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mixed-effects linear regression model with repeated measures  (SAS institute)

 
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    SAS institute mixed-effects linear regression model with repeated measures
    Mixed Effects Linear Regression 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
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    Average 90 stars, based on 1 article reviews
    mixed-effects linear regression model with repeated measures - by Bioz Stars, 2026-04
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    Multivariable-adjusted regression estimates a for the difference b in the percentage of workers commuting by bicycle in 2000 and 2010 according to joint levels c of the distance (km) between the tract and the trail system c and proportion of commuting trips that cross the trail system d . a Regression models included: time-varying and tract-level variables for distance to trail system, proportion to work-related trips that cross the trail system, total work-related trips, intersection density, population density, median household income, professional workforce, workforce aged 13–34 years, total length of bicycle lanes, maximum reach of bicycle lane network, maximum reach of network comprising both bicycle lanes and off-road trails, and the time-invariant variable for commuting by bicycle in 1990. Estimated effects for changes for all time-varying variables were modeled by including a year* variable interaction term. b Differences were obtained using the ‘margins’ post-estimation command following repeated-measures random effects linear regression models <t>(-xtreg-)</t> in Stata. c P -value for interaction = 0.06. d Levels of predictor variables reflect the percentiles of the variable distribution for combined 2000 and 2010 data. For distance: 25 th = 1.08 km; 50 th = 2.83 km; 75 th = 5.91 km. For proportion of commuting trips that cross the trail: 25 th = 0.11; 50 th = 0.29; 75 th = 0.42
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    linear regression models with repeated measures proc mixed  (SAS institute)
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    Multivariable-adjusted regression estimates a for the difference b in the percentage of workers commuting by bicycle in 2000 and 2010 according to joint levels c of the distance (km) between the tract and the trail system c and proportion of commuting trips that cross the trail system d . a Regression models included: time-varying and tract-level variables for distance to trail system, proportion to work-related trips that cross the trail system, total work-related trips, intersection density, population density, median household income, professional workforce, workforce aged 13–34 years, total length of bicycle lanes, maximum reach of bicycle lane network, maximum reach of network comprising both bicycle lanes and off-road trails, and the time-invariant variable for commuting by bicycle in 1990. Estimated effects for changes for all time-varying variables were modeled by including a year* variable interaction term. b Differences were obtained using the ‘margins’ post-estimation command following repeated-measures random effects linear regression models <t>(-xtreg-)</t> in Stata. c P -value for interaction = 0.06. d Levels of predictor variables reflect the percentiles of the variable distribution for combined 2000 and 2010 data. For distance: 25 th = 1.08 km; 50 th = 2.83 km; 75 th = 5.91 km. For proportion of commuting trips that cross the trail: 25 th = 0.11; 50 th = 0.29; 75 th = 0.42
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    Multivariable-adjusted regression estimates a for the difference b in the percentage of workers commuting by bicycle in 2000 and 2010 according to joint levels c of the distance (km) between the tract and the trail system c and proportion of commuting trips that cross the trail system d . a Regression models included: time-varying and tract-level variables for distance to trail system, proportion to work-related trips that cross the trail system, total work-related trips, intersection density, population density, median household income, professional workforce, workforce aged 13–34 years, total length of bicycle lanes, maximum reach of bicycle lane network, maximum reach of network comprising both bicycle lanes and off-road trails, and the time-invariant variable for commuting by bicycle in 1990. Estimated effects for changes for all time-varying variables were modeled by including a year* variable interaction term. b Differences were obtained using the ‘margins’ post-estimation command following repeated-measures random effects linear regression models (-xtreg-) in Stata. c P -value for interaction = 0.06. d Levels of predictor variables reflect the percentiles of the variable distribution for combined 2000 and 2010 data. For distance: 25 th = 1.08 km; 50 th = 2.83 km; 75 th = 5.91 km. For proportion of commuting trips that cross the trail: 25 th = 0.11; 50 th = 0.29; 75 th = 0.42

    Journal: The International Journal of Behavioral Nutrition and Physical Activity

    Article Title: Municipal investment in off-road trails and changes in bicycle commuting in Minneapolis, Minnesota over 10 years: a longitudinal repeated cross-sectional study

    doi: 10.1186/s12966-017-0475-1

    Figure Lengend Snippet: Multivariable-adjusted regression estimates a for the difference b in the percentage of workers commuting by bicycle in 2000 and 2010 according to joint levels c of the distance (km) between the tract and the trail system c and proportion of commuting trips that cross the trail system d . a Regression models included: time-varying and tract-level variables for distance to trail system, proportion to work-related trips that cross the trail system, total work-related trips, intersection density, population density, median household income, professional workforce, workforce aged 13–34 years, total length of bicycle lanes, maximum reach of bicycle lane network, maximum reach of network comprising both bicycle lanes and off-road trails, and the time-invariant variable for commuting by bicycle in 1990. Estimated effects for changes for all time-varying variables were modeled by including a year* variable interaction term. b Differences were obtained using the ‘margins’ post-estimation command following repeated-measures random effects linear regression models (-xtreg-) in Stata. c P -value for interaction = 0.06. d Levels of predictor variables reflect the percentiles of the variable distribution for combined 2000 and 2010 data. For distance: 25 th = 1.08 km; 50 th = 2.83 km; 75 th = 5.91 km. For proportion of commuting trips that cross the trail: 25 th = 0.11; 50 th = 0.29; 75 th = 0.42

    Article Snippet: Estimated effects for changes for all time-varying variables were modeled by including a year* variable interaction term. b Differences were obtained using the ‘margins’ post-estimation command following repeated-measures random effects linear regression models (-xtreg-) in Stata. c P -value for interaction = 0.06. d Levels of predictor variables reflect the percentiles of the variable distribution for combined 2000 and 2010 data.

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