Composite endpoints for malaria case-management: not simplifying the picture?

Cairns ME, Leurent B, Milligan PJ - Malar. J. (2014)

Related In: Results  -  Collection

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Fig2: Illustration of the Newcombe method. Method from Newcombe [13], using the spreadsheet available from http://medicine.cf.ac.uk/primary-care-public-health/resources/. In [13], the difference in probability of correct diagnosis is plotted against lambda. Here the relationship of lambda to prevalence is shown in figure A), and the difference in probability of correct management is shown against prevalence in figures B)-F) for different values of R; blue lines show 95% confidence interval. R is the ratio of importance of false negatives (c1) to the importance of false positives (c2), i.e. R = c1/c2 (Table 4). At prevalence = 0 the difference is equal to the difference in the NMR between the enhanced and control groups, and at prevalence = 1 the difference is equal to the difference in PMR between the two strategies.
Mentions: The weighted difference in probability of correct management between the two strategies, denoted f, is calculated as described in [13] and summarised in Table 4. A plot of the weight, denoted by lambda (λ), against prevalence for five different scenarios is shown in Figure 2A): equal importance given to both types of error (relative importance of false negatives to false positives, R = 1), false negatives considered twice as important as false positives (R = 2), false negatives ten times as important as false positives (R = 10), false positives twice as important as false negatives (R = 0.5), false positives ten times as important as false negatives (R = 0.1). This shows that if the PMR is prioritised (R > 1), i.e. false negatives (missed malaria cases) are emphasised, lambda rises more steeply at low prevalence, and thus the PMR has more influence on the weighted difference. Conversely, if the false positives are prioritised (R < 1), then the NMR has more influence.Table 4

Bottom Line: However combining correct management of positives and negatives into a single summary measure can be misleading.Where a combined metric is necessary, then one of the established approaches to summarise the performance of diagnostic tests could be considered, although these are not without their limitations.Two graphical approaches to help understand case management performance are illustrated.

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Affiliation: MRC Tropical Epidemiology Group, London School of Hygiene and Tropical Medicine, Keppel Street, London WC1E 7HT, UK. matthew.cairns@lshtm.ac.uk.

ABSTRACT
Rapid diagnostic tests (RDTs) for infection with Plasmodium spp. offer two main potential advantages related to malaria treatment: 1) ensuring that individuals with malaria are promptly treated with an effective artemisinin-based combination therapy, and 2) ensuring that individuals without malaria do not receive an anti-malarial they do not need (and instead receive a more appropriate treatment). Some studies of the impact of RDTs on malaria case management have combined these two different successes into a binary outcome describing 'correct management'. However combining correct management of positives and negatives into a single summary measure can be misleading. The problems, which are analogous to those encountered in the evaluation of diagnostic tests, can largely be avoided if data for patients with and without malaria are presented and analysed separately. Where a combined metric is necessary, then one of the established approaches to summarise the performance of diagnostic tests could be considered, although these are not without their limitations. Two graphical approaches to help understand case management performance are illustrated.

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