PCN28 METHOD TO ESTIMATE IMPACT OF PROGNOSTIC FACTORS ON SURVIVAL OUTCOMES USING MAXIMUM LIKELIHOOD ESTIMATION

Abstract

Intermediate treatment response can result in differences in oncology survival outcomes. Quantifying the impact of this response can be performed with regression models when individual patient data (IPD) of survival times and response status for each patient are known. However, when meta-analyzing data from published sources, only the proportion of responders aggregated over the population is often known. Maximum likelihood estimation (MLE) is a procedure to estimate the hazard ratio (HR) of response based on the distribution of the observed survival data. This work demonstrates the application of MLE to a mixture of data types typically available for meta-analysis: IPD with known response status, IPD with aggregate response (such as reconstructed IPD), and median survival with aggregate response. 10 hypothetical trial populations were randomly generated using the Weibull distribution with shape (0.5), scale (0.2), and HR of response (0.8). MLE estimates of these parameters were generated from the hypothetical data using multiple combinations of survival data availability: 10 IPD with known response, 5 IPD with known response + 5 IPD with aggregate response, and 1 IPD with known response + 5 IPD with aggregate response + 4 median survival with aggregate response. Parameter estimates for each were compared for accuracy. The MLE methodology was also applied to a real-world collection of chronic lymphocytic leukemia (CLL) data. MLE of studies including IPD response data were similar (shape=0.40, scale=0.19; HR=0.89) to the actual values. The scenario with 5 studies including IPD with aggregate response also resulted in similar estimates. The final scenario including 4 studies with median survival data resulted in estimations with largest standard errors and HR=0.80. MLE-estimated HR of treatment response from published CLL data was 0.27. MLE methodology can meta-analyze multiple types of published data to estimate the overall effect of treatment response (or other covariates) on survival outcomes.