This video introduces Time Varying Confounding, Structural Nested Accelerated Failure Time Models, G-estimation, and how to deal with both administrative and competing risks censoring.
Most of the information presented here comes from:
Hernan et al. (2005). Structural accelerated failure time models for survival analysis in studies with time-varying treatments. Pharmacoepidemiology and Drug Safety. Vol. 14, pp. 477-491.
For more general information on G-estimation and structural nested models see:
Vansteelandt, S and Joffe, M. (2014). Structural Nested Models and G-estimation: The Partially Realized Promise. Statistical Science. Vol. 24, No. 4, pp. 707-731.
Citations from minute 19 of video (methods):
Robins, J. (1986). A new approach to causal inference in mortality studies with a sustained exposure period- Application to control of the healthy worker survivor effect. Mathematical models in medicine: Diseases and epidemics. Part 2. Math. Modeling. Vol. 7, pp. 1393-1512.
Robins, J. M., Hernan, M. A. and Brumback, B. (2000). Marginal structural models and causal inference in epidemiology. Epidemiology Vol. 11, pp. 550-560.
Robins, J. (1992). Estimation of the time-dependent accelerated failure time model in the presence of confounding factors. Biometrika. Vol. 79, No. 2, pp. 321-334.