The bias introduced is probably usually small, especially when the dropout rate is low. But, in those cases you get very little “enhanced power”. You would be better off just not bothering with a per-protocol analysis, as you would get the same result from an ordinary analysis based on which group the person was sorted into originally (control or not).
The only situation in which the per-protocol analysis is worth doing is one where it makes a real difference to the statistics, and that is exactly the same situation in which it introduces the risk of introducing bias. So, I think it might just never be worth it: it removes a known problem (due to dropouts, some people in the yoga group didn’t do all the yoga), with an unknown problem (the yoga group is post-selected nonrandomly), effecting exactly the same number of participants—so the same scale of problem.
In the Yoga context then I would say that if it’s really good at curing depression then surely its effect size is going to be big enough to swamp a small number of yoga dropouts.
They also only have 32 participants in the trial. I don’t know if its a real rule, but I feel like the smaller the dataset the more you should stick to really basic simple measures.
The bias introduced is probably usually small, especially when the dropout rate is low. But, in those cases you get very little “enhanced power”. You would be better off just not bothering with a per-protocol analysis, as you would get the same result from an ordinary analysis based on which group the person was sorted into originally (control or not).
The only situation in which the per-protocol analysis is worth doing is one where it makes a real difference to the statistics, and that is exactly the same situation in which it introduces the risk of introducing bias. So, I think it might just never be worth it: it removes a known problem (due to dropouts, some people in the yoga group didn’t do all the yoga), with an unknown problem (the yoga group is post-selected nonrandomly), effecting exactly the same number of participants—so the same scale of problem.
In the Yoga context then I would say that if it’s really good at curing depression then surely its effect size is going to be big enough to swamp a small number of yoga dropouts.
They also only have 32 participants in the trial. I don’t know if its a real rule, but I feel like the smaller the dataset the more you should stick to really basic simple measures.