Biostatistics Commons^™

On Additive Regression Of Expectancy, Ying Qing Chen

UW Biostatistics Working Paper Series

Regression models have been important tools to study the association between outcome variables and their covariates. The traditional linear regression models usually specify such an association by the expectations of the outcome variables as function of the covariates and some parameters. In reality, however, interests often focus on their expectancies characterized by the conditional means. In this article, a new class of additive regression models is proposed to model the expectancies. The model parameters carry practical implication, which may allow the models to be useful in applications such as treatment assessment, resource planning or short-term forecasting. Moreover, the new model …

A Linear Regression Framework For Receiver Operating Characteristic(Roc) Curve Analysis, Zheng Zhang, Margaret S. Pepe

UW Biostatistics Working Paper Series

In the field of medical diagnostic testing, the receiver operating characteristics(ROC) curve has long been used as a standard statistical tool to assess the accuracy of tests that yield continuous results. Although previous research in this area focused mostly on estimating the ROC curve, recently it has been recognized that the accuracy of a given test may fluctuate depending on certain factors, which motivates modelling covariate effects on the ROC curve. Comparing the corresponding ROC curves between two or more tests is a special case of covariate effect modelling. In this manuscript, we introduce a linear regression framework to model …

Multiple Imputation For Correcting Verification Bias, Ofer Harel, Xiao-Hua Zhou

UW Biostatistics Working Paper Series

In the case in which all subjects are screened using a common test, and only a subset of these subjects are tested using a golden standard test, it is well documented that there is a risk for bias, called verification bias. When the test has only two levels (e.g. positive and negative) and we are trying to estimate the sensitivity and specificity of the test, one is actually constructing a confidence interval for a binomial proportion. Since it is well documented that this estimation is not trivial even with complete data, we adopt Multiple imputation (MI) framework for verification bias …

New Confidence Intervals For The Difference Between Two Sensitivities At A Fixed Level Of Specificity, Gengsheng Qin, Yu-Sheng Hsu, Xiao-Hua Zhou

UW Biostatistics Working Paper Series

For two continuous-scale diagnostic tests, it is of interest to compare their sensitivities at a predetermined level of specificity. In this paper we propose three new intervals for the difference between two sensitivities at a fixed level of specificity. These intervals are easy to compute. We also conduct simulation studies to compare the relative performance of the new intervals with the existing normal approximation based interval proposed by Wieand et al (1989). Our simulation results show that the newly proposed intervals perform better than the existing normal approximation based interval in terms of coverage accuracy and interval length.

Multiple Outcomes In Health Services Research: Hypothesis Tests And Power, Donald C. Martin, Paula Diehr, Thomas D. Koepsell, Stephan D. Fihn

UW Biostatistics Working Paper Series

Health services research often is directed towards making small improvements in a number of outcomes that reflect many aspects of the patient’s life rather than a large improvement in a single well defined outcome. A researcher might choose five scales to measure different aspects of treatment outcomes and not expect any large treatment differences on any single outcome measure. O’Brien (1984) has proposed a nonparametric statistical procedure which is particularly well suited to this type of problem and that can result in considerable increases in statistical power. This paper will briefly review O’Brien’s pooled rank method and develop power calculations. …

Pooling Community Data For Community Interventions When The Number Of Pairs Is Small, Paula Diehr, Ted Lystig, Holly Andrilla, Ziding Feng

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 very small, yielding a study with low power. We examined the ability of a process known as “pooling” or “preliminary significance testing” to improve the power of community variations. In this process, one first tests whether there is significant community variation, using type 1 error of perhaps 0.25. If there is significant variation, the usual community-level test is performed. If not, a person-level test is performed. We found through Monte …

An Empirical Study Of Small-Area Variation For Icd-9 Surgical Procedures, Paula Diehr, Kevin Cain, Zhan Ye, John Loeser

UW Biostatistics Working Paper Series

Objective. Several measures of variation have been used in SAVA. One study of DRGs found that the coefficient of variation from analysis of variance (CVA) had superior performance. That work is replicated here for ICD-9 surgical procedures, and extended to age/sex-standardized rates. Results are compared with those in the literature, and recommendations are made for assessing small-area variation in future studies.

Data Sources. Data were taken from Washington State's "Episode of Illness" file of hospital discharges in the State in 1987. Up to three ICD-9 surgical procedures and a unique patient identifier were available for each discharge.

Study Design. We …

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

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

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

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

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.