Adjusting for patient crossover in clinical trials using external data: a case
study of lenalidomide for advanced multiple myeloma.
Author(s): Ishak KJ, Caro JJ, Drayson MT, Dimopoulos M, Weber D, Augustson B, Child JA,
Knight R, Iqbal G, Dunn J, Shearer A, Morgan G.
Affiliation(s): United BioSource Corporation, Montreal, QC, Canada.
jack.ishak@unitedbiosource.com
Publication date & source: 2011, Value Health. , 14(5):672-8
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.
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