Recent content by hlsmith

  1. hlsmith

    Bayes theorem with errors

  2. hlsmith

    Estimating effect of those not in the system

    It is a two part model. You first model the probability of getting the service (y/n) given you were eligible. Then you use those propensity scores in your outcome model of service (y/n) on income. If you don't have overlap in the histograms or additional reservations about residual confounding...
  3. hlsmith

    Reviewer feedback - In the statistical section, why log transformation were not done for normality and equal variance

    I think your rebuttal is great. I would agree with @Dason's comments and add, that your a priori analytic protocol was to use the nonparametric tests - thus you should stay true to that. I love quantile regression, which would also be a great fit - given you have enough data for the confidence...
  4. hlsmith

    correction for variables in a correlation

    To follow-up, it also sounds like you have two dependent variables you would like to predict. Meaning you will need to run two models or "multivariate multiple regression". If you do the former, it may be good practice to make the alpha level a little smaller, since you are looking at two...
  5. hlsmith

    Estimating effect of those not in the system

    What is the primary outcome in this setting? Income?
  6. hlsmith

    Evaluating a regression model.

    You can calculate the calibration plot in the holdout set - using the model built in the training dataset.
  7. hlsmith

    Bayes theorem with errors

    Also, can you fix your LAtEx, please. My dumbass can't fully appreciate the question in its current state.
  8. hlsmith

    Evaluating a regression model.

    Net Benefit Analysis type stats are interesting and can be used to address the tradeoff between FP and FN. But it is an all or nothing rule. I have always wondered if once you select the best cutoff, is there a way to rerun the analysis (e.g., logistic regression) in order to get estimates for...
  9. hlsmith

    Estimating effect of those not in the system

    The g-formula may be beneficial here. I'll discuss tomorrow, but you use propensity scores in a standardized model. You can also search double robust estimation. I think SAS has a package causality that may make it easy for your first time.
  10. hlsmith

    Evaluating a regression model.

    Wicklin provides code for calculating calibration curve https://blogs.sas.com/content/iml/2018/05/14/calibration-plots-in-sas.html Perhaps a start for you.
  11. hlsmith

    Estimating effect of those not in the system

    You have to have a subsample or assumptions - otherwise you would be straight making up stuff. So given you have either, you can then create weights to apply to your data. Perhaps, are there customers that start but quit, and these people can be match to people that are similar but didn't quit...
  12. hlsmith

    Evaluating a regression model.

    I don't think I knew Brier's score went 0-.25, I thought it was 0-1 bounded - I guess. It is pretty much looking at the same thing as Calibrations, but turning it into a score. The Calibration plot with confidence intervals is important in visualizing the fit. Also, discrimination is important...
  13. hlsmith

    Estimating effect of those not in the system

    Yes, you need to have some type of information on people that opt not to use your services in order to generalize or transport results to them. If you had content knowledge you could assume some of this information or as @GretaGarbo mentions - randomly subsampling them would be ideal. Very...
  14. hlsmith

    Hello!

    Welcome to the forum. We look for to your questions and contributions. Is there a certain area of medicine that you work in (e.g., emergency, palliative care, pediatrics, etc.)?
  15. hlsmith

    "Decreasing P-value" could be interpreted as "Getting Meaningful?" in time series?

    Decreasing p-value means that you have multiple pvalues taken from multiple tests and they seem to be decreasing? If so, better explain what you are doing. Or That you fit a simple linear regression and the p-value for the slope of the dependent variable regressed on time is small?