A mixture model with application to discrete competing risks data

In this paper, we modify the continuous time mixture competing risks model (Larson and Dinse, 1985) to handle discrete competing risks data. The main result of the model is an alternate regression expression for the cumulative incidence function. The structure of the regression expression for the cumulative incidence function under this model, and the proportional hazards assumption for the conditional hazard rates with piece-wise constant baseline conditional hazards, combine to allow for another means to assess the covariate effects on the cumulative incidence function. This benefit comes at some computational costs because the parameters are estimated via an EM algorithm. The proposed model is applied to real data and it is found that it improves the exercise of evaluating the covariate effects on the cumulative incidence function compared to other discrete competing risks models

Genotype by Environment Interaction (G x E) and Grain Yield Stability Analysis of Ethiopian Linseed and Niger Seed Varieties

General Background: Niger seed [(Guizotia abyssinica (L.F.) Cass.), 2n = 30] and Linseed [(Linum usitatissimum L.), n=15] are indigenous oil crops of Ethiopia. Over many years, there are a few Linseed and Niger seed varieties developed and released through intensive breeding and genetics research program in Ethiopia. However, whether these varieties are stable, adaptable to the environments of Western Ethiopia and similar agro-ecologies are not clear.Objectives: The objectives of the study were to (i) assess genotype by environment interaction (G x E) and (ii) identify stable and adaptable Linseed and Niger seed varieties for specific and wide adaptions.Materials and methods: All Niger seed and Linseed released varieties of Ethiopia between the years 1984 and 2008 were tested for multi-locations and years. Independent experiments of linseed and Niger seed varieties were evaluated in Randomized Complete Block Design replicated three times. Eight varieties of Linseed with one local variety were evaluated at Arjo, Gute and Shambu locations. In addition, five Niger seed varieties including one local variety were tested at Bako, Gute and Shambu locations.Summary of the result and application of the findings: The seed yields ranged between 0.898 tons ha-1 and 1.575 tons/ha for Linseed and between 0.600 tons ha-1 and 0.690 tons ha-1 for Niger seed. Analysis of variance using additive main effects and multiplicative interactions (AMMI) model revealed significant differences (pG0.01) for genotype, environment, genotype x environment interaction and interaction principal component (IPCA1) for Linseed, while only environment was found to be significantly different for Niger seed. Based on AMMI analysis, Kulumsa-1 was the best yielding, stable and widely adapted, while CI-1525 and Berene were high yielding but unstable and specifically adapted Linseed varieties to high yielding environments. Belay 96, Chilalo, Tole and CI-1652 were moderately stable and adapted to high yielding environments. Among Niger seed varieties, Shambu-1 and Esete-1 had comparable seed yield with moderately stable for the tested environments whereas Kuyu and local variety were unstable and not adopted to the testing environments.Key words/phrases: Adapted variety, Additive main effect and multiplicative interaction (AMMI), Genotype x environment (G x E) interaction, Stable variet

A nonparametric vertical model: An application to discrete time competing risks data with missing failure causes

Discrete time competing risks data continue to arise in social sciences, education etc., where time to failure is usually measured in discrete units. This data may also come with unknown failure causes for some subjects. This occurs against a background of very limited discrete time analysis methods that were developed to handle such data. A number of continuous time missing failure causes models have been proposed over the years. We select one of these continuous time models, the vertical model (Nicolaie et al., 2015), and present it as a nonparametric model that can be applied to discrete time competing risks data with missing failure causes. The proposed model is applied to real data and compared to the MI. It was found that the proposed model compared favorably with the MI method

Predictors of subjective recovery from recent-onset psychosis in a developing country: a mixed-methods study

Purpose This study was conducted to: (a) investigate the levels and progress of subjective recovery from recent-onset psychosis; (b) examine its predictive factors and; (c) describe perceived challenges and opportunities affecting recovery. The findings were expected to help inform recovery-oriented psychiatric care in low-income, particularly African, countries. Methods This sequential explanatory mixed-methods study involved 263 service users with recent-onset psychosis from Northwestern Ethiopia. For the quantitative part, a 9-month longitudinal study approach was employed with three time point measurements over 9 months. Predictor variables for subjective recovery from recent-onset psychosis were identified by hierarchical multiple linear regression tests. Following the quantitative survey, individual qualitative interviews were conducted with 19 participants. Interview data were transcribed and thematically analysed. Results High mean subjective recovery scores were recorded throughout the study (Questionnaire about the Process of Recovery score ranging from 44.17 to 44.65). Quality of life, internalized stigma, disability, hopelessness, satisfaction with social support, and central obesity were significant predictors of subjective recovery across the three time points. Participants' perceived challenges and opportunities affecting their recovery were categorized into four themes. Conclusion In Ethiopia, a low percentage of individuals with SMIs initiate psychiatric treatment and many discontinue this to attend spiritual healing. In this study, the Ethiopian SMI patients engaged consistently in psychiatric treatment indicated high mean subjective recovery scores. Devising mechanisms to integrate the psychiatric treatment and spiritual healing sectors are suggested. Approaches to improve quality of life, functioning, hope, internalized stigma and provide need-based social support are suggested

Evaluation of mixed-effects models for predicting Douglas-fir mortality

We examined the performance of several generalized linear fixed- and mixed-effects individual-tree mortality models for Douglas-fir stands in the Pacific Northwest. The mixed-effects models accounted for sampling and study design overdispersion. Inclusion of a random intercept term reduced model bias by 88% relative to the fixed-effects model; however, model discrimination did not substantially differ. An uninformed version of the mixed model that used only its fixed effects parameters produced predicted mortality values that exceeded the fixed-effects model bias by 31%. Overall, we did not find compelling evidence to suggest that the mixed models fit our data better than the fixed-effects model. In particular, the mixed models produced fixed-effects parameter estimates that predicted unreasonably high mortality rates for trees approaching 1 m in diameter at breast height.Keywords: Douglas-fir, Mortality, Generalized linear model, Mixed mode

Analysis and comparison of nonlinear tree height prediction strategies for Douglas-fir forests

Using an extensive Douglas-fir data set from southwest Oregon, we examined the (1) performance and suitability of selected prediction strategies, (2) contribution of relative position and stand-density measures in improving tree height (h) prediction values, and (3) effect of different subsampling designs to fill in missing h values in a new stand using a regional nonlinear model. Nonlinear mixed-effects models (NMEM) substantially improved the accuracy and precision of height prediction over the conventional nonlinear fixed-effects model (NFEM) that assumes the observations are independent, particularly when a few trees are subsampled for height. The predictive performance of a correction factor on a NFEM with relative position and stand-density measures was comparable to that of a NMEM when four or more trees were subsampled for height. When two or more heights were randomly subsampled, the NMEM efficiently explained the differences in the height–diameter relationship because of the variations in relative position of trees and stand density without having to incorporate them into the model. When only one height was subsampled, selecting the largest diameter tree in the stand would result in a lower predicted root mean square error (RMSE) than randomly selecting the height, regardless of the model form or fitting strategy used.A` l’aide d’une banque de donne´es exhaustive sur le sapin Douglas du sud-ouest de l’Oregon, nous avons examine ´ (1) la performance et la pertinence des strate´gies de pre´diction se´lectionne´es, (2) la contribution de la position relative de l’arbre et de la densite´ du peuplement pour ame´liorer la pre´diction de la hauteur des arbres et (3) l’effet de diffe´rents dispositifs d’e´chantillonnage pour imputer la hauteur manquante dans un nouveau peuplement a` l’aide d’un mode`le non line´aire re´gional. Les mode`les non line´aires a` effets mixtes (MNLEM) ame´liorent substantiellement l’exactitude et la pre´cision des pre´dictions de la hauteur comparativement au mode`le non line´aire a` effets fixes conventionnel (MNLEF). Ce dernier suppose que les observations sont inde´pendantes, particulie`rement lorsque peu d’arbres sont e´chantillonne´s pour e´valuer la hauteur. La performance pre´dictive d’un facteur de correction pour le MNLEF base´ sur la mesure de la position relative de l’arbre et de la densite´ du peuplement est comparable a` celle du MNLEM lorsque quatre arbres ou plus sont e´chantillonne´s pour e´valuer la hauteur. Lorsque deux hauteurs ou plus sont e´chantillonne´es ale´atoirement, le MNLEM explique efficacement les diffe´rences dans la relation hauteur-diame`tre dues aux variations de la position relative des arbres et de la densite´ sans avoir a` les incorporer formellement dans le mode`le. Lorsqu’une seule hauteur est e´chantillonne´e, le choix du plus gros arbre dans le peuplement pourrait entraıˆner une erreur de pre´diction plus faible que lorsque la hauteur est se´lectionne´e au hasard, peu importe la forme du mode`le ou la strate´gie d’ajustement utilise´e

Evaluation of sampling methods to quantify abundance of hardwoods and snags within conifer-dominated riparian zones

• Aims: Six sampling alternatives were examined for their ability to quantify selected attributes of snags and hardwoods in conifer-dominated riparian areas of managed headwater forests in western Oregon. • Methods: Each alternative was simulated 500 times at eight headwater forest locations based on a 0.52-ha square stem map. The alternatives were evaluated based on how well they estimated the number of hardwoods and snags per hectare and their basal area per hectare using root mean square error and percent bias. • Results: In general, 3.6-m wide systematic strips oriented perpendicular to the stream outperformed the other alternatives. However, the variance of all six sampling alternatives was quite high and further research is needed to determine an optimal sampling method for quantifying hardwood and snag attributes in forests dominated by live conifers. • Conclusion: When sampling snag and hardwood as a minor component of the overall forest composition within a riparian area, we suggest using 3.6-m strips perpendicular to the stream.Keywords: Stand structure, Monitoring, Pacific Northwest, Strip samplin

Geostatistical modeling of riparian forest microclimate and its implications for sampling

Les modèles de prédiction du microclimat pour différentes conditions de station dans les zones riveraines boisées des cours d’eau de tête de bassin sont peu développés et les procédures d’échantillonnage pour caractériser les gradients sous-jacents du microclimat riverain sont rares. Nous avons utilisé des données de microclimat riverain collectées le long de huit cours d’eau de tête de bassin dans la chaîne côtière de l’Oregon pour comparer le krigeage ordinaire (KO), le krigeage universel (KU) et le krigeage avec dérive externe (KDE) pour la prédiction localisée de la température moyenne maximale de l’air (Tair). Plusieurs caractéristiques topographiques et de la structure de la forêt ont été considérées comme paramètres spécifiques à la station. L’élévation au-dessus du cours d’eau et la distance du cours d’eau étaient les covariables les plus importantes dans les modèles de KDE qui donnaient de meilleurs résultats que le KO et le KU en termes d’écart-type. La répartition des échantillons a été optimisée sur la base de la variance de krigeage et des moyennes pondérées du critère de la plus courte distance à l’aide d’un algorithme de recuit simulé. La répartition optimisée des échantillons donnait de meilleurs résultats que la répartition systématique en termes de variance moyenne de krigeage, surtout lorsque le nombre d’échantillons était faible. Ces résultats suggèrent des méthodes pour augmenter l’efficacité du suivi du microclimat dans les zones riveraines.Predictive models of microclimate under various site conditions in forested headwater stream – riparian areas are poorly developed, and sampling designs for characterizing underlying riparian microclimate gradients are sparse. We used riparian microclimate data collected at eight headwater streams in the Oregon Coast Range to compare ordinary kriging (OK), universal kriging (UK), and kriging with external drift (KED) for point prediction of mean maximum air temperature (T air). Several topographic and forest structure characteristics were considered as site-specific parameters. Height above stream and distance to stream were the most important covariates in the KED models, which outperformed OK and UK in terms of root mean square error. Sample patterns were optimized based on the kriging variance and the weighted means of shortest distance criterion using the simulated annealing algorithm. The optimized sample patterns outperformed systematic sample patterns in terms of mean kriging variance mainly for small sample sizes. These findings suggest methods for increasing efficiency of microclimate monitoring in riparian areas