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Estimands: improving inference in randomized controlled trials in clinical nutrition in the presence of missing values

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Abstract

For randomized controlled trials, the impact of the amount and handling of missing data on the interpretation of the treatment effect has been unclear. The current use of intention to treat, per protocol, and complete-case analysis has shortcomings. The use of estimands may lead to improved estimation of treatment effects through more precise characterizations of the fate of treatments after dropout or other post-randomization events. A perspective on current and future developments with a view toward clinical nutrition is provided.

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References

  1. International Conference on Harmonisation (ICH). E9(R1): Statistical principles for clinical trials: Addendum Estimands and Sensitivity Analyses in Clinical Trials; 2017.

  2. Mallinckrodt CH. Preventing and treating missing in longitudinal clinical trials. Cambridge: Cambridge University Press; 2013.

  3. Leuchs A-K, Zinserling J, Brandt A, Wirtz D, Benda N. Choosing appropriate estimands in clinicals. Ther Innov Regul Sci. 2015;49:584–92.

    PubMed  Google Scholar 

  4. Helms RW. Precise definitions of some terminology for longitudinal clinical trials: subjects, patient populations, analysis sets, intention to treat, and related terms. Pharm Stat. 2016;15:471–85.

    Article  Google Scholar 

  5. Carpenter JR, Roger JH, Kenward MG. Analysis of longitudinal trials with protocol deviation: a framework for relevant, accessible assumptions, and inference via multiple imputation. J Biopharm Stat. 2013;23:1352–71.

    Article  Google Scholar 

  6. Permutt T. A taxonomy of estimands for regulatory clinical trials with discontinuations. Stat Med. 2016;35:2865–75.

    Article  Google Scholar 

  7. Mallinckrodt CH, Molenberghs G, Rathmann S. Choosing estimands in clinical trials with missing data. Pharm Stat. 2017;16:29–36.

    Article  Google Scholar 

  8. Geiker NRW, Ritz C, Pedersen SD, Larsen TM, Hill JO, Astrup A. A weight-loss program adapted to the menstrual cycle increases weight loss in healthy, overweight, premenopausal women: a 6-mo randomized controlled trial. Am J Clin Nutr. 2016;104:15–20.

    Article  CAS  Google Scholar 

  9. White IR, Carpenter J, Horton NJ. Including all individuals is not enough: lessons for intention-to-treat analysis. Clin Trials. 2012;9:396–407.

    Article  Google Scholar 

  10. Olsen MF, Abdissa A, Kæstel P, Tesfaye M, Yilma D, Girma T, et al. Nutrient supplements improve weight gain, lean mass, grip strength and immune recovery in HIV-patients initiating antiretroviral treatment: a randomised controlled trial in Ethiopia. BMJ. 2014;348:g3187.

    Article  Google Scholar 

  11. Perkin MR, Logan K, Tseng A, Raji B, Ayis S, Peacock J, et al. Randomized trial of introduction of allergenic foods in breast-fed infants. New Engl J Med. 2016;374:1733–43.

    Article  CAS  Google Scholar 

  12. Leuchs A-K, Brandt A, Zinserling J, Benda N. Disentangling estimands and the intention-to-treat principle. Pharm Stat. 2017;16:12–19.

    Article  Google Scholar 

  13. Johnston BC, Guyatt GH. Best (but oft-forgotten) practices: intention-to-treat, treatment adherence, and missing participant outcome data in the nutrition literature. Am J Clin Nutr. 2016;104:1197–201.

    Article  CAS  Google Scholar 

  14. Lu K. An analytic method for the placebo-based pattern-mixture model. Stat Med. 2014;33:1134–45.

    Article  Google Scholar 

  15. Holzhauer B, Akacha M, Bermann G. Choice of estimand and analysis methods in diabetes trials with rescue medication. Pharm Stat. 2015;14:433–47.

    Article  Google Scholar 

  16. Little R, Kang S. Intention-to-treat analysis with treatment discontinuation and missing data in clinical trials. Stat Med. 2015;34:2381–90.

    Article  Google Scholar 

  17. Akacha M, Bretz F, Ruberg S. Estimands in clinical trials – broadening the perspective. Stat Med. 2017;36:5–19.

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Nutrition, Exercise and Sports, Faculty of Science, University of Copenhagen, Copenhagen, Denmark

    Christian Ritz

  2. Biometrics, Leo Pharma a/s, Copenhagen, Denmark

    Birgitte Rønn

Authors
  1. Christian Ritz
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  2. Birgitte Rønn
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Correspondence to Christian Ritz.

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Ritz, C., Rønn, B. Estimands: improving inference in randomized controlled trials in clinical nutrition in the presence of missing values. Eur J Clin Nutr 72, 1291–1295 (2018). https://doi.org/10.1038/s41430-018-0207-x

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  • DOI: https://doi.org/10.1038/s41430-018-0207-x

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