RESUMO
Statistics plays an important role in medical research， and the selection of appropriate statistical methods is crucial for drawing reliable and valuable conclusions. This paper provides a brief introduction to commonly used statistical analysis methods for medical data， covering descriptive analysis， parametric test， nonparametric test， correlation analysis， regression analysis， and analysis of survival data. It focuses on discussing the assumptions of multiple linear regression， logistic regression and Cox proportional risk regression， as well as how to choose the appropriate statistical methods for analyzing and interpreting medical data based on different research objectives and data types.
RESUMO
The purpose of this article was to introduce the likelihood ratio test， six nonparametric tests， and the SAS implementation of the survival data. Based on the assumption that the survival data had the exponential distribution， the likelihood ratio test method was derived， the main difference between six nonparametric test methods was that they had different weight functions. Under the conditions of non-stratification and stratification， the seven survival data hypothesis testing methods mentioned above could be used， and their common point was that their test statistics all followed the χ2 distribution. Through two examples and by means of the SAS software， the article realized the various hypothesis tests for two or more groups of survival data， outputed and explained SAS calculation results， and made statistical and professional conclusions.