Restricted accessLetterFirst published online 2019-1
Letter to the Editor: Comparison of statistical approaches dealing with time-dependent confounding in drug effectiveness studies (SMMR,Vol. 27,Issue 6,2018)
KarimMEPetkauJGustafsonPet al.Comparison of statistical approaches dealing with time-dependent confounding in drug effectiveness studies. Stat Methods Med Res2018; 27: 1709–1722.
2.
GranJMRøyslandKWolbersMet al.A sequential cox approach for estimating the causal effect of treatment in the presence of time-dependent confounding applied to data from the Swiss HIV cohort study. Stat Med2010; 29: 2757–2768.
3.
RobinsJMHernanMABrumbackB. Marginal structural models and causal inference in epidemiology. Epidemiology2000; 11: 550–560.
4.
Kalbfleisch JD and Prentice RL. The statistical analysis of failure time data. Vol. 360. 2nd ed. John Wiley & Sons, 2011.
5.
Aalen O, Borgan Ø and Gjessing H. Survival and event history analysis: a process point of view. Springer Science & Business Media, 2008.
6.
PetersenMLSinisiSEvan der LaanMJ. Estimation of direct causal effects. Epidemiology2006; 17: 276–284.
7.
KeoghRHDanielRMVanderweeleTJet al.Analysis of longitudinal studies with repeated outcome measures: adjusting for time-dependent confounding using conventional methods. Am J Epidemiol2017; 187: 1085–1092.
8.
YoungJGHernánMAPicciottoSet al.Relation between three classes of structural models for the effect of a time-varying exposure on survival. Lifetime Data Anal2010; 16: 71–71.
9.
HavercroftWDidelezV. Simulating from marginal structural models with time-dependent confounding. Stat Med2012; 31: 4190–4206.