arXiv subjects

Udara Kumaranathunga

Publications and source records attributed to Udara Kumaranathunga.

2 recordsLinked to original sources

arXiv2026

The dangers of using three-number summaries to estimate unknown standard deviations: sensitivity analyses and some possible improvements incorporating shape

In recent years, there has been much progress toward the development of methods for converting three- and five-number summary statistics (i.e. minimum, maximum, median, and quartiles) to means and standard deviations (SDs). This is commonly done in the meta-analysis setting, where some studies report means and SDs, while other report quantile summaries. However, we show that three-number summaries, which are the most common, do not contain enough information to reliably estimate SDs. We show that very poor estimates can result, which may invalidate any inference and provide details of a sensitivity analysis that can allow researchers to have greater confidence in their results, or highlight potential sources of bias. We further explore whether nominating additional information can provide enough information regarding the unknown data shape to improve SD estimations, and in doing so introduce a new estimator using the scaled Beta distribution. Simulations and a real data example are used to highlight the advantages and disadvantages of this approach. A Web application is also provided to help researchers perform sensitivity analyses.

stat.ME↗

arXiv2024

A novel density-based approach for estimating unknown means, distribution visualisations and meta-analyses of quantiles

In meta-analysis with continuous outcomes, the use of effect sizes based on the means is the most common. It is often found, however, that only the quantile summary measures are reported in some studies, and in certain scenarios, a meta-analysis of the quantiles themselves are of interest. We propose a novel density-based approach to support the implementation of a comprehensive meta-analysis, when only the quantile summary measures are reported. The proposed approach uses flexible quantile-based distributions and percentile matching to estimate the unknown parameters without making any prior assumptions about the underlying distributions. Using simulated and real data, we show that the proposed novel density-based approach works as well as or better than the widely-used methods in estimating the means using quantile summaries without assuming a distribution apriori, and provides a novel tool for distribution visualisations. In addition to this, we introduce quantile-based meta-analysis methods for situations where a comparison of quantiles between groups themselves are of interest and found to be more suitable. Using both real and simulated data, we also demonstrate the applicability of these quantile-based methods.

stat.ME↗