Population-adjusted treatment comparisons: estimates based on Matching-Adjusted Indirect Comparison and Simulated Treatment Comparison

David Phillippo; Tony Ades; Sofia Dias; Stephen Palmer; Keith Abrams; Nicky J. Welton

Population-adjusted treatment comparisons: estimates based on Matching-Adjusted Indirect Comparison and Simulated Treatment Comparison

David Phillippo, Tony Ades, Sofia Dias, Stephen Palmer, Keith Abrams, Nicky J. Welton

Research output: Contribution to conference › Paper › peer-review

Abstract

We present the findings and recommendations of a recent NICE Technical Support Document (available from http://www.nicedsu.org.uk/) regarding the use of population-adjusted treatment comparisons in health technology appraisal.Standard methods for indirect comparisons and network meta-analysis are based on aggregate data, with the key assumption that there is no difference between trials in the distribution of effect-modifying variables. Two methods which relax this assumption, Matching-Adjusted Indirect Comparison (MAIC) and Simulated Treatment Comparison (STC), are becoming increasingly common in industry-sponsored treatment comparisons, where a company has access to individual patient data (IPD) from its own trials but only aggregate data from competitor trials. Both methods use IPD to adjust for between-trial differences in covariate distributions. Despite their increasing popularity, there is a distinct lack of clarity about how and when these methods should be applied. We review the properties of these methods, and identify the key assumptions. Notably, there is a fundamental distinction between “anchored” and “unanchored” forms of indirect comparison, where a common comparator arm is or is not utilised to control for between-trial differences in prognostic variables, with the unanchored comparison making assumptions that are infeasibly strong. Furthermore, both MAIC and STC as currently applied can only produce estimates that are valid for the populations in the competitor trials, which do not necessarily represent the decision population. We provide recommendations on how and when population adjustment methods should be used to provide statistically valid, clinically meaningful, transparent and consistent results.

Original language	Undefined/Unknown
Publication status	Published - 1 Jul 2017

Bibliographical note

38th Annual Conference of the International Society for Clinical Biostatistics, ISCB ; Conference date: 09-07-2017 Through 13-07-2017

Cite this

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title = "Population-adjusted treatment comparisons: estimates based on Matching-Adjusted Indirect Comparison and Simulated Treatment Comparison",

abstract = "We present the findings and recommendations of a recent NICE Technical Support Document (available from http://www.nicedsu.org.uk/) regarding the use of population-adjusted treatment comparisons in health technology appraisal.Standard methods for indirect comparisons and network meta-analysis are based on aggregate data, with the key assumption that there is no difference between trials in the distribution of effect-modifying variables. Two methods which relax this assumption, Matching-Adjusted Indirect Comparison (MAIC) and Simulated Treatment Comparison (STC), are becoming increasingly common in industry-sponsored treatment comparisons, where a company has access to individual patient data (IPD) from its own trials but only aggregate data from competitor trials. Both methods use IPD to adjust for between-trial differences in covariate distributions. Despite their increasing popularity, there is a distinct lack of clarity about how and when these methods should be applied. We review the properties of these methods, and identify the key assumptions. Notably, there is a fundamental distinction between “anchored” and “unanchored” forms of indirect comparison, where a common comparator arm is or is not utilised to control for between-trial differences in prognostic variables, with the unanchored comparison making assumptions that are infeasibly strong. Furthermore, both MAIC and STC as currently applied can only produce estimates that are valid for the populations in the competitor trials, which do not necessarily represent the decision population. We provide recommendations on how and when population adjustment methods should be used to provide statistically valid, clinically meaningful, transparent and consistent results.",

author = "David Phillippo and Tony Ades and Sofia Dias and Stephen Palmer and Keith Abrams and Welton, {Nicky J.}",

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T1 - Population-adjusted treatment comparisons: estimates based on Matching-Adjusted Indirect Comparison and Simulated Treatment Comparison

AU - Phillippo, David

AU - Ades, Tony

AU - Dias, Sofia

AU - Palmer, Stephen

AU - Abrams, Keith

AU - Welton, Nicky J.

N1 - 38th Annual Conference of the International Society for Clinical Biostatistics, ISCB ; Conference date: 09-07-2017 Through 13-07-2017

PY - 2017/7/1

Y1 - 2017/7/1

N2 - We present the findings and recommendations of a recent NICE Technical Support Document (available from http://www.nicedsu.org.uk/) regarding the use of population-adjusted treatment comparisons in health technology appraisal.Standard methods for indirect comparisons and network meta-analysis are based on aggregate data, with the key assumption that there is no difference between trials in the distribution of effect-modifying variables. Two methods which relax this assumption, Matching-Adjusted Indirect Comparison (MAIC) and Simulated Treatment Comparison (STC), are becoming increasingly common in industry-sponsored treatment comparisons, where a company has access to individual patient data (IPD) from its own trials but only aggregate data from competitor trials. Both methods use IPD to adjust for between-trial differences in covariate distributions. Despite their increasing popularity, there is a distinct lack of clarity about how and when these methods should be applied. We review the properties of these methods, and identify the key assumptions. Notably, there is a fundamental distinction between “anchored” and “unanchored” forms of indirect comparison, where a common comparator arm is or is not utilised to control for between-trial differences in prognostic variables, with the unanchored comparison making assumptions that are infeasibly strong. Furthermore, both MAIC and STC as currently applied can only produce estimates that are valid for the populations in the competitor trials, which do not necessarily represent the decision population. We provide recommendations on how and when population adjustment methods should be used to provide statistically valid, clinically meaningful, transparent and consistent results.

AB - We present the findings and recommendations of a recent NICE Technical Support Document (available from http://www.nicedsu.org.uk/) regarding the use of population-adjusted treatment comparisons in health technology appraisal.Standard methods for indirect comparisons and network meta-analysis are based on aggregate data, with the key assumption that there is no difference between trials in the distribution of effect-modifying variables. Two methods which relax this assumption, Matching-Adjusted Indirect Comparison (MAIC) and Simulated Treatment Comparison (STC), are becoming increasingly common in industry-sponsored treatment comparisons, where a company has access to individual patient data (IPD) from its own trials but only aggregate data from competitor trials. Both methods use IPD to adjust for between-trial differences in covariate distributions. Despite their increasing popularity, there is a distinct lack of clarity about how and when these methods should be applied. We review the properties of these methods, and identify the key assumptions. Notably, there is a fundamental distinction between “anchored” and “unanchored” forms of indirect comparison, where a common comparator arm is or is not utilised to control for between-trial differences in prognostic variables, with the unanchored comparison making assumptions that are infeasibly strong. Furthermore, both MAIC and STC as currently applied can only produce estimates that are valid for the populations in the competitor trials, which do not necessarily represent the decision population. We provide recommendations on how and when population adjustment methods should be used to provide statistically valid, clinically meaningful, transparent and consistent results.

M3 - Paper

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