Regression models for grouped survival data: Estimation and sensitivity analysis

In this study, regression models are evaluated for grouped survival data when the effect of censoring time is considered in the model and the regression structure is modeled through four link functions. The methodology for grouped survival data is based on life tables, and the times are grouped in k intervals so that ties are eliminated. Thus, the data modeling is performed by considering the discrete models of lifetime regression. The model parameters are estimated by using the maximum likelihood and jackknife methods. To detect influential observations in the proposed models, diagnostic measures based on case deletion, which are denominated 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 those measures, the local influence and the total influential estimate are also employed. Various simulation studies are performed and compared to the performance of the four link functions of the regression models for grouped survival data for different parameter settings, sample sizes and numbers of intervals. Finally, a data set is analyzed by using the proposed regression models. (C) 2010 Elsevier B.V. All rights reserved.

Regression models for bivariate survival data

Multivariate lifetime data arise in various forms including recurrent event data when individuals are followed to observe the sequence of occurrences of a certain type of event; correlated lifetime when an individual is followed for the occurrence of two or more types of events, or when distinct individuals have dependent event times. In most studies there are covariates such as treatments, group indicators, individual characteristics, or environmental conditions, whose relationship to lifetime is of interest. This leads to a consideration of regression models.The well known Cox proportional hazards model and its variations, using the marginal hazard functions employed for the analysis of multivariate survival data in literature are not sufficient to explain the complete dependence structure of pair of lifetimes on the covariate vector. Motivated by this, in Chapter 2, we introduced a bivariate proportional hazards model using vector hazard function of Johnson and Kotz (1975), in which the covariates under study have different effect on two components of the vector hazard function. The proposed model is useful in real life situations to study the dependence structure of pair of lifetimes on the covariate vector . The well known partial likelihood approach is used for the estimation of parameter vectors. We then introduced a bivariate proportional hazards model for gap times of recurrent events in Chapter 3. The model incorporates both marginal and joint dependence of the distribution of gap times on the covariate vector . In many fields of application, mean residual life function is considered superior concept than the hazard function. Motivated by this, in Chapter 4, we considered a new semi-parametric model, bivariate proportional mean residual life time model, to assess the relationship between mean residual life and covariates for gap time of recurrent events. The counting process approach is used for the inference procedures of the gap time of recurrent events. In many survival studies, the distribution of lifetime may depend on the distribution of censoring time. In Chapter 5, we introduced a proportional hazards model for duration times and developed inference procedures under dependent (informative) censoring. In Chapter 6, we introduced a bivariate proportional hazards model for competing risks data under right censoring. The asymptotic properties of the estimators of the parameters of different models developed in previous chapters, were studied. The proposed models were applied to various real life situations.

Analysis of the Stayability in Milk Buffaloes using Survival Models

In this study the trait Stayability (SA) was evaluated according to the year of cull after first calvin, i.e., SA 1 to 6 for 1 to 6 years from first calving in lactating females from bubaline milk herds spread in nine farms located in São Paulo state. Informations were used regarding 1027 lactating Murrah breed buffaloes. The statistical analyses were made using LIFEREG (SAS, 1999) procedure. The SA was evaluated using the fixed effects: farm production, birth year, calving season (Season 1- April to September and Season 2 October - March) and class of milk yield at 270 days. The age at first calving (AFC) was considered as a random effect. The mean observed for total milk yield was 1458.75Kg. Calving Season 2 encloses 65.6% of births. The means of cull age, in months, and the percentage of SA were, respectively: 10.69 e 69% (SA1), 19.30 e 63% (SA2), 26.4 e 54% (SA3), 33.15 e 42% (SA4), 38.53 e 36% (SA5) e 42.65 e 26% (SA6). It is verified that most of culls happens after the first lactation, among the sixth and eleventh month after first calving. It was observed that the factors: farm production, birth year and class of milk yield at 270 days affected significantly all SAs. Factors like calving season and the age at first calving (AFC) were only significant for SAL Being significant the factor AFC in level of 1% and factor time in 10%. For other SAs these factors were not statistically significant.

VeriStrat(®) has a prognostic value for patients with advanced non-small cell lung cancer treated with erlotinib and bevacizumab in the first line: Pooled analysis of SAKK19/05 and NTR528.

BACKGROUND: VeriStrat(®) is a serum proteomic test used to determine whether patients with advanced non-small cell lung cancer (NSCLC) who have already received chemotherapy are likely to have good or poor outcomes from treatment with gefitinib or erlotinib. The main objective of our retrospective study was to evaluate the role of VS as a marker of overall survival (OS) in patients treated with erlotinib and bevacizumab in the first line. PATIENTS AND METHODS: Patients were pooled from two phase II trials (SAKK19/05 and NTR528). For survival analyses, a log-rank test was used to determine if there was a statistically significant difference between groups. The hazard ratio (HR) of any separation was assessed using Cox proportional hazards models. RESULTS: 117 patients were analyzed. VeriStrat classified patients into two groups which had a statistically significant difference in duration of OS (p=0.0027, HR=0.480, 95% confidence interval: 0.294-0.784). CONCLUSION: VeriStrat has a prognostic role in patients with advanced, nonsquamous NSCLC treated with erlotinib and bevacizumab in the first line. Further work is needed to study the predictive role of VeriStrat for erlotinib and bevacizumab in chemotherapy-untreated patients.

Effects of cobalt-chromium everolimus eluting stents or bare metal stent on fatal and non-fatal cardiovascular events: patient level meta-analysis

Objectives: To examine the safety and effectiveness of cobalt-chromium everolimus eluting stents compared with bare metal stents. Design: Individual patient data meta-analysis of randomised controlled trials. Cox proportional regression models stratified by trial, containing random effects, were used to assess the impact of stent type on outcomes. Hazard ratios with 95% confidence interval for outcomes were reported. Data sources and study selection: Medline, Embase, the Cochrane Central Register of Controlled Trials. Randomised controlled trials that compared cobalt-chromium everolimus eluting stents with bare metal stents were selected. The principal investigators whose trials met the inclusion criteria provided data for individual patients. Primary outcomes: The primary outcome was cardiac mortality. Secondary endpoints were myocardial infarction, definite stent thrombosis, definite or probable stent thrombosis, target vessel revascularisation, and all cause death. Results: The search yielded five randomised controlled trials, comprising 4896 participants. Compared with patients receiving bare metal stents, participants receiving cobalt-chromium everolimus eluting stents had a significant reduction of cardiac mortality (hazard ratio 0.67, 95% confidence interval 0.49 to 0.91; P=0.01), myocardial infarction (0.71, 0.55 to 0.92; P=0.01), definite stent thrombosis (0.41, 0.22 to 0.76; P=0.005), definite or probable stent thrombosis (0.48, 0.31 to 0.73; P<0.001), and target vessel revascularisation (0.29, 0.20 to 0.41; P<0.001) at a median follow-up of 720 days. There was no significant difference in all cause death between groups (0.83, 0.65 to 1.06; P=0.14). Findings remained unchanged at multivariable regression after adjustment for the acuity of clinical syndrome (for instance, acute coronary syndrome v stable coronary artery disease), diabetes mellitus, female sex, use of glycoprotein IIb/IIIa inhibitors, and up to one year v longer duration treatment with dual antiplatelets. Conclusions: This meta-analysis offers evidence that compared with bare metal stents the use of cobalt-chromium everolimus eluting stents improves global cardiovascular outcomes including cardiac survival, myocardial infarction, and overall stent thrombosis.

Effects of cobalt-chromium everolimus eluting stents or bare metal stent on fatal and non-fatal cardiovascular events: patient level meta-analysis

Objectives: To examine the safety and effectiveness of cobalt-chromium everolimus eluting stents compared with bare metal stents. Design: Individual patient data meta-analysis of randomised controlled trials. Cox proportional regression models stratified by trial, containing random effects, were used to assess the impact of stent type on outcomes. Hazard ratios with 95% confidence interval for outcomes were reported. Data sources and study selection: Medline, Embase, the Cochrane Central Register of Controlled Trials. Randomised controlled trials that compared cobalt-chromium everolimus eluting stents with bare metal stents were selected. The principal investigators whose trials met the inclusion criteria provided data for individual patients. Primary outcomes: The primary outcome was cardiac mortality. Secondary endpoints were myocardial infarction, definite stent thrombosis, definite or probable stent thrombosis, target vessel revascularisation, and all cause death. Results: The search yielded five randomised controlled trials, comprising 4896 participants. Compared with patients receiving bare metal stents, participants receiving cobalt-chromium everolimus eluting stents had a significant reduction of cardiac mortality (hazard ratio 0.67, 95% confidence interval 0.49 to 0.91; P=0.01), myocardial infarction (0.71, 0.55 to 0.92; P=0.01), definite stent thrombosis (0.41, 0.22 to 0.76; P=0.005), definite or probable stent thrombosis (0.48, 0.31 to 0.73; P<0.001), and target vessel revascularisation (0.29, 0.20 to 0.41; P<0.001) at a median follow-up of 720 days. There was no significant difference in all cause death between groups (0.83, 0.65 to 1.06; P=0.14). Findings remained unchanged at multivariable regression after adjustment for the acuity of clinical syndrome (for instance, acute coronary syndrome v stable coronary artery disease), diabetes mellitus, female sex, use of glycoprotein IIb/IIIa inhibitors, and up to one year v longer duration treatment with dual antiplatelets. Conclusions: This meta-analysis offers evidence that compared with bare metal stents the use of cobalt-chromium everolimus eluting stents improves global cardiovascular outcomes including cardiac survival, myocardial infarction, and overall stent thrombosis.

Statistical analysis of survey-based event history data with application to modeling of unemployment duration

Longitudinal surveys are increasingly used to collect event history data on person-specific processes such as transitions between labour market states. Surveybased event history data pose a number of challenges for statistical analysis. These challenges include survey errors due to sampling, non-response, attrition and measurement. This study deals with non-response, attrition and measurement errors in event history data and the bias caused by them in event history analysis. The study also discusses some choices faced by a researcher using longitudinal survey data for event history analysis and demonstrates their effects. These choices include, whether a design-based or a model-based approach is taken, which subset of data to use and, if a design-based approach is taken, which weights to use. The study takes advantage of the possibility to use combined longitudinal survey register data. The Finnish subset of European Community Household Panel (FI ECHP) survey for waves 1–5 were linked at person-level with longitudinal register data. Unemployment spells were used as study variables of interest. Lastly, a simulation study was conducted in order to assess the statistical properties of the Inverse Probability of Censoring Weighting (IPCW) method in a survey data context. The study shows how combined longitudinal survey register data can be used to analyse and compare the non-response and attrition processes, test the missingness mechanism type and estimate the size of bias due to non-response and attrition. In our empirical analysis, initial non-response turned out to be a more important source of bias than attrition. Reported unemployment spells were subject to seam effects, omissions, and, to a lesser extent, overreporting. The use of proxy interviews tended to cause spell omissions. An often-ignored phenomenon classification error in reported spell outcomes, was also found in the data. Neither the Missing At Random (MAR) assumption about non-response and attrition mechanisms, nor the classical assumptions about measurement errors, turned out to be valid. Both measurement errors in spell durations and spell outcomes were found to cause bias in estimates from event history models. Low measurement accuracy affected the estimates of baseline hazard most. The design-based estimates based on data from respondents to all waves of interest and weighted by the last wave weights displayed the largest bias. Using all the available data, including the spells by attriters until the time of attrition, helped to reduce attrition bias. Lastly, the simulation study showed that the IPCW correction to design weights reduces bias due to dependent censoring in design-based Kaplan-Meier and Cox proportional hazard model estimators. The study discusses implications of the results for survey organisations collecting event history data, researchers using surveys for event history analysis, and researchers who develop methods to correct for non-sampling biases in event history data.

Essays on numerically efficient inference in nonlinear and non-Gaussian state space models, and commodity market analysis.

The first two articles build procedures to simulate vector of univariate states and estimate parameters in nonlinear and non Gaussian state space models. We propose state space speci fications that offer more flexibility in modeling dynamic relationship with latent variables. Our procedures are extension of the HESSIAN method of McCausland[2012]. Thus, they use approximation of the posterior density of the vector of states that allow to : simulate directly from the state vector posterior distribution, to simulate the states vector in one bloc and jointly with the vector of parameters, and to not allow data augmentation. These properties allow to build posterior simulators with very high relative numerical efficiency. Generic, they open a new path in nonlinear and non Gaussian state space analysis with limited contribution of the modeler. The third article is an essay in commodity market analysis. Private firms coexist with farmers' cooperatives in commodity markets in subsaharan african countries. The private firms have the biggest market share while some theoretical models predict they disappearance once confronted to farmers cooperatives. Elsewhere, some empirical studies and observations link cooperative incidence in a region with interpersonal trust, and thus to farmers trust toward cooperatives. We propose a model that sustain these empirical facts. A model where the cooperative reputation is a leading factor determining the market equilibrium of a price competition between a cooperative and a private firm

The persistent colombian conflict bubnational analysis of the duration of violence

The growing empirical literature on the analysis of civil war has recently included the study of conflict duration at the cross-country level. This paper presents, for the first time, a within-country analysis of the determinants of violence duration. I focus on the experience of the Colombian armed conflict. While the conflict has been active for about five decades, local violence ebbs and flows and areas experiencing continuous conflict coexist with places that have been able to resile and where violence is mostly absent. I examine a wide range of factors potentially associated with violence duration at the municipal level, including scale variables, geographical conditions, economic and social variables, institutions and state presence, inequality, government intervention, and victimization variables. I characterize a few variables robustly correlated with the persistence of localized conflict, both across specifications and using different econometric models of duration analysis.

A bivariate regression model for matched paired survival data: local influence and residual analysis

The use of bivariate distributions plays a fundamental role in survival and reliability studies. In this paper, we consider a location scale model for bivariate survival times based on the proposal of a copula to model the dependence of bivariate survival data. For the proposed model, we consider inferential procedures based on maximum likelihood. Gains in efficiency from bivariate models are also examined in the censored data setting. For different parameter settings, sample sizes and censoring percentages, various simulation studies are performed and compared to the performance of the bivariate regression model for matched paired survival data. Sensitivity analysis methods such as local and total influence are presented and derived under three perturbation schemes. The martingale marginal and the deviance marginal residual measures are used to check the adequacy of the model. Furthermore, we propose a new measure which we call modified deviance component residual. The methodology in the paper is illustrated on a lifetime data set for kidney patients.

A new class of survival regression models with heavy-tailed errors: robustness and diagnostics

Birnbaum-Saunders models have largely been applied in material fatigue studies and reliability analyses to relate the total time until failure with some type of cumulative damage. In many problems related to the medical field, such as chronic cardiac diseases and different types of cancer, a cumulative damage caused by several risk factors might cause some degradation that leads to a fatigue process. In these cases, BS models can be suitable for describing the propagation lifetime. However, since the cumulative damage is assumed to be normally distributed in the BS distribution, the parameter estimates from this model can be sensitive to outlying observations. In order to attenuate this influence, we present in this paper BS models, in which a Student-t distribution is assumed to explain the cumulative damage. In particular, we show that the maximum likelihood estimates of the Student-t log-BS models attribute smaller weights to outlying observations, which produce robust parameter estimates. Also, some inferential results are presented. In addition, based on local influence and deviance component and martingale-type residuals, a diagnostics analysis is derived. Finally, a motivating example from the medical field is analyzed using log-BS regression models. Since the parameter estimates appear to be very sensitive to outlying and influential observations, the Student-t log-BS regression model should attenuate such influences. The model checking methodologies developed in this paper are used to compare the fitted models.

FTY720 in combination with cyclosporine - an analysis of skin allograft survival and renal function

Acute and chronic nephrotoxicity caused by CsA Continuous administration impair kidney allograft survival. Several clinical and experimental protocols have shown benefits to the kidney after decreasing CsA dose, withdrawing the drug or delaying its introduction after transplantation.FTY720 is a new Compound that has immunosuppressive characteristics and increase allograft survival in animal models without causing the side effects of calcineurin inhibitors (CNIs). FTY720 described mechanism of action that consists to alter the lymphocyte migration pattern without impairment of the immune system response against pathogens.In our mice model, FTY720 administered alone or in combination with CsA during 21 days increased skin allograft survival in a fully mismatched strain combination and did not cause significant changes in renal function. Moreover, renal structure was normal in all groups suggesting that at low doses (10 mg/kg/day) CsA can be associated during short-term period to other immunosuppressive drugs, i.e. FTY720 without affecting the kidney.Combination of immunosuppressive compounds with FTY720 and/or delayed introduction of low cyclosporine dose Could prevent graft rejection and avoid nephrotoxicity. (c) 2006 Elsevier B.V. All rights reserved.

Survival rate, load to fracture, and finite element analysis of incisors and canines restored with ceramic veneers having varied preparation design

Purpose: To evaluate the survival rate, success rate, load to fracture, and finite element analysis (FEA) of maxillary central incisors and canines restored using ceramic veneers and varying preparation designs.Methods and Materials: Thirty human maxillary central incisors and 30 canines were allocated to the following four groups (n=15) based on the preparation design and type of tooth: Gr1 = central incisor with a conservative preparation; Gr2 = central incisor with a conventional preparation with palatal chamfer; Gr3 = canine with a conservative preparation; Gr4 = canine with a conventional preparation with palatal chamfer. Ceramic veneers (lithium disilicate) were fabricated and adhesively cemented (Variolink Veneer). The specimens were subjected to 4 x 106 mechanical cycles and evaluated at every 500,000 cycles to detect failures. Specimens that survived were subjected to a load to fracture test. Bidimensional models were modeled (Rhinoceros 4.0) and evaluated (MSC.Patrans 2005r2 and MSC.Marc 2005r2) on the basis of their maximum principal stress (MPS) values. Survival rate values were analyzed using the Kaplan-Meier test (alpha = 0.05) and load to fracture values were analyzed using the Student t-test (alpha = 0.05).Results: All groups showed 100% survival rates. The Student t-test did not show any difference between the groups for load to fracture. FEA showed higher MPS values in the specimens restored using veneers with conventional preparation design with palatal chamfer.Conclusion: Preparation design did not affect the fracture load of canines and central incisors, but the veneers with conventional preparation design with palatal chamfer exhibited a tendency to generate higher MPS values.

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.

On Causal Mediation Analysis with a Survival Outcome

Suppose that having established a marginal total effect of a point exposure on a time-to-event outcome, an investigator wishes to decompose this effect into its direct and indirect pathways, also know as natural direct and indirect effects, mediated by a variable known to occur after the exposure and prior to the outcome. This paper proposes a theory of estimation of natural direct and indirect effects in two important semiparametric models for a failure time outcome. The underlying survival model for the marginal total effect and thus for the direct and indirect effects, can either be a marginal structural Cox proportional hazards model, or a marginal structural additive hazards model. The proposed theory delivers new estimators for mediation analysis in each of these models, with appealing robustness properties. Specifically, in order to guarantee ignorability with respect to the exposure and mediator variables, the approach, which is multiply robust, allows the investigator to use several flexible working models to adjust for confounding by a large number of pre-exposure variables. Multiple robustness is appealing because it only requires a subset of working models to be correct for consistency; furthermore, the analyst need not know which subset of working models is in fact correct to report valid inferences. Finally, a novel semiparametric sensitivity analysis technique is developed for each of these models, to assess the impact on inference, of a violation of the assumption of ignorability of the mediator.