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Articles 211 - 215 of 215
Full-Text Articles in Physical Sciences and Mathematics
Breaking The Matches In A Paired T-Test For Community Interventions When The Number Of Pairs Is Small, Paula Diehr, Don C. Martin, Thomas D. Koepsell, Allen D. Cheadle
Breaking The Matches In A Paired T-Test For Community Interventions When The Number Of Pairs Is Small, Paula Diehr, Don C. Martin, Thomas D. Koepsell, Allen D. Cheadle
UW Biostatistics Working Paper Series
There is considerable interest in community interventions for health promotion, where the community is the experimental unit. Because such interventions are expensive, the number of experimental units (communities) is usually small. Because of the small number of communities involved, investigators often match treatment and control communities on demographic variables before randomization to minimize the possibility of a bad split. Unfortunately, matching has been shown to decrease the power of the design when the number of pairs is small, unless the matching variable is very highly correlated with the outcome variable (in this case, with change in the health behavior). We …
The Multiple Admission Factor (Maf) In Small Area Variation Analysis, Kevin Cain, Paula Diehr
The Multiple Admission Factor (Maf) In Small Area Variation Analysis, Kevin Cain, Paula Diehr
UW Biostatistics Working Paper Series
Small area variation analysis are often based on area-level data such as the total number of hospital admissions within an area, rather than person-level data. Such analysis often make the assumption that the number of admissions within a small area follow a Poisson distribution. This may not be a reasonable assumption when multiple admissions per person are possible. In this case, the multiple admission factor (MAF) can be used to adjust for the extra variance introduced by multiple admissions. In this article, data from Washington State are used to estimate the multiple admission rate and the MAF for each modifed …
Regression Models For Bivariate Binary Responses, Juni Palmgren
Regression Models For Bivariate Binary Responses, Juni Palmgren
UW Biostatistics Working Paper Series
We discuss maximum likelihood inference for the bivariate logistic model, specified in terms of the marginal logits and the log odds ratio. Using the exponential family nonlinear model formulation the model fitting can be done in GLIM. The procedure is illustrated by modelling survival of unilateral and bilateral total hip arthroplasties as function of patient specific and hip specific covariates. We compare maximum likelihood inference with inference obtained from solving likelihood equations under the assumption of within block independence and using robust standard errors for the estimates. Simulations indicate that the latter procedure is effcient for block specific covariates but …
Sample Size Calculations And Optimal Followup Time In Health Services Research Using Utilization Rates, Paula Diehr
Sample Size Calculations And Optimal Followup Time In Health Services Research Using Utilization Rates, Paula Diehr
UW Biostatistics Working Paper Series
It is not always possible to estimate the sample sizes needed in health services research because special formulas are needed, and the necessary data may not be available to use in the formulas. We provide some useful formulas for the sample size required in comparing the means of two groups. These include the special case where the two groups are not of equal size either because one is known to have a higher variability or because one group has already been chosen and its size is thus fixed. We also explore the relationship of the mean to the standard deviation …
Statistical Measures For Admission Rates, Paula Diehr
Statistical Measures For Admission Rates, Paula Diehr
UW Biostatistics Working Paper Series
Hospital admission rates are often shown and interpreted without consideration of their inherent variability, which may lead to faulty conclusions. This may be because theoretically correct variance estimates are not known for the type of estimates usually used; i.e., total admissions divided by total person-months of observation. Here, correct methods for testing and estimation are shown for situations where they exist. For other types of data, approximate procedures are proposed and their properties examined theoretically and empirically, yielding recommendations for exact and approximate estimation and testing methods for admission rates in common situations.