Examining Ways to Handle Non-Random Missingness in CEA through Econometric and Statistics Lenses
Jackie Yenerall (),
George Davis and
No 205690, 2015 AAEA & WAEA Joint Annual Meeting, July 26-28, San Francisco, California from Agricultural and Applied Economics Association
Missing data in experiments can bias estimates if not appropriately addressed. This is of particular concern in cost-effectiveness analysis where bias in either the cost or effect estimate could bias the entire cost effectiveness estimate. Complicated experimental designs, such as cluster randomized trials (CRT) or longitudinal data call for even greater care when addressing missingness. The purpose of this paper is to compare two sample selection models designed to address bias resulting from non-random missingless when applied to a longitudinal CRT. From the statistics literature we consider the Diggle Kenward model and from the econometrics literature we consider the Heckman model. Both of these models will be used to analyze the twelve-month outcomes of a worksite weight loss program, as well as used in a simulation experiment.
Keywords: Health Economics and Policy; Research Methods/ Statistical Methods (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ecm
References: View references in EconPapers View complete reference list from CitEc
Citations: Track citations by RSS feed
Downloads: (external link)
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:ags:aaea15:205690
Access Statistics for this paper
More papers in 2015 AAEA & WAEA Joint Annual Meeting, July 26-28, San Francisco, California from Agricultural and Applied Economics Association Contact information at EDIRC.
Bibliographic data for series maintained by AgEcon Search ().