Although randomized controlled experiments remain the gold standard for medical research, observational studies—studies in which the researcher does not assign or alter any factor of interest—still play an important role.
Some research questions lend themselves exclusively to observational designs. In many instances, the factor of interest cannot be controlled by the investigator. Consider a study on the impact of race on the effectiveness of pegylated interferon and ribavirin in treating chronic hepatitis C virus infection (1). The researcher cannot randomly assign being Caucasian to one group of patients and being African American to another group; such a study must be observational. Similarly, genome-wide association studies must be observational because researchers cannot alter people's genetic makeup. Other research questions might not involve relating a factor of interest to an outcome. Studies that seek only to characterize an outcome in a single population of interest will, by definition, be observational. For example, a recent study sought to determine the proportion of patients who lost independence or physical function 1 year after suffering a myocardial infarction (MI) (2).
Even for research questions that could be answered by a randomized trial, an observational study might be advantageous. Randomized trials are costly and time-consuming to run and entail navigating and complying with complex regulations (see references 3 and 4 for examples). Using data from a registry or a previously completed trial investigating a different research question would allow analysis of data far more quickly. In fact, more data might be available from registries than could be obtained in a new trial. For example, consider early studies on the efficacy of coronary artery bypass grafting (CABG) compared with percutaneous coronary intervention (PCI). A pooled analysis of eight randomized controlled trials involved a total of only 3,371 patients (an average 421 patients per trial) (5), whereas an analysis of a single registry included 6,814 patients (6). Observational data also can allow researchers to consider a wider group of patients than would be possible in a controlled trial; many randomized experiments exclude large subpopulations, particularly those that have other comorbidities or significant risk factors. Finally, in many instances, it can be unethical to consider randomizing some factors that are known to compromise patient health, such as smoking status or physical activity level.
Although using observational data for biomedical research can have advantages over implementing and running a randomized controlled trial, there are many pitfalls in using observational data that can undermine the validity of conclusions drawn from such analysis. Investigators must be aware of these potential problems so that they can be mitigated when possible and acknowledged as limitations otherwise.
This chapter focuses on scenarios in which the data have not been collected to specifically answer the question of interest—that is, when general registry data or data from a previous trial are used. (Observational studies in which the investigator designs the data collection to answer ...