Common methodological issues and suggested solutions in bone research

Abstract

Bone research is a dynamic area of scientific investigation that usually encompasses multidisciplines. Virtually all basic cellular research, clinical research and epidemiologic research rely on statistical concepts and methodology for inference. This paper discusses common issues and suggested solutions concerning the application of statistical thinking in bone research, particularly in clinical and epidemiological investigations. The issues are sample size estimation, biases and confounders, analysis of longitudinal data, categorization of continuous data, selection of significant variables, over-fitting, P-values, false positive finding, confidence interval, and Bayesian inference. It is hoped that by adopting the suggested measures the scientific quality of bone research can improve.

Keywords: Bayesian inference; Collider bias; Confidence interval; P-value; Statistical methods.

Fig. 1

Illustration of selection bias. There was a significant association between exposure and outcome in the population (left panel), but if a subset of individuals in red box were selected from the population, the association can be statistically non-significant (right panel).

Fig. 2

Illustration of confounding variable and collider variable. A confounder is a variable that causes both exposure and outcome variables. A collider is a variable that is caused by both exposure and outcome variables. Regression model can be used to adjust for the effect of confounder, but it should not be used to adjust for the effect of collider.