Responder Analysis: Loser x 4

research methods measurement
21

Hopefully the irony of using % reduction as the example isn’t lost given all the issues with that outcome. I wonder though dichotomania aside isn’t this example just showing a mismatch between utility function/analysis? If the mean treatment effect really is the relevant scale for making treatment decisions then of course you shouldn’t be basing anything on thresholds.

At the risk of being kicked off the island wouldn’t a semi-parametric ordinal just be testing the treatment effect on the difference at all thresholds? Could certainly be reported in terms of mean differences from an efficiency standpoint but is there anything wrong perse with being interested in increasing some exceedance threshold and then using a PO model to assess that efficiently/provide a more transportable summary treatment effect and than summarizing differences in numbers of patients meeting that threshold?

I guess from a decision theory perspective we’d be interested in maximizing expected value of utility but is there a reason to assume it has to be linear in the mean difference?

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22

why not look at the whole study period, eg, random coefficients modelling and look at slopes? or a summary statistic like max or auc? I think the clinicians are interested in: how long will the patient have sustained weight loss. And they like max weight loss too - they try to read these values off published plots of mean over time (which can’t be done). Why not time-to-event analysis then, ie time to cessation of weight loss? but then compliance is made more relevant. Maybe it’s so messy that they fall back on simple linear regression, although mean of % change seems the most difficult to translate into something meaningful

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23

% change does not have the right mathematical properties to be used in statistical analysis.

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