A review of methods for comparing treatments evaluated in studies that form disconnected networks of evidence

A network meta-analysis allows a simultaneous comparison between treatments evaluated in randomised controlled trials that share at least one treatment with at least one other study. Estimates of treatment effects may be required for treatments across disconnected networks of evidence, which requires a different statistical approach and modelling assumptions to account for imbalances in prognostic variables and treatment effect modifiers between studies. In this paper, we review and discuss methods for comparing treatments evaluated in studies that form disconnected networks of evidence. Several methods have been proposed but assessing which are appropriate often depends on the clinical context as well as the availability of data. Most methods account for sampling variation but do not always account for others sources of uncertainty. We suggest that further research is required to assess the properties of methods and the use of approaches that allow the incorporation of external information to reflect parameter and structural uncertainty.

[1]  Eric Q. Wu,et al.  Comparative Efficacy of Vildagliptin and Sitagliptin in Japanese Patients with Type 2 Diabetes Mellitus , 2011, Clinical drug investigation.

[2]  Irina Proskorovsky,et al.  Simulation and Matching-Based Approaches for Indirect Comparison of Treatments , 2015, PharmacoEconomics.

[3]  Elizabeth A Stuart,et al.  Matching methods for causal inference: A review and a look forward. , 2010, Statistical science : a review journal of the Institute of Mathematical Statistics.

[4]  P. Lorigan,et al.  Systematic review and network meta-analysis of overall survival comparing 3 mg/kg ipilimumab with alternative therapies in the management of pretreated patients with unresectable stage III or IV melanoma. , 2012, The oncologist.

[5]  Anthony O'Hagan,et al.  Robust meta‐analytic‐predictive priors in clinical trials with historical control information , 2014, Biometrics.

[6]  Nicky J Welton,et al.  NICE DSU Technical Support Document 18: Methods for population-adjusted indirect comparisons in submissions to NICE , 2016 .

[7]  Zhimei Liu,et al.  An adjusted indirect comparison of everolimus and sorafenib therapy in sunitinib-refractory metastatic renal cell carcinoma patients using repeated matched samples , 2011, Expert opinion on pharmacotherapy.

[8]  J. P. Perez Ruixo,et al.  Time course of bone mineral density changes with denosumab compared with other drugs in postmenopausal osteoporosis: a dose-response-based meta-analysis. , 2014, The Journal of clinical endocrinology and metabolism.

[9]  Alan Brnabic,et al.  Inclusion Of Multiple Studies In Matching Adjusted Indirect Comparisons (Maic) , 2015 .

[10]  Donna Niedzwiecki,et al.  Meta-analysis of phase II cooperative group trials in metastatic stage IV melanoma to determine progression-free and overall survival benchmarks for future phase II trials. , 2008, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[11]  D. Moher,et al.  Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement , 2009, BMJ.

[12]  Catherine P. Bradshaw,et al.  The use of propensity scores to assess the generalizability of results from randomized trials , 2011, Journal of the Royal Statistical Society. Series A,.

[13]  D. Moher,et al.  Preferred Reporting Items for Systematic Reviews and Meta-Analyses: The PRISMA Statement , 2009, BMJ : British Medical Journal.

[14]  Nicky J Welton,et al.  NICE DSU Technical Support Document 1: Introduction to Evidence Synthesis for Decision Making , 2011 .

[15]  A. E. Ades,et al.  Absolute or relative effects? Arm‐based synthesis of trial data , 2015, Research synthesis methods.

[16]  David H. Salinger,et al.  A dose–response Meta‐Analysis for Quantifying Relative Efficacy of Biologics in Rheumatoid Arthritis , 2011, Clinical pharmacology and therapeutics.

[17]  S. Duffull,et al.  A model‐based meta‐analysis of the influence of factors that impact adherence to medications , 2015, Journal of clinical pharmacy and therapeutics.

[18]  Gary C. Brown,et al.  Comparative effectiveness , 2009, Current opinion in ophthalmology.

[19]  David C Hoaglin,et al.  An indirect comparison of everolimus versus sorafenib in metastatic renal cell carcinoma – a flawed analysis? , 2012, Expert opinion on pharmacotherapy.

[20]  S. Palmer,et al.  Indirect Treatment Comparison of Talimogene Laherparepvec Compared with Ipilimumab and Vemurafenib for the Treatment of Patients with Metastatic Melanoma , 2016, Advances in Therapy.

[21]  Jae Eun Ahn,et al.  Longitudinal aggregate data model-based meta-analysis with NONMEM: approaches to handling within treatment arm correlation , 2010, Journal of Pharmacokinetics and Pharmacodynamics.

[22]  Jens Hainmueller,et al.  Entropy Balancing for Causal Effects: A Multivariate Reweighting Method to Produce Balanced Samples in Observational Studies , 2012, Political Analysis.

[23]  S M Goring,et al.  Disconnected by design: analytic approach in treatment networks having no common comparator , 2016, Research synthesis methods.

[24]  D Mawdsley,et al.  Model‐Based Network Meta‐Analysis: A Framework for Evidence Synthesis of Clinical Trial Data , 2016, CPT: pharmacometrics & systems pharmacology.

[25]  Liang Jin,et al.  Utilization of model-based meta-analysis to delineate the net efficacy of taspoglutide from the response of placebo in clinical trials , 2014, Saudi pharmaceutical journal : SPJ : the official publication of the Saudi Pharmaceutical Society.

[26]  Stephen Senn,et al.  Controversies concerning randomization and additivity in clinical trials , 2004, Statistics in medicine.

[27]  Alex J. Sutton,et al.  Evidence Synthesis for Decision Making 1 , 2013, Medical decision making : an international journal of the Society for Medical Decision Making.

[28]  Andrea Manca,et al.  NICE DSU TECHNICAL SUPPORT DOCUMENT 17: THE USE OF OBSERVATIONAL DATA TO INFORM ESTIMATES OF TREATMENT EFFECTIVENESS IN TECHNOLOGY APPRAISAL: METHODS FOR COMPARATIVE INDIVIDUAL PATIENT DATA , 2015 .

[29]  Mei Lu,et al.  Comparative Efficacy of Guanfacine Extended Release Versus Atomoxetine for the Treatment of Attention-Deficit/Hyperactivity Disorder in Children and Adolescents: Applying Matching-Adjusted Indirect Comparison Methodology , 2013, CNS Drugs.

[30]  Nicola J Cooper,et al.  Network meta-analysis of multiple outcome measures accounting for borrowing of information across outcomes , 2014, BMC Medical Research Methodology.

[31]  J. Gibbs,et al.  Quantitative Model of the Relationship Between Dipeptidyl Peptidase‐4 (DPP‐4) Inhibition and Response: Meta‐Analysis of Alogliptin, Saxagliptin, Sitagliptin, and Vildagliptin Efficacy Results , 2012, Journal of clinical pharmacology.

[32]  Alex J. Sutton,et al.  Evidence Synthesis for Decision Making 7 , 2013, Medical decision making : an international journal of the Society for Medical Decision Making.

[33]  Mei Lu,et al.  Matching-adjusted indirect comparisons: a new tool for timely comparative effectiveness research. , 2012, Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research.

[34]  Douglas G Altman,et al.  Validity of indirect comparison for estimating efficacy of competing interventions: empirical evidence from published meta-analyses , 2003, BMJ : British Medical Journal.

[35]  Mats O Karlsson,et al.  A linearization approach for the model‐based analysis of combined aggregate and individual patient data , 2014, Statistics in medicine.

[36]  Troy Guthrie,et al.  Talimogene Laherparepvec Improves Durable Response Rate in Patients With Advanced Melanoma. , 2015, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[37]  David J Spiegelhalter,et al.  A re-evaluation of random-effects meta-analysis , 2009, Journal of the Royal Statistical Society. Series A,.

[38]  J. Weber,et al.  Exposure–Response Relationships of the Efficacy and Safety of Ipilimumab in Patients with Advanced Melanoma , 2013, Clinical Cancer Research.

[39]  M. Kalaycio,et al.  Targeted therapy for patients with chronic myeloid leukemia: clinical trial experience and challenges in inter-trial comparisons , 2012, Leukemia & lymphoma.

[40]  Eric Q. Wu,et al.  Comparative efficacy of nilotinib and dasatinib in newly diagnosed chronic myeloid leukemia: a matching-adjusted indirect comparison of randomized trials , 2011, Current medical research and opinion.

[41]  B. Monz,et al.  A novel model-based meta-analysis to indirectly estimate the comparative efficacy of two medications: an example using DPP-4 inhibitors, sitagliptin and linagliptin, in treatment of type 2 diabetes mellitus , 2013, BMJ Open.

[42]  B. Hamrén,et al.  Longitudinal Model‐Based Meta‐Analysis in Rheumatoid Arthritis: An Application Toward Model‐Based Drug Development , 2012, Clinical pharmacology and therapeutics.

[43]  Donald E Mager,et al.  Population‐based meta‐analysis of furosemide pharmacokinetics , 2014, Biopharmaceutics & drug disposition.

[44]  D R Mould,et al.  Model‐Based Meta‐Analysis: An Important Tool for Making Quantitative Decisions During Drug Development , 2012, Clinical pharmacology and therapeutics.

[45]  Gavin Giovannoni,et al.  No Evidence of Disease Activity: Indirect Comparisons of Oral Therapies for the Treatment of Relapsing–Remitting Multiple Sclerosis , 2014, Advances in Therapy.

[46]  Eric Q. Wu,et al.  Comparative Effectiveness Without Head-to-Head Trials , 2012, PharmacoEconomics.

[47]  Léo R. Belzile,et al.  A Bayesian view of doubly robust causal inference , 2016, 1701.04093.

[48]  Fang Chen,et al.  Use of historical control data for assessing treatment effects in clinical trials , 2014, Pharmaceutical statistics.

[49]  Lei Nie,et al.  Likelihood reweighting methods to reduce potential bias in noninferiority trials which rely on historical data to make inference , 2013, 1311.7485.

[50]  F. Wiegand,et al.  Population pharmacodynamic modeling of various extended-release formulations of methylphenidate in children with attention deficit hyperactivity disorder via meta-analysis , 2012, Journal of Pharmacokinetics and Pharmacodynamics.

[51]  Roland R. Ramsahai,et al.  Evaluating treatment effectiveness in patient subgroups: a comparison of propensity score methods with an automated matching approach , 2012, The international journal of biostatistics.

[52]  D. Mould Models for Disease Progression: New Approaches and Uses , 2012, Clinical pharmacology and therapeutics.

[53]  S. Shoaf,et al.  Population‐based meta‐analysis of hydrochlorothiazide pharmacokinetics , 2013, Biopharmaceutics & drug disposition.

[54]  Alex J. Sutton,et al.  Evidence Synthesis for Decision Making 2 , 2013, Medical decision making : an international journal of the Society for Medical Decision Making.

[55]  H. Kaufman,et al.  Oncolytic viruses: a new class of immunotherapy drugs , 2015, Nature Reviews Drug Discovery.

[56]  Richard M Nixon,et al.  Network meta-analysis combining individual patient and aggregate data from a mixture of study designs with an application to pulmonary arterial hypertension , 2015, BMC Medical Research Methodology.

[57]  Roland R. Ramsahai,et al.  From sample average treatment effect to population average treatment effect on the treated: combining experimental with observational studies to estimate population treatment effects , 2015 .

[58]  F. Mercier,et al.  A Model-Based Meta-analysis to Compare Efficacy and Tolerability of Tramadol and Tapentadol for the Treatment of Chronic Non-Malignant Pain , 2014, Pain and Therapy.

[59]  D. DeAngelo,et al.  One-year and long-term molecular response to nilotinib and dasatinib for newly diagnosed chronic myeloid leukemia: a matching-adjusted indirect comparison , 2015, Current medical research and opinion.

[60]  Alan Brnabic,et al.  Reweighting Rct Evidence To Better Reflect Real Life: A Case Study of The Innovation Medicines Initiative , 2016 .

[61]  Z. Kadziola,et al.  Alternative Weighting Approaches For Matching Adjusted Indirect Comparisons (Maic) , 2015 .

[62]  E. Stuart,et al.  The performance of inverse probability of treatment weighting and full matching on the propensity score in the presence of model misspecification when estimating the effect of treatment on survival outcomes , 2015, Statistical methods in medical research.

[63]  K. Jack Ishak,et al.  No Head-to-Head Trial? Simulate the Missing Arms , 2012, PharmacoEconomics.

[64]  Liang Zhao,et al.  Application of pharmacokinetics-pharmacodynamics/clinical response modeling and simulation for biologics drug development. , 2012, Journal of pharmaceutical sciences.

[65]  Jeremy E. Oakley,et al.  Uncertain Judgements: Eliciting Experts' Probabilities , 2006 .

[66]  M. Gibbs,et al.  Model‐Based Meta‐Analysis for Comparative Efficacy and Safety: Application in Drug Development and Beyond , 2011, Clinical pharmacology and therapeutics.

[67]  M. Danhof,et al.  Pharmacokinetic–pharmacodynamic modeling of antipsychotic drugs in patients with schizophrenia Part I: The use of PANSS total score and clinical utility , 2013, Schizophrenia Research.

[68]  Mei Lu,et al.  Comparative effectiveness research using matching‐adjusted indirect comparison: an application to treatment with guanfacine extended release or atomoxetine in children with attention‐deficit/hyperactivity disorder and comorbid oppositional defiant disorder , 2012, Pharmacoepidemiology and drug safety.