Empirical study of correlated survival times for recurrent events with proportional hazards margins and the effect of correlation and censoring.

Background: In longitudinal studies where subjects experience recurrent incidents over a period of time, such as respiratory infections, fever or diarrhea, statistical methods are required to take into account the within-subject correlation. Methods: For repeated events data with censored failure, the independent increment (AG), marginal (WLW) and conditional (PWP) models are three multiple failure models that generalize Cox"s proportional hazard model. In this paper, we revise the efficiency, accuracy and robustness of all three models under simulated scenarios with varying degrees of within-subject correlation, censoring levels, maximum number of possible recurrences and sample size. We also study the methods performance on a real dataset from a cohort study with bronchial obstruction. Results: We find substantial differences between methods and there is not an optimal method. AG and PWP seem to be preferable to WLW for low correlation levels but the situation reverts for high correlations. Conclusions: All methods are stable in front of censoring, worsen with increasing recurrence levels and share a bias problem which, among other consequences, makes asymptotic normal confidence intervals not fully reliable, although they are well developed theoretically.

Empirical study of correlated survival times for recurrent events with proportional hazards margins and the effect of correlation and censoring.

Background: In longitudinal studies where subjects experience recurrent incidents over a period of time, such as respiratory infections, fever or diarrhea, statistical methods are required to take into account the within-subject correlation. Methods: For repeated events data with censored failure, the independent increment (AG), marginal (WLW) and conditional (PWP) models are three multiple failure models that generalize Cox"s proportional hazard model. In this paper, we revise the efficiency, accuracy and robustness of all three models under simulated scenarios with varying degrees of within-subject correlation, censoring levels, maximum number of possible recurrences and sample size. We also study the methods performance on a real dataset from a cohort study with bronchial obstruction. Results: We find substantial differences between methods and there is not an optimal method. AG and PWP seem to be preferable to WLW for low correlation levels but the situation reverts for high correlations. Conclusions: All methods are stable in front of censoring, worsen with increasing recurrence levels and share a bias problem which, among other consequences, makes asymptotic normal confidence intervals not fully reliable, although they are well developed theoretically.

A parametric model for the interval censored survival times of Acacia mangium plantation in a spacing trial

Survival times for the Acacia mangium plantation in the Segaliud Lokan Project, Sabah, East Malaysia were analysed based on 20 permanent sample plots (PSPs) established in 1988 as a spacing experiment. The PSPs were established following a complete randomized block design with five levels of spacing randomly assigned to units within four blocks at different sites. The survival times of trees in years are of interest. Since the inventories were only conducted annually, the actual survival time for each tree was not observed. Hence, the data set comprises censored survival times. Initial analysis of the survival of the Acacia mangium plantation suggested there is block by spacing interaction; a Weibull model gives a reasonable fit to the replicate survival times within each PSP; but a standard Weibull regression model is inappropriate because the shape parameter differs between PSPs. In this paper we investigate the form of the non-constant Weibull shape parameter. Parsimonious models for the Weibull survival times have been derived using maximum likelihood methods. The factor selection for the parameters is based on a backward elimination procedure. The models are compared using likelihood ratio statistics. The results suggest that both Weibull parameters depend on spacing and block.

Survival of conidia of sorghum ergot (caused by Claviceps africana) on panicles, seed and soil in Australia

Macroconidia of the sorghum ergot pathogen, Claviceps africana Frederickson, Mantle & de Milliano, survived in dried honeydew on soil for 13-14 weeks in a glasshouse at ambient temperatures, but for less than half that time on seed stored in a shadehouse over summer. Those on seeds stored at 4degreesC, however, survived for over a year (58-62 weeks). During summer, conidia on ergot-infected panicles buried in soil, or on the soil surface, survived for 7.5-12 weeks, whereas over winter the survival times were 4 weeks and 19-27 weeks, respectively. Macroconidia on infected panicles held above the soil surface survived for >38 weeks (8 calendar months) over winter, suggesting that they may play a role in the perennation of C. africana in Australia.

Rationale for consolidation to improve progression-free survival in patients with non-Hodgkin's lymphoma: a review of the evidence.

Non-Hodgkin's lymphoma (NHL) comprises both indolent forms, including follicular lymphoma (FL) and marginal zone lymphoma (MZL), and aggressive forms, including diffuse large B-cell lymphoma (DLBCL) and mantle cell lymphoma (MCL). FL and DLBCL are the most common subtypes of indolent and aggressive NHL, respectively. Although these lymphomas exhibit different clinical behaviors and outcomes, the prognosis is negatively affected in both DLBCL and FL by the lack of a complete response (CR) with standard treatment options. The aim of therapy should therefore be achievement of a CR, which is not only associated with longer progression-free survival (PFS) and overall survival times, but is also a prerequisite for a cure, particularly in DLBCL. Consolidation treatment with radioimmunotherapy (RIT) is an innovative treatment approach to increase CR rates. Phase II studies have indicated promising results with yttrium-90 ((90)Y)-ibritumomab tiuxetan and iodine-131 ((131)I)-tositumomab as consolidation following induction therapy for previously untreated patients with advanced FL. More recently, investigators reported a marked increase in CR rates and significant improvements in PFS using standard chemotherapy regimens followed by (90)Y-ibritumomab tiuxetan in a phase III randomized trial in patients with previously untreated FL. Data also suggest that RIT may play a role in the treatment of high-risk DLBCL, with encouraging PFS results from a phase II trial of (90)Y-ibritumomab tiuxetan consolidation following induction with rituximab plus chemotherapy in elderly patients with previously untreated DLBCL. With the higher CR rates and longer PFS times observed in patients with FL and DLBCL, as well as encouraging early data from MZL and MCL consolidation trials, RIT appears to have an important role in the treatment of patients with NHL.

Parametric accelerated failure time models with random effects and an application to kidney transplant survival

Accelerated failure time models with a shared random component are described, and are used to evaluate the effect of explanatory factors and different transplant centres on survival times following kidney transplantation. Different combinations of the distribution of the random effects and baseline hazard function are considered and the fit of such models to the transplant data is critically assessed. A mixture model that combines short- and long-term components of a hazard function is then developed, which provides a more flexible model for the hazard function. The model can incorporate different explanatory variables and random effects in each component. The model is straightforward to fit using standard statistical software, and is shown to be a good fit to the transplant data. Copyright (C) 2004 John Wiley Sons, Ltd.

A Bayesian Analysis in the Presence of Covariates for Multivariate Survival Data: An example of Application

In this paper, we introduce a Bayesian analysis for survival multivariate data in the presence of a covariate vector and censored observations. Different ""frailties"" or latent variables are considered to capture the correlation among the survival times for the same individual. We assume Weibull or generalized Gamma distributions considering right censored lifetime data. We develop the Bayesian analysis using Markov Chain Monte Carlo (MCMC) methods.

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.

Semiparametric Normal Transformation Models for Spatially Correlated Survival Data

There is an emerging interest in modeling spatially correlated survival data in biomedical and epidemiological studies. In this paper, we propose a new class of semiparametric normal transformation models for right censored spatially correlated survival data. This class of models assumes that survival outcomes marginally follow a Cox proportional hazard model with unspecified baseline hazard, and their joint distribution is obtained by transforming survival outcomes to normal random variables, whose joint distribution is assumed to be multivariate normal with a spatial correlation structure. A key feature of the class of semiparametric normal transformation models is that it provides a rich class of spatial survival models where regression coefficients have population average interpretation and the spatial dependence of survival times is conveniently modeled using the transformed variables by flexible normal random fields. We study the relationship of the spatial correlation structure of the transformed normal variables and the dependence measures of the original survival times. Direct nonparametric maximum likelihood estimation in such models is practically prohibited due to the high dimensional intractable integration of the likelihood function and the infinite dimensional nuisance baseline hazard parameter. We hence develop a class of spatial semiparametric estimating equations, which conveniently estimate the population-level regression coefficients and the dependence parameters simultaneously. We study the asymptotic properties of the proposed estimators, and show that they are consistent and asymptotically normal. The proposed method is illustrated with an analysis of data from the East Boston Ashma Study and its performance is evaluated using simulations.

Loss of DAXX and ATRX are associated with chromosome instability and reduced survival of patients with pancreatic neuroendocrine tumors

BACKGROUND & AIMS Sporadic pancreatic neuroendocrine tumors (pNETs) are rare and genetically heterogeneous. Chromosome instability (CIN) has been detected in pNETs from patients with poor outcomes, but no specific genetic factors have been associated with CIN. Mutations in death domain-associated protein gene (DAXX) or ATR-X gene (ATRX) (which both encode proteins involved in chromatin remodeling) have been detected in 40% of pNETs, in association with activation of alternative lengthening of telomeres. We investigated whether loss of DAXX or ATRX, and consequent alternative lengthening of telomeres, are related to CIN in pNETs. We also assessed whether loss of DAXX or ATRX is associated with specific phenotypes of pNETs. METHODS We collected well-differentiated primary pNET samples from 142 patients at the University Hospital Zurich and from 101 patients at the University Hospital Bern (both located in Switzerland). Clinical follow-up data were obtained for 149 patients from general practitioners and tumor registries. The tumors were reclassified into 3 groups according to the 2010 World Health Organization classification. Samples were analyzed by immunohistochemistry and telomeric fluorescence in situ hybridization. We correlated loss of DAXX, or ATRX, expression, and activation of alternative lengthening of telomeres with data from comparative genomic hybridization array studies, as well as with clinical and pathological features of the tumors and relapse and survival data. RESULTS Loss of DAXX or ATRX protein and alternative lengthening of telomeres were associated with CIN in pNETs. Furthermore, loss of DAXX or ATRX correlated with tumor stage and metastasis, reduced time of relapse-free survival, and decreased time of tumor-associated survival. CONCLUSIONS Loss of DAXX or ATRX is associated with CIN in pNETs and shorter survival times of patients. These results support the hypothesis that DAXX- and ATRX-negative tumors are a more aggressive subtype of pNET, and could lead to identification of strategies to target CIN in pancreatic tumors.

SURVIVAL PREDICTION FOR BRAIN TUMOR PATIENTS USING GENE EXPRESSION DATA

Brain tumor is one of the most aggressive types of cancer in humans, with an estimated median survival time of 12 months and only 4% of the patients surviving more than 5 years after disease diagnosis. Until recently, brain tumor prognosis has been based only on clinical information such as tumor grade and patient age, but there are reports indicating that molecular profiling of gliomas can reveal subgroups of patients with distinct survival rates. We hypothesize that coupling molecular profiling of brain tumors with clinical information might improve predictions of patient survival time and, consequently, better guide future treatment decisions. In order to evaluate this hypothesis, the general goal of this research is to build models for survival prediction of glioma patients using DNA molecular profiles (U133 Affymetrix gene expression microarrays) along with clinical information. First, a predictive Random Forest model is built for binary outcomes (i.e. short vs. long-term survival) and a small subset of genes whose expression values can be used to predict survival time is selected. Following, a new statistical methodology is developed for predicting time-to-death outcomes using Bayesian ensemble trees. Due to a large heterogeneity observed within prognostic classes obtained by the Random Forest model, prediction can be improved by relating time-to-death with gene expression profile directly. We propose a Bayesian ensemble model for survival prediction which is appropriate for high-dimensional data such as gene expression data. Our approach is based on the ensemble "sum-of-trees" model which is flexible to incorporate additive and interaction effects between genes. We specify a fully Bayesian hierarchical approach and illustrate our methodology for the CPH, Weibull, and AFT survival models. We overcome the lack of conjugacy using a latent variable formulation to model the covariate effects which decreases computation time for model fitting. Also, our proposed models provides a model-free way to select important predictive prognostic markers based on controlling false discovery rates. We compare the performance of our methods with baseline reference survival methods and apply our methodology to an unpublished data set of brain tumor survival times and gene expression data, selecting genes potentially related to the development of the disease under study. A closing discussion compares results obtained by Random Forest and Bayesian ensemble methods under the biological/clinical perspectives and highlights the statistical advantages and disadvantages of the new methodology in the context of DNA microarray data analysis.

Prophylactic antibiotics or G(M)-CSF for the prevention of infections and improvement of survival in cancer patients receiving myelotoxic chemotherapy.

BACKGROUND Febrile neutropenia (FN) and other infectious complications are some of the most serious treatment-related toxicities of chemotherapy for cancer, with a mortality rate of 2% to 21%. The two main types of prophylactic regimens are granulocyte (macrophage) colony-stimulating factors (G(M)-CSF) and antibiotics, frequently quinolones or cotrimoxazole. Current guidelines recommend the use of colony-stimulating factors when the risk of febrile neutropenia is above 20%, but they do not mention the use of antibiotics. However, both regimens have been shown to reduce the incidence of infections. Since no systematic review has compared the two regimens, a systematic review was undertaken. OBJECTIVES To compare the efficacy and safety of G(M)-CSF compared to antibiotics in cancer patients receiving myelotoxic chemotherapy. SEARCH METHODS We searched The Cochrane Library, MEDLINE, EMBASE, databases of ongoing trials, and conference proceedings of the American Society of Clinical Oncology and the American Society of Hematology (1980 to December 2015). We planned to include both full-text and abstract publications. Two review authors independently screened search results. SELECTION CRITERIA We included randomised controlled trials (RCTs) comparing prophylaxis with G(M)-CSF versus antibiotics for the prevention of infection in cancer patients of all ages receiving chemotherapy. All study arms had to receive identical chemotherapy regimes and other supportive care. We included full-text, abstracts, and unpublished data if sufficient information on study design, participant characteristics, interventions and outcomes was available. We excluded cross-over trials, quasi-randomised trials and post-hoc retrospective trials. DATA COLLECTION AND ANALYSIS Two review authors independently screened the results of the search strategies, extracted data, assessed risk of bias, and analysed data according to standard Cochrane methods. We did final interpretation together with an experienced clinician. MAIN RESULTS In this updated review, we included no new randomised controlled trials. We included two trials in the review, one with 40 breast cancer patients receiving high-dose chemotherapy and G-CSF compared to antibiotics, a second one evaluating 155 patients with small-cell lung cancer receiving GM-CSF or antibiotics.We judge the overall risk of bias as high in the G-CSF trial, as neither patients nor physicians were blinded and not all included patients were analysed as randomised (7 out of 40 patients). We considered the overall risk of bias in the GM-CSF to be moderate, because of the risk of performance bias (neither patients nor personnel were blinded), but low risk of selection and attrition bias.For the trial comparing G-CSF to antibiotics, all cause mortality was not reported. There was no evidence of a difference for infection-related mortality, with zero events in each arm. Microbiologically or clinically documented infections, severe infections, quality of life, and adverse events were not reported. There was no evidence of a difference in frequency of febrile neutropenia (risk ratio (RR) 1.22; 95% confidence interval (CI) 0.53 to 2.84). The quality of the evidence for the two reported outcomes, infection-related mortality and frequency of febrile neutropenia, was very low, due to the low number of patients evaluated (high imprecision) and the high risk of bias.There was no evidence of a difference in terms of median survival time in the trial comparing GM-CSF and antibiotics. Two-year survival times were 6% (0 to 12%) in both arms (high imprecision, low quality of evidence). There were four toxic deaths in the GM-CSF arm and three in the antibiotics arm (3.8%), without evidence of a difference (RR 1.32; 95% CI 0.30 to 5.69; P = 0.71; low quality of evidence). There were 28% grade III or IV infections in the GM-CSF arm and 18% in the antibiotics arm, without any evidence of a difference (RR 1.55; 95% CI 0.86 to 2.80; P = 0.15, low quality of evidence). There were 5 episodes out of 360 cycles of grade IV infections in the GM-CSF arm and 3 episodes out of 334 cycles in the cotrimoxazole arm (0.8%), with no evidence of a difference (RR 1.55; 95% CI 0.37 to 6.42; P = 0.55; low quality of evidence). There was no significant difference between the two arms for non-haematological toxicities like diarrhoea, stomatitis, infections, neurologic, respiratory, or cardiac adverse events. Grade III and IV thrombopenia occurred significantly more frequently in the GM-CSF arm (60.8%) compared to the antibiotics arm (28.9%); (RR 2.10; 95% CI 1.41 to 3.12; P = 0.0002; low quality of evidence). Neither infection-related mortality, incidence of febrile neutropenia, nor quality of life were reported in this trial. AUTHORS' CONCLUSIONS As we only found two small trials with 195 patients altogether, no conclusion for clinical practice is possible. More trials are necessary to assess the benefits and harms of G(M)-CSF compared to antibiotics for infection prevention in cancer patients receiving chemotherapy.

Bayesian and survival model for tumor relapse status and disease-specific survival, with applications to breast cancer

Breast cancer is the most common non-skin cancer and the second leading cause of cancer-related death in women in the United States. Studies on ipsilateral breast tumor relapse (IBTR) status and disease-specific survival will help guide clinic treatment and predict patient prognosis.^ After breast conservation therapy, patients with breast cancer may experience breast tumor relapse. This relapse is classified into two distinct types: true local recurrence (TR) and new ipsilateral primary tumor (NP). However, the methods used to classify the relapse types are imperfect and are prone to misclassification. In addition, some observed survival data (e.g., time to relapse and time from relapse to death)are strongly correlated with relapse types. The first part of this dissertation presents a Bayesian approach to (1) modeling the potentially misclassified relapse status and the correlated survival information, (2) estimating the sensitivity and specificity of the diagnostic methods, and (3) quantify the covariate effects on event probabilities. A shared frailty was used to account for the within-subject correlation between survival times. The inference was conducted using a Bayesian framework via Markov Chain Monte Carlo simulation implemented in softwareWinBUGS. Simulation was used to validate the Bayesian method and assess its frequentist properties. The new model has two important innovations: (1) it utilizes the additional survival times correlated with the relapse status to improve the parameter estimation, and (2) it provides tools to address the correlation between the two diagnostic methods conditional to the true relapse types.^ Prediction of patients at highest risk for IBTR after local excision of ductal carcinoma in situ (DCIS) remains a clinical concern. The goals of the second part of this dissertation were to evaluate a published nomogram from Memorial Sloan-Kettering Cancer Center, to determine the risk of IBTR in patients with DCIS treated with local excision, and to determine whether there is a subset of patients at low risk of IBTR. Patients who had undergone local excision from 1990 through 2007 at MD Anderson Cancer Center with a final diagnosis of DCIS (n=794) were included in this part. Clinicopathologic factors and the performance of the Memorial Sloan-Kettering Cancer Center nomogram for prediction of IBTR were assessed for 734 patients with complete data. Nomogram for prediction of 5- and 10-year IBTR probabilities were found to demonstrate imperfect calibration and discrimination, with an area under the receiver operating characteristic curve of .63 and a concordance index of .63. In conclusion, predictive models for IBTR in DCIS patients treated with local excision are imperfect. Our current ability to accurately predict recurrence based on clinical parameters is limited.^ The American Joint Committee on Cancer (AJCC) staging of breast cancer is widely used to determine prognosis, yet survival within each AJCC stage shows wide variation and remains unpredictable. For the third part of this dissertation, biologic markers were hypothesized to be responsible for some of this variation, and the addition of biologic markers to current AJCC staging were examined for possibly provide improved prognostication. The initial cohort included patients treated with surgery as first intervention at MDACC from 1997 to 2006. Cox proportional hazards models were used to create prognostic scoring systems. AJCC pathologic staging parameters and biologic tumor markers were investigated to devise the scoring systems. Surveillance Epidemiology and End Results (SEER) data was used as the external cohort to validate the scoring systems. Binary indicators for pathologic stage (PS), estrogen receptor status (E), and tumor grade (G) were summed to create PS+EG scoring systems devised to predict 5-year patient outcomes. These scoring systems facilitated separation of the study population into more refined subgroups than the current AJCC staging system. The ability of the PS+EG score to stratify outcomes was confirmed in both internal and external validation cohorts. The current study proposes and validates a new staging system by incorporating tumor grade and ER status into current AJCC staging. We recommend that biologic markers be incorporating into revised versions of the AJCC staging system for patients receiving surgery as the first intervention.^ Chapter 1 focuses on developing a Bayesian method to solve misclassified relapse status and application to breast cancer data. Chapter 2 focuses on evaluation of a breast cancer nomogram for predicting risk of IBTR in patients with DCIS after local excision gives the statement of the problem in the clinical research. Chapter 3 focuses on validation of a novel staging system for disease-specific survival in patients with breast cancer treated with surgery as the first intervention. ^