International Journal of Epidemiology, Vol 26, 1080-1089, Copyright © 1997 by International Epidemiological Association
BC Reeves and M Quigley
BACKGROUND: Verbal autopsy (VA) is an indirect method for estimating
cause-specific mortality. In most previous studies, cause of death has been
assigned from verbal autopsy data using expert algorithms or by physician
review. Both of these methods may have poor validity. In addition,
physician review is time consuming and has to be carried out by doctors. A
range of methods exist for deriving classification rules from data. Such
rules are quick and simple to apply and in many situations perform as well
as experts. METHODS: This paper has two aims. First, it considers the
advantages and disadvantages of the three main methods for deriving
classification rules empirically; (a) linear and other discriminant
techniques, (b) probability density estimation and (c) decision trees and
rule-based methods. Second, it reviews the factors which need to be taken
into account when choosing a classification method for assigning cause of
death from VA data. RESULTS: Four main factors influence the choice of
classification method: (a) the purpose for which a classifier is being
developed, (b) the number of validated causes of death assigned to each
case, (c) the characteristics of the VA data and (d) the need for a
classifier to be comprehensible. When the objective is to estimate
mortality from a single cause of death, logistic regression should be used.
When the objective is to determine patterns of mortality, the choice of
method will depend on the above factors in ways which are elaborated in the
paper. CONCLUSION: Choice of classification method for assigning cause of
death needs to be considered when designing a VA validation study.
Comparison of the performance of classifiers derived using different
methods requires a large VA dataset, which is not currently available.
ARTICLES
A review of data-derived methods for assigning causes of death from verbal autopsy data
Department of Social Medicine, University of Bristol, UK.
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