Analysing malaria drug trials on a per-individual or per-clone basis: a comparison of methods.

Abstract

There are a variety of methods used to estimate the effectiveness of antimalarial drugs in clinical trials, invariably on a per-person basis. A person, however, may have more than one malaria infection present at the time of treatment. We evaluate currently used methods for analysing malaria trials on a per-individual basis and introduce a novel method to estimate the cure rate on a per-infection (clone) basis. We used simulated and real data to highlight the differences of the various methods. We give special attention to classifying outcomes as cured, recrudescent (infections that never fully cleared) or ambiguous on the basis of genetic markers at three loci. To estimate cure rates on a per-clone basis, we used the genetic information within an individual before treatment to determine the number of clones present. We used the genetic information obtained at the time of treatment failure to classify clones as recrudescence or new infections. On the per-individual level, we find that the most accurate methods of classification label an individual as newly infected if all alleles are different at the beginning and at the time of failure and as a recrudescence if all or some alleles were the same. The most appropriate analysis method is survival analysis or alternatively for complete data/per-protocol analysis a proportion estimate that treats new infections as successes. We show that the analysis of drug effectiveness on a per-clone basis estimates the cure rate accurately and allows more detailed evaluation of the performance of the treatment.

• Authors: Thomas Jaki, Alice Parry, Katherine Winter, Ian Hastings
• Journal: Statistics in medicine (Stat Med) Vol. 32 Issue 17 Pg. 3020-38 (Jul 30 2013) ISSN: 1097-0258 [Electronic] England
• PMID: 23258694 (Publication Type: Comparative Study, Journal Article, Research Support, Non-U.S. Gov't)
• Copyright: Copyright © 2012 John Wiley & Sons, Ltd.

Chemical References

• Antimalarials
• Genetic Markers

Topics

• Antimalarials (therapeutic use)
• Biostatistics
• Clinical Trials as Topic (statistics & numerical data)
• Data Interpretation, Statistical
• Databases, Factual
• Genes, Protozoan
• Genetic Markers
• Humans
• Malaria (drug therapy, parasitology)
• Models, Statistical
• Plasmodium (drug effects, genetics)
• Treatment Failure
• Treatment Outcome