The use of individual patient-level data (IPD) to quantify the impact of pretreatment predictors of response to treatment in chronic hepatitis B patients

Objectives: Evidence synthesis is an integral part decision-making by reimbursement agencies. When direct evidence is not available, network-meta-analysis (NMA) techniques are commonly used. This approach assumes that the trials are sufficiently similar in terms of treatment-effect modifiers. When imbalances in potential treatment-effect modifiers exist, the NMA approach may not produce fair comparisons. The objective of this study was to identify and quantify the interaction between treatment-effect and potential treatment-effect modifiers, including time-of-response measurement and baseline viral load in chronic hepatitis B (CHB) patients. Design: Retrospective patient-level data econometric analysis. Participants: 1353 individuals from two randomised controlled trials of nucleoside-naive CHB taking 0.5 mg entecavir (n=679) or 100 mg lamivudine (n=668) daily for 48 weeks. Interventions: Hepatitis B virus (HBV) DNA levels for both drugs were measured at baseline and weeks 24, 36 and 48. Generalised estimating equation for repeated binary responses was used to identify treatment-effect modifiers for response defined at <= 400 or <= 300 copies/ml. Primary outcome measures: OR at 48 weeks. Results: The OR for the time-of-response measurement and treatment-effect interaction term was 1.039 (p=0.00) and 1.035 (p=0.00) when response was defined at <= 400 or <= 300 copies/ml, respectively. The baseline HBV DNA and treatment-effect interaction OR was 0.94 (p=0.047) and 0.95 (p=0.096), respectively, for the two response definitions suggesting evidence of interaction between baseline disease activity and treatment effect. The interaction between HBeAg status and treatment effect was not statistically significant. Conclusions: The measurement time point seems to modify the relative treatment effect of entacavir compared to lamivudine, measured on the OR scale. Evidence also suggested that differences in baseline viral load may also alter relative treatment effect. Meta-analyses should account for such modifiers when generating relative efficacy estimates.