A comparison of exponentially weighted moving average-based methods for monitoring increases in incidence rate with varying population size

Lianjie Shu, Yan Su, Wei Jiang and Kwok-Leung Tsui

IISE Transactions, 2014, vol. 46, issue 8, 798-812

Abstract: Estimation of incidence rate and quick detection of its increases are important tasks in public health surveillance. In addition to being an efficient tool for online parameter estimation, the Exponentially Weighted Moving Average (EWMA) method has been widely used as an effective monitoring tool in statistical process control. Motivated by its successful applications, several EWMA-type methods are discussed for monitoring and estimating the incidence rate of adverse events in health care applications. The comparison results show that the conventional EWMA chart has a superior performance in detecting small shifts that occur at the start-up but very poor performance when shifts occur at a later time point. Instead, the adaptive EWMA method that is capable of dynamically updating its smoothing parameter can provide an overall good detection performance when shifts occur at both the first time point and a later time point. This result is validated using male thyroid cancer data in New Mexico.

Date: 2014
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/0740817X.2014.894805 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:uiiexx:v:46:y:2014:i:8:p:798-812

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/uiie20

DOI: 10.1080/0740817X.2014.894805

Access Statistics for this article

IISE Transactions is currently edited by Jianjun Shi

More articles in IISE Transactions from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().