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Unformatted text preview: Group Randomized Clinical Trials: Statistical Analysis This section presents approaches for statistical analysis of grouprandomized or clusterrandomized clinical trials. Recall from last lecture that you cant just do the same analysis you would if the assignment to group were random or if there were no withingroup correlation. The choice of analysis depends, as usual, on: The research question (or specific aim); The study design (what treatments, how assigned, when were patients observed); The endpoint or outcome measure (dichotomous, continuous, survival). A classification scheme for models for relationship of outcome to treatment and covariates Group and clusterrandomized studies need to reduce variance as much as possible, because of withingroup clustering. Usually this leads to some modelbased analysis. The choice of model depends on the outcome, the relationship to fixed effects (treatment, other covariates), and the sources of random variation. Is there just one source of random variation (typically patienttopatient variance), or more? If only one source, betweenpatient, we can use standard approaches such as proportional hazards, linear models, logistic regression. If we have two or more sources of random variation, we will need mixed models. Several measurements per patient and withinpatient random variation as well as betweenpatient: random effects models for repeated measures. Patients nested within randomly chosen groups (e.g. clinic, town, physician practice): hierarchical or nested random effects models. Is the outcome measure continuous, with residuals that are approximately normal (or can be transformed to yield normal distribution)? Gaussian data: usually some linear model setting. NonGaussian data: generalized linear model, allowing for variation to be binomial Poisson, or other. Survival data: typically uses proportional hazards models. Is the relationship between the outcome and the predictor linear or other? Linear relationship: more typical for continuous data. Nonlinear relationship, e.g. logistic: some appropriate link function, e.g. logit. The linear mixed model is widely discussed in literature; Laird an Ware is a standard paper. The model is usually written as: Y = X + ZA + e . The outcome Y represents a vector of observations from the same person (in repeated measures data) or from the same group or cluster. The observed values are the sum of fixed effects based on treatment and covariates, random effects A at the level of the cluster, and random effects e at the level of the observation within cluster. The random effects are assumed here to be normally distributed....
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This note was uploaded on 12/30/2010 for the course BST 252 taught by Professor Tsodikov during the Winter '06 term at UC Davis.
 Winter '06
 Tsodikov
 The Land

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