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We invite statisticians to reflect on the evolving landscape of estimands, encouraging thoughtful consideration of estimation techniques and a deeper exploration of causal inference in clinical trials. The journey through this nuanced statistical terrain unfolds, offering valuable insights for both seasoned professionals and newcomers to the field.

Reference:

Role play reference:

Keene ON, Ruberg S, Schacht A, Akacha M, Lawrance R, Berglind A, Wright D. What matters most? Different stakeholder perspectives on estimands for an invented case study in COPD. Pharmaceutical Statistics. 2020 Jul;19(4):370-87.

Other references:

Naitee Ting (2023) Emerging insights and commentaries – MMRM vs LOCF, Journal of Biopharmaceutical Statistics, 33:2, 253-255, DOI: 10.1080/10543406.2023.2184828

https://www.tandfonline.com/doi/abs/10.1080/10543406.2023.2184828

Wright D, Bratton DJ, Drury T, Keene ON, Rehal S, White IR. Response to Comment on” Emerging insights and commentaries–MMRM vs LOCF by Naitee Ting”. Journal of Biopharmaceutical Statistics. 2023 Sep 23:1-3.

Keene ON. Adherence, per-protocol effects, and the estimands framework. Pharmaceutical Statistics. 2023;1‐4. doi:10.1002/pst.232

Keene ON. Intent-to-treat analysis in the presence of off-treatment or missing data. Pharmaceutical Statistics 2011, 10:191–195, doi: 10.1002/pst.421.

Keene ON, Wright D, Phillips A, Wright M. Why ITT analysis is not always the answer for estimating treatment effects in clinical trials. Contemporary Clinical Trials. 2021 Sep 1;108:106494.

Keene ON, Lynggaard H, Englert S, Lanius V, Wright D. Why estimands are needed to define treatment effects in clinical trials. BMC Medicine. 2023 Jul 27;21(1):276.