Sample size and power calculations for detecting changes in malaria transmission using antibody seroconversion rate

Malaria Journal

Methodology

Carlos Daniel Paulino¹  Chris Drakeley²  Nuno Sepúlveda³ 

[1] Center of Statistics and Applications of University of Lisbon, Faculdade de Ciências da Universidade de Lisboa, Bloco C6-Piso 4, 1749-1016, Lisbon, Portugal;Instituto Superior Técnico, Universidade Técnica de Lisboa, Avenida Rovisco Pais, 1049-001, Lisbonn, Portugal;London School of Hygiene and Tropical Medicine, Keppel Street, WC1E 7HT, London, UK;London School of Hygiene and Tropical Medicine, Keppel Street, WC1E 7HT, London, UK;Center of Statistics and Applications of University of Lisbon, Faculdade de Ciências da Universidade de Lisboa, Bloco C6-Piso 4, 1749-1016, Lisbon, Portugal;

关键词: Intervention;    Malaria transmission;    Bias;    Precision;    Sample size;   

DOI  :  10.1186/s12936-015-1050-3

 received in 2015-06-20, accepted in 2015-12-12,  发布年份 2015

来源: Springer

【 摘 要 】

BackgroundSeveral studies have highlighted the use of serological data in detecting a reduction in malaria transmission intensity. These studies have typically used serology as an adjunct measure and no formal examination of sample size calculations for this approach has been conducted.MethodsA sample size calculator is proposed for cross-sectional surveys using data simulation from a reverse catalytic model assuming a reduction in seroconversion rate (SCR) at a given change point before sampling. This calculator is based on logistic approximations for the underlying power curves to detect a reduction in SCR in relation to the hypothesis of a stable SCR for the same data. Sample sizes are illustrated for a hypothetical cross-sectional survey from an African population assuming a known or unknown change point.ResultsOverall, data simulation demonstrates that power is strongly affected by assuming a known or unknown change point. Small sample sizes are sufficient to detect strong reductions in SCR, but invariantly lead to poor precision of estimates for current SCR. In this situation, sample size is better determined by controlling the precision of SCR estimates. Conversely larger sample sizes are required for detecting more subtle reductions in malaria transmission but those invariantly increase precision whilst reducing putative estimation bias.ConclusionsThe proposed sample size calculator, although based on data simulation, shows promise of being easily applicable to a range of populations and survey types. Since the change point is a major source of uncertainty, obtaining or assuming prior information about this parameter might reduce both the sample size and the chance of generating biased SCR estimates.

【 授权许可】

CC BY   
© Sepúlveda et al. 2015

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