Modelling survival data to account for model uncertainty: a single model or model averaging?

This study considered the problem of predicting survival, based on three alternative models: a single Weibull, a mixture of Weibulls and a cure model. Instead of the common procedure of choosing a single “best” model, where “best” is defined in terms of goodness of fit to the data, a Bayesian model averaging (BMA) approach was adopted to account for model uncertainty. This was illustrated using a case study in which the aim was the description of lymphoma cancer survival with covariates given by phenotypes and gene expression. The results of this study indicate that if the sample size is sufficiently large, one of the three models emerge as having highest probability given the data, as indicated by the goodness of fit measure; the Bayesian information criterion (BIC). However, when the sample size was reduced, no single model was revealed as “best”, suggesting that a BMA approach would be appropriate. Although a BMA approach can compromise on goodness of fit to the data (when compared to the true model), it can provide robust predictions and facilitate more detailed investigation of the relationships between gene expression and patient survival. Keywords: Bayesian modelling; Bayesian model averaging; Cure model; Markov Chain Monte Carlo; Mixture model; Survival analysis; Weibull distribution

Isolation of human lymphatic malformation endothelial cells, their in vitro characterization and in vivo survival in a mouse xenograft model

Human lymphatic vascular malformations (LMs), also known as cystic hygromas or lymphangioma, consist of multiple lymphatic endothelial cell-lined lymph-containing cysts. No animal model of this disease exists. To develop a mouse xenograft model of human LM, CD34NegCD31Pos LM lymphatic endothelial cells (LM-LEC) were isolated from surgical specimens and compared to foreskin CD34NegCD31Pos lymphatic endothelial cells (LECs). Cells were implanted into a mouse tissue engineering model for 1, 2 and 4 weeks. In vitro LM-LECs showed increased proliferation and survival under starvation conditions (P < 0.0005 at 48 h, two-way ANOVA), increased migration (P < 0.001, two-way ANOVA) and formed fewer (P = 0.029, independent samples t test), shorter tubes (P = 0.029, independent samples t test) than foreskin LECs. In vivo LM-LECs implanted into a Matrigel™-containing mouse chamber model assembled to develop vessels with dilated cystic lumens lined with flat endothelium, morphology similar to that of clinical LMs. Human foreskin LECs failed to survive implantation. In LM-LEC implanted chambers the percent volume of podoplaninPos vessels was 1.18 ± 2.24 % at 1 week, 6.34 ± 2.68 % at 2 weeks and increasing to 7.67 ± 3.60 % at 4 weeks. In conclusion, the significantly increased proliferation, migration, resistance to apoptosis and decreased tubulogenesis of LM-LECs observed in vitro is likely to account for their survival and assembly into stable LM-like structures when implanted into a mouse vascularised chamber model. This in vivo xenograft model will provide the basis of future studies of LM biology and testing of potential pharmacological interventions for patients with lymphatic malformations.

Prolonged cardiac xenograft survival in guinea pig-to-rat model by a highly active cobra venom factor

A highly active cobra venom factor (CVF) was isolated from the venom of Naja kaouthia by sequential column chromatography. It displays strong anticomplementary activity, and has 1515 U of anti complementary activity per mg protein. A single dose of 0.1 mg/kg CVF given i.v. to rats completely abrogated complement activity for nearly 5 days. Given 0.02 mg/kg of CVF. the complement activity of rats was reduced by more than 96.5% in 6 It. In guinea pig-to-rat heart transplant model, rats treated with a single dose of 0.05 mg/kg CVF had significantly prolonged xenograft survival (56.12 +/- 6.27 h in CVF-treated rats vs. 0.19 +/- 0.07 h in control rats, P < 0.001). (C) 2003 Elsevier Ltd. All rights reserved.

Cardiac beta ARK1 inhibition prolongs survival and augments beta blocker therapy in a mouse model of severe heart failure.

Chronic human heart failure is characterized by abnormalities in beta-adrenergic receptor (betaAR) signaling, including increased levels of betaAR kinase 1 (betaARK1), which seems critical to the pathogenesis of the disease. To determine whether inhibition of betaARK1 is sufficient to rescue a model of severe heart failure, we mated transgenic mice overexpressing a peptide inhibitor of betaARK1 (betaARKct) with transgenic mice overexpressing the sarcoplasmic reticulum Ca(2+)-binding protein, calsequestrin (CSQ). CSQ mice have a severe cardiomyopathy and markedly shortened survival (9 +/- 1 weeks). In contrast, CSQ/betaARKct mice exhibited a significant increase in mean survival age (15 +/- 1 weeks; P < 0.0001) and showed less cardiac dilation, and cardiac function was significantly improved (CSQ vs. CSQ/betaARKct, left ventricular end diastolic dimension 5.60 +/- 0.17 mm vs. 4.19 +/- 0.09 mm, P < 0.005; % fractional shortening, 15 +/- 2 vs. 36 +/- 2, P < 0.005). The enhancement of the survival rate in CSQ/betaARKct mice was substantially potentiated by chronic treatment with the betaAR antagonist metoprolol (CSQ/betaARKct nontreated vs. CSQ/betaARKct metoprolol treated, 15 +/- 1 weeks vs. 25 +/- 2 weeks, P < 0.0001). Thus, overexpression of the betaARKct resulted in a marked prolongation in survival and improved cardiac function in a mouse model of severe cardiomyopathy that can be potentiated with beta-blocker therapy. These data demonstrate a significant synergy between an established heart-failure treatment and the strategy of betaARK1 inhibition.

Keratinocyte Growth-Factor Promotes Epithelial Survival and Resolution in a Human Model of Lung Injury

Rationale: Increasing epithelial repair and regeneration may hasten resolution of lung injury in patients with the Acute Respiratory Distress Syndrome (ARDS). In animal models of ARDS, Keratinocyte Growth Factor (KGF) reduces injury and increases epithelial proliferation and repair. The effect of KGF in the human alveolus is unknown.

Objectives: To test whether KGF can attenuate alveolar injury in a human model of ARDS.

Methods: Volunteers were randomized to intravenous KGF (60 μg/kg) or placebo for 3 days, before inhaling 50μg lipopolysaccharide. Six hours later, subjects underwent bronchoalveolar lavage (BAL) to quantify markers of alveolar inflammation and cell-specific injury.

Measurements and Main Results: KGF did not alter leukocyte infiltration or markers of permeability in response to LPS. KGF increased BAL concentrations of Surfactant Protein D (SP-D), MMP-9, IL-1Ra, GM-CSF and CRP. In vitro, BAL fluid from KGF-treated subjects (KGF BAL) inhibited pulmonary fibroblast proliferation, but increased alveolar epithelial proliferation. Active MMP-9 increased alveolar epithelial wound repair. Finally, BAL from the KGF pre-treated group enhanced macrophage phagocytic uptake of apoptotic epithelial cells and bacteria compared with BAL from the placebo-treated group. This effect was blocked by inhibiting activation of the GM-CSF receptor.

Conclusions: KGF treatment increases BAL SP-D, a marker of type II alveolar epithelial cell proliferation in a human model of ALI. Additionally KGF increases alveolar concentrations of the anti-inflammatory cytokine IL-1Ra, and mediators that drive epithelial repair (MMP-9) and enhance macrophage clearance of dead cells and bacteria (GM-CSF).

Anti-VEGFA therapy reduces tumor growth and extends survival in a murine model of ovarian granulosa cell tumor

Les tumeurs des cellules de la granulosa (GCTs) sont des tumeurs avec un potentiel malin ayant tendance à récidiver, provoquant ainsi la mort dans 80% des cas de stade avancé consécutif à une rechute. Bien que les GCTs représentent 5% des tumeurs ovariennes, peu d’études ont évalué les protocoles de traitement adjuvant pour la maladie avancée ou récurrente. Notre but était d’évaluer l’efficacité de la voie de signalisation du facteur de croissance de l’endothélium vasculaire A (VEGFA) comme cible pour le traitement de la GCT utilisant le modèle murin transgénique Ptentm1Hwu/tm1Hwu; Ctnnb1tm1Mmt/+; Amhr2tm3(cre)Bhr/+ (PCA) qui reproduit le stade avancé de la maladie humaine. Un anticorps anti-VEGFA a été administré une fois par semaine par voie intrapéritonéale (IP) à partir de 3 semaines d’âge. La thérapie anti-VEGFA a permis une réduction de la taille des tumeurs à 6 semaines d’âge (p<0.05) et une prolongation de la survie des animaux traités, lorsque comparé aux animaux contrôles. L’analyse des GCTs a montré une réduction significative de la prolifération cellulaire (p<0.05) et de la densité microvasculaire (p<0.01) mais aucune différence significative n’a été détectée dans l’apoptose cellulaire. p44/p42 MAPK, un effecteur de la signalisation pour le récepteur 2 de VEGFA (VEGFR2) associé à la prolifération cellulaire, était moins activé dans les tumeurs traitées (p<0.05). Par contre, l’activation d’AKT, un effecteur impliqué dans la survie cellulaire, était similaire d’un groupe à l’autre. Ces résultats suggèrent que l’anticorps anti-VEGFA réduit la prolifération cellulaire et la densité microvasculaire chez les souris PCA par inhibition de la voie de signalisation VEGFR2-MAPK, inhibant ainsi la croissance tumorale. En conclusion, l’efficacité de la thérapie anti- VEGFA mérite d’être évaluée en essais contrôlés randomisés pour le traitement des GCTs chez l’homme.

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 Lactobacillus plantarum in model solutions and fruit juices

The aim of the work was to study the survival of Lactobacillus plantarum NCIMB 8826 in model solutions and develop a mathematical model describing its dependence on pH, citric acid and ascorbic acid. A Central Composite Design (CCD) was developed studying each of the three factors at five levels within the following ranges, i.e., pH (3.0-4.2), citric acid (6-40 g/L), and ascorbic acid (100-1000 mg/L). In total, 17 experimental runs were carried out. The initial cell concentration in the model solutions was approximately 1 × 10(8)CFU/mL; the solutions were stored at 4°C for 6 weeks. Analysis of variance (ANOVA) of the stepwise regression demonstrated that a second order polynomial model fits well the data. The results demonstrated that high pH and citric acid concentration enhanced cell survival; one the other hand, ascorbic acid did not have an effect. Cell survival during storage was also investigated in various types of juices, including orange, grapefruit, blackcurrant, pineapple, pomegranate, cranberry and lemon juice. The model predicted well the cell survival in orange, blackcurrant and pineapple, however it failed to predict cell survival in grapefruit and pomegranate, indicating the influence of additional factors, besides pH and citric acid, on cell survival. Very good cell survival (less than 0.4 log decrease) was observed after 6 weeks of storage in orange, blackcurrant and pineapple juice, all of which had a pH of about 3.8. Cell survival in cranberry and pomegranate decreased very quickly, whereas in the case of lemon juice, the cell concentration decreased approximately 1.1 logs after 6 weeks of storage, albeit the fact that lemon juice had the lowest pH (pH~2.5) among all the juices tested. Taking into account the results from the compositional analysis of the juices and the model, it was deduced that in certain juices, other compounds seemed to protect the cells during storage; these were likely to be proteins and dietary fibre In contrast, in certain juices, such as pomegranate, cell survival was much lower than expected; this could be due to the presence of antimicrobial compounds, such as phenolic compounds.

Investigation of the factors influencing the survival of Bifidobacterium longum in model acidic solutions and fruit juices

The survival of Bifidobacterium longum NCIMB 8809 was studied during refrigerated storage for 6 weeks in model solutions, based on which a mathematical model was constructed describing cell survival as a function of pH, citric acid, protein and dietary fibre. A Central Composite Design (CCD) was developed studying the influence of four factors at three levels, i.e., pH (3.2–4), citric acid (2–15 g/l), protein (0–10 g/l), and dietary fibre (0–8 g/l). In total, 31 experimental runs were carried out. Analysis of variance (ANOVA) of the regression model demonstrated that the model fitted well the data. From the regression coefficients it was deduced that all four factors had a statistically significant (P < 0.05) negative effect on the log decrease [log10N0 week−log10N6 week], with the pH and citric acid being the most influential ones. Cell survival during storage was also investigated in various types of juices, including orange, grapefruit, blackcurrant, pineapple, pomegranate and strawberry. The highest cell survival (less than 0.4 log decrease) after 6 weeks of storage was observed in orange and pineapple, both of which had a pH of about 3.8. Although the pH of grapefruit and blackcurrant was similar (pH ∼3.2), the log decrease of the former was ∼0.5 log, whereas of the latter was ∼0.7 log. One reason for this could be the fact that grapefruit contained a high amount of citric acid (15.3 g/l). The log decrease in pomegranate and strawberry juices was extremely high (∼8 logs). The mathematical model was able to predict adequately the cell survival in orange, grapefruit, blackcurrant, and pineapple juices. However, the model failed to predict the cell survival in pomegranate and strawberry, most likely due to the very high levels of phenolic compounds in these two juices.

A flexible model for survival data with a cure rate: a Bayesian approach

In this paper we deal with a Bayesian analysis for right-censored survival data suitable for populations with a cure rate. We consider a cure rate model based on the negative binomial distribution, encompassing as a special case the promotion time cure model. Bayesian analysis is based on Markov chain Monte Carlo (MCMC) methods. We also present some discussion on model selection and an illustration with a real dataset.

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 semi-Markov multistate model for estimation of the mean quality-adjusted survival for non-progressive processes

We discuss the estimation of the expected value of the quality-adjusted survival, based on multistate models. We generalize an earlier work, considering the sojourn times in health states are not identically distributed, for a given vector of covariates. Approaches based on semiparametric and parametric (exponential and Weibull distributions) methodologies are considered. A simulation study is conducted to evaluate the performance of the proposed estimator and the jackknife resampling method is used to estimate the variance of such estimator. An application to a real data set is also included.

Estimation of the mean quality-adjusted survival using a multistate model for the sojourn times

In clinical trials, it may be of interest taking into account physical and emotional well-being in addition to survival when comparing treatments. Quality-adjusted survival time has the advantage of incorporating information about both survival time and quality-of-life. In this paper, we discuss the estimation of the expected value of the quality-adjusted survival, based on multistate models for the sojourn times in health states. Semiparametric and parametric (with exponential distribution) approaches are considered. A simulation study is presented to evaluate the performance of the proposed estimator and the jackknife resampling method is used to compute bias and variance of the estimator. (C) 2007 Elsevier B.V. All rights reserved.

Determining the sensitivity of the Antarctic amphipod Orchomenella pinguides to metals using a joint model of survival response to exposure concentration and duration

Developing water quality guidelines for Antarctic marine environments requires understanding the sensitivity of local biota to contaminant exposure. Antarctic invertebrates have shown slower contaminant responses in previous experiments compared to temperate and tropical species in standard toxicity tests. Consequently, test methods which take into account environmental conditions and biological characteristics of cold climate species need to be developed. This study investigated the effects of five metals on the survival of a common Antarctic amphipod, Orchomenella pinguides. Multiple observations assessing mortality to metal exposure were made over the 30 days exposure period. Traditional toxicity tests with quantal data sets are analysed using methods such as maximum likelihood regression (probit analysis) and Spearman–Kärber which treat individual time period endpoints independently. A new statistical model was developed to integrate the time-series concentration–response data obtained in this study. Grouped survival data were modelled using a generalized additive mixed model (GAMM) which incorporates all the data obtained from multiple observation times to derive time integrated point estimates. The sensitivity of the amphipod, O. pinguides, to metals increased with increasing exposure time. Response times varied for different metals with amphipods responding faster to copper than to cadmium, lead or zinc. As indicated by 30 days lethal concentration (LC50) estimates, copper was the most toxic metal (31 µg/L), followed by cadmium (168 µg/L), lead (256 µg/L) and zinc (822 µg/L). Nickel exposure (up to 1.12 mg/L) did not affect amphipod survival. Using longer exposure durations and utilising the GAMM model provides an improved methodology for assessing sensitivities of slow responding Antarctic marine invertebrates to contaminants.