Dealing with indeterminate outcomes in antimalarial drug efficacy trials: a comparison between complete case analysis, multiple imputation and inverse probability weighting
<p><strong>Background</strong></p> Antimalarial 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 recrudescenc...
| Autores principales: | , , , , , |
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| Formato: | Journal article |
| Lenguaje: | English |
| Publicado: |
BioMed Central
2019
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| _version_ | 1826268410321829888 |
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| author | Dahal, P Stepniewska, K Guerin, P D’Alessandro, U Price, R Simpson, J |
| author_facet | Dahal, P Stepniewska, K Guerin, P D’Alessandro, U Price, R Simpson, J |
| author_sort | Dahal, P |
| collection | OXFORD |
| description | <p><strong>Background</strong></p> Antimalarial 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. <p><strong>Methods</strong></p> 3369 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. <p><strong>Results</strong></p> The 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. <p><strong>Conclusions</strong></p> The 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. |
| first_indexed | 2024-03-06T21:09:15Z |
| format | Journal article |
| id | oxford-uuid:3d92b1a7-e5e7-40e5-8fc8-19fa07f3f9b8 |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-06T21:09:15Z |
| publishDate | 2019 |
| publisher | BioMed Central |
| record_format | dspace |
| spelling | oxford-uuid:3d92b1a7-e5e7-40e5-8fc8-19fa07f3f9b82022-03-26T14:20:14ZDealing with indeterminate outcomes in antimalarial drug efficacy trials: a comparison between complete case analysis, multiple imputation and inverse probability weightingJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:3d92b1a7-e5e7-40e5-8fc8-19fa07f3f9b8EnglishSymplectic Elements at OxfordBioMed Central2019Dahal, PStepniewska, KGuerin, PD’Alessandro, UPrice, RSimpson, J<p><strong>Background</strong></p> Antimalarial 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. <p><strong>Methods</strong></p> 3369 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. <p><strong>Results</strong></p> The 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. <p><strong>Conclusions</strong></p> The 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. |
| spellingShingle | Dahal, P Stepniewska, K Guerin, P D’Alessandro, U Price, R Simpson, J Dealing with indeterminate outcomes in antimalarial drug efficacy trials: a comparison between complete case analysis, multiple imputation and inverse probability weighting |
| title | Dealing with indeterminate outcomes in antimalarial drug efficacy trials: a comparison between complete case analysis, multiple imputation and inverse probability weighting |
| title_full | Dealing with indeterminate outcomes in antimalarial drug efficacy trials: a comparison between complete case analysis, multiple imputation and inverse probability weighting |
| title_fullStr | Dealing with indeterminate outcomes in antimalarial drug efficacy trials: a comparison between complete case analysis, multiple imputation and inverse probability weighting |
| title_full_unstemmed | Dealing with indeterminate outcomes in antimalarial drug efficacy trials: a comparison between complete case analysis, multiple imputation and inverse probability weighting |
| title_short | Dealing with indeterminate outcomes in antimalarial drug efficacy trials: a comparison between complete case analysis, multiple imputation and inverse probability weighting |
| title_sort | dealing with indeterminate outcomes in antimalarial drug efficacy trials a comparison between complete case analysis multiple imputation and inverse probability weighting |
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