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An efficient algorithm to assess multivariate surrogate endpoints in a causal inference framework. Comput Stat Data Anal 2022. [DOI: 10.1016/j.csda.2022.107494] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
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Markozannes G, Vourli G, Ntzani E. A survey of methodologies on causal inference methods in meta-analyses of randomized controlled trials. Syst Rev 2021; 10:170. [PMID: 34108033 PMCID: PMC8188671 DOI: 10.1186/s13643-021-01726-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/15/2020] [Accepted: 06/01/2021] [Indexed: 11/25/2022] Open
Abstract
BACKGROUND Meta-analyses of randomized controlled trials (RCTs) have been considered as the highest level of evidence in the pyramid of the evidence-based medicine. However, the causal interpretation of such results is seldom studied. METHODS We systematically searched for methodologies pertaining to the implementation of a causally explicit framework for meta-analysis of randomized controlled trials and discussed the interpretation and scientific relevance of such causal estimands. We performed a systematic search in four databases to identify relevant methodologies, supplemented with hand-search. We included methodologies that described causality under counterfactuals and potential outcomes framework. RESULTS We only identified three efforts explicitly describing a causal framework on meta-analysis of RCTs. Two approaches required individual participant data, while for the last one, only summary data were required. All three approaches presented a sufficient framework under which a meta-analytical estimate is identifiable and estimable. However, several conceptual limitations remain, mainly in regard to the data generation process under which the selected RCTs rise. CONCLUSIONS We undertook a review of methodologies on causal inference methods in meta-analyses. Although all identified methodologies provide valid causal estimates, there are limitations in the assumptions regarding the data generation process and sampling of the potential RCTs to be included in the meta-analysis which pose challenges to the interpretation and scientific relevance of the identified causal effects. Despite both causal inference and meta-analysis being extensively studied in the literature, limited effort exists of combining those two frameworks.
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Affiliation(s)
- Georgios Markozannes
- Department of Hygiene and Epidemiology, University of Ioannina School of Medicine, Ioannina, Greece.
| | - Georgia Vourli
- Deptartment of Hygiene, Epidemiology and Medical Statistics, Medical School, National and Kapodistrian University of Athens, Athens, Greece
| | - Evangelia Ntzani
- Department of Hygiene and Epidemiology, University of Ioannina School of Medicine, Ioannina, Greece
- Center for Evidence Synthesis in Health, Department of Health Services, Policy and Practice, School of Public Health, Brown University, Providence, RI, USA
- Institute of Biosciences, University Research Center of Ioannina, University of Ioannina, Ioannina, Greece
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Van der Elst W, Abad AA, Coppenolle H, Meyvisch P, Molenberghs G. The individual-level surrogate threshold effect in a causal-inference setting with normally distributed endpoints. Pharm Stat 2021; 20:1216-1231. [PMID: 34018666 DOI: 10.1002/pst.2141] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2020] [Revised: 04/06/2021] [Accepted: 05/09/2021] [Indexed: 11/09/2022]
Abstract
In the meta-analytic surrogate evaluation framework, the trial-level coefficient of determination R trial 2 quantifies the strength of the association between the expected causal treatment effects on the surrogate (S) and the true (T) endpoints. Burzykowski and Buyse supplemented this metric of surrogacy with the surrogate threshold effect (STE), which is defined as the minimum value of the causal treatment effect on S for which the predicted causal treatment effect on T exceeds zero. The STE supplements R trial 2 with a more direct clinically interpretable metric of surrogacy. Alonso et al. proposed to evaluate surrogacy based on the strength of the association between the individual (rather than expected) causal treatment effects on S and T. In the current paper, the individual-level surrogate threshold effect (ISTE) is introduced in the setting where S and T are normally distributed variables. ISTE is defined as the minimum value of the individual causal treatment effect on S for which the lower limit of the prediction interval around the individual causal treatment effect on T exceeds zero. The newly proposed methodology is applied in a case study, and it is illustrated that ISTE has an appealing clinical interpretation. The R package surrogate implements the methodology and a web appendix (supporting information) that details how the analyses can be conducted in practice is provided.
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Affiliation(s)
- Wim Van der Elst
- The Janssen Pharmaceutical companies of Johnson & Johnson, Beerse, Belgium
| | | | - Hans Coppenolle
- The Janssen Pharmaceutical companies of Johnson & Johnson, Beerse, Belgium
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Ciani O, Grigore B, Blommestein H, de Groot S, Möllenkamp M, Rabbe S, Daubner-Bendes R, Taylor RS. Validity of Surrogate Endpoints and Their Impact on Coverage Recommendations: A Retrospective Analysis across International Health Technology Assessment Agencies. Med Decis Making 2021; 41:439-452. [PMID: 33719711 PMCID: PMC8108112 DOI: 10.1177/0272989x21994553] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2020] [Accepted: 01/21/2021] [Indexed: 12/03/2022]
Abstract
BACKGROUND Surrogate endpoints (i.e., intermediate endpoints intended to predict for patient-centered outcomes) are increasingly common. However, little is known about how surrogate evidence is handled in the context of health technology assessment (HTA). OBJECTIVES 1) To map methodologies for the validation of surrogate endpoints and 2) to determine their impact on acceptability of surrogates and coverage decisions made by HTA agencies. METHODS We sought HTA reports where evaluation relied on a surrogate from 8 HTA agencies. We extracted data on the methods applied for surrogate validation. We assessed the level of agreement between agencies and fitted mixed-effects logistic regression models to test the impact of validation approaches on the agency's acceptability of the surrogate endpoint and their coverage recommendation. RESULTS Of the 124 included reports, 61 (49%) discussed the level of evidence to support the relationship between the surrogate and the patient-centered endpoint, 27 (22%) reported a correlation coefficient/association measure, and 40 (32%) quantified the expected effect on the patient-centered outcome. Overall, the surrogate endpoint was deemed acceptable in 49 (40%) reports (k-coefficient 0.10, P = 0.004). Any consideration of the level of evidence was associated with accepting the surrogate endpoint as valid (odds ratio [OR], 4.60; 95% confidence interval [CI], 1.60-13.18, P = 0.005). However, we did not find strong evidence of an association between accepting the surrogate endpoint and agency coverage recommendation (OR, 0.71; 95% CI, 0.23-2.20; P = 0.55). CONCLUSIONS Handling of surrogate endpoint evidence in reports varied greatly across HTA agencies, with inconsistent consideration of the level of evidence and statistical validation. Our findings call for careful reconsideration of the issue of surrogacy and the need for harmonization of practices across international HTA agencies.
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Affiliation(s)
- Oriana Ciani
- />Centre for Research on Health and Social Care Management, SDA Bocconi, Milan, Lombardia, Italy
- />Evidence Synthesis & Modelling for Health Improvement, University of Exeter Medical School, Exeter, Devon, UK
| | - Bogdan Grigore
- Evidence Synthesis & Modelling for Health Improvement, University of Exeter Medical School, Exeter, Devon, UK
| | - Hedwig Blommestein
- Institute for Medical Technology Assessment, Erasmus School of Health Policy & Management, Erasmus University Rotterdam, Rotterdam, The Netherlands
| | - Saskia de Groot
- Institute for Medical Technology Assessment, Erasmus School of Health Policy & Management, Erasmus University Rotterdam, Rotterdam, The Netherlands
| | - Meilin Möllenkamp
- Hamburg Center for Health Economics, Universität Hamburg, Hamburg, Germany
| | - Stefan Rabbe
- Hamburg Center for Health Economics, Universität Hamburg, Hamburg, Germany
| | - Rita Daubner-Bendes
- />Syreon Research Institute, Budapest, Hungary
- />MRC/CSO Social and Public Health Sciences Unit & Robertson Centre for Biostatistics, Institute of Health and Well Being, University of Glasgow, Glasgow, Scotland, UK
| | - Rod S. Taylor
- />Evidence Synthesis & Modelling for Health Improvement, University of Exeter Medical School, Exeter, Devon, UK
- />MRC/CSO Social and Public Health Sciences Unit & Robertson Centre for Biostatistics, Institute of Health and Well Being, University of Glasgow, Glasgow, Scotland, UK
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Meyvisch P, Alonso A, Van der Elst W, Molenberghs G. On the relationship between association and surrogacy when both the surrogate and true endpoint are binary outcomes. Stat Med 2020; 39:3867-3878. [PMID: 32875590 DOI: 10.1002/sim.8698] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2019] [Revised: 06/14/2020] [Accepted: 06/25/2020] [Indexed: 11/12/2022]
Abstract
The relationship between association and surrogacy has been the focus of much debate in the surrogate marker literature. Recently, the individual causal association (ICA) has been introduced as a metric of surrogacy in the causal inference framework, when both the surrogate and the true endpoint are normally distributed and when both are binary. Earlier work on the normal case has demonstrated that, although the ICA and the adjusted association are related metrics, their relationship strongly depends on unidentifiable parameters and, consequently, the association between both endpoints conveys little information on the validity of the surrogate. In addition, in the normal setting, the magnitude of the ICA does not depend on the mean of the outcomes. The latter implies that identifiable parameters such as mean responses and treatment effects provide no information on the validity of the surrogate. In the present work it is shown that this is fundamentally different in the binary case. We demonstrate that the observed association between the outcomes as well as the success rates in both treatment groups are quite predictive for the ICA. It is shown that finding a good surrogate will be more likely when the association between the endpoints is large, there are sizeable treatment effects and the success rates for both endpoints are similar in both treatment groups. These results are demonstrated using extensive simulations and illustrated on a case study in multi-drug resistant tuberculosis.
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Affiliation(s)
- Paul Meyvisch
- Galapagos NV, Mechelen, Belgium.,I-BioStat, KU Leuven, Leuven, Belgium.,I-BioStat, Universiteit Hasselt, Diepenbeek, Belgium
| | | | - Wim Van der Elst
- The Janssen Pharmaceutical Companies of Johnson & Johnson, Beerse, Belgium
| | - Geert Molenberghs
- I-BioStat, KU Leuven, Leuven, Belgium.,I-BioStat, Universiteit Hasselt, Diepenbeek, Belgium
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Flórez AJ, Alonso Abad A, Molenberghs G, Van Der Elst W. Generating random correlation matrices with fixed values: An application to the evaluation of multivariate surrogate endpoints. Comput Stat Data Anal 2020. [DOI: 10.1016/j.csda.2019.106834] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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Van der Elst W, Alonso AA, Geys H, Meyvisch P, Bijnens L, Sengupta R, Molenberghs G. Univariate Versus Multivariate Surrogates in the Single-Trial Setting. Stat Biopharm Res 2019. [DOI: 10.1080/19466315.2019.1575276] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Affiliation(s)
- Wim Van der Elst
- The Janssen Pharmaceutical Companies of Johnson & Johnson, Beerse, Belgium
| | | | - Helena Geys
- The Janssen Pharmaceutical Companies of Johnson & Johnson, Beerse, Belgium
| | | | - Luc Bijnens
- The Janssen Pharmaceutical Companies of Johnson & Johnson, Beerse, Belgium
| | - Rudradev Sengupta
- The Janssen Pharmaceutical Companies of Johnson & Johnson, Beerse, Belgium
| | - Geert Molenberghs
- I-BioStat, KU Leuven & UHasselt, Leuven, Belgium and Hasselt, Belgium
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Hilgers RD, Bogdan M, Burman CF, Dette H, Karlsson M, König F, Male C, Mentré F, Molenberghs G, Senn S. Lessons learned from IDeAl - 33 recommendations from the IDeAl-net about design and analysis of small population clinical trials. Orphanet J Rare Dis 2018; 13:77. [PMID: 29751809 PMCID: PMC5948846 DOI: 10.1186/s13023-018-0820-8] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2017] [Accepted: 05/01/2018] [Indexed: 01/01/2023] Open
Abstract
BACKGROUND IDeAl (Integrated designs and analysis of small population clinical trials) is an EU funded project developing new statistical design and analysis methodologies for clinical trials in small population groups. Here we provide an overview of IDeAl findings and give recommendations to applied researchers. METHOD The description of the findings is broken down by the nine scientific IDeAl work packages and summarizes results from the project's more than 60 publications to date in peer reviewed journals. In addition, we applied text mining to evaluate the publications and the IDeAl work packages' output in relation to the design and analysis terms derived from in the IRDiRC task force report on small population clinical trials. RESULTS The results are summarized, describing the developments from an applied viewpoint. The main result presented here are 33 practical recommendations drawn from the work, giving researchers a comprehensive guidance to the improved methodology. In particular, the findings will help design and analyse efficient clinical trials in rare diseases with limited number of patients available. We developed a network representation relating the hot topics developed by the IRDiRC task force on small population clinical trials to IDeAl's work as well as relating important methodologies by IDeAl's definition necessary to consider in design and analysis of small-population clinical trials. These network representation establish a new perspective on design and analysis of small-population clinical trials. CONCLUSION IDeAl has provided a huge number of options to refine the statistical methodology for small-population clinical trials from various perspectives. A total of 33 recommendations developed and related to the work packages help the researcher to design small population clinical trial. The route to improvements is displayed in IDeAl-network representing important statistical methodological skills necessary to design and analysis of small-population clinical trials. The methods are ready for use.
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Affiliation(s)
- Ralf-Dieter Hilgers
- Department of Medical Statistics, RWTH Aachen University, Pauwelsstr. 19, D-52074, Aachen, Germany.
| | - Malgorzata Bogdan
- Department of Medical Statistics, RWTH Aachen University, Pauwelsstr. 19, D-52074, Aachen, Germany
| | - Carl-Fredrik Burman
- Department of Medical Statistics, RWTH Aachen University, Pauwelsstr. 19, D-52074, Aachen, Germany
| | - Holger Dette
- Department of Medical Statistics, RWTH Aachen University, Pauwelsstr. 19, D-52074, Aachen, Germany
| | - Mats Karlsson
- Department of Medical Statistics, RWTH Aachen University, Pauwelsstr. 19, D-52074, Aachen, Germany
| | - Franz König
- Department of Medical Statistics, RWTH Aachen University, Pauwelsstr. 19, D-52074, Aachen, Germany
| | - Christoph Male
- Department of Medical Statistics, RWTH Aachen University, Pauwelsstr. 19, D-52074, Aachen, Germany
| | - France Mentré
- Department of Medical Statistics, RWTH Aachen University, Pauwelsstr. 19, D-52074, Aachen, Germany
| | - Geert Molenberghs
- Department of Medical Statistics, RWTH Aachen University, Pauwelsstr. 19, D-52074, Aachen, Germany
| | - Stephen Senn
- Department of Medical Statistics, RWTH Aachen University, Pauwelsstr. 19, D-52074, Aachen, Germany
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