7 results on '"Indirect treatment comparison"'
Search Results
2. Advancing unanchored simulated treatment comparisons: A novel implementation and simulation study.
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Ren, Shijie, Ren, Sa, Welton, Nicky J., and Strong, Mark
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TECHNOLOGY assessment , *MEDICAL technology , *TREATMENT effectiveness - Abstract
Population‐adjusted indirect comparisons, developed in the 2010s, enable comparisons between two treatments in different studies by balancing patient characteristics in the case where individual patient‐level data (IPD) are available for only one study. Health technology assessment (HTA) bodies increasingly rely on these methods to inform funding decisions, typically using unanchored indirect comparisons (i.e., without a common comparator), due to the need to evaluate comparative efficacy and safety for single‐arm trials. Unanchored matching‐adjusted indirect comparison (MAIC) and unanchored simulated treatment comparison (STC) are currently the only two approaches available for population‐adjusted indirect comparisons based on single‐arm trials. However, there is a notable underutilisation of unanchored STC in HTA, largely due to a lack of understanding of its implementation. We therefore develop a novel way to implement unanchored STC by incorporating standardisation/marginalisation and the NORmal To Anything (NORTA) algorithm for sampling covariates. This methodology aims to derive a suitable marginal treatment effect without aggregation bias for HTA evaluations. We use a non‐parametric bootstrap and propose separately calculating the standard error for the IPD study and the comparator study to ensure the appropriate quantification of the uncertainty associated with the estimated treatment effect. The performance of our proposed unanchored STC approach is evaluated through a comprehensive simulation study focused on binary outcomes. Our findings demonstrate that the proposed approach is asymptotically unbiased. We argue that unanchored STC should be considered when conducting unanchored indirect comparisons with single‐arm studies, presenting a robust approach for HTA decision‐making. [ABSTRACT FROM AUTHOR] more...
- Published
- 2024
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3. A comprehensive review and shiny application on the matching‐adjusted indirect comparison.
- Author
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Jiang, Ziren, Cappelleri, Joseph C., Gamalo, Margaret, Chen, Yong, Thomas, Neal, and Chu, Haitao
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TECHNOLOGY assessment , *ESTIMATION theory , *MEDICAL technology , *RESEARCH personnel , *DECISION making - Abstract
Population‐adjusted indirect comparison (PAIC) is an increasingly used technique for estimating the comparative effectiveness of different treatments for the health technology assessments when head‐to‐head trials are unavailable. Three commonly used PAIC methods include matching‐adjusted indirect comparison (MAIC), simulated treatment comparison (STC), and multilevel network meta‐regression (ML‐NMR). MAIC enables researchers to achieve balanced covariate distribution across two independent trials when individual participant data are only available in one trial. In this article, we provide a comprehensive review of the MAIC methods, including their theoretical derivation, implicit assumptions, and connection to calibration estimation in survey sampling. We discuss the nuances between anchored and unanchored MAIC, as well as their required assumptions. Furthermore, we implement various MAIC methods in a user‐friendly R Shiny application Shiny‐MAIC. To our knowledge, it is the first Shiny application that implements various MAIC methods. The Shiny‐MAIC application offers choice between anchored or unanchored MAIC, choice among different types of covariates and outcomes, and two variance estimators including bootstrap and robust standard errors. An example with simulated data is provided to demonstrate the utility of the Shiny‐MAIC application, enabling a user‐friendly approach conducting MAIC for healthcare decision‐making. The Shiny‐MAIC is freely available through the link: https://ziren.shinyapps.io/Shiny_MAIC/. [ABSTRACT FROM AUTHOR] more...
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- 2024
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4. Methodological considerations for novel approaches to covariate‐adjusted indirect treatment comparisons.
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Remiro‐Azócar, Antonio, Heath, Anna, and Baio, Gianluca
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EXTRAPOLATION , *TECHNOLOGY assessment - Abstract
We examine four important considerations in the development of covariate adjustment methodologies for indirect treatment comparisons. First, we consider potential advantages of weighting versus outcome modeling, placing focus on bias‐robustness. Second, we outline why model‐based extrapolation may be required and useful, in the specific context of indirect treatment comparisons with limited overlap. Third, we describe challenges for covariate adjustment based on data‐adaptive outcome modeling. Finally, we offer further perspectives on the promise of doubly robust covariate adjustment frameworks. [ABSTRACT FROM AUTHOR] more...
- Published
- 2023
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5. Parametric G‐computation for compatible indirect treatment comparisons with limited individual patient data.
- Author
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Remiro‐Azócar, Antonio, Heath, Anna, and Baio, Gianluca
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TREATMENT effectiveness , *REGRESSION analysis , *CAUSAL inference , *TECHNOLOGY assessment , *STATISTICAL weighting - Abstract
Population adjustment methods such as matching‐adjusted indirect comparison (MAIC) are increasingly used to compare marginal treatment effects when there are cross‐trial differences in effect modifiers and limited patient‐level data. MAIC is based on propensity score weighting, which is sensitive to poor covariate overlap and cannot extrapolate beyond the observed covariate space. Current outcome regression‐based alternatives can extrapolate but target a conditional treatment effect that is incompatible in the indirect comparison. When adjusting for covariates, one must integrate or average the conditional estimate over the relevant population to recover a compatible marginal treatment effect. We propose a marginalization method based on parametric G‐computation that can be easily applied where the outcome regression is a generalized linear model or a Cox model. The approach views the covariate adjustment regression as a nuisance model and separates its estimation from the evaluation of the marginal treatment effect of interest. The method can accommodate a Bayesian statistical framework, which naturally integrates the analysis into a probabilistic framework. A simulation study provides proof‐of‐principle and benchmarks the method's performance against MAIC and the conventional outcome regression. Parametric G‐computation achieves more precise and more accurate estimates than MAIC, particularly when covariate overlap is poor, and yields unbiased marginal treatment effect estimates under no failures of assumptions. Furthermore, the marginalized regression‐adjusted estimates provide greater precision and accuracy than the conditional estimates produced by the conventional outcome regression, which are systematically biased because the measure of effect is non‐collapsible. [ABSTRACT FROM AUTHOR] more...
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- 2022
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6. Methods for population adjustment with limited access to individual patient data: A review and simulation study.
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Remiro‐Azócar, Antonio, Heath, Anna, and Baio, Gianluca
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SURVIVAL rate , *TREATMENT effectiveness , *ERROR rates , *LOG-rank test , *EVALUATION methodology , *SAMPLE size (Statistics) - Abstract
Population‐adjusted indirect comparisons estimate treatment effects when access to individual patient data is limited and there are cross‐trial differences in effect modifiers. Popular methods include matching‐adjusted indirect comparison (MAIC) and simulated treatment comparison (STC). There is limited formal evaluation of these methods and whether they can be used to accurately compare treatments. Thus, we undertake a comprehensive simulation study to compare standard unadjusted indirect comparisons, MAIC and STC across 162 scenarios. This simulation study assumes that the trials are investigating survival outcomes and measure continuous covariates, with the log hazard ratio as the measure of effect. MAIC yields unbiased treatment effect estimates under no failures of assumptions. The typical usage of STC produces bias because it targets a conditional treatment effect where the target estimand should be a marginal treatment effect. The incompatibility of estimates in the indirect comparison leads to bias as the measure of effect is non‐collapsible. Standard indirect comparisons are systematically biased, particularly under stronger covariate imbalance and interaction effects. Standard errors and coverage rates are often valid in MAIC but the robust sandwich variance estimator underestimates variability where effective sample sizes are small. Interval estimates for the standard indirect comparison are too narrow and STC suffers from bias‐induced undercoverage. MAIC provides the most accurate estimates and, with lower degrees of covariate overlap, its bias reduction outweighs the loss in precision under no failures of assumptions. An important future objective is the development of an alternative formulation to STC that targets a marginal treatment effect. [ABSTRACT FROM AUTHOR] more...
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- 2021
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7. GetReal in network meta-analysis: a review of the methodology.
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Efthimiou, Orestis, Debray, Thomas P. A., Valkenhoef, Gert, Trelle, Sven, Panayidou, Klea, Moons, Karel G. M., Reitsma, Johannes B., Shang, Aijing, and Salanti, Georgia
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PAIRED comparisons (Mathematics) , *CLINICAL trials , *META-analysis , *EMPIRICAL research , *DISCOURSE analysis - Abstract
Pairwise meta-analysis is an established statistical tool for synthesizing evidence from multiple trials, but it is informative only about the relative efficacy of two specific interventions. The usefulness of pairwise meta-analysis is thus limited in real-life medical practice, where many competing interventions may be available for a certain condition and studies informing some of the pairwise comparisons may be lacking. This commonly encountered scenario has led to the development of network meta-analysis (NMA). In the last decade, several applications, methodological developments, and empirical studies in NMA have been published, and the area is thriving as its relevance to public health is increasingly recognized. This article presents a review of the relevant literature on NMA methodology aiming to pinpoint the developments that have appeared in the field. Copyright © 2016 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR] more...
- Published
- 2016
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