Evidence map›Paper›PMID 31775647›Full record

ArticleBMC medical research methodology2019

Dealing with indeterminate outcomes in antimalarial drug efficacy trials: a comparison between complete case analysis, multiple imputation and inverse probability weighting.

Prabin Dahal, Kasia Stepniewska, Philippe J Guerin, Umberto D'Alessandro, Ric N Price, Julie A Simpson

Open access · goldAbstract readMulticenter Study
In one paragraph

Article in BMC medical research methodology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed, 1 pooled it
1.2field-weighted citation impact, top 18% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed, 1 synthesis or guideline pooled it, 7 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Article
  4. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors at 5 institutions in 5 countries.

Prabin DahalWorldWide Antimalarial Resistance Network (WWARN), Oxford, UK. prabin.dahal@ndm.ox.ac.uk.ORCID 0000-0002-2158-846X
Kasia StepniewskaWorldWide Antimalarial Resistance Network (WWARN), Oxford, UK.ORCID 0000-0002-1713-6209
Philippe J GuerinWorldWide Antimalarial Resistance Network (WWARN), Oxford, UK.ORCID 0000-0002-6008-2963
Umberto D'AlessandroMedical Research Council Unit, Fajara, The Gambia.
Ric N PriceWorldWide Antimalarial Resistance Network (WWARN), Oxford, UK.
Julie A SimpsonCentre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Australia. julieas@unimelb.edu.au.ORCID 0000-0002-2660-2013
University of Oxford · GBAngkor Hospital for Children · KHCharles Darwin University · AUMRC Unit the Gambia · GMThe University of Melbourne · AU

Funding

Wellcome Trust 200909Wellcome Trust 200909/Z/16/Z
6 · The paper itself

Abstract

backgroundAntimalarial clinical efficacy studies for uncomplicated Plasmodium falciparum malaria frequently encounter situations in which molecular genotyping is unable to discriminate between parasitic recurrence, either new infection or recrudescence. The current WHO guideline recommends excluding these individuals with indeterminate outcomes in a complete case (CC) analysis. Data from the four artemisinin-based combination (4ABC) trial was used to compare the performance of multiple imputation (MI) and inverse probability weighting (IPW) against the standard CC analysis for dealing with indeterminate recurrences.

methods3369 study participants from the multicentre study (4ABC trial) with molecularly defined parasitic recurrence treated with three artemisinin-based combination therapies were used to represent a complete dataset. A set proportion of recurrent infections (10, 30 and 45%) were reclassified as missing using two mechanisms: a completely random selection (mechanism 1); missingness weakly dependent (mechanism 2a) and strongly dependent (mechanism 2b) on treatment and transmission intensity. The performance of MI, IPW and CC approaches in estimating the Kaplan-Meier (K-M) probability of parasitic recrudescence at day 28 was then compared. In addition, the maximum likelihood estimate of the cured proportion was presented for further comparison (analytical solution). Performance measures (bias, relative bias, standard error and coverage) were reported as an average from 1000 simulation runs.

resultsThe CC analyses resulted in absolute underestimation of K-M probability of day 28 recrudescence by up to 1.7% and were associated with reduced precision and poor coverage across all the scenarios studied. Both MI and IPW method performed better (greater consistency and greater efficiency) compared to CC analysis. In the absence of censoring, the analytical solution provided the most consistent and accurate estimate of cured proportion compared to the CC analyses.

conclusionsThe widely used CC approach underestimates antimalarial failure; IPW and MI procedures provided efficient and consistent estimates and should be considered when reporting the results of antimalarial clinical trials, especially in areas of high transmission, where the proportion of indeterminate outcomes could be large. The analytical solution estimating the cured proportion could provide an alternative approach, in scenarios with minimal censoring due to loss to follow-up or new infections.

Indexed as

Plasmodium falciparumAntimalarialsArtemisininsData Interpretation, StatisticalHumansLikelihood FunctionsMalaria, FalciparumProbabilityRecurrenceTreatment OutcomeAntimalarialsartemisininArtemisininsEfficacyIndeterminate outcomesInverse probability weightingMultiple imputationPlasmodium falciparum

Identifiers

PMID31775647
PMCPMC6882216
OpenAlexW2990883835

What OpenQuestion holds

Textmetadata
LicenceCC BY
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.