Unlike experimental studies where the researcher has a strong degree of control over sources of bias and variation, observational studies present major hurdles to conducting research that provides strong clinical evidence. Issues such as missing data, confounding bias and effect modification mean that we need to use statistical modelling to adjust or offset these problems.

In this series, biostatistical models used for the analysis of continuous clinical outcomes will be discussed.

• Research objectives and modelling approach
• Risk factors, confounders and effect modifiers
• Linear regression and ANOVA
• The General Linear Model