Network meta-analysis including treatment by covariate interactions: consistency can vary across covariate values

TY - JOUR

T1 - Network meta-analysis including treatment by covariate interactions

T2 - consistency can vary across covariate values

AU - Donegan, Sarah

AU - Welton, Nicky J.

AU - Tudur Smith, Catrin

AU - D'Alessandro, Umberto

AU - Dias, Sofia

N1 - © 2017 The Authors

PY - 2017/12/1

Y1 - 2017/12/1

N2 - Background: Many reviews aim to compare numerous treatments and report results stratified by subgroups (eg, by disease severity). In such cases, a network meta-analysis model including treatment by covariate interactions can estimate the relative effects of all treatment pairings for each subgroup of patients. Two key assumptions underlie such models: consistency of treatment effects and consistency of the regression coefficients for the interactions. Consistency may differ depending on the covariate value at which consistency is assessed. For valid inference, we need to be confident of consistency for the relevant range of covariate values. In this paper, we demonstrate how to assess consistency of treatment effects from direct and indirect evidence at various covariate values. Methods: Consistency is assessed using visual inspection, inconsistency estimates, and probabilities. The method is applied to an individual patient dataset comparing artemisinin combination therapies for treating uncomplicated malaria in children using the covariate age. Results: The magnitude of the inconsistency appears to be decreasing with increasing age for each comparison. For one comparison, direct and indirect evidence differ for age 1 (P =.05), and this brings results for age 1 for all comparisons into question. Conclusion: When fitting models including interactions, the consistency of direct and indirect evidence must be assessed across the range of covariates included in the trials. Clinical inferences are only valid for covariate values for which results are consistent.

AB - Background: Many reviews aim to compare numerous treatments and report results stratified by subgroups (eg, by disease severity). In such cases, a network meta-analysis model including treatment by covariate interactions can estimate the relative effects of all treatment pairings for each subgroup of patients. Two key assumptions underlie such models: consistency of treatment effects and consistency of the regression coefficients for the interactions. Consistency may differ depending on the covariate value at which consistency is assessed. For valid inference, we need to be confident of consistency for the relevant range of covariate values. In this paper, we demonstrate how to assess consistency of treatment effects from direct and indirect evidence at various covariate values. Methods: Consistency is assessed using visual inspection, inconsistency estimates, and probabilities. The method is applied to an individual patient dataset comparing artemisinin combination therapies for treating uncomplicated malaria in children using the covariate age. Results: The magnitude of the inconsistency appears to be decreasing with increasing age for each comparison. For one comparison, direct and indirect evidence differ for age 1 (P =.05), and this brings results for age 1 for all comparisons into question. Conclusion: When fitting models including interactions, the consistency of direct and indirect evidence must be assessed across the range of covariates included in the trials. Clinical inferences are only valid for covariate values for which results are consistent.

KW - Consistency, meta-regression, indirect comparison, network meta-analysis, individual patient data, treatment by covariate interaction

U2 - 10.1002/jrsm.1257

DO - 10.1002/jrsm.1257

M3 - Article

SN - 1759-2879

VL - 8

SP - 485

EP - 495

JO - Research Synthesis Methods

JF - Research Synthesis Methods

IS - 4

ER -