%0 Journal Article
%T How many repeated measures in repeated measures designs? Statistical issues for comparative trials
%A Andrew J Vickers
%J BMC Medical Research Methodology
%D 2003
%I BioMed Central
%R 10.1186/1471-2288-3-22
%X The degree to which adding a further measure increases statistical power can be derived from simple formulae. This "marginal benefit" can be used to inform the optimal number of repeat assessments.Although repeating assessments can have dramatic effects on power, marginal benefit of an additional measure rapidly decreases as the number of measures rises. There is little value in increasing the number of either baseline or post-treatment assessments beyond four, or seven where baseline assessments are taken. An exception is when correlations between measures are low, for instance, episodic conditions such as headache.The proposed method offers a rational basis for determining the number of repeat measures in repeat measures designs.Many studies measure a continuous endpoint repeatedly over time. In some cases, this is because researchers wish to judge the time course of a symptom or to evaluate how the effect of a treatment changes over time. For example, in a study of thoracic surgery, patients were evaluated every three months after thoracic surgery to determine the incidence and duration of chronic postoperative pain. The researchers found that the incidence of pain at one year was high and only slightly lower than at three months, showing that post-thoracotomy pain is common and persistent[1]. In such studies, the number and timing of repeated measures needs to be decided on a study-by-study basis depending on the scientific interests of the investigators.Measures may also be repeated in order obtain a more precise estimate of an endpoint. In simple terms, measure a patient once and they may be having a particularly good or bad day; measure them several times and you are more likely to get a fair picture of how they are doing in general. Repeat assessment reduces intra-patient variability and thus increases study power. This is of particular relevance to comparative studies. For instance, in a randomized trial of soy and placebo for cancer-related hot flashes, pa
%U http://www.biomedcentral.com/1471-2288/3/22