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REVIEW 3 major objections 7 minor 35 references

Methodological Advances and Challenges in Indirect Treatment Comparisons: A Review of International Guidelines and HAS TC Case Studies

T0 review · 3 major / 7 minor · reviewed 2026-08-07 · deepseek-v4-flash

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Pith's one-line read Only 13.3% of indirect treatment comparisons submitted to the French Transparency Committee from 2021 to 2023 influenced reimbursement decisions, with acceptance ranging from 34.4% in genetic diseases to 4.2% for network meta-analyses.

desk verdict Useful French HTA dataset, but the 13.3% 'influenced' headline loses the paper's own distinction between accepted and merely not-rejected ITCs. read the letter →

arxiv 2506.11587 v1 pith:QJNQFHGC submitted 2025-06-13 stat.ME

Matthias Monnereau , Ana Jarne , Axel Benoist , Clémence Fradet , Maurice Perol , Thomas Filleron , Louise Baschet This is my paper · ORCID
classification stat.ME
keywords indirecttreatmentcomparisonhealthtechnologyassessmentFrenchTransparencyCommitteenetworkmeta-analysisreimbursementdecisionssingle-armtrialsevidencesynthesisreal-worlddata
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

Indirect treatment comparisons (ITCs) compare a new drug against a comparator using data from separate trials rather than a head-to-head study. The paper tries to establish that, despite their growing use in French reimbursement submissions, these comparisons rarely shape the final decision. In 138 Transparency Committee opinions published between 2021 and 2023, the authors identified 195 ITCs and found that only 26 (13.3%) influenced the committee's decision-making. Acceptance was uneven: 34.4% in genetic diseases, 11.1% in autoimmune diseases, and 10.0% in oncology, and methods using individual patient data performed better (23.1%) than network meta-analyses (4.2%). The paper also reviews international guidelines and concludes they largely agree, so the gap is not mainly conflicting regulatory standards but practical failure to meet existing methodological expectations.

What carries the argument

The central instrument is an acceptability classification built from the Transparency Committee's published summaries. Each ITC was tagged as acceptable, not rejected, not acceptable, or unclear depending on whether and how the decision summary mentioned the ITC, with the ITC rather than the drug opinion as the unit of analysis. A 41-variable extraction grid and a three-branch taxonomy of limitations (data, methodology, uncertainty) let the authors quantify acceptance by therapeutic area, by statistical method, and by whether the ITC was the primary evidence source.

What would settle it

Look for a sample of the 169 ITCs classified as not influencing decisions in the committee's internal records or in interviews with committee members: if a substantial share were actually discussed and used in the SMR or ASMR reasoning, the 13.3% estimate is an artifact of written reporting. A simpler check would re-run the same dataset coding influence from whether the ITC changed the final rating relative to the manufacturer's requested rating and compare the two acceptance rates.

Watch

Extended reading notes

Core claim

Across all French Transparency Committee opinions from 2021 to 2023, 138 contained at least one indirect comparison, yielding 195 ITCs for analysis. Only 13.3% were considered in the ASMR decision; 86.7% were not. When an ITC was the primary source of comparative effectiveness, the proportion of important clinical benefit fell to 60.9% versus 73.4% when randomized controlled trials anchored the comparison, and the proportion of insufficient benefit rose from 9.6% to 18.8%. The committee's most frequent criticisms were heterogeneity or risk of bias (59%), lack of or unclear data (48%), statistical-methodology problems (29%), study-design concerns (27%), small sample size (25%), and variability in outcome definition or timing (20%). The authors read the low acceptance of network meta-analyses and Bucher comparisons (4.2%) as a sign that strict homogeneity and consistency conditions are often unmet, while the higher acceptance of unadjusted comparisons reflects settings of extreme unmet medical need rather than methodological preference.

Load-bearing premise

The classification of an ITC as influencing the decision depends entirely on whether the Transparency Committee's written ASMR summary mentions it, so an ITC that was weighed but not cited is counted as not influencing the decision.

Editorial extensions

If this is right

  • Manufacturers submitting ITCs to French reimbursement reviews should expect most to be ignored in the final ASMR decision, even when the ITC is the only comparative evidence.
  • The method gradient means network meta-analyses and Bucher comparisons are unlikely to influence decisions unless homogeneity and consistency are convincingly demonstrated, whereas IPD-based or unadjusted comparisons in rare and genetic diseases have a better chance.
  • Relying on an ITC as the primary source of comparative evidence is associated with a lower probability of an important clinical benefit rating and a higher probability of an insufficient rating.
  • Because international guidelines largely agree, a single well-conducted ITC dossier could in principle serve multiple European HTA bodies, with the main national divergence centered on population-adjusted methods.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The 13.3% figure is only as good as the written summaries: a natural validation would compare the summary-based classification against the committee's internal deliberation records or manufacturers' own accounts of what was discussed.
  • The higher acceptance of unadjusted comparisons is probably confounded with disease context, since companies tend to submit them only where unmet need is extreme and treatment effects are dramatic; matching on disease area could separate method quality from context.
  • If the 2023 HAS doctrine changes ITC requirements, the acceptance rate among opinions from 2024 onward would be a direct test of whether clearer guidance actually raises the influence of indirect comparisons.
  • The guideline comparison suggests that a common European ITC reporting template could reduce duplication, a policy implication the paper notes but does not test.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 7 minor

Summary. The manuscript presents a pragmatic review of indirect treatment comparison (ITC) guidelines from seven HTA and multistakeholder bodies, together with a systematic analysis of all French Transparency Committee (TC) opinions published between 2021 and 2023 that mention at least one ITC. The authors extracted 195 ITCs from 138 TC opinions, classified each ITC's acceptability on the basis of how it was treated in the TC's ASMR summary, and reported acceptance rates by disease area, by statistical method, and by whether the ITC was the primary source of evidence. The paper's central quantitative claim is that only 13.3% of submitted ITCs influenced TC decision-making, with higher acceptance in genetic diseases and for IPD-based methods, and it also describes the most common limitations identified by the TC.

Significance. If the classification issues were resolved, this would be a valuable and comprehensive descriptive study of ITC use in French HTA decision-making. Its strengths include a census-like sampling frame (all TC opinions in a three-year window), a structured 41-variable extraction template, manual review with a documented 10% quality-assurance check, and a transparent list of the most frequently cited limitations. The guideline comparison in Table 1 is a useful synthesis. The paper is, however, descriptive rather than methodological, and the headline influence rate is not currently supported by the paper's own category definitions. The dataset and extraction approach could serve as a reproducible baseline for future evaluations if the terminological and classification ambiguities are corrected.

major comments (3)
  1. [Abstract; Methods (Overall acceptability); Results (paragraph beginning 'Among the 195 indirect comparisons')] The headline 'Only 13.3% of these ITCs influenced TC decision-making' conflates the category 'Acceptable' with 'Not rejected.' The Methods define three distinct outcomes: 'Acceptable,' 'Not rejected' (considered for the decision-making but with clear stated issues), and 'Not acceptable.' The Results report 26/195 (13.3%) as 'considered,' and the text around Figure 1 implies 6 accepted ITCs (3.1%) and 20 not rejected ITCs (10.3%). If 'influence' is reserved for ITCs the TC positively relied on, the rate is 3.1%; if it includes ITCs described as having 'clear stated issues,' the abstract's wording needs an explicit definition. The abstract, Results, and Discussion should report the three categories separately and use consistent terminology such as 'considered,' 'accepted,' and 'not rejected' rather than using 'influenced' for the combined category.
  2. [Results, paragraph after Figure 1] The sentence 'the percentage of accepted indirect comparisons is higher when the ITC is the main source for comparing effectiveness (21.6% vs 5.8%)' directly contradicts the preceding sentence reporting 3% acceptance in both the main-source and non-main-source groups. The values 21.6% (20/92) and 5.8% (6/103) correspond to the combined 'accepted or not rejected' category, not to 'accepted' alone. This incorrect label propagates to the discussion of IPD-based versus NMA-based acceptance rates and should be corrected, with the accepted and not-rejected subcategories reported separately throughout.
  3. [Methods (Overall acceptability definition)] The acceptability classification depends entirely on whether and how the ITC is mentioned in the TC's ASMR summary. This assumes that the written summary fully captures the role of the ITC in the actual decision. A TC could weigh an ITC without citing it, or mention it without substantive influence. This measurement-validity assumption should be acknowledged as a limitation and, if possible, tested by comparing a sample of classifications against the full TC dossiers rather than only the opinion summaries.
minor comments (7)
  1. [Throughout] The terms 'accepted,' 'considered,' and 'influenced' are used interchangeably; please standardize the terminology to match the definitions in the Methods.
  2. [Abstract and Results] The abstract refers to 'important clinical benefit' while the Results report 'important SMR'; use one consistent term for this outcome.
  3. [Methods, first sentence] The sentence 'This article are based on two complementary works' should read 'This article is based on two complementary works.'
  4. [Methods, TC opinion screening] The automated keyword algorithm is described only as 'based on key words' with a reference to Supplementary Methods; please provide the exact search strategy and validation results in the main text or a fully available supplement.
  5. [Table 1 legend] The table uses both 'X' and 'O' but the legend explains only 'X' and 'o'; unify the case and clearly define all symbols.
  6. [Figure 2 and accompanying text] Some acceptance-rate comparisons are based on very small denominators (e.g., unadjusted comparisons 3/8); please report exact counts with confidence intervals or explicit cautions about small-sample comparisons.
  7. [Discussion] The statement 'This is in line with HTA recommendations... (HAS, 2019)' cites a year not present in the reference list; either add the reference or correct the citation.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: this is a descriptive coding review, not a derivation; the 13.3% headline is an operational category issue, not a logical loop.

full rationale

This paper is a descriptive, empirically coded review rather than a mathematical derivation. It reviews HTA guidelines and manually codes 195 ITCs from 138 TC opinions using a predefined extraction grid. The central statistics (13.3% considered, acceptance by disease area and method, limitation frequencies, SMR comparisons) are direct tabulations of the coded data, not outputs of a model fitted to those same data. No parameter is fitted and then redesignated as a prediction, and no equation is used whose definition pre-implies the result. The only adjacent concern is that 'influenced' is operationalized as 'considered' in the TC summary, with 'Not rejected' (considered but with stated issues) counted alongside 'Acceptable'; this is a coding and interpretation limitation, not a circular derivation, because the paper does not derive the category from the outcome it is measuring. The guideline comparison relies on standard HTA documents and published literature, with no load-bearing self-citation or imported uniqueness theorem. Therefore there is no significant circularity.

Assumptions & free parameters 0 free parameters · 3 assumptions · 0 invented entities

No free parameters or invented entities. The analysis relies on domain assumptions about the transparency of TC summaries, completeness of keyword screening, and reproducibility of manual categorization.

assumptions (3)
  • domain assumption The ASMR summary text in each TC opinion accurately reflects the role of each ITC in the decision-making process.
    The paper defines ITC acceptability based on whether the ITC is mentioned in the decision-making summary (Methods, category definitions).
  • domain assumption The automated keyword search of HAS opinions captured all opinions containing at least one ITC.
    The screening relied on an automated algorithm described only in Supplementary Materials; if it missed opinions, the sample is incomplete.
  • domain assumption The manual extraction and categorization of ITC limitations is reproducible across reviewers.
    Only 10% of opinions were double-reviewed, and no inter-rater reliability statistic is reported (Methods).

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Cite this review

Pith. "Pith review of Methodological Advances and Challenges in Indirect Treatment Comparisons: A Review of International Guidelines and HAS TC Case Studies." pith.science (2026). https://pith.science/paper/QJNQFHGC

@misc{pith2026250611587,
  author       = {Pith},
  title        = {Pith review of: Methodological Advances and Challenges in Indirect Treatment Comparisons: A Review of International Guidelines and HAS TC Case Studies},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/QJNQFHGC}},
  note         = {Machine review of arXiv:2506.11587}
}
read the original abstract

To evaluate methodological challenges and regulatory considerations of indirect treatment comparisons (ITCs) with the analysis of international health technology assessment guidelines and French Transparency Committee (TC) decisions. We conducted a pragmatic review of ITC guidelines from major health technology assessment (HTA) bodies and multistakeholder organizations. Then, we analyzed TC opinions published between 2021-2023. We extracted data on ITC methodology, therapeutic areas, acceptability, and limitations expressed by the TC. The targeted review of the main guidelines showed mainly agreements between HTA bodies and multistakeholder organizations, with some specificities. 138 TC opinions containing 195 ITCs were analyzed. Only 13.3% of these ITCs influenced TC decision-making. ITCs were more frequently accepted in genetic diseases (34.4%) compared to oncology (10.0%) and autoimmune diseases (11.1%). Methods using individual patient data showed higher acceptance rates (23.1%) than network meta-analyses (4.2%). Main limitations included heterogeneity/bias risk (59%), lack of data (48%), statistical methodology issues (29%), study design concerns (27%), small sample size (25%), and outcome definition variability (20%). When ITCs were the primary source of evidence, the proportion of important clinical benefit was lower (60.9% vs. 73.4%) than when randomized controlled trials were available. While ITCs are increasingly submitted, particularly where direct evidence is impractical, their influence on reimbursement decisions remains limited. There is a need for clear and accessible guides so manufacturers can produce clearer and more robust ITCs that follow regulatory guidelines, from the planning phase to execution.

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

35 extracted references · 33 canonical work pages

  1. [1]

    Use of External Comparators for Health Technology Assessment Submissions Based on Single-Arm Trials

    Patel D, Grimson F, Mihaylova E, Wagner P, Warren J, van Engen A, et al. Use of External Comparators for Health Technology Assessment Submissions Based on Single-Arm Trials. Value Health. 1 août 2021;24(8):1118‑25

    work page 2021

  2. [2]

    CO163 The Increasing Use of Population- Adjusted Indirect Comparisons in the NICE Health Technology Assessment (HTA) Submission Process and the Response to These Methods

    Pooley N, Kisomi M, Embleton N, Langham S. CO163 The Increasing Use of Population- Adjusted Indirect Comparisons in the NICE Health Technology Assessment (HTA) Submission Process and the Response to These Methods. Value Health. 1 déc 2022;25(12):S49

    work page 2022

  3. [3]

    Data Standards for Drug and Biological Product Submissions Containing Real-World Data - Guidance for Industry

    FDA, Center for Drug Evaluation and Research, Center for Biologics Evaluation and Research. Data Standards for Drug and Biological Product Submissions Containing Real-World Data - Guidance for Industry. https://www.fda.gov/regulatory-information/search-fda-guidance- documents/data-standards-drug-and-biological-product-submissions-containing-real-world-dat...

    work page 2023

  4. [4]

    Real-World Data: Assessing Registries to Support Regulatory Decision-Making for Drug and Biological Products

    FDA, Center for Drug Evaluation and Research (CDER), Center for Biologics Evaluation and Research (CBER), Oncology Center of Excellence (OCE). Real-World Data: Assessing Registries to Support Regulatory Decision-Making for Drug and Biological Products. déc 2023

    work page 2023

  5. [5]

    Indirect comparisons: Methods and validity

    Haute Autorité de Santé. Indirect comparisons: Methods and validity. 2009

    work page 2009

  6. [6]

    Guide méthodologique : Choix méthodologiques pour l’évaluation économique à la HAS

    Haute Autorité de Santé. Guide méthodologique : Choix méthodologiques pour l’évaluation économique à la HAS. https://www.has-sante.fr/upload/docs/application/pdf/2020- 07/guide_methodologique_evaluation_economique_has_2020_vf.pdf. Accessed October 18, 2023

    work page 2020

  7. [7]

    Guide méthodologique « Etudes en vie réelle pour l’évaluation des médicaments et dispositifs médicaux »

    Haute Autorité de Santé. Guide méthodologique « Etudes en vie réelle pour l’évaluation des médicaments et dispositifs médicaux ». https://www.has- sante.fr/upload/docs/application/pdf/2021-06/guide_etude_en_vie_reelle_medicaments__dm.pdf. Accessed September 18, 2024

    work page 2021

  8. [8]

    Rapid access to innovative medicinal products while ensuring relevant health technology assessment

    Vanier A, Fernandez J, Kelley S, Alter L, Semenzato P, Alberti C, et al. Rapid access to innovative medicinal products while ensuring relevant health technology assessment. Position of the French National Authority for Health. BMJ Evid-Based Med. 14 févr 2023;bmjebm-2022- 112091

    work page 2023

Show all 35 references
  1. [9]

    Doctrine de la commission de la transparence (CT) Principes d’évaluation de la CT relatifs aux médicaments en vue de leur accès au remboursement

    Haute Autorité de Santé. Doctrine de la commission de la transparence (CT) Principes d’évaluation de la CT relatifs aux médicaments en vue de leur accès au remboursement. https://www.has-sante.fr/upload/docs/application/pdf/2021-03/doctrine_ct.pdf. Accessed December 1, 2023

    2021

  2. [10]

    Guideline on Clinical Trials in Small Populations

    EMA. Guideline on Clinical Trials in Small Populations. https://www.ema.europa.eu/en/documents/scientific-guideline/guideline-clinical-trials-small- populations_en.pdf. Accessed December 1, 2023

    2023

  3. [11]

    Guideline on the clinical evaluation of anticancer medicinal products

    EMA. Guideline on the clinical evaluation of anticancer medicinal products. EMA; 2020

    2020

  4. [12]

    Reflection paper on establishing efficacy based on single-arm trials submitted as pivotal evidence in a marketing authorisation - Considerations on evidence from single-arm trials

    EMA/CHMP. Reflection paper on establishing efficacy based on single-arm trials submitted as pivotal evidence in a marketing authorisation - Considerations on evidence from single-arm trials. https://www.ema.europa.eu/en/documents/scientific-guideline/reflection-paper-establish...

    2023

  5. [13]

    Methods Guideline-D4.3.2-Methodological-Guideline-on-Direct-and-indirect- comparisons-V1.0.pdf

    EUnetHTA. Methods Guideline-D4.3.2-Methodological-Guideline-on-Direct-and-indirect- comparisons-V1.0.pdf. https://www.eunethta.eu/wp-content/uploads/2022/08/EUnetHTA-21- Deliverable-D4.3.2-Methodological-Guideline-on-Direct-and-indirect-comparisons-V1.0.pdf. Accessed December 18, 2023

    2022

  6. [14]

    EUnetHTA 21 - Individual Practical Guideline Document D4.3.1: DIRECT AND INDIRECT COMPARISONS [Internet]

    EUnetHTA. EUnetHTA 21 - Individual Practical Guideline Document D4.3.1: DIRECT AND INDIRECT COMPARISONS [Internet]. https://www.eunethta.eu/wp- content/uploads/2022/12/EUnetHTA-21-D4.3.1-Direct-and-indirect-comparisons-v1.0.pdf. Accessed October 10, 2023

    2022

  7. [15]

    Practical Guideline for Quantitative Evidence Synthesis: Direct and Indirect Comparisons

    HTA CG. Practical Guideline for Quantitative Evidence Synthesis: Direct and Indirect Comparisons. 2024

    2024

  8. [16]

    Methodological Guideline for Quantitative Evidence Synthesis: Direct and Indirect Comparisons

    HTA CG. Methodological Guideline for Quantitative Evidence Synthesis: Direct and Indirect Comparisons. 2024

    2024

  9. [17]

    Use of indirect and mixed treatment comparisons for technology assessment

    Sutton A, Ades AE, Cooper N, Abrams K. Use of indirect and mixed treatment comparisons for technology assessment. PharmacoEconomics. sept 2008;26(9):753‑67

    2008

  10. [18]

    Checking consistency in mixed treatment comparison meta-analysis

    Dias S, Welton NJ, Caldwell DM, Ades AE. Checking consistency in mixed treatment comparison meta-analysis. Stat Med. 30 mars 2010;29(7‑8):932‑44

    2010

  11. [19]

    Evidence Synthesis of Treatment Efficacy in Decision Making: A Reviewer’s Checklist

    Ades AE, Caldwell DM, Reken S, Welton NJ, Sutton AJ, Dias S. Evidence Synthesis of Treatment Efficacy in Decision Making: A Reviewer’s Checklist. (NICE Decision Support Unit Technical Support Documents). http://www.ncbi.nlm.nih.gov/books/NBK395872/. Accessed January 10, 2023

    2023

  12. [20]

    NICE DSU Technical Support Document 3: Heterogeneity: subgroups, meta-regression,bias and bias-adjustment

    Dias S, Sutton AJ, Welton NJ, Ades A. NICE DSU Technical Support Document 3: Heterogeneity: subgroups, meta-regression,bias and bias-adjustment. https://www.sheffield.ac.uk/nice-dsu/tsds/evidence-synthesis. Accessed May 20, 2023

    2023

  13. [21]

    Guide to the methods of technology appraisal

    NICE. Guide to the methods of technology appraisal. 2013; https://www.nice.org.uk/process/pmg9/chapter/foreword. Accessed May 20, 2023

    2013

  14. [22]

    NICE DSU Technical Support Document 4: Inconsistency in networks of evidence based on randomised controlled trials

    Dias S, Welton NJ, Sutton AJ, Caldwell DM, Lu G, Ades A. NICE DSU Technical Support Document 4: Inconsistency in networks of evidence based on randomised controlled trials. https://www.sheffield.ac.uk/nice-dsu/tsds/evidence-synthesis. Accessed May 20, 2023

    2023

  15. [23]

    Appendix K: Network meta-analysis reporting standards | Tools and resources | Developing NICE guidelines: the manual | Guidance | NICE

    NICE. Appendix K: Network meta-analysis reporting standards | Tools and resources | Developing NICE guidelines: the manual | Guidance | NICE. https://www.nice.org.uk/process/pmg20/resources/developing-nice-guidelines-the-manual- appendices-2549710189/chapter/appendix-k-network...

    2023

  16. [24]

    NICE DSU Technical Support Document 2 : A general linear modelling framework for pair-wise and network meta-analysis of randomised controlled trials

    Dias S, Welton NJ, Sutton AJ, Ades A. NICE DSU Technical Support Document 2 : A general linear modelling framework for pair-wise and network meta-analysis of randomised controlled trials. https://www.sheffield.ac.uk/nice-dsu/tsds/evidence-synthesis. Accessed May 20, 2023

    2023

  17. [25]

    Assessing evidence inconsistency in mixed treatment comparisons

    Lu G, Ades A. Assessing evidence inconsistency in mixed treatment comparisons. J Am Stat Assoc. juin 2006;101 (474):447‑59

    2006

  18. [26]

    Methods for Population- Adjusted Indirect Comparisons in Health Technology Appraisal

    Phillippo DM, Ades AE, Dias S, Palmer S, Abrams KR, Welton NJ. Methods for Population- Adjusted Indirect Comparisons in Health Technology Appraisal. Med Decis Mak Int J Soc Med Decis Mak. févr 2018;38(2):200‑11

    2018

  19. [27]

    Population Adjustment Methods for Indirect Comparisons: A Review of National Institute for Health and Care Excellence Technology Appraisals

    Phillippo DM, Dias S, Elsada A, Ades AE, Welton NJ. Population Adjustment Methods for Indirect Comparisons: A Review of National Institute for Health and Care Excellence Technology Appraisals. Int J Technol Assess Health Care. janv 2019;35(3):221‑8

    2019

  20. [28]

    real-world evidence framework

    NICE. real-world evidence framework. www.nice.org.uk/corporate/ecd9. Accessed October 18, 2023

    2023

  21. [29]

    Meta-Analyses of Randomized Controlled Clinical Trials to Evaluate the Safety of Human Drugs or Biological Products Guidance for Industry

    FDA. Meta-Analyses of Randomized Controlled Clinical Trials to Evaluate the Safety of Human Drugs or Biological Products Guidance for Industry. U.S. Department of Health and Human Services Food and Drug Administration Center for Drug Evaluation and Research (CDER) Center for B...

    2024

  22. [30]

    Considerations for the Design and Conduct of Externally Controlled Trials for Drug and Biological Products (Draft guidance)

    FDA. Considerations for the Design and Conduct of Externally Controlled Trials for Drug and Biological Products (Draft guidance). https://www.fda.gov/regulatory-information/search-fda- guidance-documents/considerations-design-and-conduct-externally-controlled-trials-drug-and- ...

    2024

  23. [31]

    Conducting indirect- treatment-comparison and network-meta-analysis studies: report of the ISPOR Task Force on Indirect Treatment Comparisons Good Research Practices: part 2

    Hoaglin DC, Hawkins N, Jansen JP, Scott DA, Itzler R, Cappelleri JC, et al. Conducting indirect- treatment-comparison and network-meta-analysis studies: report of the ISPOR Task Force on Indirect Treatment Comparisons Good Research Practices: part 2. Value Health J Int Soc Pha...

    2011

  24. [32]

    Jansen JP, Fleurence R, Devine B, Itzler R, Barrett A, Hawkins N, et al. Interpreting indirect treatment comparisons and network meta-analysis for health-care decision making: report of the ISPOR Task Force on Indirect Treatment Comparisons Good Research Practices: part 1. Val...

    2011

  25. [33]

    Jansen JP, Trikalinos T, Cappelleri JC, Daw J, Andes S, Eldessouki R, et al. Indirect Treatment Comparison/Network Meta-Analysis Study Questionnaire to Assess Relevance and Credibility to Inform Health Care Decision Making: An ISPOR-AMCP-NPC Good Practice Task Force Report. Va...

    2014

  26. [34]

    ISPOR | International Society For Pharmacoeconomics and Outcomes Research

    Willke RJ. ISPOR | International Society For Pharmacoeconomics and Outcomes Research. 2017. Comparative-Effectiveness Research: New Progress in Assessing the Evidence in Today’s Health Care Marketplace. https://www.ispor.org/publications/journals/value-outcomes- spotlight/abst...

    2017

  27. [35]

    Yes”,”No

    IQWIG. General Methods - Version 7.0. Gen Methods. https://www.iqwig.de/en/about- us/methods/methods-paper/. Accessed September 19, 2023. SUPPLEMENTARY MATERIAL Appendix 1 – Variables extracted - Common by opinion: o Automatically extracted:  Review ID  Evamed Number  link,...

    2023

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