Robustness Beyond Statistical Significance: the Role of Robustness Index in Medical Studies

EasyChair Preprint 12265

Abstract

In medical research, the quest for robustness goes beyond mere statistical significance. While statistical significance remains a cornerstone of hypothesis testing, it alone does not adequately capture the reliability and generalizability of findings. The concept of robustness encompasses the ability of results to withstand variations in data, assumptions, and methodologies, thereby ensuring the validity of conclusions across diverse contexts. This paper introduces the notion of a robustness index as a complementary measure to statistical significance in medical studies. The robustness index quantifies the stability and consistency of study outcomes under different conditions, providing researchers and clinicians with a more comprehensive understanding of the reliability of findings. Unlike traditional significance testing, which focuses solely on p-values and hypothesis rejection, the robustness index evaluates the resilience of results to uncertainties and perturbations. By embracing a holistic approach to data analysis that prioritizes robustness alongside significance, researchers can foster a culture of accountability and trustworthiness in medical research. Ultimately, the integration of robustness indices into study protocols and reporting standards can contribute to more reliable and actionable evidence, advancing the collective goal of improving patient outcomes and healthcare delivery.

Keyphrases: Generalizability, Healthcare delivery, Methodological rigor, Patient Outcomes, Reliability, Robustness index, bias detection, medical studies, reproducibility, statistical significance

@booklet{EasyChair:12265,
  author    = {Haney Zaki},
  title     = {Robustness Beyond Statistical Significance: the Role of Robustness Index in Medical Studies},
  howpublished = {EasyChair Preprint 12265},
  year      = {EasyChair, 2024}}