Comparative Effectiveness Without Head-to-Head Trials

Abstract

The absence of head-to-head trials is a common challenge in comparative effectiveness research and health technology assessment. Indirect cross-trial treatment comparisons are possible, but can be biased by cross-trial differences in patient characteristics. Using only published aggregate data, adjustment for such biases may be impossible. Although individual patient data (IPD) would permit adjustment, they are rarely available for all trials. However, many researchers have the opportunity to access IPD for trials of one treatment, a new drug for example, but only aggregate data for trials of comparator treatments. We propose a method that leverages all available data in this setting by adjusting average patient characteristics in trials with IPD to match those reported for trials without IPD. Treatment outcomes, including continuous, categorical and censored time-to-event outcomes, can then be compared across balanced trial populations. The proposed method is illustrated by a comparison of adalimumab and etanercept for the treatment of psoriasis. IPD from trials of adalimumab versus placebo (n = 1025) were re-weighted to match the average baseline characteristics reported for a trial of etanercept versus placebo (n = 330). Re-weighting was based on the estimated propensity of enrolment in the adalimumab versus etanercept trials. Before matching, patients in the adalimumab trials had lower mean age, greater prevalence of psoriatic arthritis, less prior use of systemic treatment or phototherapy, and a smaller mean percentage of body surface area affected than patients in the etanercept trial. After matching, these and all other available baseline characteristics were well balanced across trials. Symptom improvements of ≥75% and ≥90% (as measured by the Psoriasis Area and Severity Index [PASI] score at week 12) were experienced by an additional 17.2% and 14.8% of adalimumab-treated patients compared with the matched etanercept-treated patients (respectively, both p < 0.001). Mean percentage PASI score improvements from baseline were also greater for adalimumab than for etanercept at weeks 4, 8 and 12 (all p < 0.05). Matching adjustment ensured that this indirect comparison was not biased by differences in mean baseline characteristics across trials, supporting the conclusion that adalimumab was associated with significantly greater symptom reduction than etanercept for the treatment of moderate to severe psoriasis.

One-year and long-term molecular response to nilotinib and dasatinib for newly diagnosed chronic myeloid leukemia: a matching-adjusted indirect comparison

“…9,12 However, methods that rely solely on aggregated data cannot fully adjust for baseline differences. 9,13 Ideally, individual patient data (IPD) should be available for each trial so to adjust for differences between trial populations using regression techniques. 13 However, access to all of the IPD for all of the trials is usually not available, and adjustment for observed differences between the trial populations using IPD from both trials is not conducted.…”

“…9,13 Ideally, individual patient data (IPD) should be available for each trial so to adjust for differences between trial populations using regression techniques. 13 However, access to all of the IPD for all of the trials is usually not available, and adjustment for observed differences between the trial populations using IPD from both trials is not conducted. 13 To overcome this, IPD from one of the comparator trials can be used to conduct a matchingadjusted indirect comparison (MAIC to adjust for baseline differences between trial populations by J u s t A c c e p t e d Curr Med Res Opin Downloaded from informahealthcare.com by Dicle Univ.…”

“…13 However, access to all of the IPD for all of the trials is usually not available, and adjustment for observed differences between the trial populations using IPD from both trials is not conducted. 13 To overcome this, IPD from one of the comparator trials can be used to conduct a matchingadjusted indirect comparison (MAIC to adjust for baseline differences between trial populations by J u s t A c c e p t e d Curr Med Res Opin Downloaded from informahealthcare.com by Dicle Univ. on 11/04/14 Page 4 weighting patients in the trial with IPD so that the baseline characteristics match the published baseline characteristics of the trial without available IPD.…”

“…on 11/04/14 Page 4 weighting patients in the trial with IPD so that the baseline characteristics match the published baseline characteristics of the trial without available IPD. 9,13,14 Two prior studies have indirectly compared the short-term benefits of nilotinib and dasatinib in the treatment of newly diagnosed CML-CP. 15,16 The first study used MAIC to compare the rates of MMR, PFS, and overall survival by 12 months in the open-label, phase III trials (ENESTnd 1 and DASISION 2 ) of nilotinib and dasatinib, respectively.…”

Comparative efficacy and safety of daclatasvir/asunaprevir versus IFN-based regimens in genotype 1b hepatitis C virus infection

“…In cases where trials of different treatments could be linked via direct comparisons to the same reference treatment, a network meta-analysis (NMA) was conducted to synthesize the direct and indirect evidence for each pairwise treatment comparison across included trials [28][29][30][31][32][33][34][35][36]. Matching-adjusted indirect comparisons (MAICs) were also used to compare outcomes across trials with adjustment for cross-trial differences in patient populations [37][38][39]. The MAICs utilized individual patient data (IPD) from DCV + ASV trials and aggregate data for comparator regimens including single-arm trials.…”