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mixed-effects model repeated measurement (mmrm) model  (SAS institute)
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Change from baseline in 4‐m gait speed at Month 6 based on multiple imputation in the PP population. Missing data are imputed using multiple imputation. Analysis is based on mixed‐effect model for repeated measurements <t>(MMRM)</t> with treatment, visit, center, gender, and treatment * visit as fixed effects and baseline value as a covariate. LS, least square; SE, standard error.
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Change from baseline in 4‐m gait speed at Month 6 based on multiple imputation in the PP population. Missing data are imputed using multiple imputation. Analysis is based on mixed‐effect model for repeated measurements <t>(MMRM)</t> with treatment, visit, center, gender, and treatment * visit as fixed effects and baseline value as a covariate. LS, least square; SE, standard error.
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Change from baseline in 4‐m gait speed at Month 6 based on multiple imputation in the PP population. Missing data are imputed using multiple imputation. Analysis is based on mixed‐effect model for repeated measurements <t>(MMRM)</t> with treatment, visit, center, gender, and treatment * visit as fixed effects and baseline value as a covariate. LS, least square; SE, standard error.
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mixed-effects model with repeated measures  (SAS institute)
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Change from baseline in 4‐m gait speed at Month 6 based on multiple imputation in the PP population. Missing data are imputed using multiple imputation. Analysis is based on mixed‐effect model for repeated measurements <t>(MMRM)</t> with treatment, visit, center, gender, and treatment * visit as fixed effects and baseline value as a covariate. LS, least square; SE, standard error.
Mixed Effects Model With Repeated Measures, supplied by SAS institute, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/result/mixed-effects model with repeated measures/product/SAS institute
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mixed-effects model with repeated measures - by Bioz Stars, 2026-04
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Change from baseline in 4‐m gait speed at Month 6 based on multiple imputation in the PP population. Missing data are imputed using multiple imputation. Analysis is based on mixed‐effect model for repeated measurements <t>(MMRM)</t> with treatment, visit, center, gender, and treatment * visit as fixed effects and baseline value as a covariate. LS, least square; SE, standard error.
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multivariate mixed effect models for repeated measures (mmrm) - by Bioz Stars, 2026-04
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mixed-effects model for repeated measures  (GraphPad Software Inc)
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Change from baseline in 4‐m gait speed at Month 6 based on multiple imputation in the PP population. Missing data are imputed using multiple imputation. Analysis is based on mixed‐effect model for repeated measurements <t>(MMRM)</t> with treatment, visit, center, gender, and treatment * visit as fixed effects and baseline value as a covariate. LS, least square; SE, standard error.
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https://www.bioz.com/result/mixed-effects model for repeated measures/product/GraphPad Software Inc
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mixed-effects model for repeated measures based on a restricted maximum likelihood generalized linear mixed model  (GraphPad Software Inc)
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Change from baseline in 4‐m gait speed at Month 6 based on multiple imputation in the PP population. Missing data are imputed using multiple imputation. Analysis is based on mixed‐effect model for repeated measurements <t>(MMRM)</t> with treatment, visit, center, gender, and treatment * visit as fixed effects and baseline value as a covariate. LS, least square; SE, standard error.
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Average 90 stars, based on 1 article reviews
mixed-effects model for repeated measures based on a restricted maximum likelihood generalized linear mixed model - by Bioz Stars, 2026-04
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mixed-effects models for repeated measures  (SAS institute)
90
SAS institute mixed-effects models for repeated measures
Change from baseline in 4‐m gait speed at Month 6 based on multiple imputation in the PP population. Missing data are imputed using multiple imputation. Analysis is based on mixed‐effect model for repeated measurements <t>(MMRM)</t> with treatment, visit, center, gender, and treatment * visit as fixed effects and baseline value as a covariate. LS, least square; SE, standard error.
Mixed Effects Models For Repeated Measures, supplied by SAS institute, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/result/mixed-effects models for repeated measures/product/SAS institute
Average 90 stars, based on 1 article reviews
mixed-effects models for repeated measures - by Bioz Stars, 2026-04
90/100 stars
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Change from baseline in 4‐m gait speed at Month 6 based on multiple imputation in the PP population. Missing data are imputed using multiple imputation. Analysis is based on mixed‐effect model for repeated measurements (MMRM) with treatment, visit, center, gender, and treatment * visit as fixed effects and baseline value as a covariate. LS, least square; SE, standard error.

Journal: Journal of Cachexia, Sarcopenia and Muscle

Article Title: BIO101 in Sarcopenic Seniors at Risk of Mobility Disability: Results of a Double‐Blind Randomised Interventional Phase 2b Trial

doi: 10.1002/jcsm.13750

Figure Lengend Snippet: Change from baseline in 4‐m gait speed at Month 6 based on multiple imputation in the PP population. Missing data are imputed using multiple imputation. Analysis is based on mixed‐effect model for repeated measurements (MMRM) with treatment, visit, center, gender, and treatment * visit as fixed effects and baseline value as a covariate. LS, least square; SE, standard error.

Article Snippet: A mixed‐effects model repeated measurement (MMRM) model with fixed factors of treatment, centres, baseline score and sex was used (in SAS v9.3) to estimate the CFB of the 400MWT GS at Month 6/9 between each active arm and the placebo group (after adjustment for multiplicity due to the two doses of active treatment by the Hochberg procedure).

Techniques: