Tutorial on discrete hazard functions.
Risk analysis requires estimation of hazard functions. A hazard rate is the conditional probability of adverse sentinel event occurring in the next time period, given that it has not yet occurred. This tutorial shows how hazard functions are estimated from survival functions, the probability of goin...
| Publicado en: | Quality Management in Health Care Vol. 16; no. 4; pp. 311 - 321 |
|---|---|
| Autor principal: | |
| Formato: | Journal Article |
| Publicado: |
Lippincott Williams & Wilkins
Oct-Dec2007
|
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=105829465&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 105829465 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 10638628 0O9 jtl: Quality Management in Health Care issn: 10638628 maglogo: N pubinfo: dt: Oct-Dec2007 vid: 16 iid: 4 pid: 5086 pub: Lippincott Williams & Wilkins place: Baltimore, Maryland artinfo: ui: 105829465 2009711704 NLM18049384 105829465 ppf: 311 ppct: 10 formats: tig: atl: Tutorial on discrete hazard functions. aug: au: Alemi F affil: Health Administration and Policy, College of Health an Human Services, George Mason University, 4400 University Dr, Fairfax, VA 22030; falemi@gmu.edu sug: subj: Probability Safety Health Facilities Models, Statistical Risk Assessment Methods Risk Assessment Statistics and Numerical Data United States ab: Risk analysis requires estimation of hazard functions. A hazard rate is the conditional probability of adverse sentinel event occurring in the next time period, given that it has not yet occurred. This tutorial shows how hazard functions are estimated from survival functions, the probability of going through a time period without the sentinel event. Survival functions are built on cumulative distribution functions, which measure the probability of occurrence of sentinel event in current and prior time periods. Cumulative distribution functions are calculated from probability density functions, which give the probability of an event occurring at a particular time period. Probability density functions are typically estimated from incidence reports, which are readily available to safety officers. Sometimes, these functions are estimated by making assumptions about the shape of the distribution function. For discrete data, the typical probability density functions are Bernoulli, Binominal, Geometric, and Poisson distributions. This tutorial starts with estimating a probability distribution and then proceeds to calculation of hazard and relative risk rates. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|