What is Overadjustment?
Overadjustment refers to the practice of controlling for too many variables in a statistical analysis, particularly those that do not need to be controlled for and might introduce bias. In the context of
cancer research, overadjustment can distort the true relationship between exposure and outcome, leading to misleading conclusions.
Why is Overadjustment a Concern in Cancer Research?
In cancer studies, researchers often aim to identify
risk factors and understand their impact on cancer incidence or survival. Overadjustment can mask the true effects of these risk factors, making it difficult to identify genuine
associations or to quantify the actual risk. This can result in skewed public health guidelines and ineffective treatment strategies.
How Does Overadjustment Happen?
Overadjustment typically occurs when researchers adjust for variables that are
intermediate variables or
colliders. Intermediate variables lie on the causal pathway between the exposure and outcome, while colliders are influenced by both the exposure and outcome. Adjusting for these can introduce bias and obscure the true relationship.
Examples of Overadjustment in Cancer Studies
Consider a study investigating the link between
smoking and lung cancer. If researchers adjust for a variable like
COPD, which is influenced by smoking, they might underestimate the effect of smoking on lung cancer. Another example is adjusting for
BMI in a study on diet and colorectal cancer, where BMI might be influenced by diet and could act as an intermediate variable.
How to Avoid Overadjustment?
Researchers can avoid overadjustment by carefully selecting variables for adjustment. This involves understanding the
causal pathways and using methods such as
DAGs to visualize relationships between variables. Consulting with a biostatistician can also help ensure appropriate variable selection.
Impact of Overadjustment on Cancer Treatment and Prevention
Overadjustment can lead to incorrect conclusions about the effectiveness of
treatment strategies and preventive measures. This might result in patients receiving suboptimal care or misallocation of resources to interventions that are not genuinely effective. Therefore, it is crucial for researchers to avoid overadjustment to ensure accurate and reliable findings.
Conclusion
Overadjustment is a significant issue in cancer research that can lead to biased results and misguided clinical and public health decisions. By carefully selecting variables for adjustment and using appropriate statistical techniques, researchers can mitigate the risk of overadjustment and contribute to more accurate and actionable findings in the fight against cancer.
