1. Data Quality: Ensuring the accuracy, completeness, and consistency of data is critical. 2. Confounding Variables: Adjusting for factors that can influence outcomes, such as age, gender, and comorbidities, is complex. 3. Bias: Minimizing biases such as selection bias and information bias is essential for reliable results. 4. Generalizability: Results from specific populations or settings may not be applicable to broader groups.
