Related Experiment Video
Updated: May 28, 2026
10:46
A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
Published on: December 9, 2015
A Poisson regression approach for modelling spatial autocorrelation between geographically referenced observations.
Mohammadreza Mohebbi1, Rory Wolfe, Damien Jolley
1Department of Epidemiology and Preventive Medicine, Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, Australia. Mohammadreza.Mohebbi@monash.edu
BMC Medical Research Methodology
|October 4, 2011
Summary
Standard epidemiological methods often ignore spatial correlation, potentially biasing results. Accounting for spatial autocorrelation in esophageal cancer incidence analysis in Iran is crucial for accurate parameter estimates and reliable standard errors.
Area of Science:
- Epidemiology
- Spatial Statistics
- Cancer Registry Analysis
Background:
- Traditional epidemiological methods often overlook spatial correlation in data.
- Ignoring spatial autocorrelation can lead to biased parameter estimates and inaccurate standard errors in regression analyses.
Purpose of the Study:
- To assess the impact of spatial correlation on esophageal cancer incidence in the Caspian region of Iran.
- To compare nonspatial and spatial regression models for analyzing esophageal cancer incidence data.
Main Methods:
- Utilized age-standardized incidence ratios (SIRs) of esophageal cancer (EC) from 2001-2005.
- Employed Poisson regression models with nonspatial and spatial random effects, incorporating distance-based and neighborhood-based autocorrelation structures.
- Model comparison was performed using Bayesian information criterion (BIC), Akaike's information criterion (AIC), and adjusted pseudo R2.
Main Results:
- A Gaussian semivariogram indicated a 225 km effective range for spatial autocorrelation in EC incidence.
- The Moran's I index confirmed significant geographical clustering of EC.
- Modeling residual spatial dependence altered point and interval estimates of covariate effects compared to nonspatial models.
Conclusions:
- The spatial patterns of EC incidence and differences in estimates highlight the necessity of accounting for spatial correlation in the Caspian region.
- Spatial smoothing techniques require careful application in epidemiological analyses.
Related Concept Videos
01:09Poisson Probability Distribution
A Poisson probability distribution is a discrete probability distribution. It gives the probability of a number of events occurring in a fixed interval of time or space if these events happen at a known average rate and independently of the time since the last event. For example, a book editor might be interested in the number of words spelled incorrectly in a particular book. It might be that, on average, there are five words spelled incorrectly in 100 pages. The interval is 100 pages.
The...
The...
01:11Residuals and Least-Squares Property
The vertical distance between the actual value of y and the estimated value of y. In other words, it measures the vertical distance between the actual data point and the predicted point on the line
If the observed data point lies above the line, the residual is positive, and the line underestimates the actual data value for y. If the observed data point lies below the line, the residual is negative, and the line overestimates the actual data value for y.
The process of fitting the best-fit...
If the observed data point lies above the line, the residual is positive, and the line underestimates the actual data value for y. If the observed data point lies below the line, the residual is negative, and the line overestimates the actual data value for y.
The process of fitting the best-fit...
01:11Regression Analysis
Regression analysis is a statistical tool that describes a mathematical relationship between a dependent variable and one or more independent variables.
In regression analysis, a regression equation is determined based on the line of best fit– a line that best fits the data points plotted in a graph. This line is also called the regression line. The algebraic equation for the regression line is called the regression equation. It is represented as:
In regression analysis, a regression equation is determined based on the line of best fit– a line that best fits the data points plotted in a graph. This line is also called the regression line. The algebraic equation for the regression line is called the regression equation. It is represented as:
01:25Poisson's And Laplace's Equation
The electric potential of the system can be calculated by relating it to the electric charge densities that give rise to the electric potential. The differential form of Gauss's law expresses the electric field's divergence in terms of the electric charge density.
01:06Model Approaches for Pharmacokinetic Data: Distributed Parameter Models
Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
01:15Scatter Plot
The most common and easiest way to display the relationship between two variables, x and y, is a scatter plot. A scatter plot shows the direction of a relationship between the variables. A clear direction happens when there is either: