I'm trying to convey some findings in which a score from 1-10 seems to predict disease status (binary).

I predict yhat and plot yhat with a quadratic fit against my predictor (score).

It looks accurate but when I add a confidence interval to the quadratic fit, it's VERY narrow. Too narrow for me to believe it.

Does a predicted plot deflate the confidence interval of the original data? If so, does anyone have a suggestion on how I convey my data in a similar format (i.e. for every increase in score, percentage of success increases by y) with a confidence interval?

First plot:

This plot is obtained by entering `twoway qfitci effect score` where effect is a binary variable denoting whether or not the patient had the desired effect, 1 being effect, and score being a nominal/continuous variable where 1 is the lowest and 10 is the highest score, with the hypothesis that a higher score increases probability of effect. `qfitci` is a quadratic fitting plot with CI in gray.


CI of original data plot `twoway qfit effect score`:

[![enter image description here][1]][1]

2nd plot:

This plot is obtained by running a logistic regression model: `logistic effect score age gender` in stata. This command returns OR's as opposed to the `logit` command which returns coefficients in e. This model is then predicted using `predict yhat` in stata which creates a new variable `yhat` with the predicted probabilities of the model.

DATA (CSV): https://gofile.io/?c=sxNnuM

CI of prediction plot `twoway qfitci yhat score`: [![enter image description here][2]][2]


  [1]: https://i.sstatic.net/60sAV.png
  [2]: https://i.sstatic.net/lnzpW.png