What Effect Is Really Being Measured? An Alternative Explanation of Paradoxical Phenomena in Studies of Osteoarthritis Progression

Many observational studies have found that risk factors for incidence of a disease may differ from risk factors for its sequelae (i.e., recurrence or progression of the disease). For example, several risk factors (such as female sex or obesity) for incident knee osteoarthritis (OA) are not associated with OA progression, and a few risk factors that increase risk of incident OA are inversely associated with OA progression (such as high bone mineral density and low levels of vitamin C) (1– 4). The paradoxical phenomena have also been identified in other diseases as well, such as the “obesity paradox,” whereby overweight or obese patients with preexisting cardiovascular disease have improved survival and a lower risk of recurrent major cardiovascular events than normal-weight patients (5). Another example is the “lipid paradox,” in which high levels of total cholesterol or low-density lipoprotein are associated with increased risk of cardiovascular disease among the general population (6), but are not, or even inversely, associated with risk of cardiovascular disease among patients with rheumatic diseases (7). While various explanations have been proposed for such paradoxical phenomena, including that risk factors for incident OA may be biologically different from those for OA progression or that findings from studies of risk factors for disease progression may be susceptible to selection bias (e.g., collider stratification bias) (8), we proposed that such paradoxical phenomena can be due to lack of clarity in the research question. Specifically, we posited that lack of clarity regarding the types of effects that can be determined from a study, i.e., total, direct, and indirect effects, may constitute a major reason why research findings for risk factors of OA progression appear paradoxical. In this work, we demonstrated the importance of carefully considering the actual intent of the research question in OA studies and ensuring that the study design allows the particular question to be answered. In the first section, we used a hypothetical randomized clinical trial (RCT) as a prototype to answer a well-defined research question and emphasize the critical characteristics of an RCT that enable one to make valid inferences. In the second section, we illustrated how an observational study can lead to incorrect inferences in the context of an ill-defined research question. We demonstrated that under such circumstances, the findings and inference may not be relevant to the investigators’ intended research question and would fail to provide insight into the prevention and treatment of disease, even if appropriate analytic methods were used. Finally, we offered a few suggestions that may help avoid such potential bias.