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Determining individual trajectories of joint space loss: improved statistical methods for monitoring knee osteoarthritis disease progression

Determining individual trajectories of joint space loss: improved statistical methods for monitoring knee osteoarthritis disease progression
Determining individual trajectories of joint space loss: improved statistical methods for monitoring knee osteoarthritis disease progression
Objectives
Knee osteoarthritis (KOA) progression is frequently monitored by calculating the change in knee joint space width (JSW) measurements. Such differences are small and sensitive to measurement error. We aimed to assess the utility of two alternative statistical modelling methods for monitoring KOA.

Material and methods
We used JSW on radiographs from both the control arm of the Strontium Ranelate Efficacy in Knee Osteoarthritis trial (SEKOIA), a 3-year multicentre, double-blind, placebo-controlled phase three trial, and the Osteoarthritis Initiative (OAI), an open-access longitudinal dataset from the USA comprising participants followed over 8 years. Individual estimates of annualised change obtained from frequentist linear mixed effect (LME) and Bayesian hierarchical modelling, were compared with annualised crude change, and the association of these parameters with change in WOMAC pain was examined.

Results
Mean annualised JSW changes were comparable for all estimates, a reduction of around 0.14 mm/y in SEKOIA and 0.08 mm/y in OAI. The standard deviation (SD) of change estimates was lower with LME and Bayesian modelling than crude change (SEKOIA SD = 0.12, 0.12 and 0.21 respectively; OAI SD = 0.08, 0.08 and 0.11 respectively). Estimates from LME and Bayesian modelling were statistically significant predictors of change in pain in SEKOIA (LME P-value = 0.04, Bayes P-value = 0.04), while crude change did not predict change in pain (P-value = 0.10).

Conclusions
Implementation of LME or Bayesian modelling in clinical trials and epidemiological studies, would reduce sample sizes by enabling all study participants to be included in analysis regardless of incomplete follow up, and precision of change estimates would improve. They provide increased power to detect associations with other measures.
Longitudinal, Osteoarthritis, Progression
1063-4584
59-67
Parsons, Camille
9730e5c3-0382-4ed7-8eaa-6932ab09ec15
Judge, Andrew
53ccba98-13f0-4a06-b2ff-59a35616c990
Mayer, Renate
d696ef78-f01b-4e97-a266-0c362eec8089
Bruyere, Olivier
7d127754-d7d6-4328-8f76-212df27727b6
Petit Dop, Florence
0af40637-efb0-4e14-a41f-9a6c9f891916
Chapurlat, Roland
d9410f8c-4e4a-465b-8fb6-67efaadaad4c
Reginster, Jean-Yves
db56b103-184d-46e1-9600-f47f7a09a492
Cooper, Cyrus
e05f5612-b493-4273-9b71-9e0ce32bdad6
Inskip, Hazel
5fb4470a-9379-49b2-a533-9da8e61058b7
Parsons, Camille
9730e5c3-0382-4ed7-8eaa-6932ab09ec15
Judge, Andrew
53ccba98-13f0-4a06-b2ff-59a35616c990
Mayer, Renate
d696ef78-f01b-4e97-a266-0c362eec8089
Bruyere, Olivier
7d127754-d7d6-4328-8f76-212df27727b6
Petit Dop, Florence
0af40637-efb0-4e14-a41f-9a6c9f891916
Chapurlat, Roland
d9410f8c-4e4a-465b-8fb6-67efaadaad4c
Reginster, Jean-Yves
db56b103-184d-46e1-9600-f47f7a09a492
Cooper, Cyrus
e05f5612-b493-4273-9b71-9e0ce32bdad6
Inskip, Hazel
5fb4470a-9379-49b2-a533-9da8e61058b7

Parsons, Camille, Judge, Andrew, Mayer, Renate, Bruyere, Olivier, Petit Dop, Florence, Chapurlat, Roland, Reginster, Jean-Yves, Cooper, Cyrus and Inskip, Hazel (2020) Determining individual trajectories of joint space loss: improved statistical methods for monitoring knee osteoarthritis disease progression. Osteoarthritis and Cartilage, 29 (1), 59-67. (doi:10.1016/j.joca.2020.09.009).

Record type: Article

Abstract

Objectives
Knee osteoarthritis (KOA) progression is frequently monitored by calculating the change in knee joint space width (JSW) measurements. Such differences are small and sensitive to measurement error. We aimed to assess the utility of two alternative statistical modelling methods for monitoring KOA.

Material and methods
We used JSW on radiographs from both the control arm of the Strontium Ranelate Efficacy in Knee Osteoarthritis trial (SEKOIA), a 3-year multicentre, double-blind, placebo-controlled phase three trial, and the Osteoarthritis Initiative (OAI), an open-access longitudinal dataset from the USA comprising participants followed over 8 years. Individual estimates of annualised change obtained from frequentist linear mixed effect (LME) and Bayesian hierarchical modelling, were compared with annualised crude change, and the association of these parameters with change in WOMAC pain was examined.

Results
Mean annualised JSW changes were comparable for all estimates, a reduction of around 0.14 mm/y in SEKOIA and 0.08 mm/y in OAI. The standard deviation (SD) of change estimates was lower with LME and Bayesian modelling than crude change (SEKOIA SD = 0.12, 0.12 and 0.21 respectively; OAI SD = 0.08, 0.08 and 0.11 respectively). Estimates from LME and Bayesian modelling were statistically significant predictors of change in pain in SEKOIA (LME P-value = 0.04, Bayes P-value = 0.04), while crude change did not predict change in pain (P-value = 0.10).

Conclusions
Implementation of LME or Bayesian modelling in clinical trials and epidemiological studies, would reduce sample sizes by enabling all study participants to be included in analysis regardless of incomplete follow up, and precision of change estimates would improve. They provide increased power to detect associations with other measures.

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Accepted/In Press date: 2 September 2020
e-pub ahead of print date: 24 November 2020
Keywords: Longitudinal, Osteoarthritis, Progression

Identifiers

Local EPrints ID: 445514
URI: http://eprints.soton.ac.uk/id/eprint/445514
ISSN: 1063-4584
PURE UUID: a704762e-f79c-4b77-9873-e086b81d3b8c
ORCID for Cyrus Cooper: ORCID iD orcid.org/0000-0003-3510-0709
ORCID for Hazel Inskip: ORCID iD orcid.org/0000-0001-8897-1749

Catalogue record

Date deposited: 14 Dec 2020 17:31
Last modified: 26 Nov 2021 05:39

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Contributors

Author: Camille Parsons
Author: Andrew Judge
Author: Renate Mayer
Author: Olivier Bruyere
Author: Florence Petit Dop
Author: Roland Chapurlat
Author: Jean-Yves Reginster
Author: Cyrus Cooper ORCID iD
Author: Hazel Inskip ORCID iD

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