Application of survival analysis to determine the persimmon cv. 'Rama-forte' sensory shelf-life.

2014

Pre-hospital risk factors for inpatient death from severe febrile illness in malian children.

BACKGROUND: Inpatient case fatality from severe malaria remains high in much of sub-Saharan Africa. The majority of these deaths occur within 24 hours of admission, suggesting that pre-hospital management may have an impact on the risk of case fatality. METHODS: Prospective cohort study, including questionnaire about pre-hospital treatment, of all 437 patients admitted with severe febrile illness (presumed to be severe malaria) to the paediatric ward in Sikasso Regional Hospital, Mali, in a two-month period. FINDINGS: The case fatality rate was 17.4%. Coma, hypoglycaemia and respiratory distress at admission were associated with significantly higher mortality. In multiple logistic regression models and in a survival analysis to examine pre-admission risk factors for case fatality, the only consistent and significant risk factor was sex. Girls were twice as likely to die as boys (AOR 2.00, 95% CI 1.08-3.70). There was a wide variety of pre-hospital treatments used, both modern and traditional. None had a consistent impact on the risk of death across different analyses. Reported use of traditional treatments was not associated with post-admission outcome. INTERPRETATION: Aside from well-recognised markers of severity, the main risk factor for death in this study was female sex, but this study cannot determine the reason why. Differences in pre-hospital treatments were not associated with case fatality.

Modelagem em análise de sobrevivencia com eventos recorrentes aplicada a dados da área médica

Pós-graduação em Biometria - IBB

Radiation therapy duration influences overall survival in patients with cervical carcinoma.

OBJECTIVE: This article analyses the influence of treatment duration on survival in patients with invasive carcinoma of the cervix treated by radical radiation therapy. METHOD: Three hundred and sixty patients with FIGO stage IB-IIIB carcinoma of the cervix were treated in Lausanne (Switzerland) with external radiation and brachytherapy as first line therapy. Median therapy duration was 45 days. Patients were classified according to the duration of the therapies, taking 60 days (the 75th percentile) as an arbitrary cut-off. RESULTS: The 5-year survival was 61% (S.E. = 3%) for the therapy duration group of less than 60 days and 53% (S.E. = 7%) for the group of more than 60 days. In terms of univariate hazard ratio (HR), the relative difference between the two groups corresponds to a 50% increase of deaths (HR = 1.53, 95% CI = 1.03-2.28) for the longer therapy duration group (P = 0.044). In a multivariate analysis, the magnitude of estimated relative hazards for the longer therapies are confirmed though significance was reduced (HR = 1.52, 95% CI = 0.94-2.45, P = 0.084). CONCLUSION: These findings suggest that short treatment duration is a factor associated with longer survival in carcinoma of the cervix.

Analysis of culling probability in dairy buffalo using survival models

In order to contribute to the genetic breeding programs of buffaloes, this study aimed to determine the influence of environmental effects on the stayability (ST) of dairy female Murrah buffalo in the herd. Data from 1016 buffaloes were used. ST was defined as the ability of the female to remain in the herd for 1, 2, 3, 4, 5 or 6 years after the first calving. Environmental effects were studied by survival analysis, adjusted to the fixed effects of farm, year and season of birth, class of first-lactation milk yield and age at first calving. The data were analyzed using the LIFEREG procedure of the SAS program that fits parametric models to failure time data (culling or ST = 0), and estimates parameters by maximum likelihood estimation. Breeding farm, year of birth and first-lactation milk yield significantly influenced (P < 0.0001) the ST to the specific ages (1 to 6 years after the first calving). Buffaloes that were older at first calving presented higher probabilities of being culled 1 year after the first calving, without any effect on culling at older ages. Buffaloes with a higher milk yield at first calving presented a lower culling probability and remained for a longer period of time in the herd. The effects of breeding farm, year of birth and first-lactation milk yield should be included in models used for the analysis of ST in buffaloes. Copyright © The Animal Consortium 2010.

One-year survival and neurological outcome after pediatric cardiopulmonary resuscitation.

OBJECTIVE: Reported survival after cardiopulmonary resuscitation (CPR) in children varies considerably. We aimed to identify predictors of 1-year survival and to assess long-term neurological status after in- or outpatient CPR. DESIGN: Retrospective review of the medical records and prospective follow-up of CPR survivors. SETTING: Tertiary care pediatric university hospital. PATIENTS AND METHODS: During a 30-month period, 89 in- and outpatients received advanced CPR. Survivors of CPR were prospectively followed-up for 1 year. Neurological outcome was assessed by the Pediatric Cerebral Performance Category scale (PCPC). Variables predicting 1-year survival were identified by multivariable logistic regression analysis. INTERVENTIONS: None. RESULTS: Seventy-one of the 89 patients were successfully resuscitated. During subsequent hospitalization do-not-resuscitate orders were issued in 25 patients. At 1 year, 48 (54%) were alive, including two of the 25 patients with out-of-hospital CPR. All patients died, who required CPR after trauma or near drowning, when CPR began >10 min after arrest or with CPR duration >60 min. Prolonged CPR (21-60 min) was compatible with survival (five of 19). At 1 year, 77% of the survivors had the same PCPC score as prior to CPR. Predictors of survival were location of resuscitation, CPR during peri- or postoperative care, and duration of resuscitation. A clinical score (0-15 points) based on these three items yielded an area under the ROC of 0.93. CONCLUSIONS: Independent determinants of long-term survival of pediatric resuscitation are location of arrest, underlying cause, and duration of CPR. Long-term survivors have little or no change in neurological status.

New validated prognostic models and prognostic calculators in patients with low-grade gliomas diagnosed by central pathology review: a pooled analysis of EORTC/RTOG/NCCTG phase III clinical trials.

Background In a previous study, the European Organisation for Research and Treatment of Cancer (EORTC) reported a scoring system to predict survival of patients with low-grade gliomas (LGGs). A major issue in the diagnosis of brain tumors is the lack of agreement among pathologists. New models in patients with LGGs diagnosed by central pathology review are needed. Methods Data from 339 EORTC patients with LGGs diagnosed by central pathology review were used to develop new prognostic models for progression-free survival (PFS) and overall survival (OS). Data from 450 patients with centrally diagnosed LGGs recruited into 2 large studies conducted by North American cooperative groups were used to validate the models. Results Both PFS and OS were negatively influenced by the presence of baseline neurological deficits, a shorter time since first symptoms (<30 wk), an astrocytic tumor type, and tumors larger than 5 cm in diameter. Early irradiation improved PFS but not OS. Three risk groups have been identified (low, intermediate, and high) and validated. Conclusions We have developed new prognostic models in a more homogeneous LGG population diagnosed by central pathology review. This population better fits with modern practice, where patients are enrolled in clinical trials based on central or panel pathology review. We could validate the models in a large, external, and independent dataset. The models can divide LGG patients into 3 risk groups and provide reliable individual survival predictions. Inclusion of other clinical and molecular factors might still improve models' predictions.

Transition probabilities and duration analysis among disability states: Some evidence from Spanish data

In this paper we study the disability transition probabilities (as well as the mortalityprobabilities) due to concurrent factors to age such as income, gender and education. Althoughit is well known that ageing and socioeconomic status influence the probability ofcausing functional disorders, surprisingly little attention has been paid to the combined effectof those factors along the individuals' life and how this affects the transition from one degreeof disability to another. The assumption that tomorrow's disability state is only a functionof the today's state is very strong, since disability is a complex variable that depends onseveral other elements than time. This paper contributes into the field in two ways: (1) byattending the distinction between the initial disability level and the process that leads tohis course (2) by addressing whether and how education, age and income differentially affectthe disability transitions. Using a Markov chain discrete model and a survival analysis, weestimate the probability by year and individual characteristics that changes the state of disabilityand the duration that it takes its progression in each case. We find that people withan initial state of disability have a higher propensity to change and take less time to transitfrom different stages. Men do that more frequently than women. Education and incomehave negative effects on transition. Moreover, we consider the disability benefits associatedto those changes along different stages of disability and therefore we offer some clues onthe potential savings of preventive actions that may delay or avoid those transitions. Onpure cost considerations, preventive programs for improvement show higher benefits thanthose for preventing deterioration, and in general terms, those focussing individuals below65 should go first. Finally the trend of disability in Spain seems not to change among yearsand regional differences are not found.

Aspectes metodològics i aplicacions de la modelització del temps de supervivència multivariant mitjançant models mixtes

Els estudis de supervivència s'interessen pel temps que passa des de l'inici de l'estudi (diagnòstic de la malaltia, inici del tractament,...) fins que es produeix l'esdeveniment d'interès (mort, curació, millora,...). No obstant això, moltes vegades aquest esdeveniment s'observa més d'una vegada en un mateix individu durant el període de seguiment (dades de supervivència multivariant). En aquest cas, és necessari utilitzar una metodologia diferent a la utilitzada en l'anàlisi de supervivència estàndard. El principal problema que l'estudi d'aquest tipus de dades comporta és que les observacions poden no ser independents. Fins ara, aquest problema s'ha solucionat de dues maneres diferents en funció de la variable dependent. Si aquesta variable segueix una distribució de la família exponencial s'utilitzen els models lineals generalitzats mixtes (GLMM); i si aquesta variable és el temps, variable amb una distribució de probabilitat no pertanyent a aquesta família, s'utilitza l'anàlisi de supervivència multivariant. El que es pretén en aquesta tesis és unificar aquests dos enfocs, és a dir, utilitzar una variable dependent que sigui el temps amb agrupacions d'individus o d'observacions, a partir d'un GLMM, amb la finalitat d'introduir nous mètodes pel tractament d'aquest tipus de dades.

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.

On estimation and influence diagnostics for log-Birnbaum-Saunders Student-t regression models: Full Bayesian analysis

The purpose of this paper is to develop a Bayesian approach for log-Birnbaum-Saunders Student-t regression models under right-censored survival data. Markov chain Monte Carlo (MCMC) methods are used to develop a Bayesian procedure for the considered model. In order to attenuate the influence of the outlying observations on the parameter estimates, we present in this paper Birnbaum-Saunders models in which a Student-t distribution is assumed to explain the cumulative damage. Also, some discussions on the model selection to compare the fitted models are given and case deletion influence diagnostics are developed for the joint posterior distribution based on the Kullback-Leibler divergence. The developed procedures are illustrated with a real data set. (C) 2010 Elsevier B.V. All rights reserved.

A hands-on approach for fitting long-term survival models under the GAMLSS framework

In many data sets from clinical studies there are patients insusceptible to the occurrence of the event of interest. Survival models which ignore this fact are generally inadequate. The main goal of this paper is to describe an application of the generalized additive models for location, scale, and shape (GAMLSS) framework to the fitting of long-term survival models. in this work the number of competing causes of the event of interest follows the negative binomial distribution. In this way, some well known models found in the literature are characterized as particular cases of our proposal. The model is conveniently parameterized in terms of the cured fraction, which is then linked to covariates. We explore the use of the gamlss package in R as a powerful tool for inference in long-term survival models. The procedure is illustrated with a numerical example. (C) 2009 Elsevier Ireland Ltd. All rights reserved.

Regression analysis of mean quality-adjusted survival time based on pseudo-observations

Regression models for the mean quality-adjusted survival time are specified from hazard functions of transitions between two states and the mean quality-adjusted survival time may be a complex function of covariates. We discuss a regression model for the mean quality-adjusted survival (QAS) time based on pseudo-observations, which has the advantage of directly modeling the effect of covariates in the QAS time. Both Monte Carlo Simulations and a real data set are studied. Copyright (C) 2009 John Wiley & Sons, Ltd.

Robust Inferences For Covariate Effects On Survival Time With Censored Linear Regression Models

Various inference procedures for linear regression models with censored failure times have been studied extensively. Recent developments on efficient algorithms to implement these procedures enhance the practical usage of such models in survival analysis. In this article, we present robust inferences for certain covariate effects on the failure time in the presence of "nuisance" confounders under a semiparametric, partial linear regression setting. Specifically, the estimation procedures for the regression coefficients of interest are derived from a working linear model and are valid even when the function of the confounders in the model is not correctly specified. The new proposals are illustrated with two examples and their validity for cases with practical sample sizes is demonstrated via a simulation study.