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Crossover clinical trials can provide substantial benefits by eliminating inter-patient variation from treatment comparisons and by allowing multiple observations of each patient. They are particularly useful when sample sizes are necessarily small. These advantages proved particularly valuable in an assessment of clot prevention in children undergoing haemodialysis. Only small numbers of children are treated at any given time in any single dialysis unit, but each patient is obliged to attend two or three times each week, suggesting the use of a crossover trial with many periods. Standard crossover trials described in the literature (i) typically have fewer than 10 periods and (ii) are based on a model of questionable applicability to this study. This paper describes the derivation of an optimal crossover trial with 30 periods, which was used to compare two anticoagulants using nine patients. There is also a discussion of the analysis of the data obtained in the trial, which had a distribution markedly different from that anticipated when the study was designed., (Copyright © 2013 John Wiley & Sons, Ltd.)

In common with most forms of designed experiment, crossover trials can be affected by missing data. Attempts to devise designs that can mitigate the possible effects of missing data, such as loss of efficiency, or even inestimability of certain contrasts, have been proposed. However, a potentially serious effect of missing data that has not been addressed in designs hitherto is that the treatment effects may be biassed because of the nature of the missingness process. We investigate this problem in two-treatment, two-period crossover designs. In particular, we consider the robustness of the analysis under a missing at random assumption when, in fact, the data are non-ignorably missing. We show that the conventional AB/BA design still has good properties, although the design with sequences AB, BA, AA, and BB may be preferred if the chance of dropout depends primarily on the difference between the responses in the two periods.

Missing data arise in crossover trials, as they do in any form of clinical trial. Several papers have addressed the problems that missing data create, although almost all of these assume that the probability that a planned observation is missing does not depend on the value that would have been observed; that is, the data are missing at random (MAR). In many applications, this assumption is likely to be untenable; in which case, the data are missing not at random (MNAR). We investigate the effect on estimates of the treatment effect that assume data are MAR when data are actually MNAR. We also propose using the assumption of no carryover treatment effect, which is usually required for this design, to permit the estimation of a treatment effect when data are MNAR. The results are applied to a trial comparing two treatments for neuropathic pain and show that the estimate of treatment effect is sensitive to the assumption of MAR., (Copyright © 2012 John Wiley & Sons, Ltd.)

This paper presents a review of crossover designs for use in medical applications which have three or more treatment periods. Only outcomes which can be analysed as continuous variables are considered. Designs which purport to allow for carryover effects are reviewed in detail, as are methods for analysing data collected in such trials. In practice, it is often possible to eliminate carryover by interposing sufficiently long 'washout' periods between successive treatments, and suitable designs for this case are also mentioned. Much current practice revolves around a model which has been widely criticized: the shortcomings of this model and the implications of possible remedies, for design as well as analysis, are discussed.