I have a situation where the parameter estimates from lrm identify a binary predictor variable ("X") as clearly non-significant (p>0.3), but the ANOVA of that same model gives X a chi^2-df rank of > 200, and adjudicates X and one interaction of X and a continuous measure as highly significant. The N is massive and X has two categories, each with > 100,000 observations. I would expect X to have a significant impact on the outcome.
The full model includes a large number of continuous (coded with rcs with 3 knots) and categorical variables, as well as a plethora of interactions between the categorical and continuous variables. Only one of the interactions between the binary variable and the other categorical or continuous variables is statistically significant.
Can anyone offer a suggestion on what might explain this discordance? ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
