Discussion
diabetic
REGRESSION, CVD
In this study, we estimated the effect of time-varying intensive glucose lowering therapy vs. standard therapy among type 2 diabetic patients on cardiovascular and all-cause mortality outcomes using the G-estimation and compared it with conventional regression model. We used this method because conventional methods may result in biased effect estimates when there are time-varying confounders affected by prior treatment. In ACCORD trial, non-compliance was high especially in the standard therapy group, and treatment at each visit (after baseline) was influenced by previous values of HbA1c. Specifically, the patients with higher baseline and lagged values of HbA1c in standard therapy were more likely to give up their assigned treatment. In such situations, conventional methods may result in biased effect estimates because of over-adjustment and selection biasesThe estimates obtained from time-dependent model are also subject to selection and over-adjustment biases because of inappropriate adjustment of time-varying confounders. G-estimation overcomes this deficiency by estimating the effect of received treatment under the assumption of sequential randomization (conditional exchangeability) at each visit, given the measured confounders. The effect of interest is the effect that would have been observed if all patients in the trial had compliance to the study protocolThe G-estimate in the current study indicated protective effect of treatment on mortality, but failed to show the same effect on CVD. This might be explained in part by the association of HbA1c with all-cause mortality that has been reported by several recent meta-analyses and large-scale observational studiesG-estimation has been applied in a number of observational studies