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Prediction of renal insufficiency in Pima Indians with nephropathy of type 2 diabetes mellitus

AMERICAN JOURNAL OF KIDNEY DISEASES 2002年第2期40卷 252-264页

作者： Goldfarb-Rumyantzev, AS Pappas, L Univ Utah Sch Med Div Nephrol Salt Lake City UT 84112 USA Univ Utah Sch Med Div Hypertens Salt Lake City UT 84112 USA

Background: A high prevalence and early onset of type 2 diabetes in Pima Indians is well known. Our objective is to use several statistical models to identify predictors of glomerular filtration rate (GFR) deterioration and develop an algorithm to predict GFR 4 years after the initial evaluation. Methods: All records (n = 86) were randomly assigned to a training set (n = 60) and a testing set (n = 26). Linear regression, generalized additive, tree-based, and artificial neural network models were used to identify predictors of outcome and develop a prediction algorithm. Results: Proteinuria remained the single most important predictor of long-term renal function;other predictors included baseline GFR, blood pressure, plasma renin activity, lipid profile, age, weight/body mass index, and diabetes duration. All four models achieved a good correlation (r = 0.73 to 0.78) between observed and predicted 4-year GFRs on a separate (testing) data set. Best results in predicting the value of GFR were achieved using a tree-based model with six terminal nodes (r = 0.78;root mean squared prediction error = 38.9). The tree-based and generalized additive models achieved high positive (91%) and negative (100%) predictive values in identifying subjects, who developed depressed GFRs in 4 years. An artificial neural network achieved the highest area under the receiver operating characteristic curve (0.91). Conclusion: GFR depression within 4 years can be predicted with a precision that suggests potential clinical utility. A tree-based model with six terminal nodes has shown the best results in predicting the actual value of GFR, whereas an artificial neural network is the model of choice to identify the group of patients that will develop renal insufficiency. (C) 2002 by the National Kidney Foundation, Inc.

关键词： prediction model artificial neural network tree-based model generalized additive model Pima Indians diabetic nephropathy renal insufficiency outcome

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Vegetation distribution in relation to topographically driven processes in southwestern Australia

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APPLIED VEGETATION SCIENCE 2002年第1期5卷 147-158页

作者： Dirnböck, T Hobbs, RJ Lambeck, RJ Caccetta, PA Univ Vienna Inst Ecol & Conservat Biol A-1090 Vienna Austria CSIRO Perth WA 6014 Australia CSIRO Wembley WA 6014 Australia Greening Australia Fremantle WA Australia

This study assesses the utility of modelling approaches to predict vegetation distribution in agricultural landscapes of southwestern Australia. Climate surfaces, hydrologic and erosion process models are used to link, vegetation to environmental variables. generalized additive models (GAM) are derived for presence/absence data of mapped vegetation types. Vegetation distribution show's significant responses to rainfall and subsequent water redistribution due to the relief: however, these variables are insufficient to effectively explain vegetation patterns at the local scale. Accordingly, prediction accuracy remains low (kappa-values below, 0.5). The striking unpredictability of the local distribution of the vegetation in the Wheatbelt is discussed with regard to the performance of topographically driven processes in subdued landscapes and with regard to geological, historical and biological factors determining the southwestern Australian plant species distribution.

关键词： digital elevation model fragmented landscape generalized additive model hydrological modeling topographical variable vegetation mapping

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Air pollution and mortality: Estimating regional and national dose-response relationships

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JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION 2002年第457期97卷 100-111页

作者： Dominici, F Daniels, M Zeger, SL Samet, JM Johns Hopkins Univ Dept Biostat Bloomberg Sch Publ Hlth Baltimore MD 21205 USA Iowa State Univ Dept Stat Ames IA USA Johns Hopkins Univ Dept Epidemiol Baltimore MD 21205 USA

We analyzed a national data base of air pollution and mortality for the 88 largest U.S. cities for the period 1987-1994, to estimate relative rates of mortality associated with airborne particulate matter smaller than 10 microns (PM,,) and the form of the relationship between PM10 concentration and mortality. To estimate city-specific relative rates of mortality associated with PM,,, we built log-linear models that included nonparametric adjustments for weather variables and longer term trends. To estimate PM, mortality dose-response curves, we modeled the logarithm of the expected value of daily mortality as a function of PM10 using natural cubic splines with unknown numbers and locations of knots. we also developed spatial models to investigate the heterogeneity of relative mortality rates and of the shapes of PM10 mortality dose-response curves across cities and geographical regions. To determine whether variability in effect estimates can be explained by city-specific factors, we explored the dependence of relative mortality rates on mean pollution levels, demographic variables, reliability of the pollution data, and specific constituents of particulate matter. We implemented estimation with simulation-based methods, including data augmentation to impute the missing data of the city-specific covariates and the reversible jump Markov chain Monte Carlo (RJMCMC) to sample from the posterior distribution of the parameters in the hierarchical spline model. We found that previous-day PM,, concentrations were positively associated with total mortality in most the locations, with a .5% increment for a 10 mug/m(3) increase in PM10. The effect was strongest in the Northeast region, where the increase in the death rate was twice as high as the average for the other cities, Overall, we found that the pooled concentration-response relationship for the nation was linear.

关键词： air pollution data augmentation generalized additive model hierarchical model natural cubic spline particulate matter relative rate

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modeling spatial distribution of amphibian populations: a GIS approach based on habitat matrix permeability

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BIODIVERSITY AND CONSERVATION 2002年第12期11卷 2143-2165页

作者： Ray, N Lehmann, A Joly, P Univ Geneva Anthropol & Ecol Dept LEBA CH-1211 Geneva 4 Switzerland Univ Lyon 1 CNRS UMR 5023 Lyon France

Predictions of occurrence of two amphibian species, the common toad and the alpine newt, were made using information on land use surrounding breeding ponds. A geographical information system (GIS) was used to compile a landuse map, from which permeability estimates ( friction) were derived. Potential migration zones based on friction and maximum migration distance were then modeled. Contacts between several migration zones suggest the potential for migration between ponds by adult individuals. The ability of the migration zones to enhance predictions of species occurrence was tested using generalized additive models (GAMs), and several landscape variables were selected as determinants of amphibian presence. The area of a migration zone and the number of ponds within that zone were positively related to both toad and newt presence, suggesting the importance of buffer habitats around each pond in amphibian conservation. Toad presence declined with cultivated field area and newt presence declined with vineyard area, suggesting the negative effect of agricultural activity on amphibians. The friction-based modeling approach improved the prediction of toad presence when compared to a more classical analysis of habitat composition within a circular zone centered on the focal pond. Prediction of newt presence was, however, less accurate than prediction of toad presence. Despite its exploratory nature and the subjectivity of permeability estimates, this study shows the usefulness of GIS in the functional analysis of a landscape, with potential applications in biological conservation. It also highlights the need for improving our knowledge of habitat use by animals.

关键词： Amphibian Bufo bufo dispersal model generalized additive model geographical information system least-cost path migration modeling permeability Switzerland Triturus alpestris

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Applications: generalized additive modelling of Mixed Distribution Markov models with Application to Melbourne's Rainfall

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Australian & New Zealand Journal of Statistics 2002年第2期42卷

作者： Rob J. Hyndman Gary K. Grunwald Department of Econometrics and Business Statistics Monash University Clayton VIC 3168 Australia Department of Preventive Medicine and Biometrics University of Colorado Health Sciences Center Denver CO 80262 USA

The paper considers the modelling of time series using a generalized additive model with first‐order Markov structure and mixed transition density having a discrete component at zero and a continuous component with positive sample space. Such models have application, for example, in modelling daily occurrence and intensity of rainfall, and in modelling numbers and sizes of insurance claims. The paper shows how these methods extend the usual sinusoidal seasonal assumption in standard chain‐dependent models by assuming a general smooth pattern of occurrence and intensity over time. These models can be fitted using standard statistical software. The methods of Grunwald & Jones (2000) can be used to combine these separate occurrence and intensity models into a single model for amount. The models are used to investigate the relationship between the Southern Oscillation Index and Melbourne's rainfall, illustrated with 36 years of rainfall data from Melbourne, Australia.

关键词： binary time series droughts dry spells gamma time series generalized additive model generalized linear model Markov model mixture distribution non‐Gaussian time series southern oscillation index.

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modeling spatially explicit forest structural attributes using generalized additive models

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JOURNAL OF VEGETATION SCIENCE 2001年第1期12卷 15-26页

作者： Frescino, TS Edwards, TC Moisen, GG US Forest Serv USDA Rocky Mt Res Stn Ogden UT 84401 USA Utah State Univ Grad Degree Program Fisheries & Wildlife Logan UT 84322 USA Utah State Univ USGS Biol Resources Div Utah Cooperat Fish & Wildlife Res Unit Logan UT 84322 USA

We modelled forest composition and structural diversity in the Uinta Mountains, Utah, as functions of satellite spectral data and spatially-explicit environmental variables through generalized additive models. Measures of vegetation composition and structural diversity were available from existing forest inventory data. Satellite data included raw spectral data from the Landsat Thematic Mapper (TM) a GAP Analysis classified TM, and a vegetation index based on raw spectral data from an advanced very high resolution radiometer (AVHRR). Environmental predictor variables included maps of temperature, precipitation. elevation, aspect, slope, and geology. Spatially-explicit predictions were generated for the presence of forest and lodgepole cover types, basal area of forest trees, percent cover of shrubs, and density of snags. The maps were validated using an independent set of field data collected from the Evanston ranger district within the Uinta Mountains. Within the Evanston ranger district, model predictions were 88% and 80% accurate for forest presence and lodgepole pine (Pinus contorta), respectively. An average 62% of the predictions of basal area, shrub cover, and snag density fell within a 15% deviation from the field validation values. The addition of TM spectral data and the GAP Analysis TM-classified data contributed significantly to the models' predictions, while AVHRR had less significance.

关键词： accuracy assessment AVHRR forest attribute model generalized additive model Geogaphical Information Systems Landsat Thematic Mapper vegetation modelling

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Simple incorporation of interactions into additive models

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BIOMETRICS 2001年第2期57卷 539-545页

作者： Coull, BA Ruppert, D Wand, MP Harvard Univ Sch Publ Hlth Dept Biostat Boston MA 02115 USA Cornell Univ Sch Operat Res & Ind Engn Ithaca NY 14853 USA

Often, the functional form of covariate effects in an additive model varies across groups defined by levels of a categorical variable. This structure represents a factor-by-curve interaction. This article presents penalized spline models that incorporate factor-by-curve interactions into additive models. A mixed model formulation for penalized splines allows for straightforward model fitting and smoothing parameter selection. We illustrate the proposed model by applying it to pollen ragweed data in which seasonal trends vary by year.

关键词： generalized additive model generalized linear mixed model penalized spline pollen forecasting varying-coefficient model

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Competitive interactions between tree species in New Zealand's old-growth indigenous forests

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ECOLOGY 2001年第9期82卷 2560-2573页

作者： Leathwick, JR Austin, MP Landcare Res New Zealand Private Bag 3127 Hamilton New Zealand CSIRO Wildlife & Ecol Canberra ACT 2601 Australia

New Zealand's four broad-leaved evergreen tree species from the genus Nothofagus all show pronounced distributional disjunctions, independent of environmental factors known to influence tree distributions. Here, we use these disjunctions as the basis for a natural removal experiment to investigate competitive interactions between Nothofagus and a range of other widespread conifer and broad-leaved tree species. We first model the abundance of non-Nothofagus species as a function of environment, using generalized additive models (GAMs) and an extensive data set sampling much of New Zealand's remaining old-growth forests. We then assess the effects of competitive interaction with Nothofagus by adding statistical terms describing (1) Nothofagus abundance, and (2) interactions between Nothofagus abundance and annual temperature, the dominant environmental gradient. Results indicate substantial reductions in the abundance of many species as Nothofagus abundance increases. The magnitude of this reduction varies with position along the dominant environmental gradient;species overlapping most strongly with Nothofagus are generally most sensitive to increases in Nothofagus abundance. In addition, both the shapes of species responses to mean annual temperature and the positions of their optima change as Nothofagus abundance increases. This demonstration of competition using community compositional data has implications both for vegetation theory and for prediction of the likely impacts of global warming on New Zealand's forest pattern.

关键词： broad-leaved evergreen climate variables conifer GAM regression generalized additive model interspecific competition landform New Zealand Nothofagus old-growth forest removal experiment temperature gradient

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Can we trust gradients extracted by Detrended Correspondence Analysis?

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JOURNAL OF VEGETATION SCIENCE 2000年第4期11卷 565-572页

作者： Ejrnæs, R Univ Copenhagen Dept Plant Ecol DK-1353 Copenhagen K Denmark

In this study, data from dry temperate grassland vegetation in Denmark are used to compare the predictive power of gradients obtained by ordination. One of the problems of ordination methods based on weighted averaging, namely the assumption of symmetric, unimodal response curves, is investigated by smoothing species responses to simple and complex gradients respectively. It was found that species response types to a pH gradient are diverse and often deviate from a unimodal, symmetrical shape, Bimodal responses were also found, but they disappeared when more influential gradients were considered. Many species showed truncated responses with optima near gradient ends. In order to assess the impact of unrealistic response assumptions and reported problems with instability on the performance of DCA, the predictive potentials of measured variables and coenoclines extracted by DCA were compared. Despite field data violating the assumption of unimodal response, DCA was found to extract gradients predicting species abundance better than the environmental variables available. Shortcomings and advantages of indirect and direct methods in plant ecology are discussed.

关键词： Correspondence Analysis cross-validation Denmark dry grassland generalized additive model multiple regression response curve smoothing

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Transitional regression models, with application to environmental time series

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JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION 2000年第449期95卷 16-27页

作者： Brumback, BA Ryan, LM Schwartz, JD Neas, LM Stark, PC Burge, HA Harvard Univ Sch Publ Hlth Dept Biostat Boston MA 02115 USA Harvard Univ Sch Publ Hlth Dept Environm Hlth Boston MA 02115 USA

Environmental epidemiologists often encounter time series data in the form of discrete or other nonnormal outcomes: for example, in modeling the relationship between air pollution and hospital admissions or mortality rates. We present a case study examining the association between pollen counts and meteorologic covariates. Although such time series data are inadequately described by standard methods for Gaussian time series, they are often autocorrelated, and warrant an analysis beyond those provided by ordinary generalized linear models (GLMs). Transitional regression models (TRMs), signifying nonlinear regression models expressed in terms of conditional means and variances given past observations, provide a unifying framework for two mainstream approaches to extending the GLM for autocorrelated data. The first approach models current outcomes with a GLM that incorporates past outcomes as covariates, whereas the second models individual outcomes with marginal GLMs and then couples the error terms With an autoregressive covariance matrix. Although the two approaches coincide for the Gaussian GLM, which serves as a helpful introductory example, in general they yield fundamentally different models. We analyze the pollen study using TRM's of both types and present parameter estimates together with asymptotic and bootstrap standard errors. In several cases we find evidence of residual autocorrelation;however, when we relax the TRM to allow for a nonparametric smooth trend, the autocorrelation disappears. This kind of trade-off between autocorrelation and flexibility is to be expected, and has a natural interpretation in terms of the covariance function for a nonparametric smoother. We provide an algorithm for fitting these flexible TRM's that is relatively easy to program with the generalized additive model software in S-PLUS.

关键词： conditional least squares generalized additive model generalized linear model moving blocks bootstrap non-Gaussian time series time-varying overdispersion

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