The HICS Physiology blog

                                                                                                           Tests of Correlation Correlation analysis is a cornerstone of medical statistics, used to quantify the strength and direction of association between two variables. In clinical research, it helps investigators understand whether changes in one biological, physiological, or behavioral measure are associated with changes in another. Correlation does not imply causation, but it provides essential groundwork for hypothesis generation, risk factor identification, and model building. This write‑up provides a comprehensive overview of the major tests of correlation used in medical statistics, their assumptions, interpr...

                                                                                                                                 Non‑Inferiority and Superiority in Clinical Trials: Concepts, Rationale, and Statistical Approaches Clinical trials are designed to evaluate whether a new intervention provides meaningful benefit compared with an existing standard of care or placebo. Traditionally, the gold standard has been the superiority trial , which aims to demonstrate that a new treatment is better than its comparator. However, as medicine has evolved—particularly in areas where effective treatments already exist— non‑inferiority trials have become increasingly impor...

                                                                                             The Statistical Selection Crisis      For many researchers, the most daunting moment of a study isn’t the grueling months of data collection, but the silent morning spent staring at a spreadsheet of raw numbers. The pressure to produce a significant p-value often triggers a "statistical selection crisis": a frantic search for any test that seems to "fit" the data. In medical research, "association" is not a one-size-fits-all term. It is a mathematical relationship defined by the architecture of your data. Choosing the wrong tool doesn't just lead to an incorrect p-value; it can fundamentally misrepresent the clinical reality you are trying to uncover...

  Dr Manoj Raju Prabandhakam Aster Narayanadri Hospital, Tirupati             Appropriate interpretation of research results from the clinical significance point is crucial for clinical decision-making and Evidence-based practice.   Clinical significance measures the practical, real-world impact of a treatment, distinct from statistical significance, which only indicates that findings are likely not due to chance (p< 0.05). From a clinical point of view, the statistically significant difference among groups is not of prime importance. (1) Before diving into clinical significance, the six points about p -value to consider are: (2) 1.       P  values can be indicative of the level of discrepancy between the data and a specific statistical model. 2.     P  values do not quantify the probability of the tested hypothesis being true or the probability of the data resulting solely from random chance. ...