# Search results

1. ### Determining stats based on "activity"

Might be fun to make heat maps of time.
2. ### Can random effect only model positive correlations?

What do you think they are referencing with negative 'correlations'? Are they writing about model estimates or covariance between values? Unclear to me. I am not an expert in this area, but I wouldn't understand why model estimates couldn't be negative. Beta reg can be used with 0-1 bounded...
3. ### Standard deviation of an average of a number of results

Yeah, @Dason is right here. In MC simulation studies you use the percentiles of interest. So order all of the outputs and find the 2.5 and 97.5 values.
4. ### Can random effect only model positive correlations?

Can you post their exact wording/review. Because I am also not following what their concern may be.

Skewed!
6. ### Cox Regression Assumption

why are the doses different? Are they dependent on another variable (e.g., bioassay)?
7. ### Cox Regression Assumption

Right censoring: Insufficient follow-up; Lost to follow-up; Patient withdraws from study. Left censoring: Already had event prior to study period You have 56 censored and 239 events.
8. ### IV going from p > .05 to p< .05 in Hierarchical regression?!

Do you have any theory to support the models our is this just an exploration excursion. If the latter, you will find all kinds of things, but it doesn't mean they are true and will generalize. Giant corn - like Peruvian varieties that I can get in corn nut form and in my ceviche?
9. ### (SPSS- Logistic Regression) Interpretation of moderation effect with one non-significant main effect

Think of this, you have two exposures - smoking and asbestos exposure, both smoking and asbestos exposure cause lung cancer. Exposure to both result in a multiplicative increase in cancer risk. In the small sample you fail to find an effect beyond chance with the single exposures and find an...
10. ### Cox Regression Assumption

So you are saying only 56/295 had a documented outcome?
11. ### (SPSS- Logistic Regression) Interpretation of moderation effect with one non-significant main effect

Agreed and I will recommend always plotting interactions.
12. ### Cox Regression Assumption

I solicited a recommendation for a related article. This one on RMST seems like a nice entry level piece and may have STATA code attached.
13. ### Cox Regression Assumption

Non-proportional hazards means the horizontal distance between the survival curves (for categorical covariates) is not uniform. So estimates of the hazards wont explicitly represent the change pattern accurately. The proportional hazards can be partially seen by looking at the plot and crossing...
14. ### Cox Regression Assumption

Do you know the cause of the crossing. This may help you frame it. Causes can include: people switching treatments, differential disease progression, heterogeneous treatment effects (subgroups), and accelerated or delayed treatment effect. NPH is likely present in every study to some extent...
15. ### Cox Regression Assumption

General text from web, "In the case of categorical covariates, graphs of the Kaplan-Meier estimates of the survival function provide quick and easy checks of proportional hazards. If proportional hazards holds, the graphs of the survival function should look “parallel”, in the sense that they...
16. ### Help writing hypotheses!

We appreciate you are interested in stats. Though, I will note that a failure to reject a null hypothesis at a set alpha is not evidence towards the null being true. As noted it could be a sample size issue. To formally test a null value, a threshold is usually stated for a margin of similarity...
17. ### is there a statistical test for this

Would it be acceptable to fit a line to data?

:)
19. ### Make me pretty (Density Plot)

My chat options are different from yours?
20. ### Make me pretty (Density Plot)

In the below figure, I would like the word 'Density', not to be on the graph. So by default it includes the var name. Any suggestions? diff_1 = rnorm(250, 30, 5) Density = diff_1 p <- mcmc_areas( data.frame(Density), prob = 0.95, # 80% intervals prob_outer = 0.999, # 99% point_est =...