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Comparison of non-parametric methods for ungrouping coarsely aggregated data

Authors :
Silvia Rizzi
Mikael Thinggaard
Gerda Engholm
Niels Christensen
Tom Børge Johannesen
James W. Vaupel
Rune Lindahl-Jacobsen
Source :
BMC Medical Research Methodology, Vol 16, Iss 1, Pp 1-12 (2016)
Publication Year :
2016
Publisher :
BMC, 2016.

Abstract

Abstract Background Histograms are a common tool to estimate densities non-parametrically. They are extensively encountered in health sciences to summarize data in a compact format. Examples are age-specific distributions of death or onset of diseases grouped in 5-years age classes with an open-ended age group at the highest ages. When histogram intervals are too coarse, information is lost and comparison between histograms with different boundaries is arduous. In these cases it is useful to estimate detailed distributions from grouped data. Methods From an extensive literature search we identify five methods for ungrouping count data. We compare the performance of two spline interpolation methods, two kernel density estimators and a penalized composite link model first via a simulation study and then with empirical data obtained from the NORDCAN Database. All methods analyzed can be used to estimate differently shaped distributions; can handle unequal interval length; and allow stretches of 0 counts. Results The methods show similar performance when the grouping scheme is relatively narrow, i.e. 5-years age classes. With coarser age intervals, i.e. in the presence of open-ended age groups, the penalized composite link model performs the best. Conclusion We give an overview and test different methods to estimate detailed distributions from grouped count data. Health researchers can benefit from these versatile methods, which are ready for use in the statistical software R. We recommend using the penalized composite link model when data are grouped in wide age classes.

Details

Language :
English
ISSN :
14712288 and 54983827
Volume :
16
Issue :
1
Database :
Directory of Open Access Journals
Journal :
BMC Medical Research Methodology
Publication Type :
Academic Journal
Accession number :
edsdoj.fb547927cce5498382791a71578618e8
Document Type :
article
Full Text :
https://doi.org/10.1186/s12874-016-0157-8