No abstract

A method of analysis is presented for estimating the magnitude of a treatment effect among compliers in a clinical trial which is asymptotically unbiased and respects the randomization. The approach is valid even when compliers have a different baseline risk than non-compliers. Adjustments for contamination (use of the treatment by individuals in the control arm) are also developed. When the baseline failure rates in non-compliers and contaminators are the same as those who accept their allocated treatment, the method produces larger treatment effects than an 'intent-to-treat' analysis, but the confidence limits are also wider, and (even without this assumption) asymptotically the efficiencies are the same. In addition to providing a better estimate of the true effect of a treatment in compliers, the method also provides a more realistic confidence interval, which can be especially important for trials aimed at showing the equivalence of two treatments. In this case the intent-to-treat analysis can give unrealistically narrow confidence intervals if substantial numbers of patients elect to have the treatment they were not randomized to receive.

No abstract

Although chronic pain is one of the most important medical problems facing society, there has been very limited progress in the development of novel therapies for this condition. Here, we discuss high-impact research priorities to reduce the number of people transitioning from acute to chronic intractable pain.