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dc.contributor.authorPatson, Noel
dc.contributor.authorMukaka, Mavuto
dc.contributor.authorD'Alessandro, Umberto
dc.contributor.authorChapotera, Gertrude
dc.contributor.authorMwapasa, Victor
dc.contributor.authorMathanga, Don
dc.contributor.authorKazembe, Lawrence
dc.contributor.authorLaufer, Miriam K
dc.contributor.authorChirwa, Tobias
dc.date.accessioned2021-10-15T16:35:23Z
dc.date.available2021-10-15T16:35:23Z
dc.date.issued2021-10-09
dc.identifier.urihttp://hdl.handle.net/10713/16847
dc.description.abstractBackground: In drug trials, clinical adverse events (AEs), concomitant medication and laboratory safety outcomes are repeatedly collected to support drug safety evidence. Despite the potential correlation of these outcomes, they are typically analysed separately, potentially leading to misinformation and inefficient estimates due to partial assessment of safety data. Using joint modelling, we investigated whether clinical AEs vary by treatment and how laboratory outcomes (alanine amino-transferase, total bilirubin) and concomitant medication are associated with clinical AEs over time following artemisinin-based antimalarial therapy. Methods: We used data from a trial of artemisinin-based treatments for malaria during pregnancy that randomized 870 women to receive artemether-lumefantrine (AL), amodiaquine-artesunate (ASAQ) and dihydroartemisinin-piperaquine (DHAPQ). We fitted a joint model containing four sub-models from four outcomes: longitudinal sub-model for alanine aminotransferase, longitudinal sub-model for total bilirubin, Poisson sub-model for concomitant medication and Poisson sub-model for clinical AEs. Since the clinical AEs was our primary outcome, the longitudinal sub-models and concomitant medication sub-model were linked to the clinical AEs sub-model via current value and random effects association structures respectively. We fitted a conventional Poisson model for clinical AEs to assess if the effect of treatment on clinical AEs (i.e. incidence rate ratio (IRR)) estimates differed between the conventional Poisson and the joint models, where AL was reference treatment. Results: Out of the 870 women, 564 (65%) experienced at least one AE. Using joint model, AEs were associated with the concomitant medication (log IRR 1.7487; 95% CI: 1.5471, 1.9503; p < 0.001) but not the total bilirubin (log IRR: -0.0288; 95% CI: - 0.5045, 0.4469; p = 0.906) and alanine aminotransferase (log IRR: 0.1153; 95% CI: - 0.0889, 0.3194; p = 0.269). The Poisson model underestimated the effects of treatment on AE incidence such that log IRR for ASAQ was 0.2118 (95% CI: 0.0082, 0.4154; p = 0.041) for joint model compared to 0.1838 (95% CI: 0.0574, 0.3102; p = 0.004) for Poisson model. Conclusion: We demonstrated that although the AEs did not vary across the treatments, the joint model yielded efficient AE incidence estimates compared to the Poisson model. The joint model showed a positive relationship between the AEs and concomitant medication but not with laboratory outcomes. Trial registration: ClinicalTrials.gov: NCT00852423.en_US
dc.description.urihttps://doi.org/10.1186/s12874-021-01412-9en_US
dc.language.isoenen_US
dc.publisherSpringer Natureen_US
dc.relation.ispartofBMC Medical Research Methodologyen_US
dc.rights© 2021. The Author(s).en_US
dc.subjectAdverse eventsen_US
dc.subjectConcomitant medicationen_US
dc.subjectDrug safetyen_US
dc.subjectJoint modelen_US
dc.subjectRandomised controlled trialsen_US
dc.titleJoint modelling of multivariate longitudinal clinical laboratory safety outcomes, concomitant medication and clinical adverse events: application to artemisinin-based treatment during pregnancy clinical trialen_US
dc.typeArticleen_US
dc.identifier.doi10.1186/s12874-021-01412-9
dc.identifier.pmid34627141
dc.source.volume21
dc.source.issue1
dc.source.beginpage208
dc.source.endpage
dc.source.countryEngland


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