eprintid: 10150985
rev_number: 7
eprint_status: archive
userid: 699
dir: disk0/10/15/09/85
datestamp: 2022-06-30 10:52:38
lastmod: 2022-06-30 10:52:38
status_changed: 2022-06-30 10:52:38
type: article
metadata_visibility: show
sword_depositor: 699
creators_name: Kahan, Brennan C
creators_name: Morris, Tim P
creators_name: Goulão, Beatriz
creators_name: Carpenter, James
title: Estimands for factorial trials
ispublished: inpress
divisions: UCL
divisions: J38
divisions: D65
divisions: B02
keywords: 2 × 2, ICH-E9 addendum, estimand, factorial trial, randomized controlled trial
note: © 2022 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd. This is an open access article under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/)
abstract: Factorial trials offer an efficient method to evaluate multiple interventions in a single trial, however the use of additional treatments can obscure research objectives, leading to inappropriate analytical methods and interpretation of results. We define a set of estimands for factorial trials, and describe a framework for applying these estimands, with the aim of clarifying trial objectives and ensuring appropriate primary and sensitivity analyses are chosen. This framework is intended for use in factorial trials where the intent is to conduct "two-trials-in-one" (ie, to separately evaluate the effects of treatments A and B), and is comprised of four steps: (i) specifying how additional treatment(s) (eg, treatment B) will be handled in the estimand, and how intercurrent events affecting the additional treatment(s) will be handled; (ii) designating the appropriate factorial estimator as the primary analysis strategy; (iii) evaluating the interaction to assess the plausibility of the assumptions underpinning the factorial estimator; and (iv) performing a sensitivity analysis using an appropriate multiarm estimator to evaluate to what extent departures from the underlying assumption of no interaction may affect results. We show that adjustment for other factors is necessary for noncollapsible effect measures (such as odds ratio), and through a trial re-analysis we find that failure to consider the estimand could lead to inappropriate interpretation of results. We conclude that careful use of the estimands framework clarifies research objectives and reduces the risk of misinterpretation of trial results, and should become a standard part of both the protocol and reporting of factorial trials.
date: 2022-06-25
date_type: published
publisher: Wiley
official_url: https://doi.org/10.1002/sim.9510
oa_status: green
full_text_type: pub
language: eng
primo: open
primo_central: open_green
verified: verified_manual
elements_id: 1963503
doi: 10.1002/sim.9510
medium: Print-Electronic
lyricists_name: Morris, Timothy
lyricists_name: Kahan, Brennan
lyricists_name: Carpenter, James
lyricists_id: TNMOR17
lyricists_id: BKAHA03
lyricists_id: JCARP26
actors_name: Gibb, Diana
actors_name: Payne, Roxanne
actors_id: MGIBB48
actors_id: RPAYN74
actors_role: owner
actors_role: impersonator
funding_acknowledgements: MC_UU_00004/09 [UK MRC]; MC_UU_00004/07 [UK MRC]
full_text_status: public
publication: Statistics in Medicine
event_location: England
citation:        Kahan, Brennan C;    Morris, Tim P;    Goulão, Beatriz;    Carpenter, James;      (2022)    Estimands for factorial trials.                   Statistics in Medicine        10.1002/sim.9510 <https://doi.org/10.1002/sim.9510>.    (In press).    Green open access   
 
document_url: https://discovery.ucl.ac.uk/id/eprint/10150985/1/Statistics%20in%20Medicine%20-%202022%20-%20Kahan%20-%20Estimands%20for%20factorial%20trials.pdf