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Comparison of non-parametric methods for ungrouping coarsely aggregated data
Silvia Rizzi1,2, Mikael Thinggaard3,4, Gerda Engholm5
1Max Planck Odense Center on the Biodemography of Aging, J.B. Winsløws Vej 9, Odense, 5000, Denmark. srizzi@health.sdu.dk.
BMC Medical Research Methodology
|May 25, 2016
Summary
Estimating detailed health data distributions from coarse histograms is crucial. The penalized composite link model best reconstructs distributions from wide age groups, outperforming other methods.
Area of Science:
- Biostatistics
- Public Health
- Epidemiology
Background:
- Histograms are common in health sciences for summarizing data, often using age-specific distributions.
- Coarse histogram intervals can lead to information loss and difficulties in comparing datasets.
- Estimating detailed distributions from grouped data is essential when intervals are too broad.
Purpose of the Study:
- To compare methods for ungrouping count data to estimate detailed distributions.
- To evaluate the performance of spline interpolation, kernel density estimators, and a penalized composite link model.
- To identify the most effective method for reconstructing distributions from grouped health data.
Main Methods:
- A literature search identified five methods for ungrouping count data.
- Two spline interpolation methods and two kernel density estimators were compared.
- A penalized composite link model was also evaluated using simulation and empirical data from the NORDCAN Database.
Main Results:
- All methods could estimate varied distributions, handle unequal interval lengths, and accommodate zero counts.
- Methods showed similar performance with narrow 5-year age classes.
- The penalized composite link model demonstrated superior performance with coarser age intervals and open-ended groups.
Conclusions:
- The penalized composite link model is recommended for data grouped in wide age classes.
- These methods offer versatile solutions for health researchers to estimate detailed distributions from grouped count data.
- The methods are available in the statistical software R for practical application.
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