Comparative effectiveness research often looks at how well different treatments work over time. One way to analyze this is by using Inverse Probability Weighting (IPW) to adjust for missing data and confounding factors. However, Multiple Imputation (MI) may be a better method for handling missing data, as it consistently gives more accurate and precise results compared to IPW. A study comparing the two methods found that MI provided lower bias and more precise estimates across various scenarios. In a real-world example involving biologic drugs for severe rheumatoid arthritis patients, MI was shown to be more effective in handling missing data in outcomes and confounders measured over time.