On basic life testing issues in medical research using non‐parametric hypothesis testing

Abstract

The probability life distributions usually describe the time to event or survival time. Therefore, these life distributions play a crucial role in the analysis and projection of the maximum life expectancy using the Laplace transform technique for nonparametric hypothesis testing. A flexible framework for monitoring randomized clinical trials utilizing prior knowledge of certain phenomena under uncertainty is provided by the Laplace transform. By considering the previous data, medical professionals can also utilize these estimators to calculate the chance of tumor recurrence, cardiovascular death, and AIDS for HIV patients. However, censoring occurs in clinical trials and medical studies when the precise event's time of occurrence is unknown. The present study aims to propose a new test for exponential better than used (EBU). The suggested statistical test's efficiency and critical values are computed and compared with those of previous classes. Furthermore, the suggested test statistic is provided in bivariate form as well. Finally, the methodology is then tested using medical data in order to provide evidence that it is applied efficiently.