Sleep disorders in Multiple System Atrophy: a longitudinal study

Introduction: A higher frequency of sleep and breathing disorders in Multiple System Atrophy (MSA) populations is documented in literature. The analysis of disease progression and prognosis in patients with sleep and breathing disorders could shed light on specific neuropathology and pathophysiology of MSA. Objective: To characterize sleep disorders and their longitudinal modifications during disease course in MSA patients, and to determine their prognostic value. Methods: This is a retrospective and prospective cohort study including 182 MSA patients (58.8% males). Type of onset was defined by the first reported motor or autonomic symptom/sign related to MSA. The occurrence of symptoms/signs and milestones of disease progression and their latency were collected. REM sleep behaviour disorder (RBD) and stridor were video-polysomnography (VPSG)-confirmed. VPSG recordings were analysed in a standardized fashion during the disease course. Survival data were based on time to death from the first symptom of disease. Results: Isolated RBD represented the first MSA symptom in 30% of patients, preceding disease onset according to international criteria with a median of 3(1–5) years. Patients developing early stridor or presenting with RBD at disease onset showed a more rapid and severe disease progression. These features had independent negative prognostic value for survival. Sleep architecture was characterized by peculiar features which could represent negative markers in MSA prognosis. Patients with stridor treated with tracheostomy showed a reduced risk of death. Conclusions: This is one of the first studies focusing on longitudinal progression of sleep in MSA. Sleep disorders are key features of disease, playing a role in presentation, prognosis and progression. In our MSA cohort, RBD represented the most frequent mode of disease presentation. Moreover, some specific clinical and instrumental sleep features could represent a hallmark of MSA and could be involved in prognosis and, in particular, in sudden death and death during sleep.

Nonparametric estimation of the survival function for ordered multivariate failure time data: a comparative study

In longitudinal studies of disease, patients may experience several events through a follow-up period. In these studies, the sequentially ordered events are often of interest and lead to problems that have received much attention recently. Issues of interest include the estimation of bivariate survival, marginal distributions and the conditional distribution of gap times. In this work we consider the estimation of the survival function conditional to a previous event. Different nonparametric approaches will be considered for estimating these quantities, all based on the Kaplan-Meier estimator of the survival function. We explore the finite sample behavior of the estimators through simulations. The different methods proposed in this article are applied to a data set from a German Breast Cancer Study. The methods are used to obtain predictors for the conditional survival probabilities as well as to study the influence of recurrence in overall survival.

The prognostic value of health-related quality-of-life data in predicting survival in glioblastoma cancer patients: results from an international randomised phase III EORTC Brain Tumour and Radiation Oncology Groups, and NCIC Clinical Trials Group study.

This is one of the few studies that have explored the value of baseline symptoms and health-related quality of life (HRQOL) in predicting survival in brain cancer patients. Baseline HRQOL scores (from the EORTC QLQ-C30 and the Brain Cancer Module (BN 20)) were examined in 490 newly diagnosed glioblastoma cancer patients for the relationship with overall survival by using Cox proportional hazards regression models. Refined techniques as the bootstrap re-sampling procedure and the computation of C-indexes and R(2)-coefficients were used to try and validate the model. Classical analysis controlled for major clinical prognostic factors selected cognitive functioning (P=0.0001), global health status (P=0.0055) and social functioning (P<0.0001) as statistically significant prognostic factors of survival. However, several issues question the validity of these findings. C-indexes and R(2)-coefficients, which are measures of the predictive ability of the models, did not exhibit major improvements when adding selected or all HRQOL scores to clinical factors. While classical techniques lead to positive results, more refined analyses suggest that baseline HRQOL scores add relatively little to clinical factors to predict survival. These results may have implications for future use of HRQOL as a prognostic factor in cancer patients.

Global surveillance of cancer survival 1995-2009 : analysis of individual data for 25 676 887 patients from 279 population-based registries in 67 countries (CONCORD-2)

BACKGROUND: Worldwide data for cancer survival are scarce. We aimed to initiate worldwide surveillance of cancer survival by central analysis of population-based registry data, as a metric of the effectiveness of health systems, and to inform global policy on cancer control. METHODS: Individual tumour records were submitted by 279 population-based cancer registries in 67 countries for 25·7 million adults (age 15-99 years) and 75 000 children (age 0-14 years) diagnosed with cancer during 1995-2009 and followed up to Dec 31, 2009, or later. We looked at cancers of the stomach, colon, rectum, liver, lung, breast (women), cervix, ovary, and prostate in adults, and adult and childhood leukaemia. Standardised quality control procedures were applied; errors were corrected by the registry concerned. We estimated 5-year net survival, adjusted for background mortality in every country or region by age (single year), sex, and calendar year, and by race or ethnic origin in some countries. Estimates were age-standardised with the International Cancer Survival Standard weights. FINDINGS: 5-year survival from colon, rectal, and breast cancers has increased steadily in most developed countries. For patients diagnosed during 2005-09, survival for colon and rectal cancer reached 60% or more in 22 countries around the world; for breast cancer, 5-year survival rose to 85% or higher in 17 countries worldwide. Liver and lung cancer remain lethal in all nations: for both cancers, 5-year survival is below 20% everywhere in Europe, in the range 15-19% in North America, and as low as 7-9% in Mongolia and Thailand. Striking rises in 5-year survival from prostate cancer have occurred in many countries: survival rose by 10-20% between 1995-99 and 2005-09 in 22 countries in South America, Asia, and Europe, but survival still varies widely around the world, from less than 60% in Bulgaria and Thailand to 95% or more in Brazil, Puerto Rico, and the USA. For cervical cancer, national estimates of 5-year survival range from less than 50% to more than 70%; regional variations are much wider, and improvements between 1995-99 and 2005-09 have generally been slight. For women diagnosed with ovarian cancer in 2005-09, 5-year survival was 40% or higher only in Ecuador, the USA, and 17 countries in Asia and Europe. 5-year survival for stomach cancer in 2005-09 was high (54-58%) in Japan and South Korea, compared with less than 40% in other countries. By contrast, 5-year survival from adult leukaemia in Japan and South Korea (18-23%) is lower than in most other countries. 5-year survival from childhood acute lymphoblastic leukaemia is less than 60% in several countries, but as high as 90% in Canada and four European countries, which suggests major deficiencies in the management of a largely curable disease. INTERPRETATION: International comparison of survival trends reveals very wide differences that are likely to be attributable to differences in access to early diagnosis and optimum treatment. Continuous worldwide surveillance of cancer survival should become an indispensable source of information for cancer patients and researchers and a stimulus for politicians to improve health policy and health-care systems. FUNDING: Canadian Partnership Against Cancer (Toronto, Canada), Cancer Focus Northern Ireland (Belfast, UK), Cancer Institute New South Wales (Sydney, Australia), Cancer Research UK (London, UK), Centers for Disease Control and Prevention (Atlanta, GA, USA), Swiss Re (London, UK), Swiss Cancer Research foundation (Bern, Switzerland), Swiss Cancer League (Bern, Switzerland), and University of Kentucky (Lexington, KY, USA).

Survival analysis statistics applied to threshold data obtained from the ascending forced-choice method of limits

Sensory thresholds are often collected through ascending forced-choice methods. Group thresholds are important for comparing stimuli or populations; yet, the method has two problems. An individual may correctly guess the correct answer at any concentration step and might detect correctly at low concentrations but become adapted or fatigued at higher concentrations. The survival analysis method deals with both issues. Individual sequences of incorrect and correct answers are adjusted, taking into account the group performance at each concentration. The technique reduces the chance probability where there are consecutive correct answers. Adjusted sequences are submitted to survival analysis to determine group thresholds. The technique was applied to an aroma threshold and a taste threshold study. It resulted in group thresholds similar to ASTM or logarithmic regression procedures. Significant differences in taste thresholds between younger and older adults were determined. The approach provides a more robust technique over previous estimation methods.

COM-Poisson cure rate survival models and an application to a cutaneous melanoma data

In this paper, we develop a flexible cure rate survival model by assuming the number of competing causes of the event of interest to follow the Conway-Maxwell Poisson distribution. This model includes as special cases some of the well-known cure rate models discussed in the literature. Next, we discuss the maximum likelihood estimation of the parameters of this cure rate survival model. Finally, we illustrate the usefulness of this model by applying it to a real cutaneous melanoma data. (C) 2009 Elsevier B.V. All rights reserved.

Estimating Multiple Tumor Transition Rates Based on Data from Survival/Sacrifice Experiments

In this paper, we focus on the model for two types of tumors. Tumor development can be described by four types of death rates and four tumor transition rates. We present a general semi-parametric model to estimate the tumor transition rates based on data from survival/sacrifice experiments. In the model, we make a proportional assumption of tumor transition rates on a common parametric function but no assumption of the death rates from any states. We derived the likelihood function of the data observed in such an experiment, and an EM algorithm that simplified estimating procedures. This article extends work on semi-parametric models for one type of tumor (see Portier and Dinse and Dinse) to two types of tumors.

[Survival, clinical data and quality of life 10 years after heart transplantation: a prospective study]

AIM: To study prospectively patients after heart transplantation with respect to quality of life, mortality, morbidity, and clinical parameters before and up to 10 years after the operation. METHODS: Sixty patients (47.9 +/- 10.9 years, 57 men, 3 women) were transplanted at the University of Vienna Hospital, Department for Heart and Thorax Surgery and were included in this study. They were assessed when set on the waiting list, then exactly one, 5 and 10 years after the transplantation. The variables evaluated included physical and emotional complaints, well-being, mortality and morbidity. In the sample of patients who survived 10 years (n = 23), morbidity (infections, malignancies, graft arteriosclerosis, and rejection episodes) as well as quality of life were evaluated. RESULTS: Actuarial survival rates were 83.3, 66.7, 48.3% at 1, 5, and 10 years after transplantation, respectively. During the first year, infections were the most important reasons for premature death. As a cause of mortality, malignancies were found between years 1 and 5, and graft arteriosclerosis between years 5 and 10. Physical complaints diminished significantly after the operation, but grew significantly during the period from 5 to 10 years (p < 0.001). However, trembling (p < 0.05) and paraesthesies (p < 0.01) diminished continuously. Emotional complaints such as depression and dysphoria (both p < 0.05) increased until the tenth year after their nadir at year 1. In long-time survivors, 3 malignancies (lung, skin, thyroidea) were diagnosed 6 to 9 years postoperatively. Three patients (13%) had signs of graft arteriosclerosis at year 10; 9 (40%) patients suffered from rejection episodes during the course of 10 years. There were no serious rejection episodes deserving immediate therapy. Quality of life at 10 years is good in these patients. CONCLUSIONS: Heart transplantation is a successful therapy for patients with terminal heart disease. Long-term survivors feel well after 10 years and report a good quality of life.

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.

Survival with Newly Diagnosed Metastatic Prostate Cancer in the "Docetaxel Era": Data from 917 Patients in the Control Arm of the STAMPEDE Trial (MRC PR08, CRUK/06/019)

BACKGROUND Prostate cancer (PCa) is the second most common disease among men worldwide. It is important to know survival outcomes and prognostic factors for this disease. Recruitment for the largest therapeutic randomised controlled trial in PCa-the Systemic Therapy in Advancing or Metastatic Prostate Cancer: Evaluation of Drug Efficacy: A Multi-Stage Multi-Arm Randomised Controlled Trial (STAMPEDE)-includes men with newly diagnosed metastatic PCa who are commencing long-term androgen deprivation therapy (ADT); the control arm provides valuable data for a prospective cohort. OBJECTIVE Describe survival outcomes, along with current treatment standards and factors associated with prognosis, to inform future trial design in this patient group. DESIGN, SETTING, AND PARTICIPANTS STAMPEDE trial control arm comprising men newly diagnosed with M1 disease who were recruited between October 2005 and January 2014. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS Overall survival (OS) and failure-free survival (FFS) were reported by primary disease characteristics using Kaplan-Meier methods. Hazard ratios and 95% confidence intervals (CIs) were derived from multivariate Cox models. RESULTS AND LIMITATIONS A cohort of 917 men with newly diagnosed M1 disease was recruited to the control arm in the specified interval. Median follow-up was 20 mo. Median age at randomisation was 66 yr (interquartile range [IQR]: 61-71), and median prostate-specific antigen level was 112 ng/ml (IQR: 34-373). Most men (n=574; 62%) had bone-only metastases, whereas 237 (26%) had both bone and soft tissue metastases; soft tissue metastasis was found mainly in distant lymph nodes. There were 238 deaths, 202 (85%) from PCa. Median FFS was 11 mo; 2-yr FFS was 29% (95% CI, 25-33). Median OS was 42 mo; 2-yr OS was 72% (95% CI, 68-76). Survival time was influenced by performance status, age, Gleason score, and metastases distribution. Median survival after FFS event was 22 mo. Trial eligibility criteria meant men were younger and fitter than general PCa population. CONCLUSIONS Survival remains disappointing in men presenting with M1 disease who are started on only long-term ADT, despite active treatments being available at first failure of ADT. Importantly, men with M1 disease now spend the majority of their remaining life in a state of castration-resistant relapse. PATIENT SUMMARY Results from this control arm cohort found survival is relatively short and highly influenced by patient age, fitness, and where prostate cancer has spread in the body.

Gastric cancer survival amongst Hispanics- A Hispanic paradox? A case series study using the SEER 1973--2005 data registry

Stomach cancer is the fourth most common cancer in the world, and ranked 16th in the US in 2008. The age-adjusted rates among Hispanics were 2.8 times that of non-Hispanic Whites in 1998-2002. In spite of that, previous research has found that Hispanics with non-cardia adenocarcinoma of the stomach have a slightly better survival than non-Hispanic Whites. However, such previous research did not include a comparison with African-Americans, and it was limited to data released for the years 1973-2000 in the nine original Surveillance, Epidemiology, and End Results Cancer Registries. This finding was interpreted as related to the Hispanic Paradox, a phenomenon that refers to the fact that Hispanics in the USA tend to paradoxically have substantially better health than other ethnic groups in spite of what their aggregate socio-economic indicators would predict. We extended such research to the SEER 17 Registry, 1973-2005, with varying years of diagnosis per registry, and compared the survival of non-cardia adenocarcinoma of the stomach according to ethnicity (Hispanics, non-Hispanic Whites and African-Americans), while controlling for age, gender, marital status, stage of disease and treatment using Cox regression survival analysis. We found that Hispanic ethnicity by itself did not confer an advantage on survival from non-cardia adenocarcinoma of the stomach, but that being born abroad was independently associated with the apparent 'Hispanic Paradox' previously reported, and that such advantage was seen among foreign born persons across all race/ethnic groups.^

Mixture modeling for joint analysis of survival, discrete, and continuous data

Mixture modeling is commonly used to model categorical latent variables that represent subpopulations in which population membership is unknown but can be inferred from the data. In relatively recent years, the potential of finite mixture models has been applied in time-to-event data. However, the commonly used survival mixture model assumes that the effects of the covariates involved in failure times differ across latent classes, but the covariate distribution is homogeneous. The aim of this dissertation is to develop a method to examine time-to-event data in the presence of unobserved heterogeneity under a framework of mixture modeling. A joint model is developed to incorporate the latent survival trajectory along with the observed information for the joint analysis of a time-to-event variable, its discrete and continuous covariates, and a latent class variable. It is assumed that the effects of covariates on survival times and the distribution of covariates vary across different latent classes. The unobservable survival trajectories are identified through estimating the probability that a subject belongs to a particular class based on observed information. We applied this method to a Hodgkin lymphoma study with long-term follow-up and observed four distinct latent classes in terms of long-term survival and distributions of prognostic factors. Our results from simulation studies and from the Hodgkin lymphoma study demonstrated the superiority of our joint model compared with the conventional survival model. This flexible inference method provides more accurate estimation and accommodates unobservable heterogeneity among individuals while taking involved interactions between covariates into consideration.^