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Cross Variable T-Tests

I have a dataset with a categorical variable (treatment).  
The data columns have comparisons that take place at different times (1, 2, 4, and 6).
For each comparison variable there are columns for "Device applied" and "No Device".  

So a person could be either an active or placebo (treatment) with either a device applied or not applied for each comparison variable.  

For example ... "Comparision 01" could have four different combinations ...
Treatment - No device
Treatment - Device
Placebo - No device
Placebo - Device

Each of these would have its own mean.  What I'm trying to figure out how to do in JMP is to do cross-comparison T-tests which would compare "Placebo - No device" against "Treatment - device".  I can compare Active to active or Placebo to placebo, but I'm not seeing any way to compare across the groups.

Do I just take the results from "Distribution" with Treatment as a "By" splitter and then do a "Test mean" by plugging in the comparison I want into the Hypothesis test?   That seems a bit clunky.

AnalyticsRe: Cross Variable T-Tests

This case is a classic 'two-way ANOVA" scenario Set up your data table with Condition (Treatment or Placebo), Device (Yes or No) and the Y (response). Select Analyze > Fit Model. Select Condition and Device, click Macros, and select Full Factorial. Select Y and click A. Click Run.

 

Everything you need is there.

Re: Cross Variable T-Tests

First of all, I do not recommend using the Student t-test in this post hoc analysis. You will experience inflated type I error rates. Use Tukey's method instead. Also, are you asking for the t-ratios? Why is that necessary?

 

This sounds like two separate, parallel studies. You obtain a response for Treatment (Placebo, Active) and another response for Device (Yes, No). Is that interpretation correct? I assumed that each subject was observed once with a combination of Treatment and Device.

Re: Cross Variable T-Tests

You used Subject for 'blocking,' which is smart. It will have a random effect, though. You should compute the Treatment difference for a 'matched pairs' analysis. Otherwise, the subject variation will obscure the Treatment effect.

 

Alternative, use Fit Least Squares with Treatment, Device Status, and Subject as the effecfs. You can include an interaction term. Then you can use the various tests in this platform.