ADJUSTING VARIANCE PARAMETERS TO INCORPORATE UNCERTAINTY INTO HEALTH ECONOMICS MODELS FOLLOWING TREATMENT SWITCHING
Author(s)
Ray J1, Bennett I2, Paracha N2
1F. Hoffmann-La Roche, Basel, Switzerland, 2F. Hoffman-La Roche, Basel, Switzerland
Presentation Documents
OBJECTIVES: Trials designed to estimate the efficacy of new oncological treatments commonly permit patients switch from standard care (SC) to active treatment. Clinical regulators focus on the safety and efficacy of multiple endpoints, whereas clear demonstration of an overall survival (OS) gain is prioritized by many HTA agencies. When patients randomized to receive SC subsequently receive active treatment following disease progression, OS gains are confounded. Several statistical methods have been proposed to adjust for the impact of patients switching treatment to estimate counterfactual survival, one of which is the RPSFT method. Health economic models using these adjusted survival times may fail to introduce the additional uncertainty following the application of this method when implementing parametric survival analysis, increasing the likelihood of an incorrect decision. METHODS: Twenty datasets of 400 patients each were simulated assuming a Weibull distribution, where 70% of patients initially treated with SC switched to receive the active treatment following progression. Two scenarios were compared. Initially, Weibull survival estimates and covariance parameters were estimated using the RPSFT-adjusted survival times. These were compared with a similar approach with the addition of multiplying the components of the covariance matrix that involve uncertainty around the SC treatment parameter by the adjustment factor identified in the RPSFT statistical model for each individual simulated dataset. RESULTS: Mean undiscounted incremental life expectancy between the two scenarios were compared. Once the covariance matrices were sampled 5000 times using PSA, inflated confidence intervals in the second scenario suggested that failing to incorporate the additional uncertainty could lead to incorrect decisions. The probability of the new treatment being considered less efficacious compared to SC was 0.6% compared to 5.68% with appropriately accounting for the increased uncertainty. CONCLUSIONS: Failing to account for uncertainty when applying treatment switching methods in health economic models could misinform decisions determining patients’ access to treatments.
Conference/Value in Health Info
2016-10, ISPOR Europe 2016, Vienna, Austria
Value in Health, Vol. 19, No. 7 (November 2016)
Code
PRM197
Topic
Methodological & Statistical Research
Topic Subcategory
Confounding, Selection Bias Correction, Causal Inference
Disease
Oncology