What distinguishes clinical trial statistics from preclinical methods and why robust approaches are important

Statistics are an essential part of the analysis and design of clinical trials. During the design phase, statistics are used to plan and structure the study to avoid bias, ensure validity, efficiency and ethical behavior, and optimize the chances of obtaining meaningful results. Various types of bias can occur when there are deviations from the protocol. These include attrition bias due to differential loss of participants from different groups (e.g., a higher dropout rate in the control group) leading to biased estimates of treatment effects, ascertainment bias when treatment allocation is known ahead of time, inadvertently affecting the assessment of outcomes (e.g., more attention or additional treatments/tests for intervention group subjects), reporting bias arising from outcome switching or failure to adjust for multiplicity that can occur when multiple outcomes are assessed, and selection bias when there are systematic differences in characteristics between the groups compared in a study (e.g., poor randomization), as in these situations the likelihood of obtaining false positive associations increases.

In the analysis phase, the main goal is to draw valid and reliable conclusions from the data collected. This includes evaluating the efficacy and safety of the treatment under study and carefully extrapolating from the study population to the broader target population. It remains essential to consistently consider the pre-specified analysis plan throughout the study design process and to ensure consistency with the overall study design in order to maintain scientific integrity, prevent data manipulation, facilitate interpretation and reproducibility, and comply with regulations and maintain ethical standards.