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10-27-2016 01:42 AM

Hi,

I want to compare two methods with a Bland Altman plot. The difference between measurement 1 and measurement 2 are non-normally distributed data, therefore, I want to log-transform the data. I get the bias, SD and LOA on the log-transformed data, and can make the BA-plot.

My problem is when I am transforming back the LOA to original scale, which I want to insert on the original BA-plot, for easier interpretation. How do I calculate the slopes of the LOA and how do I know the intersection with the y-axis?

I also want to make 95 % CI of the mean difference of this data, how can I do this on log transformed data when I want the 95 % CI to be on the original scale? (for comparison to other normally-distributed data). Or should I just calculate 95 % CI of the mean difference on non-normally distributed data?

Thanks!!

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10-27-2016 10:06 AM

Not sure I understood but I will try to answer.

*My problem is when I am transforming back the LOA to original scale, which I want to insert on the original BA-plot, for easier interpretation.*

When I plot with a log scale i am not doing any transformation, I specify "TYPE=LOG" in my GTL or SGPLOT code, SAS does the job.

Generally speaking avoid any back-transforming of your data, maybe create a macro that does the plot depending of the type of analysis you want (linear or logarithmic).

__ "How do I calculate the slopes of the LOA".__.. If you mean the regression line use PROC REG:

```
ods select none;
proc reg data=inputDS;
model yvar = xvar ;
ods output ParameterEstimates=outputDS;
run;quit;
data _null_;
set outputDS;
if variable eq 'Intercept' then call symput('Int', put(estimate, BEST.));
else call symput('Slope', put(estimate, BEST.));
run;
ods select all;
```

*how do I know the intersection with the y-axis?*

*y = slope*x +intercept *intersection with y-axis is your intercept.

*I also want to make 95 % CI of the mean difference of this data, how can I do this on log transformed data when I want the 95 % CI to be on the original scale? (for comparison to other normally-distributed data). Or should I just calculate 95 % CI of the mean difference on non-normally distributed data?*

As I said, avoid back-transforming, calculate the 95%CI on original scale data.

Cheers

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