Estimating the sample mean and standard deviation from the sample size, median, range and/or interquartile range

TL;DR

This paper improves methods for estimating sample mean and standard deviation from summary statistics like median, range, and quartiles, enhancing meta-analysis accuracy.

Contribution

It introduces new estimation techniques incorporating sample size and quartiles, addressing limitations of previous methods.

Findings

Proposed methods outperform existing estimators in simulations.

New guidance table aids meta-analysis with various summary statistics.

Enhanced estimators provide more accurate pooled estimates.

Abstract

In systematic reviews and meta-analysis, researchers often pool the results of the sample mean and standard deviation from a set of similar clinical trials. A number of the trials, however, reported the study using the median, the minimum and maximum values, and/or the first and third quartiles. Hence, in order to combine results, one may have to estimate the sample mean and standard deviation for such trials. In this paper, we propose to improve the existing literature in several directions. First, we show that the sample standard deviation estimation in Hozo et al. (2005) has some serious limitations and is always less satisfactory in practice. Inspired by this, we propose a new estimation method by incorporating the sample size. Second, we systematically study the sample mean and standard deviation estimation problem under more general settings where the first and third quartiles are also available for the trials. Through simulation studies, we demonstrate that the proposed methods greatly improve the existing methods and enrich the literature. We conclude our work with a summary table that serves as a comprehensive guidance for performing meta-analysis in different situations.