The Differences and Similarities Between Two-Sample T-Test and Paired T-Test.

In clinical research, comparisons of the results from experimental and control groups are often encountered. The two-sample t-test (also called independent samples t-test) and the paired t-test are probably the most widely used tests in statistics for the comparison of mean values between two samples. However, confusion exists with regard to the use of the two test methods, resulting in their inappropriate use. In this paper, we discuss the differences and similarities between these two t-tests. Three examples are used to illustrate the calculation procedures of the two-sample t-test and paired t-test.

Sample Size Calculations for Comparing Groups with Continuous Outcomes.

Sample size justification is required for all clinical studies. However, to many biomedical and clinical researchers, power and sample size analysis seems like a magic trick of statisticians. In this note, we discuss power and sample size calculations and show that biomedical and clinical investigators play a significant role in making such analyses possible and meaningful. Thus, power analysis is really an interactive process and scientific researchers and statisticians are equal partners in the research enterprise.

Inconsistency Between Univariate and Multiple Logistic Regressions.

Logistic regression is a popular statistical method in studying the effects of covariates on binary outcomes. It has been widely used in both clinical trials and observational studies. However, the results from the univariate regression and from the multiple logistic regression tend to be conflicting. A covariate may show very strong effect on the outcome in the multiple regression but not in the univariate regression, and vice versa. These facts have not been well appreciated in biomedical research. Misuse of logistic regression is very prevalent in medical publications. In this paper, we study the inconsistency between the univariate and multiple logistic regressions and give advice in the model section in multiple logistic regression analysis.

Two Paradoxes in Linear Regression Analysis.

Regression is one of the favorite tools in applied statistics. However, misuse and misinterpretation of results from regression analysis are common in biomedical research. In this paper we use statistical theory and simulation studies to clarify some paradoxes around this popular statistical method. In particular, we show that a widely used model selection procedure employed in many publications in top medical journals is wrong. Formal procedures based on solid statistical theory should be used in model selection.