Change Point Estimation in Monitoring Survival Time

Precise identification of the time when a change in a hospital outcome has occurred enables clinical experts to search for a potential special cause more effectively. In this paper, we develop change point estimation methods for survival time of a clinical procedure in the presence of patient mix in a Bayesian framework. We apply Bayesian hierarchical models to formulate the change point where there exists a step change in the mean survival time of patients who underwent cardiac surgery. The data are right censored since the monitoring is conducted over a limited follow-up period. We capture the effect of risk factors prior to the surgery using a Weibull accelerated failure time regression model. Markov Chain Monte Carlo is used to obtain posterior distributions of the change point parameters including location and magnitude of changes and also corresponding probabilistic intervals and inferences. The performance of the Bayesian estimator is investigated through simulations and the result shows that precise estimates can be obtained when they are used in conjunction with the risk-adjusted survival time CUSUM control charts for different magnitude scenarios. The proposed estimator shows a better performance where a longer follow-up period, censoring time, is applied. In comparison with the alternative built-in CUSUM estimator, more accurate and precise estimates are obtained by the Bayesian estimator. These superiorities are enhanced when probability quantification, flexibility and generalizability of the Bayesian change point detection model are also considered.

Estimation of the time of a linear trend in monitoring survival time

Change point estimation is recognized as an essential tool of root cause analyses within quality control programs as it enables clinical experts to search for potential causes of change in hospital outcomes more effectively. In this paper, we consider estimation of the time when a linear trend disturbance has occurred in survival time following an in-control clinical intervention in the presence of variable patient mix. To model the process and change point, a linear trend in the survival time of patients who underwent cardiac surgery is formulated using hierarchical models in a Bayesian framework. The data are right censored since the monitoring is conducted over a limited follow-up period. We capture the effect of risk factors prior to the surgery using a Weibull accelerated failure time regression model. We use Markov Chain Monte Carlo to obtain posterior distributions of the change point parameters including the location and the slope size of the trend and also corresponding probabilistic intervals and inferences. The performance of the Bayesian estimator is investigated through simulations and the result shows that precise estimates can be obtained when they are used in conjunction with the risk-adjusted survival time cumulative sum control chart (CUSUM) control charts for different trend scenarios. In comparison with the alternatives, step change point model and built-in CUSUM estimator, more accurate and precise estimates are obtained by the proposed Bayesian estimator over linear trends. These superiorities are enhanced when probability quantification, flexibility and generalizability of the Bayesian change point detection model are also considered.

NAA and STS effects on bract survival time, carbohydrate content, respiration rate and carbohydrate balance of potted Bougainvillea spectabilis Willd

The aims of this work were to deepen the knowledge on the physiology of bract abscission in Bougainvillea spectabilis ‘Killie Campbell’ plants, in what relates to respiration and carbon balance. Using the effects induced by Silver Thiosulphate (STS) and/or Naphtalene Acetic Acid (NAA, at high concentration: 500 mg.l-1) on bract abscission under interior conditions, the relationship between bract survival time (longevity) and, respiration rate or carbohydrate levels, was investigated. Treatments that included NAA were the ones that reduced significantly bract abscission. Unexpectedly, the higher the levels of bract soluble and total carbohydrates, measured at day 10 postproduction (PP), the higher the abscission of bracts. These results show, for the first time, that abscission can positively correlate with non structural carbohydrates levels in the organ that abscise. Bract respiration rate was significantly affected by treatment and postproduction day (PP). Treatments that had higher bract respiration rates (WATER and STS) also had higher levels of non structural carbohydrates in the bracts. Bract respiration rate decreased from day 10 to day 17 PP by approximately 50% (on average of all treatments) and was negatively correlated with bract survival time. In the carbon balance per gram of bract dry weight, the treatments WATER and STS, showed the largest decrease in the content of total carbohydrates and had the highest consumption of carbohydrates through respiration. So, these were the bracts that needed to import a higher amount of carbohydrates per gram of bract dry weight. In the carbon balance for the whole mass of bracts and adjacent stems in an average plant, the treatments WATER and STS continued to allow for the largest decreases in total carbohydrate during postproduction. However, and contradicting the results per gram of bract dry weight, the highest total consumption of carbohydrates by respiration was obtained for the NAA and STS+NAA treatments. It makes sense that bracts that last longer have lower individual carbon consumption while, at the plant level, the increased number of remaining bracts causes a higher overall expenditure. Respiration rate has been used as an indicator of flower longevity, this correlation is here extended for the flower+bract system. Plants that had higher bract respiration rates, most probably, had a higher flow of carbohydrates through the bracts (and flowers), which, in the end, was sensed as a higher carbohydrate level.

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.

Daily controlled physiotherapy increases survival time in dogs with suspected degenerative myelopathy

The purposes of the study reported here were to evaluate the signalment and clinical presentation in 50 dogs with degenerative myelopathy, to evaluate whether mean survival time was significantly affected by various means of physiotherapy performed in 22 dogs, and to determine whether neurologic status, anatomic localization, or age at onset had an influence on survival time in dogs that received physiotherapy. We found a significant (P < .05) breed predisposition for the German Shepherd Dog, Kuvasz, Hovawart, and Bernese Mountain Dog. Mean age at diagnosis was 9.1 years, and both sexes were affected equally. The anatomic localization of the lesion was spinal cord segment T3-L3 in 56% (n = 28) and L3-S3 in 44% (n = 22) of the dogs. Animals that received intensive (n = 9) physiotherapy had longer (P < .05) survival time (mean 255 days), compared with that for animals with moderate (n = 6; mean 130 days) or no (n = 7; mean 55 days) physiotherapy. In addition, our results indicate that affected dogs which received physiotherapy remained ambulatory longer than did animals that did not receive physical treatment.

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.

Evaluation of serum biochemical marker concentrations and survival time in dogs with protein-losing enteropathy

OBJECTIVE: To evaluate serum concentrations of biochemical markers and survival time in dogs with protein-losing enteropathy (PLE). DESIGN: Prospective study. ANIMALS: 29 dogs with PLE and 18 dogs with food-responsive diarrhea (FRD). PROCEDURES: Data regarding serum concentrations of various biochemical markers at the initial evaluation were available for 18 of the 29 dogs with PLE and compared with findings for dogs with FRD. Correlations between biochemical marker concentrations and survival time (interval between time of initial evaluation and death or euthanasia) for dogs with PLE were evaluated. RESULTS: Serum C-reactive protein concentration was high in 13 of 18 dogs with PLE and in 2 of 18 dogs with FRD. Serum concentration of canine pancreatic lipase immunoreactivity was high in 3 dogs with PLE but within the reference interval in all dogs with FRD. Serum α1-proteinase inhibitor concentration was less than the lower reference limit in 9 dogs with PLE and 1 dog with FRD. Compared with findings in dogs with FRD, values of those 3 variables in dogs with PLE were significantly different. Serum calprotectin (measured by radioimmunoassay and ELISA) and S100A12 concentrations were high but did not differ significantly between groups. Seventeen of the 29 dogs with PLE were euthanized owing to this disease; median survival time was 67 days (range, 2 to 2,551 days). CONCLUSIONS AND CLINICAL RELEVANCE: Serum C-reactive protein, canine pancreatic lipase immunoreactivity, and α1-proteinase inhibitor concentrations differed significantly between dogs with PLE and FRD. Most initial biomarker concentrations were not predictive of survival time in dogs with PLE.

A subset of skin tumor modifier loci determines survival time of tumor-bearing mice

Studies of mouse models of human cancer have established the existence of multiple tumor modifiers that influence parameters of cancer susceptibility such as tumor multiplicity, tumor size, or the probability of malignant progression. We have carried out an analysis of skin tumor susceptibility in interspecific Mus musculus/Mus spretus hybrid mice and have identified another seven loci showing either significant (six loci) or suggestive (one locus) linkage to tumor susceptibility or resistance. A specific search was carried out for skin tumor modifier loci associated with time of survival after development of a malignant tumor. A combination of resistance alleles at three markers [D6Mit15 (Skts12), D7Mit12 (Skts2), and D17Mit7 (Skts10)], all of which are close to or the same as loci associated with carcinoma incidence and/or papilloma multiplicity, is significantly associated with increased survival of mice with carcinomas, whereas the reverse combination of susceptibility alleles is significantly linked to early mortality caused by rapid carcinoma growth (χ2 = 25.22; P = 5.1 × 10−8). These data indicate that host genetic factors may be used to predict carcinoma growth rate and/or survival of individual backcross mice exposed to the same carcinogenic stimulus and suggest that mouse models may provide an approach to the identification of genetic modifiers of cancer survival in humans.

Survival analysis of time-to-event data in respiratory health research studies

This article provides a review of techniques for the analysis of survival data arising from respiratory health studies. Popular techniques such as the Kaplan–Meier survival plot and the Cox proportional hazards model are presented and illustrated using data from a lung cancer study. Advanced issues are also discussed, including parametric proportional hazards models, accelerated failure time models, time-varying explanatory variables, simultaneous analysis of multiple types of outcome events and the restricted mean survival time, a novel measure of the effect of treatment.

Survival analysis of time-to-event data in respiratory health research studies

This article provides a review of techniques for the analysis of survival data arising from respiratory health studies. Popular techniques such as the Kaplan–Meier survival plot and the Cox proportional hazards model are presented and illustrated using data from a lung cancer study. Advanced issues are also discussed, including parametric proportional hazards models, accelerated failure time models, time-varying explanatory variables, simultaneous analysis of multiple types of outcome events and the restricted mean survival time, a novel measure of the effect of treatment.