Optimal treatment of replicate measurements in anthropometric studies

Anthropometric studies often include replicates of each measurement to decrease error. The optimal method to combine these measurements is uncertain.

To identify the optimal method to combine replicate measures for analysis.

The authors carried out 10 000 Monte Carlo simulations to explore the effect of six approaches to combine replicate measurements in a hypothetical two-group intervention study (n = 100 per arm) in which the outcome, infant length at age 1 year, was measured two or three times. One group had a true value with a normal distribution N (mean = 76, SD = 2.4 cm). Statistical power was estimated to detect a 1 cm difference between the groups, based on a t-test.

Under a realistic scenario with a measurement error distribution N (0, 0.8), highest power was reached by use of the mean and the median of pairwise averages. However, when a portion of the data (≥2%) were contaminated by greater error (e.g. due to data entry), the median of three measurements outperformed all other methods while the mean had the lowest performance.

Obtaining three rather than two measures and using the median of the three replicates is a safe and robust approach to combine participants' raw data values for use in subsequent analyses.