Details:

Alternative Title:
BMC Med Res Methodol
Personal Author:
Petersen, Martin R ; Deddens, James A
Petersen, Martin R ; Deddens, James A Less -
Description:
Background

It is usually preferable to model and estimate prevalence ratios instead of odds ratios in cross-sectional studies when diseases or injuries are not rare. Problems with existing methods of modeling prevalence ratios include lack of convergence, overestimated standard errors, and extrapolation of simple univariate formulas to multivariable models. We compare two of the newer methods using simulated data and real data from SAS online examples.

Methods

The Robust Poisson method, which uses the Poisson distribution and a sandwich variance estimator, is compared to the log-binomial method, which uses the binomial distribution to obtain maximum likelihood estimates, using computer simulations and real data.

Results

For very high prevalences and moderate sample size, the Robust Poisson method yields less biased estimates of the prevalence ratios than the log-binomial method. However, for moderate prevalences and moderate sample size, the log-binomial method yields slightly less biased estimates than the Robust Poisson method. In nearly all cases, the log-binomial method yielded slightly higher power and smaller standard errors than the Robust Poisson method.

Conclusion

Although the Robust Poisson often gives reasonable estimates of the prevalence ratio and is very easy to use, the log-binomial method results in less bias in most common situations, and because it fits the correct model and obtains maximum likelihood estimates, it generally results in slightly higher power, smaller standard errors, and, unlike the Robust Poisson, it always yields estimated prevalences between zero and one.


Subject:
[+]
Analysis Of Variance
Computer Simulation
Cross-Sectional Studies
Likelihood Functions
Logistic Models
Models, Statistical
Poisson Distribution
Prevalence
Research Article

Source:
BMC Med Res Methodol. 2008; 8:9.
Document Type:
Journal Article
Collection(s):
CDC Public Access
Main Document Checksum:
[+]
urn:sha256:d1295b844e7cbb8979df31352a7b7079ac2a91bef234fd91edb97b1cb1e19167
File Type:

Supporting Files

• 	1471-2288-8-9.nxml	txt
  	license.txt	txt

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