Objectives In some trials, particularly in oncology, patients whose disease progresses under the comparator treatment are crossed over into the experimental arm. This unplanned crossover can introduce bias in analyses because patients who crossover likely have a different prognosis than those who do not cross over; for instance, sicker patients not responding to standard therapy or those expected to benefit the most may be selectively chosen to receive the experimental treatment. Standard statistical methods cannot adequately correct for this bias. We describe an approach designed to minimize the impact of crossover, and illustrate this by using data from two randomized trials in multiple myeloma (MM). Methods The MM-009/010 trials compared lenalidomide and high-dose dexamethasone (Len+Dex) with dexamethasone alone (Dex). Nearly half (47%) of the patients randomized to Dex crossed over to Len with or without Dex (Len+/-Dex) at disease progression or study unblinding. Data from these trials was used to predict survival in an economic model evaluating the cost-effectiveness of lenalidomide. To adjust for crossover, the prediction equations were calibrated to match survival with Dex or Dex-equivalent therapies in trials conducted by the Medical Research Council (MRC) in the United Kingdom. To adjust for differences between the MM and MRC trial populations, a prediction equation was developed from the MRC data and used to predict survival by setting predictors to mean values for patients in the MM-009/010 trials. The expected survival with Dex without crossover was then predicted from the calibrated MM-009/010 equation (i.e., adjusted to match survival predicted from the MRC equation). Results The adjusted median overall survival predicted by the MRC equation was 19.5 months (95%CI, 16.6–22.9) for patients with one prior therapy, and 11.6 months (95% CI, 9.5–14.2) for patients with >1 prior therapy. These estimates are considerably shorter than was observed in the clinical trials: 33.6 months (27.1-NE) and 27.3 months (95% CI, 23.3–33.3) as of December 2005. Conclusion The calibration method described here is simple to implement, provided that suitable data are available; it can be implemented with other types of endpoints in any therapeutic area.