## Change from baseline transformation

Regular Contributor
Posts: 182

# Change from baseline transformation

Dear all,

I have a dataset containing data of patients with cancer. There are 60 subjects in this study, from which 30 got the standard treatment of chemotherapy, and 30 got chemotherapy along with nutritional supplements such as Turmeric and others. For each subject, the white blood count level was measured at 2 time points. Chemotherapy reduces the level of the white blood count, and the aim of the study is to show that in the treatment group, the decrease is more moderate, that the level of white blood count did not reduce as much.

I have a few questions regarding the analysis required (I am using SAS 9.4):

1. I wish to calculate the change from baseline. What is the right way to do it, absolute change from baseline, i.e. WBC2 - WBC1, or the percentage change from baseline, i.e. WBC2/WBC1 ?

2. If I use the absolute change from baseline, does it mean that a t-test or an equivalent is not a good choice, as I need to account for  the baseline measurement, leading me to a regression model ?

3. The data in both groups is not normally distributed (in one group the violation is minor, in the other it is more major). The sample size is not sufficiently large for the use of the central limit theorem (right?). A log transformation fails, since the absolute change from baseline gives negative values. How do I get out of this situation ? (I read somewhere about Fisher's transformation but as far as I know that is for correlations, and I am not sure I understand how to do it, theoretically and technically in SAS).

The mean of absolute change from baseline in the control group is -1.5, while in the treatment group -0.5. SD are 1.5 and 2 respectively.

Any advice will be most appreciated ! Thank you in advance !

Super User
Posts: 23,776

## Re: Change from baseline transformation

1. That's subject specific. See what others in your field are doing.

2. You can still use a t-test, your hypothesis is different, but it should be statistically fine.

3. Are the distributions similar? That's the important part. The t-test is fairly robust for N=30 unless you have  very skewed distribution.

You can also use non parametric methods as well - see PROC NPAR1WAY

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