Individual Covariates (individual + covariate)

Distribution by Scientific Domains

Selected Abstracts

The role of group size and environmental factors on survival in a cooperatively breeding tropical passerine

Summary 1Variation in survival, a major determinant of fitness, may be caused by individual or environmental characteristics. Furthermore, interactions between individuals may influence survival through the negative feedback effects of density dependence. Compared to species in temperate regions, we have little knowledge about population processes and variation in fitness in tropical bird species. 2To investigate whether variation in survival could be explained by population size or climatic variables we used capture,recapture models in conjunction with a long-term data set from an island population of the territorial, cooperatively breeding Seychelles warbler (Acrocephalus sechellensis). The lack of migration out of the study population means that our results are not confounded by dispersal. 3Annual survival was high, both for adults (84%) and juveniles (61%), and did not differ between the sexes. Although there was significant variation in survival between years, this variation could not be explained by overall population size or weather variables. 4For territorial species, resource competition will work mainly on a local scale. The size of a territory and number of individuals living in it will therefore be a more appropriate measure of density than overall population density. Consequently, both an index of territory quality per individual (food availability) and local density, measured as group size, were included as individual covariates in our analyses. 5Local density had a negative effect on survival; birds living in larger groups had lower survival probabilities than those living in small groups. Food availability did not affect survival. 6Our study shows that, in a territorial species, although density-dependent effects might not be detectable at the population level they can be detected at the individual territory level , the scale at which individuals compete. These results will help to provide a better understanding of the small-scale processes involved in the dynamics of a population in general, but in particular in tropical species living in relatively stable environments. [source]


Daniel P. McMillen
ABSTRACT In contrast to the rigid structure of standard parametric hedonic analysis, nonparametric estimators control for misspecified spatial effects while using highly flexible functional forms. Despite these advantages, nonparametric procedures are still not used extensively for spatial data analysis due to perceived difficulties associated with estimation and hypothesis testing. We demonstrate that nonparametric estimation is feasible for large datasets with many independent variables, offering statistical tests of individual covariates and tests of model specification. We show that fixed parameterization of distance to the nearest rapid transit line is a misspecification and that pricing of access to this amenity varies across neighborhoods within Chicago. [source]

Factors influencing Soay sheep survival

E. A. Catchpole
We present a survival analysis of Soay sheep mark recapture and recovery data. Unlike previous conditional analyses, it is not necessary to assume equality of recovery and recapture probabilities; instead these are estimated by maximum likelihood. Male and female sheep are treated separately, with the higher numbers and survival probabilities of the females resulting in a more complex model than that used for the males. In both cases, however, age and time aspects need to be included and there is a strong indication of a reduction in survival for sheep aged 7 years or more. Time variation in survival is related to the size of the population and selected weather variables, by using logistic regression. The size of the population significantly affects the survival probabilities of male and female lambs, and of female sheep aged 7 or more years. March rainfall and a measure of the North Atlantic oscillation are found to influence survival significantly for all age groups considered, for both males and females. Either of these weather variables can be used in a model. Several phenotypic and genotypic individual covariates are also fitted. The only covariate which is found to influence survival significantly is the type of horn of first-year female sheep. There is a substantial variation in the recovery probabilities over time, reflecting in part the increased effort when a population crash was expected. The goodness of fit of the model is checked by using graphical procedures. [source]

Application of a propensity score to adjust for channelling bias with NSAIDs,

S. V. Morant
Abstract Purpose To compare the relative risks of upper GI haemorrhage (UGIH) in users of Newer versus Older, non-specific NSAIDs when adjusted for channelling bias by regression on individual covariates, a propensity score and both. Methods Cohort study of patients prescribed NSAIDs between June 1987 and January 2000. Exposure to Newer and Older non-specific NSAIDs was identified, and risk factors evaluated for each patient. Results of multiple covariate analyses and the propensity scoring technique to assess potential channelling bias in comparisons between Newer and Older non-specific NSAIDs were compared. Results This study included 7.1 thousand patient years (tpy) exposure to meloxicam, 1.6,tpy exposure to coxibs, and 628,tpy exposure to Older non-specific NSAIDs. Patients receiving Newer NSAIDs were older, more likely to have a history of GI symptoms, and at higher risk for GI complications. Adjusting for these risk factors reduced the relative risks of UGIH on meloxicam and coxibs versus Older non-specific NSAIDs to 0.84 (95%CI 0.60, 1.17) and 0.36 (0.14, 0.97) respectively. Conclusions Channelling towards high GI risk patients occurred in the prescribing of Newer NSAIDs. Propensity scores highlighted the markedly different risk profiles of users of Newer and Older non-specific NSAID. Correcting for channelling bias, coxib exposure, but not meloxicam exposure, was associated with less UGIH than Older non-specific NSAID exposure. In the present study, corrections made by regression on a propensity score and on individual covariates were similar. Copyright © 2004 John Wiley & Sons, Ltd. [source]

Analysis of Capture,Recapture Models with Individual Covariates Using Data Augmentation

BIOMETRICS, Issue 1 2009
J. Andrew Royle
Summary I consider the analysis of capture,recapture models with individual covariates that influence detection probability. Bayesian analysis of the joint likelihood is carried out using a flexible data augmentation scheme that facilitates analysis by Markov chain Monte Carlo methods, and a simple and straightforward implementation in freely available software. This approach is applied to a study of meadow voles (Microtus pennsylvanicus) in which auxiliary data on a continuous covariate (body mass) are recorded, and it is thought that detection probability is related to body mass. In a second example, the model is applied to an aerial waterfowl survey in which a double-observer protocol is used. The fundamental unit of observation is the cluster of individual birds, and the size of the cluster (a discrete covariate) is used as a covariate on detection probability. [source]

Prospective study of the relationship between the systemic inflammatory response, prognostic scoring systems and relapse-free and cancer-specific survival in patients undergoing potentially curative resection for renal cancer

Sara Ramsey
OBJECTIVE To examine the prognostic value of markers of systemic inflammatory response, together with established scoring systems, in predicting relapse-free and cancer-specific survival in patients with primary operable renal cancer, as there is increasing evidence that such markers provide prognostic information, in addition to scoring systems, in patients with metastatic renal cancer. PATIENTS AND METHODS In all, 83 patients undergoing potentially curative nephrectomy for localized renal cancer were recruited. The University of California Los Angeles Integrated Staging System (UISS), ,Stage Size Grade Necrosis' (SSIGN) and Kattan scores were constructed. The systemic inflammatory response was assessed by counting white cells, neutrophils, lymphocytes and platelets, and measuring albumin and C-reactive protein (CRP) concentrations. RESULTS On multivariate analysis of the significant individual covariates, T stage (hazard ratio 2.38, 95% confidence interval 1.06, 5.36, P = 0.037), necrosis (3.73, 1.26,11.05, P = 0.018) and CRP (4.31, 1.20,15.49, P = 0.025) were significant independent predictors of relapse-free survival. On multivariate analysis of significant scoring systems and CRP, only UISS (3.50, 1.66,7.40, P = 0.001), SSIGN (2.83, 1.19,6.72, P = 0.018) and CRP (4.14, 1.16,14.73, P = 0.028) were significant independent predictors of relapse-free survival. CONCLUSION Elevated circulating CRP levels appear to be better than other markers of the systemic inflammatory response, and independent of established scoring systems, in predicting relapse-free and cancer-specific survival in patients undergoing potentially curative nephrectomy for renal cancer. [source]

Regression modelling of correlated data in ecology: subject-specific and population averaged response patterns

John Fieberg
Summary 1.,Statistical methods that assume independence among observations result in optimistic estimates of uncertainty when applied to correlated data, which are ubiquitous in applied ecological research. Mixed effects models offer a potential solution and rely on the assumption that latent or unobserved characteristics of individuals (i.e. random effects) induce correlation among repeated measurements. However, careful consideration must be given to the interpretation of parameters when using a nonlinear link function (e.g. logit). Mixed model regression parameters reflect the change in the expected response within an individual associated with a change in that individual's covariates [i.e. a subject-specific (SS) interpretation], which may not address a relevant scientific question. In particular, a SS interpretation is not natural for covariates that do not vary within individuals (e.g. gender). 2.,An alternative approach combines the solution to an unbiased estimating equation with robust measures of uncertainty to make inferences regarding predictor,outcome relationships. Regression parameters describe changes in the average response among groups of individuals differing in their covariates [i.e. a population-averaged (PA) interpretation]. 3.,We compare these two approaches [mixed models and generalized estimating equations (GEE)] with illustrative examples from a 3-year study of mallard (Anas platyrhynchos) nest structures. We observe that PA and SS responses differ when modelling binary data, with PA parameters behaving like attenuated versions of SS parameters. Differences between SS and PA parameters increase with the size of among-subject heterogeneity captured by the random effects variance component. Lastly, we illustrate how PA inferences can be derived (post hoc) from fitted generalized and nonlinear-mixed models. 4.,Synthesis and applications. Mixed effects models and GEE offer two viable approaches to modelling correlated data. The preferred method should depend primarily on the research question (i.e. desired parameter interpretation), although operating characteristics of the associated estimation procedures should also be considered. Many applied questions in ecology, wildlife management and conservation biology (including the current illustrative examples) focus on population performance measures (e.g. mean survival or nest success rates) as a function of general landscape features, for which the PA model interpretation, not the more commonly used SS model interpretation may be more natural. [source]

Discrete-time survival trees

Imad Bou-hamad
MSC 2000: Primary 62N99; secondary 62G08 Abstract Tree-based methods are frequently used in studies with censored survival time. Their structure and ease of interpretability make them useful to identify prognostic factors and to predict conditional survival probabilities given an individual's covariates. The existing methods are tailor-made to deal with a survival time variable that is measured continuously. However, survival variables measured on a discrete scale are often encountered in practice. The authors propose a new tree construction method specifically adapted to such discrete-time survival variables. The splitting procedure can be seen as an extension, to the case of right-censored data, of the entropy criterion for a categorical outcome. The selection of the final tree is made through a pruning algorithm combined with a bootstrap correction. The authors also present a simple way of potentially improving the predictive performance of a single tree through bagging. A simulation study shows that single trees and bagged-trees perform well compared to a parametric model. A real data example investigating the usefulness of personality dimensions in predicting early onset of cigarette smoking is presented. The Canadian Journal of Statistics 37: 17-32; 2009 © 2009 Statistical Society of Canada Arbres de survie à temps discret Les méthodes d'arbres sont fréquemment utilisées lors d'études impliquant des données censurées. La structure d'un arbre ainsi que la facilité avec laquelle il peut être interprété font de lui un outil utile afin d'identifier des facteurs de pronostique et de prédire les probabilités de survie conditionnelles d'un individu étant donné ses covariables. Les méthodes existantes ont été développées pour traiter une variable temporelle continue. En pratique, il arrive fréquemment que la variable mesurant le temps de survie soit mesurée selon une échelle discrète. Les auteurs proposent une nouvelle méthode pour construire un arbre qui est spécialement adaptée aux variables de survie à temps discret. Le critère de division peut être vu comme étant une extension, au cas de censure à droite, du critère d'entropie pour une variable catégorielle. La sélection de l'arbre final est basée sur une méthode d'élagage combinée avec une correction bootstrap. Les auteurs présentent également une méthode simple pour améliorer, potentiellement, la performance d'un seul arbre avec le bagging. Une étude par simulation montre que des arbres seuls et des arbres "baggés" performent bien comparativement à un modèle paramétrique. Les auteurs présentent aussi une illustration de la nouvelle méthode avec des vraies données qui investiguent l'utilité d'utiliser des dimensions de la personnalité afin de prévoir le début de l'utilisation de la cigarette. La revue canadienne de statistique 37: 17-32; 2009 © 2009 Société statistique du Canada [source]