Recent content by hlsmith

  1. hlsmith

    handling missing data

    If I is a survey, did you have monotonic missingness, but disproportionately higher in males?
  2. hlsmith

    handling missing data

    Do you have a theory for the missingness? Can the missingness be explained by other variables in dataset or extraneous variables not in the dataset?
  3. hlsmith

    Demdromorphometrist wanna meet Waloddi Weibull

    Note: Weibull is commonly used in proportional hazard regression in health care data, specially with the outcome of mortality. What is the question you are wanting to possibly apply the distribution toward? I would wonder if there are possible issues: with people in a sample from a population...
  4. hlsmith

    Question about chi-square test and single columns

    Please provide more information, given your description I am led to think you are trying to conduct single sample tests of groups against constant.
  5. hlsmith

    handling missing data

    General question, what type of missingness do you have. Not all missingness can be successfully imputed.
  6. hlsmith

    95% CI for rare incidence rate increasing in time.

    What is the purpose of the graph or conclusions made? If it is for a trend, you could formally model for a positive trend.
  7. hlsmith

    95% CI for rare incidence rate increasing in time.

    I follow you and was thinking the same thing in regards to examining years. I was also thinking there is likely an exact Poisson calculation. Not this but something like it. It is typical to use exact methods when sample sizes are small.
  8. hlsmith

    95% CI for rare incidence rate increasing in time.

    Not sure if this is correct, but incidents are the same as risks. So I wonder if there is a formula for rare event CI. I am sure that will be easy enough to search for.
  9. hlsmith

    Trend analysis

    When was the protocol implemented? if it was within the about timeline you should add a vertical reference line, to distinguish the control and intervention periods. Were there different variable to control for besides the passing of time that may influence the MRI readings/diagnosis that need...
  10. hlsmith

    Variance contribution testing

    So all variables are at the county level? What do you mean by "has a large variance"? It may be beneficial to look to the oncology or epidemiology literature to see if similar study control for a covariance structure between tangential counties.
  11. hlsmith

    Survival Analysis

    K-M curves are typically like the initial bivariate analyses before logistic regression. As noted you would use Proportional Hazard Regression (e.g., Cox). The interpretation of the model is comparable to that of logistic regression, effect of exposure while holding covariates constant. Just...
  12. hlsmith

    Confidence interval for Difference in Differences?

    Yes, I refer to this article about once a year or so to remember some of the intricacies.
  13. hlsmith

    To assess meaningfulness between an independent variable and a dependent one, is just the p value alone adequate and/or the correlation?

    Just keep sampling variability in mind which can be seen when looking at 95% confidence intervals. Estimates from smaller samples have the potential to vary given variability in the selected sample. You become more certain of the estimate with larger samples.
  14. hlsmith

    To assess meaningfulness between an independent variable and a dependent one, is just the p value alone adequate and/or the correlation?

    I like the above posts. I will add as well that a statistically significant result (e.g., slope) can help rule out chance, though if its effect is small an investigator may want to remember that it may not take that large of an effect of an omitted variable to account for the effect. Such as an...
  15. hlsmith

    Confidence interval for Difference in Differences?

    I see you used SAS code, so check out this reference. You may be able to adjust the code to output CIs. Also, I am using this reference in case you did not randomize group assignments, so you have a risk of non-ignorability of treatment assignment - AKA confounding based on baseline covariate...