THE IMPACT OF ESTIMATING MORTALITY RATES IN COST-EFFECTIVENESS ANALYSIS.: A SIMULATION STUDY

Purpose:

Current decision analytic modeling guidelines suggest the use of life tables for the derivation of all-cause mortality probabilities. There exist two main methods for estimating mortality probabilities; the period method, which is based on data from one year’s life table, and the cohort method which projects future mortality rates based on historical life tables. This simulation study aims at identifying the impact of using cohort versus period methods on the outcomes of cost effectiveness analyses.

Method:

A simulation study was designed based on a two-state Markov model (alive-death) that compared a hypothetical intervention against no intervention. The model was populated with age-specific all-cause mortality probabilities estimated using the period and cohort methods. Mortality and population data were extracted from the Human Mortality Database. The cohort mortality probabilities were estimated using the Lee-Carter method. The model outcomes were total costs, total life years (LY) and incremental net benefit (INB), assuming a hypothetical threshold of $50,000/LY. The proportional distance between the INBs of the two mortality estimation methods (pINB) was the outcome of each simulation. The following parameters were simultaneously varied: discounting rate (0- 0.07), intervention effect (relative risk of mortality: 0.5-0.9), age at intervention ( birth-40 years old), duration of intervention effect (1 year/10 years/ lifelong), acute/chronic intervention, time gap between intervention start and intervention effect (immediate/in 10 years). Simulations were conducted for two countries, one with an average increase in life expectancy (Canada) and one with rapid increase in life expectancy (Taiwan). The impact of each parameter on the pINB was measured as the proportion of total variation explained by the parameter.

Result:

The  mortality estimation method had a large impact on the pINB across the parameter combinations (0%-63.2%). The impact was greater when varying  age at intervention (21% of variation explained),  discount rates (8.8%),  countries (7%) and  time gap between intervention start and intervention effect (8%). The impact of the magnitude of the intervention effect was less pronounced. 

Conclusion:

When using cohort versus period methods, substantial differences were observed in model outcomes. Given that the magnitude and the direction of the impact of mortality estimation methods on the model outcomes is multifactorial, decisions on the mortality estimation method used in economic evaluations should be considered after conducting sensitivity analyses using both methods.