Literature DB >> 25795232

Simulation and matching-based approaches for indirect comparison of treatments.

K Jack Ishak1, Irina Proskorovsky, Agnes Benedict.   

Abstract

Estimates of the relative effects of competing treatments are rarely available from head-to-head trials. These effects must therefore be derived from indirect comparisons of results from different studies. The feasibility of comparisons relies on the network linking treatments through common comparators; the reliability of these may also be impacted when the studies are heterogeneous or when multiple intermediate comparisons are needed to link two specific treatments of interest. Simulated treatment comparison and matching-adjusted indirect comparison have been developed to address these challenges. These focus on comparisons of outcomes for two specific treatments of interest by using patient-level data for one treatment (the index) and published results for the other treatment (the comparator) from compatible studies, taking into account possible confounding due to population differences. This paper provides an overview of how and when these approaches can be used as an alternative or to complement standard MTC approaches.

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Year:  2015        PMID: 25795232     DOI: 10.1007/s40273-015-0271-1

Source DB:  PubMed          Journal:  Pharmacoeconomics        ISSN: 1170-7690            Impact factor:   4.981


  15 in total

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Journal:  BMJ       Date:  2003-03-01

3.  Network meta-analysis for indirect treatment comparisons.

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Journal:  Stat Med       Date:  2002-08-30       Impact factor: 2.373

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Journal:  Stat Med       Date:  2007-03-15       Impact factor: 2.373

6.  Matching-adjusted indirect comparisons: a new tool for timely comparative effectiveness research.

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Review 7.  Overview of parametric survival analysis for health-economic applications.

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Journal:  Pharmacoeconomics       Date:  2013-08       Impact factor: 4.981

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Authors:  James E Signorovitch; Eric Q Wu; Andrew P Yu; Charles M Gerrits; Evan Kantor; Yanjun Bao; Shiraz R Gupta; Parvez M Mulani
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Review 9.  Simultaneous comparison of multiple treatments: combining direct and indirect evidence.

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10.  Practical methods for incorporating summary time-to-event data into meta-analysis.

Authors:  Jayne F Tierney; Lesley A Stewart; Davina Ghersi; Sarah Burdett; Matthew R Sydes
Journal:  Trials       Date:  2007-06-07       Impact factor: 2.279

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  14 in total

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4.  Methods for Population-Adjusted Indirect Comparisons in Health Technology Appraisal.

Authors:  David M Phillippo; Anthony E Ades; Sofia Dias; Stephen Palmer; Keith R Abrams; Nicky J Welton
Journal:  Med Decis Making       Date:  2017-08-19       Impact factor: 2.583

5.  Multilevel network meta-regression for population-adjusted treatment comparisons.

Authors:  David M Phillippo; Sofia Dias; A E Ades; Mark Belger; Alan Brnabic; Alexander Schacht; Daniel Saure; Zbigniew Kadziola; Nicky J Welton
Journal:  J R Stat Soc Ser A Stat Soc       Date:  2020-06-07       Impact factor: 2.483

6.  A Review of Two Regulatory Approved Anti-CD19 CAR T-Cell Therapies in Diffuse Large B-Cell Lymphoma: Why Are Indirect Treatment Comparisons Not Feasible?

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7.  Matching-adjusted indirect comparisons of annualized bleeding rate and utilization of BAY 94-9027 versus three recombinant factor VIII agents for prophylaxis in patients with severe hemophilia A.

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8.  The need for comparative data in spondyloarthritis.

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Journal:  Arthritis Res Ther       Date:  2019-01-22       Impact factor: 5.156

9.  Matching-Adjusted Indirect Comparison of the Efficacy of Apalutamide and Enzalutamide with ADT in the Treatment of Non-Metastatic Castration-Resistant Prostate Cancer.

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Journal:  Adv Ther       Date:  2019-12-07       Impact factor: 3.845

Review 10.  A scoping review of indirect comparison methods and applications using individual patient data.

Authors:  Areti Angeliki Veroniki; Sharon E Straus; Charlene Soobiah; Meghan J Elliott; Andrea C Tricco
Journal:  BMC Med Res Methodol       Date:  2016-04-27       Impact factor: 4.615

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