Hello,
I'm trying to do sequential cox proportional hazards models (5 total using one outcome and then another 5 using another outcome, so 10 total) using imputed data (5 imputations), but it structured as panel data and split into 2-year time blocks using stsplit. There's approximately ~80 variables and 2.4 million observations in the analytic sample, I imputed the variables with most missing data, and there's ~600 events for the first set of 5 models and ~2200 for the other set of 5 models. When I run the models, I get over inflated p-values (0.4-0.9) and very wide confidence intervals. I have a hunch as to why the p-values are so high and the confidence intervals so wide, but I could use some input from others on if it's because of the type of model I'm using given the number of variables, events, and sample size or if it's for another reason. Additionally, these models are clustered at the state-level. Appreciate any insight you all can provide.
I'm trying to do sequential cox proportional hazards models (5 total using one outcome and then another 5 using another outcome, so 10 total) using imputed data (5 imputations), but it structured as panel data and split into 2-year time blocks using stsplit. There's approximately ~80 variables and 2.4 million observations in the analytic sample, I imputed the variables with most missing data, and there's ~600 events for the first set of 5 models and ~2200 for the other set of 5 models. When I run the models, I get over inflated p-values (0.4-0.9) and very wide confidence intervals. I have a hunch as to why the p-values are so high and the confidence intervals so wide, but I could use some input from others on if it's because of the type of model I'm using given the number of variables, events, and sample size or if it's for another reason. Additionally, these models are clustered at the state-level. Appreciate any insight you all can provide.
