Optimal treatment of replicate measurements in anthropometric studies

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

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

Methods: 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.

Results: 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.

Conclusion: 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.