People who have the same chronic condition often receive different treatments. The choice of treatment sometimes depends on things like the patient’s age, sex, and treatment history. Also, doctors often change treatment over time depending on how the patient responds. When treatment varies in these ways, it is called dynamic. Researchers find it difficult to analyze data about the effectiveness of dynamic treatments because the way the treatments are used changes over time. For example, how well the medicine being studied works might depend on how well a medicine the patient took in the past worked. Usual research methods don’t handle this difficulty well.
Funded by PCORI Pilot project, MTPPI research team proposed using an analysis method called the “parametric g-formula” to address this difficulty. This method has not yet been used with existing data from medical records or health insurance companies. Using electronic health record data stored in a registry of patients with chronic kidney disease called the United States Renal Data System (USRDS) We compared the g-formula method with a more commonly used method of analysis called “inverse probability weighting” (IPW) to learn the advantages and disadvantages of using these methods to study dynamic treatments.