![]() There are statistical methods for dealing with such missing-data and " censoring" problems. In most longitudinal studies of human subjects, patients may withdraw from the trial or become " lost to follow-up". Most clinical trials are analyzed using repeated-measurements ANOVA ( analysis of variance) or mixed models that include random effects. The data is analyzed using the statistical method that was specified in the clinical trial protocol, which must have been approved by the appropriate institutional review boards and regulatory agencies before the trial can begin. However, the two-period design is often taught in non-statistical textbooks, partly because of its simplicity. Statisticians suggest that designs should have four periods, which is more efficient than the two-period design, even if the study must be truncated to three periods. In most crossover trials each subject receives all treatments, in a random order. Nearly all crossover are designed to have "balance", whereby all subjects receive the same number of treatments and participate for the same number of periods. A crossover trial has a repeated measures design in which each patient is assigned to a sequence of two or more treatments, of which one may be a standard treatment or a placebo. When the trial has a repeated measures design, the same measures are collected multiple times for each subject. In a randomized clinical trial, the subjects are randomly assigned to different arms of the study which receive different treatments. Randomized, controlled crossover experiments are especially important in health care. ![]() ![]() Crossover designs are common for experiments in many scientific disciplines, for example psychology, pharmaceutical science, and medicine. While crossover studies can be observational studies, many important crossover studies are controlled experiments, which are discussed in this article. In medicine, a crossover study or crossover trial is a longitudinal study in which subjects receive a sequence of different treatments (or exposures). For the chemical reaction/mechanism analysis, see crossover experiment (chemistry). ![]()
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