Good Morning Everyone, or whatever time of day it is.
I have talked myself into a circle and I'm not sure which path to follow at this point. So, I thought I would ask the experts.
What I am trying to do is determine if there is a statistically significant difference in the time it takes to reach a diagnosis based on the result of the diagnosis.
For example, does it take significantly longer to reach a final diagnosis based on whether a tumor is malignant or benign.
Calculating the median number of days to final diagnosis I believe I have figured out successfully.
But then I have found myself a bit stuck. I considered ANOVA but the time data violates the independence assumption.
I considered proc lifetest, conducting a peto test, and probdf but I'm uncertain which to use in this scenario.
Any guidance would be helpful. Thanks
Person 1 is diagnosed in 12 days
Person 2 is diagnosed in 24 days
Person 3 is diagnosed in 17 days
...
I don't see any time series here.
But then I have found myself a bit stuck. I considered ANOVA but the time data violates the independence assumption.
Explain further. If the variable of interest is number of days to diagnosis, there is no violation of the independence assumption as this is not time series data.
That is where I've talked myself into a circle. Doing a wilcoxon analysis seemed like it would be appropriate, but I haven't used time data in ... 12 years. Can you help me wrap my head around how this isn't time series data? I need a better understanding here so I can defend my methodology if challenged.
Person 1 is diagnosed in 12 days
Person 2 is diagnosed in 24 days
Person 3 is diagnosed in 17 days
...
I don't see any time series here.
PROC LIFETEST is probably the simplest way to calculate the median value for a time to event variable, so long as not more than 50% of the observations are censored. The examples in the documentation are really quite good. Run those, and then see how to change to your variables.
SteveDenham
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ANOVA, or Analysis Of Variance, is used to compare the averages or means of two or more populations to better understand how they differ. Watch this tutorial for more.
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