Analysing malaria drug trials on a per‐individual or per‐clone basis: a comparison of methods. (19th December 2012)
- Record Type:
- Journal Article
- Title:
- Analysing malaria drug trials on a per‐individual or per‐clone basis: a comparison of methods. (19th December 2012)
- Main Title:
- Analysing malaria drug trials on a per‐individual or per‐clone basis: a comparison of methods
- Authors:
- Jaki, Thomas
Parry, Alice
Winter, Katherine
Hastings, Ian - Abstract:
- <abstract abstract-type="main" id="sim5706-abs-0001"> <title> <x xml:space="preserve">Abstract</x> </title> <p id="sim5706-para-0001">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<abstract abstract-type="main" id="sim5706-abs-0001"> <title> <x xml:space="preserve">Abstract</x> </title> <p id="sim5706-para-0001">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. Copyright © 2012 John Wiley &amp; Sons, Ltd.</p> </abstract> … (more)
- Is Part Of:
- Statistics in medicine. Volume 32:Number 17(2013)
- Journal:
- Statistics in medicine
- Issue:
- Volume 32:Number 17(2013)
- Issue Display:
- Volume 32, Issue 17 (2013)
- Year:
- 2013
- Volume:
- 32
- Issue:
- 17
- Issue Sort Value:
- 2013-0032-0017-0000
- Page Start:
- 3020
- Page End:
- 3038
- Publication Date:
- 2012-12-19
- Subjects:
- Medical statistics -- Periodicals
Statistique médicale -- Périodiques
Statistiques médicales -- Périodiques
610.727 - Journal URLs:
- http://onlinelibrary.wiley.com/ ↗
- DOI:
- 10.1002/sim.5706 ↗
- Languages:
- English
- ISSNs:
- 0277-6715
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 8453.576000
British Library DSC - BLDSS-3PM
British Library STI - ELD Digital store - Ingest File:
- 3510.xml