Validating the assumptions of population adjustment : application of multilevel network meta-regression to a network of treatments for plaque psoriasis

BACKGROUND: Network meta-analysis (NMA) and indirect comparisons combine aggregate data (AgD) from multiple studies on treatments of interest but may give biased estimates if study populations differ. Population adjustment methods such as multilevel network meta-regression (ML-NMR) aim to reduce bias by adjusting for differences in study populations using individual patient data (IPD) from 1 or more studies under the conditional constancy assumption. A shared effect modifier assumption may also be necessary for identifiability. This article aims to demonstrate how the assumptions made by ML-NMR can be assessed in practice to obtain reliable treatment effect estimates in a target population. METHODS: We apply ML-NMR to a network of evidence on treatments for plaque psoriasis with a mix of IPD and AgD trials reporting ordered categorical outcomes. Relative treatment effects are estimated for each trial population and for 3 external target populations represented by a registry and 2 cohort studies. We examine residual heterogeneity and inconsistency and relax the shared effect modifier assumption for each covariate in turn. RESULTS: Estimated population-average treatment effects were similar across study populations, as differences in the distributions of effect modifiers were small. Better fit was achieved with ML-NMR than with NMA, and uncertainty was reduced by explaining within- and between-study variation. We found little evidence that the conditional constancy or shared effect modifier assumptions were invalid. CONCLUSIONS: ML-NMR extends the NMA framework and addresses issues with previous population adjustment approaches. It coherently synthesizes evidence from IPD and AgD studies in networks of any size while avoiding aggregation bias and noncollapsibility bias, allows for key assumptions to be assessed or relaxed, and can produce estimates relevant to a target population for decision-making. HIGHLIGHTS: Multilevel network meta-regression (ML-NMR) extends the network meta-analysis framework to synthesize evidence from networks of studies providing individual patient data or aggregate data while adjusting for differences in effect modifiers between studies (population adjustment). We apply ML-NMR to a network of treatments for plaque psoriasis with ordered categorical outcomes. We demonstrate for the first time how ML-NMR allows key assumptions to be assessed. We check for violations of conditional constancy of relative effects (such as unobserved effect modifiers) through residual heterogeneity and inconsistency and the shared effect modifier assumption by relaxing this for each covariate in turn. Crucially for decision making, population-adjusted treatment effects can be produced in any relevant target population. We produce population-average estimates for 3 external target populations, represented by the PsoBest registry and the PROSPECT and Chiricozzi 2019 cohort studies

5-HT2B Receptor Antagonists Inhibit Fibrosis and Protect from RV Heart Failure

Objective. The serotonin (5-HT) pathway was shown to play a role in pulmonary hypertension (PH), but its functions in right ventricular failure (RVF) remain poorly understood. The aim of the current study was to investigate the effects of Terguride (5-HT2A and 2B receptor antagonist) or SB204741 (5-HT2B receptor antagonist) on right heart function and structure upon pulmonary artery banding (PAB) in mice. Methods. Seven days after PAB, mice were treated for 14 days with Terguride (0.2 mg/kg bid) or SB204741 (5 mg/kg day). Right heart function and remodeling were assessed by right heart catheterization, magnetic resonance imaging (MRI), and histomorphometric methods. Total secreted collagen content was determined in mouse cardiac fibroblasts isolated from RV tissues. Results. Chronic treatment with Terguride or SB204741 reduced right ventricular fibrosis and showed improved heart function in mice after PAB. Moreover, 5-HT2B receptor antagonists diminished TGF-beta1 induced collagen synthesis of RV cardiac fibroblasts in vitro. Conclusion. 5-HT2B receptor antagonists reduce collagen deposition, thereby inhibiting right ventricular fibrosis. Chronic treatment prevented the development and progression of pressure overload-induced RVF in mice. Thus, 5-HT2B receptor antagonists represent a valuable novel therapeutic approach for RVF