They say that the log-transformed this (and two other) variables. Whether that was necessary or not is another question. Having done that, it would be normal for them only to report the log values, and coefficients based on them. If they added "ln" to the labels inconsistently, that's a bit sloppy but not a huge problem, I'd have thought.bob sterman wrote: ↑Sun Sep 27, 2020 5:58 pm- Why do they take the unusual step of reporting mean 25(OH)D as a natural logarithm? i.e. as ln 25(OH)D? I don't think they used the natural log in any tests?

They should have presented the results with continuous predictors as well, but if their aim was really to show that deficient levels of Vitamin D are a problem then the dichotomy doesn't seem to me to be totally worthless.bob sterman wrote: ↑Sun Sep 27, 2020 5:58 pm- Why don't they treat 25(OH)D as a continuous variable and test whether it predicts a binary outcome (e.g. survival vs death)? They have dichotomized vitamin D status. What's the betting they tried dichotomizing it in various ways before settling on this particular threshold.

I count 68 white and 7 red dots above the line, versus 125 and 28 below. Accordingly:bob sterman wrote: ↑Sun Sep 27, 2020 5:58 pm- In any case, they state (for Figure 1) that "the number of red dots (inpatient mortality) above the solid line is significantly less compared to the dots below the line", the solid line being the 30 ng/mL 25(OH)D line. Well, counting the dots I can't get this result with a chi-square.

Code: Select all

```
> chisq.test(matrix(c(68,7,125,28), nrow=2))
Pearson's Chi-squared test with Yates' continuity correction
data: matrix(c(68, 7, 125, 28), nrow = 2)
X-squared = 2.4626, df = 1, p-value = 0.1166
```

Code: Select all

```
> chisq.test(matrix(c(68,7,125,28), nrow=2), correct=FALSE)
Pearson's Chi-squared test
data: matrix(c(68, 7, 125, 28), nrow = 2)
X-squared = 3.1145, df = 1, p-value = 0.0776
```