IDEAS home Printed from https://ideas.repec.org/a/spr/pharme/v28y2010i10p935-945.html
   My bibliography  Save this article

Comparative Effectiveness Without Head-to-Head Trials

Author

Listed:
  • James Signorovitch
  • Eric Wu
  • Andrew Yu
  • Charles Gerrits
  • Evan Kantor
  • Yanjun Bao
  • Shiraz Gupta
  • Parvez Mulani

Abstract

The absence of head-to-head trials is a common challenge in comparative effectiveness research and health technology assessment. Indirect cross-trial treatment comparisons are possible, but can be biased by cross-trial differences in patient characteristics. Using only published aggregate data, adjustment for such biases may be impossible. Although individual patient data (IPD) would permit adjustment, they are rarely available for all trials. However, many researchers have the opportunity to access IPD for trials of one treatment, a new drug for example, but only aggregate data for trials of comparator treatments. We propose a method that leverages all available data in this setting by adjusting average patient characteristics in trials with IPD to match those reported for trials without IPD. Treatment outcomes, including continuous, categorical and censored time-to-event outcomes, can then be compared across balanced trial populations. The proposed method is illustrated by a comparison of adalimumab and etanercept for the treatment of psoriasis. IPD from trials of adalimumab versus placebo (n=1025) were re-weighted to match the average baseline characteristics reported for a trial of etanercept versus placebo (n=330). Reweighting was based on the estimated propensity of enrolment in the adalimumab versus etanercept trials. Before matching, patients in the adalimumab trials had lower mean age, greater prevalence of psoriatic arthritis, less prior use of systemic treatment or phototherapy, and a smaller mean percentage of body surface area affected than patients in the etanercept trial. After matching, these and all other available baseline characteristics were well balanced across trials. Symptom improvements of ≥75% and ≥90% (as measured by the Psoriasis Area and Severity Index [PASI] score at week 12) were experienced by an additional 17.2% and 14.8% of adalimumab-treated patients compared with the matched etanercept-treated patients (respectively, both p > 0.001). Mean percentage PASI score improvements frombaseline were also greater for adalimumab than for etanercept at weeks 4, 8 and 12 (all p > 0.05). Matching adjustment ensured that this indirect comparison was not biased by differences in mean baseline characteristics across trials, supporting the conclusion that adalimumab was associated with significantly greater symptom reduction than etanercept for the treatment of moderate to severe psoriasis. Copyright Adis Data Information BV 2010

Suggested Citation

  • James Signorovitch & Eric Wu & Andrew Yu & Charles Gerrits & Evan Kantor & Yanjun Bao & Shiraz Gupta & Parvez Mulani, 2010. "Comparative Effectiveness Without Head-to-Head Trials," PharmacoEconomics, Springer, vol. 28(10), pages 935-945, October.
  • Handle: RePEc:spr:pharme:v:28:y:2010:i:10:p:935-945
    DOI: 10.2165/11538370-000000000-00000
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.2165/11538370-000000000-00000
    Download Restriction: Access to full text is restricted to subscribers.

    File URL: https://libkey.io/10.2165/11538370-000000000-00000?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Keisuke Hirano & Guido W. Imbens & Geert Ridder, 2003. "Efficient Estimation of Average Treatment Effects Using the Estimated Propensity Score," Econometrica, Econometric Society, vol. 71(4), pages 1161-1189, July.
    2. Andrew Yu & Scott Johnson & Si-Tien Wang & Pavel Atanasov & Jackson Tang & Eric Wu & Jingdong Chao & Parvez Mulani, 2009. "Cost Utility of Adalimumab versus Infliximab Maintenance Therapies in the United States for Moderately to Severely Active Crohn’s Disease," PharmacoEconomics, Springer, vol. 27(7), pages 609-621, July.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Lauren Cappiello & Zhiwei Zhang & Changyu Shen & Neel M. Butala & Xinping Cui & Robert W. Yeh, 2021. "Adjusting for population differences using machine learning methods," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 70(3), pages 750-769, June.
    2. David M. Phillippo & Sofia Dias & A. E. Ades & Mark Belger & Alan Brnabic & Alexander Schacht & Daniel Saure & Zbigniew Kadziola & Nicky J. Welton, 2020. "Multilevel network meta‐regression for population‐adjusted treatment comparisons," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(3), pages 1189-1210, June.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. van de Walle, Dominique & Mu, Ren, 2007. "Fungibility and the flypaper effect of project aid: Micro-evidence for Vietnam," Journal of Development Economics, Elsevier, vol. 84(2), pages 667-685, November.
    2. de Brauw, Alan & Gilligan, Daniel O. & Hoddinott, John & Roy, Shalini, 2014. "The Impact of Bolsa Família on Women’s Decision-Making Power," World Development, Elsevier, vol. 59(C), pages 487-504.
    3. Alexandre Belloni & Victor Chernozhukov & Denis Chetverikov & Christian Hansen & Kengo Kato, 2018. "High-dimensional econometrics and regularized GMM," CeMMAP working papers CWP35/18, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    4. Matias Busso & Patrick Kline, 2008. "Do Local Economic Development Programs Work? Evidence from the Federal Empowerment Zone Program," Cowles Foundation Discussion Papers 1639, Cowles Foundation for Research in Economics, Yale University.
    5. Clément de Chaisemartin & Luc Behaghel, 2020. "Estimating the Effect of Treatments Allocated by Randomized Waiting Lists," Econometrica, Econometric Society, vol. 88(4), pages 1453-1477, July.
    6. Gunther Bensch & Jörg Peters, 2013. "Alleviating Deforestation Pressures? Impacts of Improved Stove Dissemination on Charcoal Consumption in Urban Senegal," Land Economics, University of Wisconsin Press, vol. 89(4), pages 676-698.
    7. Leonardo Becchetti & Pierluigi Conzo & Alessandro Romeo, 2014. "Violence, trust, and trustworthiness: evidence from a Nairobi slum," Oxford Economic Papers, Oxford University Press, vol. 66(1), pages 283-305, January.
    8. Ruoxuan Xiong & Allison Koenecke & Michael Powell & Zhu Shen & Joshua T. Vogelstein & Susan Athey, 2021. "Federated Causal Inference in Heterogeneous Observational Data," Papers 2107.11732, arXiv.org, revised Apr 2023.
    9. Kitagawa, Toru & Muris, Chris, 2016. "Model averaging in semiparametric estimation of treatment effects," Journal of Econometrics, Elsevier, vol. 193(1), pages 271-289.
    10. Rajeev Dehejia, 2013. "The Porous Dialectic: Experimental and Non-Experimental Methods in Development Economics," WIDER Working Paper Series wp-2013-011, World Institute for Development Economic Research (UNU-WIDER).
    11. Futoshi Yamauchi & Yanyan Liu, 2013. "Impacts of an Early Stage Education Intervention on Students' Learning Achievement: Evidence from the Philippines," Journal of Development Studies, Taylor & Francis Journals, vol. 49(2), pages 208-222, February.
    12. Jessamyn Schaller & Mariana Zerpa, 2019. "Short-Run Effects of Parental Job Loss on Child Health," American Journal of Health Economics, MIT Press, vol. 5(1), pages 8-41, Winter.
    13. Susan Athey & Guido W. Imbens & Stefan Wager, 2018. "Approximate residual balancing: debiased inference of average treatment effects in high dimensions," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 80(4), pages 597-623, September.
    14. Töpfer, Marina, 2017. "Detailed RIF decomposition with selection: The gender pay gap in Italy," Hohenheim Discussion Papers in Business, Economics and Social Sciences 26-2017, University of Hohenheim, Faculty of Business, Economics and Social Sciences.
    15. Katie Meara & Francesco Pastore & Allan Webster, 2020. "The gender pay gap in the USA: a matching study," Journal of Population Economics, Springer;European Society for Population Economics, vol. 33(1), pages 271-305, January.
    16. Burt S. Barnow & Jeffrey Smith, 2015. "Employment and Training Programs," NBER Chapters, in: Economics of Means-Tested Transfer Programs in the United States, Volume 2, pages 127-234, National Bureau of Economic Research, Inc.
    17. Gernandt, Johannes & Maier, Michael & Pfeiffer, Friedhelm & Rat-Wirtzler, Julie, 2006. "Distributional effects of the high school degree in Germany," ZEW Discussion Papers 06-088, ZEW - Leibniz Centre for European Economic Research.
    18. Malerba, Daniele, 2020. "Poverty alleviation and local environmental degradation: An empirical analysis in Colombia," World Development, Elsevier, vol. 127(C).
    19. Pedro H. C. Sant'Anna & Xiaojun Song & Qi Xu, 2022. "Covariate distribution balance via propensity scores," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(6), pages 1093-1120, September.
    20. Victor Chernozhukov & Iván Fernández‐Val & Blaise Melly, 2013. "Inference on Counterfactual Distributions," Econometrica, Econometric Society, vol. 81(6), pages 2205-2268, November.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:pharme:v:28:y:2010:i:10:p:935-945. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.