Overdispersion in health care performance data: Laney's approach
نویسندگان
چکیده
منابع مشابه
Control charts for health care monitoring under overdispersion
An attractive way to control attribute data from high quality processes is to wait till r ≥ 1 failures have occurred. The choice of r in such negative binomial charts is dictated by how much the failure rate is supposed to change during Outof-Control. However, these results have been derived for the case of homogeneous data. Especially in health care monitoring, (groups of) patients will often ...
متن کاملDETECTING OVERDISPERSION IN DATA WITH CENSORING by
HSING-VI CHANG. Testing Overdispersion in Data With Censoring. (Under the direction of Chirayath M. Suchindran.) The term overdispersion refers to the situation that the variance of the outcome exceeds the nominal variance. Overdispersion in general has two effects. The first effect is that summary statistics have a larger variance than anticipated under the simple model. The second is a possib...
متن کاملFuzzy MCDM Approach for Health-Care Performance Assessment in Istanbul
Performance measurement in the health-care sector is a challenging task due to the wide variety in performance metrics and their interpretation. It is essential to develop a robust methodology to evaluate health-care performance since substantial and increasing amount of public resources are dedicated to health-care. With this goal in mind, this paper proposes a fuzzy decision making framework ...
متن کاملA revised approach to performance measurement for health-care estates.
The purpose of the research was to show how lean asset thinking can be applied to UK health-care facilities using different measures to compare the estates contribution to the business of health-care providers. The challenge to conventional wisdom matches that posed by 'Lean Production' to 'Mass Manufacturing'. Data envelope analysis examined the income generated and patient-occupied area as ou...
متن کاملA Bayesian Approach to Account for Misclassification and Overdispersion in Count Data
Count data are subject to considerable sources of what is often referred to as non-sampling error. Errors such as misclassification, measurement error and unmeasured confounding can lead to substantially biased estimators. It is strongly recommended that epidemiologists not only acknowledge these sorts of errors in data, but incorporate sensitivity analyses into part of the total data analysis....
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Quality and Safety in Health Care
سال: 2006
ISSN: 1475-3898,1475-3901
DOI: 10.1136/qshc.2006.017830