Analyzing 'change scores' in an RCT: Alternative to ANCOVA — assumptions violated

data analysis
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I am working on an RCT measuring change in scores from baseline; Total N = 60 with 3 arms (1:1:1, no dropouts). I was going for ANCOVA comparing the follow up values between the 3 groups while adjusting for baseline ones.

However, the variances are quite heterogeneous, and outliers exist. I am not sure if the model would be robust in this scenario. I thought of rank transformation, but is there a better alternative? Perhaps a “non-parametric” ANCOVA or another suitable linear model?

I appreciate your input!

2

I think the proportional odds model implemented in rms package will be appropriate.

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3

Yes semiparametric ordinal models such as the PO model are good choices. But since it is an RCT isn’t the statistical analysis plan already in place?

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4

Thank you so much for your input! Re SAP: The initially-assigned statistician, who later had to leave, powered the trial to analyze the follow up scores while not adjusting for baseline. I did this bit as planned, and the results are great, but I thought adjustment would be more informative.

5

Adjustment is more effective because it doesn’t make any of these assumptions

  • baseline is relevant (estimated slope can be zero)
  • baseline is linearly related to Y
  • slope is 1.0 for baseline if it is linearly related to Y

But for power/sample size calculations we frequently approximate the final analysis by using the standard deviation of change scores.

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