Estimating the treatment effect from non-randomized studies: The example of reduced intensity conditioning allogeneic stem cell transplantation in hematological diseases
1 Département de Biostatistique et Informatique Médicale, Hôpital Saint-Louis, AP-HP, Paris 75010, France
2 INSERM, UMRS 717, Paris 75010, France
3 Université Denis Diderot Paris 7, Paris 75010, France
4 Service d’Hématologie Greffe, Hôpital Saint-Louis, AP-HP, Paris 75010, France
5 Service d'Hématologie Clinique, Hôpital Avicenne, AP-HP, Bobigny 93100, France
6 Service d'Immuno-Hématologie, Hôpital Saint-Louis, AP-HP, Paris 75010, France
7 Service d'Onco-Hématologie, Hôpital Saint-Louis, AP-HP, Paris 75010, France
BMC Blood Disorders 2012, 12:10 doi:10.1186/1471-2326-12-10Published: 16 August 2012
In some clinical situations, for which RCT are rare or impossible, the majority of the evidence comes from observational studies, but standard estimations could be biased because they ignore covariates that confound treatment decisions and outcomes.
Three observational studies were conducted to assess the benefit of Allo-SCT in hematological malignancies of multiple myeloma, follicular lymphoma and Hodgkin’s disease. Two statistical analyses were performed: the propensity score (PS) matching approach and the inverse probability weighting (IPW) approach.
Based on PS-matched samples, a survival benefit in MM patients treated by Allo-SCT, as compared to similar non-allo treated patients, was observed with an HR of death at 0.35 (95%CI: 0.14-0.88). Similar results were observed in HD, 0.23 (0.07-0.80) but not in FL, 1.28 (0.43-3.77). Estimated benefits of Allo-SCT for the original population using IPW were erased in HR for death at 0.72 (0.37-1.39) for MM patients, 0.60 (0.19-1.89) for HD patients, and 2.02 (0.88-4.66) for FL patients.
Differences in estimated benefits rely on whether the underlying population to which they apply is an ideal randomized experimental population (PS) or the original population (IPW). These useful methods should be employed when assessing the effects of innovative treatment in non-randomized experiments.