Methods to assess evidence consistency in dose-response model based network meta-analysis.

Network meta-analysis (NMA) simultaneously estimates multiple relative treatment effects based on evidence that forms a network of treatment comparisons. Heterogeneity in treatment definitions, such as dose, can lead to a violation of the consistency assumption that underpins NMA. Model-based NMA (MBNMA) methods have been proposed that allow functional dose-response relationships to be estimated within an NMA, which avoids lumping different doses together and thereby reduces the likelihood of inconsistency. Dose-response MBNMA relies on appropriate specification of the dose-response relationship as well as consistency of relative effects. In this article we describe methods to check for inconsistency in dose-response MBNMA models. Global and local (node-splitting) tests for inconsistency are described that account for studies with ≥3 arms that are typical in dose-finding trials. We show that consistency needs to be assessed with respect to the choice of dose-response function. We illustrate the methods using a network comparing biologics for moderate-to-severe psoriasis. By comparing results from an Emax and an exponential dose-response function we show that failure to correctly characterise the dose-response can introduce apparent inconsistency. The number of comparisons for which node-splitting is possible is also shown to be dependent on the complexity of the selected dose-response function. We highlight that the nature of dose-finding studies, which typically compare multiple doses of the same agent, provide limited scope to assess inconsistency, but these study designs help guard against inconsistency in the first place. We demonstrate the importance of assessing consistency to obtain robust relative effects to inform drug-development and policy decisions.

Joining the Dots: Linking Disconnected Networks of Evidence Using Dose-Response Model-Based Network Meta-Analysis.

BACKGROUND: Network meta-analysis (NMA) synthesizes direct and indirect evidence on multiple treatments to estimate their relative effectiveness. However, comparisons between disconnected treatments are not possible without making strong assumptions. When studies including multiple doses of the same drug are available, model-based NMA (MBNMA) presents a novel solution to this problem by modeling a parametric dose-response relationship within an NMA framework. In this article, we illustrate several scenarios in which dose-response MBNMA can connect and strengthen evidence networks. METHODS: We created illustrative data sets by removing studies or treatments from an NMA of triptans for migraine relief. We fitted MBNMA models with different dose-response relationships. For connected networks, we compared MBNMA estimates with NMA estimates. For disconnected networks, we compared MBNMA estimates with NMA estimates from an "augmented" network connected by adding studies or treatments back into the data set. RESULTS: In connected networks, relative effect estimates from MBNMA were more precise than those from NMA models (ratio of posterior SDs NMA v. MBNMA: median = 1.13; range = 1.04-1.68). In disconnected networks, MBNMA provided estimates for all treatments where NMA could not and were consistent with NMA estimates from augmented networks for 15 of 18 data sets. In the remaining 3 of 18 data sets, a more complex dose-response relationship was required than could be fitted with the available evidence. CONCLUSIONS: Where information on multiple doses is available, MBNMA can connect disconnected networks and increase precision while making less strong assumptions than alternative approaches. MBNMA relies on correct specification of the dose-response relationship, which requires sufficient data at different doses to allow reliable estimation. We recommend that systematic reviews for NMA search for and include evidence (including phase II trials) on multiple doses of agents where available.

Augmenting the randomized controlled trial with real-world data to aid clinical decision making in metastatic renal cell carcinoma: a systematic review and meta-analysis.

Aim: To evaluate how efficacy outcomes from real-world data (RWD) can support those from randomized controlled trials (RCTs), in the context of first-line tyrosine kinase inhibitor treatment of metastatic renal cell carcinoma. Patients & methods: PubMed, Ovid, MEDLINE and EMBASE were searched for RCTs and RWD studies with ≥50 adult patients per arm published in 2000-2017. Outcome measures were median progression-free survival, median overall survival and objective response rate. Results: A total of 13 RCTs and 22 RWD studies met eligibility criteria; 31, 28 and 25 studies, respectively, reported median progression-free survival, median overall survival and objective response rate. Summary outcome measures were similar in RWD and RCTs. Conclusion: RWD validates efficacy-based outcomes from RCTs and may provide supportive evidence to inform clinical decisions.

Sunitinib for Metastatic Renal Cell Carcinoma: A Systematic Review and Meta-Analysis of Real-World and Clinical Trials Data.

BACKGROUND: Randomized controlled trials (RCTs) have stringent inclusion criteria and may not fully represent patients seen in everyday clinical practice. Real-world data (RWD) can provide supportive evidence for the effectiveness of medical interventions in more heterogeneous populations than RCTs. Sunitinib is a widely used first-line treatment for patients with metastatic renal cell carcinoma (mRCC). OBJECTIVE: This is the first comprehensive meta-analysis to evaluate the efficacy of sunitinib using the novel approach of combining RCTs and RWD. METHODS: RCTs and RWD studies published between 2000 and 2017 were identified from PubMed, Ovid, MEDLINE, and EMBASE. Eligible studies contained a cohort of ≥ 50 adult patients with mRCC receiving first-line sunitinib treatment. The meta-analysis combined RWD and RCT treatment groups, adjusting for data type (RCT or RWD). Recorded outcomes were median progression-free survival (mPFS), median overall survival (mOS), and objective response rate (ORR). Publication bias was assessed via review of funnel plots for each outcome measure. A random effects model to account for study heterogeneity was applied to each endpoint. Sensitivity analyses evaluated the robustness of the overall estimates. RESULTS: Of the 3611 studies identified through medical database searches, 22 (15 RWD studies, 7 RCTs) met eligibility criteria and were analyzed. mPFS (18 studies), mOS (19 studies), and ORR (15 studies) were reported for aggregate measures based on 4815, 5321, and 4183 patients, respectively. Reported mPFS (RWD, 7.5-11.0 months; RCTs, 5.6-15.1 months) and ORR data (RWD, 14.0-34.6%; RCTs, 18.8-46.9%) were consistent with the overall confidence estimates (95% confidence interval [CI]) of 9.3 (8.6-10.2) months and 27.9% (24.2-32.0), respectively. Reported mOS showed greater variation in RWD (6.8-33.2 months) compared with RCTs (21.8-31.5 months), with an overall confidence estimate (95% CI) of 23.0 (19.2-27.6) months. Inspection of funnel plots and sensitivity analyses indicated that there was no publication bias for any efficacy endpoint. Sensitivity analyses showed no evidence of lack of robustness for mPFS, mOS, or ORR. Interpretation of these results is limited by differences in trial design, cohort characteristics, and missing data. CONCLUSIONS: This novel, comprehensive meta-analysis validates sunitinib as an effective first-line treatment for patients with mRCC in both RCTs and everyday clinical practice. The methodology provides a framework for future analyses combining data from RCTs and RWD.

Many Flavors of Model-Based Meta-Analysis: Part II - Modeling Summary Level Longitudinal Responses.

Meta-analyses typically assess comparative treatment response for an end point at specific timepoints across studies. However, during drug development, it is often of interest to understand the response time-course of competitor compounds for a variety of purposes. Examples of such application include informing study design and characterizing the onset, maintenance, and offset of action. This tutorial acts as a "points for consideration" document, reviews relevant literature, and fits a longitudinal model to an example dataset.

An appraisal of meta-analysis guidelines: how do they relate to safety outcomes?

Although well developed to assess efficacy questions, meta-analyses and, more generally, systematic reviews, have received less attention in application to safety-related questions. As a result, many open questions remain on how best to apply meta-analyses in the safety setting. This appraisal attempts to: (i) summarize the current guidelines for assessing individual studies, systematic reviews, and network meta-analyses; (ii) describe several publications on safety meta-analytic approaches; and (iii) present some of the questions and issues that arise with safety data. A number of gaps in the current quality guidelines are identified along with issues to consider when performing a safety meta-analysis. While some work is ongoing to provide guidance to improve the quality of safety meta-analyses, this review emphasizes the critical need for better reporting and increased transparency regarding safety data in the systematic review guidelines. Copyright © 2016 John Wiley & Sons, Ltd.