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Results for “Statistical Regression”

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At least 433 records · Page 24Linked to original sources
PubMed2000

Graphical exploration of covariate effects on survival data through nonparametric quantile curves.

Kaplan-Meier curves provide an effective means of presenting the distributional pattern in a sample of survival data. However, in order to assess the effect of a covariate, a standard scatterplot is often difficult to interpret because of the presence of censored observations. Several authors have proposed a running median as an effective way of indicating the effect of a covariate. This article proposes a form of kernel estimation, employing double smoothing, that can be applied in a simple and efficient manner to construct an estimator of a percentile of the survival distribution as a function of one or two covariates. Permutations and bootstrap samples can be used to construct reference bands that help identify whether particular features of the estimates indicate real features of the underlying curve or whether this may be due simply to random variation. The techniques are illustrated on data from a study of kidney transplant patients.

Biometry↗
PubMed1975

[Multidimensional analysis in clinical investigation. Application to the pathogenic classification of idiopathic hypercalciuria].

Multidimensional analytical techniques enable any number of variables to be studied simultaneously. These techniques have been described and a few examples of their application to biomedical research given. A classification of so called "idiopathic hypercalciuria" was made using this approach which demonstrated the existence of three possible mechanisms of pathogenesis.

Analysis of Variance↗
PubMed1994

Effect of air pollution on lung cancer: a Poisson regression model based on vital statistics.

This article describes a Poisson regression model for time trends of mortality to detect the long-term effects of common levels of air pollution on lung cancer, in which the adjustment for cigarette smoking is not always necessary. The main hypothesis to be tested in the model is that if the long-term and common-level air pollution had an effect on lung cancer, the death rate from lung cancer could be expected to increase gradually at a higher rate in the region with relatively high levels of air pollution than in the region with low levels, and that this trend would not be expected for other control diseases in which cigarette smoking is a risk factor. Using this approach, we analyzed the trend of mortality in females aged 40 to 79, from lung cancer and two control diseases, ischemic heart disease and cerebrovascular disease, based on vital statistics in 23 wards of the Tokyo metropolitan area for 1972 to 1988. Ward-specific mean levels per day of SO2 and NO2 from 1974 through 1976 estimated by Makino (1978) were used as the ward-specific exposure measure of air pollution. No data on tobacco consumption in each ward is available. Our analysis supported the existence of long-term effects of air pollution on lung cancer.

Adult↗
PubMed2007

Statistical evidence for GLM regression parameters: a robust likelihood approach.

When a likelihood ratio is used to measure the strength of evidence for one hypothesis over another, its reliability (i.e. how often it produces misleading evidence) depends on the specification of the working model. When the working model happens to be the 'true' or 'correct' model, the probability of observing strong misleading evidence is low and controllable. But this is not necessarily the case when the working model is misspecified. Royall and Tsou (J. R. Stat. Soc., Ser. B 2003; 65:391-404) show how to adjust working models to make them robust to misspecification. Likelihood ratios derived from their 'robust adjusted likelihood' are just as reliable (asymptotically) as if the working model were correctly specified in the first place. In this paper, we apply and extend these ideas to the generalized linear model (GLM) regression setting. We provide several illustrations (both from simulated data and real data concerning rates of parasitic infection in Philippine adolescents), show how the required adjustment factor can be obtained from standard statistical software, and draw some connections between this approach and the 'sandwich estimator' for robust standard errors of regression parameters. This substantially broadens the availability and the viability of likelihood methods for measuring statistical evidence in regression settings.

Adolescent↗
PubMed2006

Numbats and aardwolves--how low is low? A re-affirmation of the need for statistical rigour in evaluating regression predictions.

Many comparative physiological studies aim to determine if a particular species differs from a prediction based on a linear allometric regression for other species. However, the judgment as to whether the species in question conforms to this allometric relationship is often not based on any formal statistical analysis. An appropriate statistical method is to compare the new species' value with the 95% confidence limits for predicting an additional datum from the relationship for the other species. We examine the basal metabolic rate (BMR) of the termitivorous numbat (Myrmecobius fasciatus) and aardwolf (Proteles cristatus) to demonstrate the use of the 95% prediction limits to determine statistically if they have a lower-than-expected BMR compared to related species. The numbat's BMR was 83.6% of expected from mass, but fell inside the 95% prediction limits for a further datum; a BMR < 72.5% of predicted was required to fall below the one-tail 95% prediction limits. The aardwolf had a BMR that was only 74.2% of predicted from the allometric equation, but it also fell well within the 95% prediction limits; a BMR of only 41.8% of predicted was necessary to fall below the one-tail 95% prediction limits. We conclude that a formal statistical approach is essential, although it is difficult to demonstrate that a single species statistically differs from a regression relationship for other species.

Animals↗