Of late, clinical trials are being designed specifically as superiority, non-inferiority or equivalence studies. Use of the CI supplements the P value by providing an estimate of actual clinical effect. However, statistical significance in terms of P only suggests whether there is any difference in probability terms. Clinical importance is best inferred by looking at the effect size, that is how much is the actual change or difference. Conflict between clinical importance and statistical significance is an important issue in biomedical research. A 99% CI will be wider than 95% CI for the same sample. Although the 95% CI is most often used in biomedical research, a CI can be calculated for any level of confidence. The factors affecting the width of the CI include the desired confidence level, the sample size and the variability in the sample. Calculation of the standard error varies depending on whether the sample statistic of interest is a mean, proportion, odds ratio (OR), and so on. Calculation of the CI of a sample statistic takes the general form: CI = Point estimate ± Margin of error, where the margin of error is given by the product of a critical value (z) derived from the standard normal curve and the standard error of point estimate. This range is the confidence interval (CI) which is estimated on the basis of a desired confidence level. It is possible to use a sample statistic and estimates of error in the sample to get a fair idea of the population parameter, not as a single value, but as a range of values. Although we work with samples, our goal is to describe and draw inferences regarding the underlying population. Biomedical research is seldom done with entire populations but rather with samples drawn from a population.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |