 72.9% of ZA-treated patients. Cumulative incidence of osteonecrosis of the jaw was similar between groups (0.7% denosumab versus 0.8% ZA). Hypocalcemia rates were 8.6% with denosumab and 3.8% with ZA. Conclusion: In this exploratory analysis, denosumab was associated with improved overall survival compared with ZA, in patients with metastatic lung cancer.

Survival Analysis of Patients with Heart Failure: Implications of Time-Varying Regression Effects in Modeling Mortality

Background: Several models have been designed to predict survival of patients with heart failure. These, while available and widely used for both stratifying and deciding upon different treatment options on the individual level, have several limitations. Specifically, some clinical variables that may influence prognosis may have an influence that change over time. Statistical models that include such characteristic may help in evaluating prognosis. The aim of the present study was to analyze and quantify the impact of modeling heart failure survival allowing for covariates with time-varying effects known to be independent predictors of overall mortality in this clinical setting. Methodology: Survival data from an inception cohort of five hundred patients diagnosed with heart failure functional class III and IV between 2002 and 2004 and followed-up to 2006 were analyzed by using the proportional hazards Cox model and variations of the Cox's model and also of the Aalen's additive model. Principal Findings: One-hundred and eighty eight (188) patients died during follow-up. For patients under study, age, serum sodium, hemoglobin, serum creatinine, and left ventricular ejection fraction were significantly associated with mortality. Evidence of time-varying effect was suggested for the last three. Both high hemoglobin and high LV ejection fraction were associated with a reduced risk of dying with a stronger initial effect. High creatinine, associated with an increased risk of dying, also presented an initial stronger effect. The impact of age and sodium were constant over time. Conclusions: The current study points to the importance of evaluating covariates with time-varying effects in heart failure models. The analysis performed suggests that variations of Cox and Aalen models constitute a valuable tool for identifying these variables. The implementation of covariates with time-varying effects into heart failure prognostication models may reduce bias and increase the specificity of such models.

Risk Factors and Survival Impact of Primary Graft Dysfunction After Lung Transplantation in a Single Institution

Background. Lung transplantation has become a standard procedure for some end-stage lung diseases, but primary graft dysfunction (PGD) is an inherent problem that impacts early and late outcomes. The aim of this study was to define the incidence, risk factors, and impact of mechanical ventilation time on mortality rates among a retrospective cohort of lung transplantations performed in a single institution. Methods. We performed a retrospective study of 118 lung transplantations performed between January 2003 and July 2010. The most severe form of PGD (grade III) as defined at 48 and 72 hours was examined for risk factors by multivariable logistic regression models using donor, recipient, and transplant variables. Results. The overall incidence of PGD at 48 hours was 19.8%, and 15.4% at 72 hours. According multivariate analysis, risk factors associated with PGD were donor smoking history for 48 hours (adjusted odds ratio [OR], 4.83; 95% confidence interval [CI], 1.236-18.896; P = .022) and older donors for 72 hours (adjusted OR, 1.046; 95% CI, 0.997-1.098; P = .022). The operative mortality was 52.9% among patients with PGD versus 20.3% at 48 hours (P = .012). At 72 hours, the mortality rate was 58.3% versus 21.2% (P = .013). The 90-days mortality was also higher among patients with PGD. The mechanical ventilation time was longer in patients with PGD III at 48 hours namely, a mean time of 72 versus 24 hours (P = .001). When PGD was defined at 72 hours, the mean ventilation time was even longer, namely 151 versus 24 hours (P < .001). The mean overall survival for patients who developed PGD at 48 hours was 490.9 versus 1665.5 days for subjects without PGD (P = .001). Considering PGD only at 72 hours, the mean survival was 177.7 days for the PGD group and 1628.9 days for the other patients (P < .001). Conclusion. PGD showed an important impacts on operative and 90-day mortality rates, mechanical ventilation time, and overall survival among lung transplant patients. PGD at 72 hours was a better predictor of lung transplant outcomes than at 48 hours. The use of donors with a smoking history or of advanced age were risk factors for the development of PGD.

Effect of acute and chronic GVHD on relapse and survival after reduced-intensity conditioning allogeneic transplantation for myeloma

We evaluated the effect of acute and chronic GVHD on relapse and survival after allogeneic hematopoietic SCT (HSCT) for multiple myeloma using non-myeloablative conditioning (NMA) and reduced-intensity conditioning (RIC). The outcomes of 177 HLA-identical sibling HSCT recipients between 1997 and 2005, following NMA (n = 98) or RIC (n = 79) were analyzed. In 105 patients, autografting was followed by planned NMA/RIC allogeneic transplantation. The impact of GVHD was assessed as a time-dependent covariate using Cox models. The incidence of acute GVHD (aGVHD; grades I-IV) was 42% (95% confidence interval (CI), 35-49%) and of chronic GVHD (cGVHD) at 5 years was 59% (95% CI, 49-69%), with 70% developing extensive cGVHD. In multivariate analysis, aGVHD (>= grade I) was associated with an increased risk of TRM (relative risk (RR) = 2.42, P = 0.016), whereas limited cGVHD significantly decreased the risk of myeloma relapse (RR = 0.35, P = 0.035) and was associated with superior EFS (RR = 0.40, P = 0.027). aGVHD had a detrimental effect on survival, especially in those receiving autologous followed by allogeneic HSCT (RR = 3.52, P = 0.001). The reduction in relapse risk associated with cGVHD is consistent with a beneficial graft-vs-myeloma effect, but this did not translate into a survival advantage. Bone Marrow Transplantation (2012) 47, 831-837; doi:10.1038/bmt.2011.192; published online 26 September 2011

Comparison of two experimental models of pulmonary hypertension

Objective: To compare two models of pulmonary hypertension (monocrotaline and monocrotaline+pneumonectomy) regarding hemodynamic severity, structure of pulmonary arteries, inflammatory markers (IL-1 and PDGF), and 45-day survival. Methods: We used 80 Sprague-Dawley rats in two study protocols: structural analysis; and survival analysis. The rats were divided into four groups: control; monocrotaline (M), pneumonectomy (P), and monocrotaline+pneumonectomy (M+P). In the structural analysis protocol, 40 rats (10/group) were catheterized for the determination of hemodynamic variables, followed by euthanasia for the removal of heart and lung tissue. The right ventricle (RV) was dissected from the interventricular septum (IS), and the ratio between RV weight and the weight of the left ventricle (LV) plus IS (RV/LV+IS) was taken as the index of RV hypertrophy. In lung tissues, we performed histological analyses, as well as using ELISA to determine IL-1 and PDGF levels. In the survival protocol, 40 animals (10/group) were followed for 45 days. Results: The M and M+P rats developed pulmonary hypertension, whereas the control and P rats did not. The RV/LV+IS ratio was significantly higher in M+P rats than in M rats, as well as being significantly higher in M and M+P rats than in control and P rats. There were no significant differences between the M and M+P rats regarding the area of the medial layer of the pulmonary arteries; IL-1 and PDGF levels; or survival. Conclusions: On the basis of our results, we cannot conclude that the monocrotaline+pneumonectomy model is superior to the monocrotaline model.