Cargando…

Power and sample-size calculations for trials that compare slopes over time: Introducing the slopepower command

Trials of interventions that aim to slow disease progression may analyze a continuous outcome by comparing its change over time—its slope—between the treated and the untreated group using a linear mixed model. To perform a sample-size calculation for such a trial, one must have estimates of the para...

Descripción completa

Detalles Bibliográficos
Autores principales: Nash, Stephen, Morgan, Katy E., Frost, Chris, Mulick, Amy
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7614632/
https://www.ncbi.nlm.nih.gov/pubmed/37476648
http://dx.doi.org/10.1177/1536867X211045512
_version_ 1783605629954293760
author Nash, Stephen
Morgan, Katy E.
Frost, Chris
Mulick, Amy
author_facet Nash, Stephen
Morgan, Katy E.
Frost, Chris
Mulick, Amy
author_sort Nash, Stephen
collection PubMed
description Trials of interventions that aim to slow disease progression may analyze a continuous outcome by comparing its change over time—its slope—between the treated and the untreated group using a linear mixed model. To perform a sample-size calculation for such a trial, one must have estimates of the parameters that govern the between- and within-subject variability in the outcome, which are often unknown. The algebra needed for the sample-size calculation can also be complex for such trial designs. We have written a new user-friendly command, slopepower, that performs sample-size or power calculations for trials that compare slope outcomes. The package is based on linear mixed-model methodology, described for this setting by Frost, Kenward, and Fox (2008, Statistics in Medicine 27: 3717–3731). In the first stage of this approach, slopepower obtains estimates of mean slopes together with variances and covariances from a linear mixed model fit to previously collected user-supplied data. In the second stage, these estimates are combined with user input about the target effectiveness of the treatment and design of the future trial to give an estimate of either a sample size or a statistical power. In this article, we present the slopepower command, briefly explain the methodology behind it, and demonstrate how it can be used to help plan a trial and compare the sample sizes needed for different trial designs.
format Online
Article
Text
id pubmed-7614632
institution National Center for Biotechnology Information
language English
publishDate 2021
record_format MEDLINE/PubMed
spelling pubmed-76146322023-07-20 Power and sample-size calculations for trials that compare slopes over time: Introducing the slopepower command Nash, Stephen Morgan, Katy E. Frost, Chris Mulick, Amy Stata J Article Trials of interventions that aim to slow disease progression may analyze a continuous outcome by comparing its change over time—its slope—between the treated and the untreated group using a linear mixed model. To perform a sample-size calculation for such a trial, one must have estimates of the parameters that govern the between- and within-subject variability in the outcome, which are often unknown. The algebra needed for the sample-size calculation can also be complex for such trial designs. We have written a new user-friendly command, slopepower, that performs sample-size or power calculations for trials that compare slope outcomes. The package is based on linear mixed-model methodology, described for this setting by Frost, Kenward, and Fox (2008, Statistics in Medicine 27: 3717–3731). In the first stage of this approach, slopepower obtains estimates of mean slopes together with variances and covariances from a linear mixed model fit to previously collected user-supplied data. In the second stage, these estimates are combined with user input about the target effectiveness of the treatment and design of the future trial to give an estimate of either a sample size or a statistical power. In this article, we present the slopepower command, briefly explain the methodology behind it, and demonstrate how it can be used to help plan a trial and compare the sample sizes needed for different trial designs. 2021-09 2021-10-04 /pmc/articles/PMC7614632/ /pubmed/37476648 http://dx.doi.org/10.1177/1536867X211045512 Text en https://creativecommons.org/licenses/by/4.0/This work is distributed under the Creative Commons Attribution 4.0 License (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Nash, Stephen
Morgan, Katy E.
Frost, Chris
Mulick, Amy
Power and sample-size calculations for trials that compare slopes over time: Introducing the slopepower command
title Power and sample-size calculations for trials that compare slopes over time: Introducing the slopepower command
title_full Power and sample-size calculations for trials that compare slopes over time: Introducing the slopepower command
title_fullStr Power and sample-size calculations for trials that compare slopes over time: Introducing the slopepower command
title_full_unstemmed Power and sample-size calculations for trials that compare slopes over time: Introducing the slopepower command
title_short Power and sample-size calculations for trials that compare slopes over time: Introducing the slopepower command
title_sort power and sample-size calculations for trials that compare slopes over time: introducing the slopepower command
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7614632/
https://www.ncbi.nlm.nih.gov/pubmed/37476648
http://dx.doi.org/10.1177/1536867X211045512
work_keys_str_mv AT nashstephen powerandsamplesizecalculationsfortrialsthatcompareslopesovertimeintroducingtheslopepowercommand
AT morgankatye powerandsamplesizecalculationsfortrialsthatcompareslopesovertimeintroducingtheslopepowercommand
AT frostchris powerandsamplesizecalculationsfortrialsthatcompareslopesovertimeintroducingtheslopepowercommand
AT mulickamy powerandsamplesizecalculationsfortrialsthatcompareslopesovertimeintroducingtheslopepowercommand