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mixed-effects model repeated measurement (mmrm) model  (SAS institute)


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    SAS institute mixed-effects model repeated measurement (mmrm) model
    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 Repeated Measurement (Mmrm) Model, 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/product/mixed-effects+model+repeated+measurement+%28mmrm%29+model/pmc11873539-126-1-20?v=SAS+institute
    Average 90 stars, based on 1 article reviews
    mixed-effects model repeated measurement (mmrm) model - by Bioz Stars, 2026-07
    90/100 stars

    Images

    1) Product Images from "BIO101 in Sarcopenic Seniors at Risk of Mobility Disability: Results of a Double‐Blind Randomised Interventional Phase 2b Trial"

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

    Journal: Journal of Cachexia, Sarcopenia and Muscle

    doi: 10.1002/jcsm.13750

    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.
    Figure Legend 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.

    Techniques Used:



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    Image Search Results


    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: