Comparing cure rates for gemtuzumab ozogamicin plus standard chemotherapy vs standard chemotherapy alone in acute myeloid leukemia patients.

Aim: Assess the suitability of standard parametric, piecewise and mixture cure models (MCMs) for modeling long-term survival of acute myeloid leukemia patients achieving remission following treatment with gemtuzumab ozogamicin (GO) + standard chemotherapy (SC) or SC alone. MCMs can model survival data comprising of statistically cured (patients in long-term remission) and uncured patients. Materials & methods: Models were fit to patient-level data corresponding to individual treatment arms. Results: Visual inspection showed that MCMs fit the clinical data best. Survival modeling with MCMs showed that treatment with GO + SC versus SC alone results in higher statistical cure rates for event-free survival (rates: 26-35% vs 21-23%) and overall survival (rates: 48-52% vs 38-44%). Conclusion: MCMs are well suited to modeling long-term survival in acute myeloid leukemia patients. Clinical trial registration: NCT00927498 (ClinicalTrials.gov).

Lay abstract To assess the effectiveness of acute myeloid leukemia (AML) treatments, researchers use statistical models to estimate the survival rate of patients who receive a particular treatment. Some patients receiving certain AML treatments can achieve long-term remission and are often considered 'cured'. Standard statistical models cannot differentiate between cured and uncured patients and so tend to underestimate the survival rates of cured patients. Mixture cure models (MCMs) can account separately for the survival of cured versus uncured patients. We tested MCMs and standard statistical models using data from a clinical trial comparing gemtuzumab ozogamicin (GO) + standard chemotherapy against standard chemotherapy alone in AML patients. Of all the models tested, MCMs generated survival extrapolations over time that most closely resembled the data from the clinical trial. Through our analyses, we demonstrated that GO + standard chemotherapy can result in higher survival rates than standard chemotherapy alone.

Extrapolating Survival Data Using Historical Trial-Based a Priori Distributions.

OBJECTIVES: To show how clinical trial data can be extrapolated using historical trial data-based a priori distributions. METHODS: Extrapolations based on 30-month pivotal multiple myeloma trial data were compared with 75-month data from the same trial. The 30-month data represent a typical decision-making scenario where early results from a clinical trial are extrapolated. Mature historical trial data with the same comparator as in the pivotal trial were incorporated in 2 stages. First, the parametric distribution selection was based on the historical trial data. Second, the shape parameter estimate of the historical trial was used to define an informative a priori distribution for the shape of the 30-month pivotal trial data. The method was compared with standard approaches, fitting parametric distributions to the 30-month data with noninformative prior. The predicted survival of each method was compared with the observed survival (ΔAUC) in the 75-month trial data. RESULTS: The Weibull had the best fit to the historical trial and the log-normal to the 30-month pivotal trial data. The ΔAUC of the Weibull with informative priors was considerably smaller compared with the standard Weibull. Also, the predicted median survival based on the Weibull with informative priors was more accurate (melphalan and prednisone [MP] 40 months, and bortezomib [V] combined with MP [VMP] 62 months) than based on the standard Weibull (MP 45 months and VMP 72 months) when compared with the observed median (MP 41.3 months and VMP 56.4 months). CONCLUSIONS: Extrapolation of clinical trial data is improved by using historical trial data-based informative a priori distributions.