What is marginal structural model?

The marginal structural model (MSM) is a statistical model used to assess the causal effect of a treatment or exposure on an outcome when there is time-dependent confounding or selection bias in the data. MSM is a type of inverse probability weighting method that reweights the observations in the data to create a pseudo-population that is balanced with respect to confounding variables and selection processes.

MSM is particularly useful in observational studies and other non-randomized studies, where the treatment or exposure is not assigned randomly and there may be time-dependent confounding or selection bias. The MSM accounts for this confounding and bias by adjusting the weights in the analysis to balance the distribution of confounders between the treated and untreated groups at each time point.

The MSM can be used for both categorical and continuous outcomes, and for time-to-event outcomes. It can also handle missing data, censoring, and non-compliance with treatment. MSM has been widely used in various fields, including epidemiology, public health, health services research, and social science.

Overall, MSM provides a powerful tool for estimating causal effects in complex observational studies. However, it requires careful consideration of model assumptions and potential sources of bias, and requires substantial statistical expertise to implement.