Outlier reduction by an option-3 measurement scheme

Detecting changes in longitudinal data is important in medical research. However, the existence of measurement outliers can cause an unexpected increase in the false alarm rate in claiming changes. To reduce the outliers, a new method has been developed. In this scheme, two measures are initially taken and, if they are closer than a specified threshold, the average of the two is considered to be the estimate of the true mean; otherwise a third measurement is taken, and the mean of the closest pair is considered to be the estimate. It is shown that this method has considerable sample size advantage over naive repeated measurements. Moreover, this scheme is robust for outlier error distribution. Evidence on outlier removal in dental attachment probing is used as an example.