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Research article
Miles D. Witham
Newcastle University
Corresponding Author
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Introduction
Frailty is the loss of ability to withstand a physiological stressor, and is associated with multiple adverse outcomes in older people. Trials to prevent or ameliorate frailty are in their infancy. A range of different outcome measures have been proposed, but current measures require either large sample sizes, long follow-up, or do not directly measure the construct of frailty.
Methods
We propose a composite outcome for frailty prevention trials, comprising progression to the frail state, death, or being too unwell to continue in a trial. To determine likely event rates, we used data from the English Longitudinal Study for Ageing, collected 4 years apart. We calculated transition rates between non-frail, prefrail, frail or loss to follow up due to death or illness. We used Markov state transition models to interpolate one- and two-year transition rates, and performed sample size calculations for a range of differences in transition rates using simple and composite outcomes.
Results
The frailty category was calculable for 4650 individuals at baseline (2226 non-frail, 1907 prefrail, 517 frail); at follow up, 1282 were non-frail, 1108 were prefrail, 318 were frail and 1936 had dropped out or were unable to complete all tests for frailty. Transition probabilities for those prefrail at baseline, measured at wave 4 were respectively 0.176, 0.286, 0.096 and 0.442 to non-frail, prefrail, frail and dead/dropped out. Interpolated transition probabilities were 0.159, 0.494, 0.113 and 0.234 at two years, and 0.108, 0.688, 0.087 and 0.117 at one year. Required sample sizes for a two-year outcome were between 1000 and 7200 for transition from prefrailty to frailty alone, 250 to 1600 for transition to the composite measure, and 75 to 350 using the composite measure with an ordinal logistic regression approach.
Conclusion
Use of a composite outcome for frailty trials offers reduced sample sizes and could ameliorate the effect of high loss to follow up inherent in such trials due to death and illness.
Keywords
Trials; frailty; outcomes; sample size; Markov
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Peer Review Timeline
CURRENT STATUS: Published
View the published article at BMC Geriatrics.
No community comments so far
Editorial decision: Accept
On 06 Feb, 2020
Editor assigned
On 29 Jan, 2020
Submission checks complete
On 28 Jan, 2020
Editor invited
On 28 Jan, 2020
No comments provided
Editorial decision: Minor revision
On 22 Jan, 2020
Editor assigned
On 07 Jan, 2020
Submission checks complete
On 06 Jan, 2020
Editor invited
On 06 Jan, 2020
Version 1
Posted 27 Aug, 2019
No comments provided
Editorial decision: Minor revision
On 09 Dec, 2019
Review #2 received
Received 06 Dec, 2019
Review #3 received
Received 06 Dec, 2019
Reviewer #3 agreed
On 25 Nov, 2019
Review #1 received
Received 16 Sep, 2019
Reviewer #2 agreed
On 02 Sep, 2019
Reviewer #1 agreed
On 26 Aug, 2019
Submission checks complete
On 21 Aug, 2019
Editor assigned
On 21 Aug, 2019
Reviewers invited
Invitations sent on 21 Aug, 2019
Editor invited
On 20 Aug, 2019
First submitted
On 25 Jul, 2019
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Subject Areas
Geriatrics & Gerontology
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A new preprint design is coming!
We have improved readability, clarity, accessibility, and opportunities for engagement.
See the published version of this preprint at BMC Geriatrics.
This is a preprint, a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Learn more about In Review
Research article
Miles D. Witham
Newcastle University
Corresponding Author
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Published
View the published article at BMC Geriatrics.
No community comments so far
Editorial decision: Accept
On 06 Feb, 2020
Editor assigned
On 29 Jan, 2020
Submission checks complete
On 28 Jan, 2020
Editor invited
On 28 Jan, 2020
No comments provided
Editorial decision: Minor revision
On 22 Jan, 2020
Editor assigned
On 07 Jan, 2020
Submission checks complete
On 06 Jan, 2020
Editor invited
On 06 Jan, 2020
Version 1
Posted 27 Aug, 2019
No comments provided
Editorial decision: Minor revision
On 09 Dec, 2019
Review #2 received
Received 06 Dec, 2019
Review #3 received
Received 06 Dec, 2019
Reviewer #3 agreed
On 25 Nov, 2019
Review #1 received
Received 16 Sep, 2019
Reviewer #2 agreed
On 02 Sep, 2019
Reviewer #1 agreed
On 26 Aug, 2019
Submission checks complete
On 21 Aug, 2019
Editor assigned
On 21 Aug, 2019
Reviewers invited
Invitations sent on 21 Aug, 2019
Editor invited
On 20 Aug, 2019
First submitted
On 25 Jul, 2019
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Subject Areas
Geriatrics & Gerontology
License:
This work is licensed under a CC BY 4.0 License. Read Full License
View the published article at BMC Geriatrics.
Introduction
Frailty is the loss of ability to withstand a physiological stressor, and is associated with multiple adverse outcomes in older people. Trials to prevent or ameliorate frailty are in their infancy. A range of different outcome measures have been proposed, but current measures require either large sample sizes, long follow-up, or do not directly measure the construct of frailty.
Methods
We propose a composite outcome for frailty prevention trials, comprising progression to the frail state, death, or being too unwell to continue in a trial. To determine likely event rates, we used data from the English Longitudinal Study for Ageing, collected 4 years apart. We calculated transition rates between non-frail, prefrail, frail or loss to follow up due to death or illness. We used Markov state transition models to interpolate one- and two-year transition rates, and performed sample size calculations for a range of differences in transition rates using simple and composite outcomes.
Results
The frailty category was calculable for 4650 individuals at baseline (2226 non-frail, 1907 prefrail, 517 frail); at follow up, 1282 were non-frail, 1108 were prefrail, 318 were frail and 1936 had dropped out or were unable to complete all tests for frailty. Transition probabilities for those prefrail at baseline, measured at wave 4 were respectively 0.176, 0.286, 0.096 and 0.442 to non-frail, prefrail, frail and dead/dropped out. Interpolated transition probabilities were 0.159, 0.494, 0.113 and 0.234 at two years, and 0.108, 0.688, 0.087 and 0.117 at one year. Required sample sizes for a two-year outcome were between 1000 and 7200 for transition from prefrailty to frailty alone, 250 to 1600 for transition to the composite measure, and 75 to 350 using the composite measure with an ordinal logistic regression approach.
Conclusion
Use of a composite outcome for frailty trials offers reduced sample sizes and could ameliorate the effect of high loss to follow up inherent in such trials due to death and illness.
Figure 1
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