Simple design and analysis strategies for solving problems in observational orthopaedic clinical research.

Abstract

Randomized controlled trials are the gold standard to establishing causal relationships in clinical research. However, these studies are expensive and time consuming to conduct. This article aims to provide orthopaedic surgeons and clinical researchers with methodology to optimize inference and minimize bias in observational studies that are often much more feasible to undertake. To mitigate the risk of bias arising from their nonexperimental design, researchers must first understand the ways in which measured covariates can influence treatment, outcomes, and missingness of follow-up data. With knowledge of these relationships, researchers can then build causal diagrams to best understand how to control sources of bias. Some common techniques for controlling for bias include matching, regression, stratification, and propensity score analysis. Selection bias may result from loss to follow-up and missing data. Strategies such as multiple imputation and time-to-event analysis can be useful for handling missingness. For longitudinal data, repeated measures allow observational studies to best summarize the impact of the intervention over time. Clinical researchers familiar with fundamental concepts of causal inference and techniques reviewed in this article will have the power to improve the quality of inferences made from clinical research in orthopaedic trauma surgery.