Browsing by Subject "Longitudinal studies"

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    Built environment determinants of bicycle volume: A longitudinal analysis
    (Journal of Transport and Land Use, 2017) Chen, Peng; Zhou, Jiangping; Sun, Feiyang
    This study examines determinants of bicycle volume in the built environment with a five-year bicycle count dataset from Seattle, Washington. A generalized linear mixed model (GLMM) is used to capture the bicycle volume change over time while controlling for temporal autocorrelations. The GLMM assumes that bicycle count follows a Poisson distribution. The model results show that (1) the variables of non-winter seasons, peak hours, and weekends are positively associated with the increase of bicycle counts over time; (2) bicycle counts are fewer in steep areas; (3) bicycle counts are greater in zones with more mixed land use, a higher percentage of water bodies, or a greater percentage of workplaces; (4) the increment of bicycle infrastructure is positively associated with the increase of bicycle volume; and (5) bicycling is more popular in neighborhoods with a greater percentage of whites and younger adults. It concludes that areas with a smaller slope variation, a higher employment density, and a shorter distance to water bodies encourage bicycling. This conclusion suggests that to best boost bicycling, decision-makers should consider building more bicycle facilities in flat areas and integrating the facilities with employment densification and open-space creation and planning.
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    Mediation analysis in longitudinal studies in the presence of measurement error and missing data
    (2018-05) Ssenkusu, John Mbaziira
    Mediation analysis hypothesizes that an exposure causes a mediator and in turn the mediator causes the outcome, so mediation is inherently longitudinal. Unfortunately, potential mediators may be measured with error and regression estimators obtained by ignoring measurement error can be severely biased. This can induce bias in the estimation of causal direct and indirect effects. In Chapter 2, using regression calibration, we show how to adjust for measurement error in longitudinal studies with repeated measurements of the mediator, and evaluate the effect of ignoring measurement error on direct and indirect effects. Rather than assuming normality for the random effects in the linear mixed effects calibration model, we correct for measurement error in the mediator allowing flexibility in the distribution of subject-specific random effects. On the other hand, longitudinal studies face challenges of missing data resulting from loss to follow-up, death, or withdrawal. In mediation analysis, multiple imputation has been shown to perform well for data missing completely at random (MCAR) and missing at random (MAR) in cross-sectional studies, but it is unclear how it performs in longitudinal studies under misspecification of the imputation model, specifically, where the misspecification ignores clustering by subject. In Chapter 3, we examine the impact of ignoring clustering on mediated effect estimates under MCAR and MAR mechanisms with varying degrees of missingness. In Chapter 4, using data from a randomized controlled trial, we examine the mediation effects on child neurodevelopment of intermittent preventive malaria treatment in pregnant women. Chapter 5 concludes and discusses future work.