Health care funding decisions and real world benefits: reducing bias by matching untreated patients


14 June 2021 - Governments and health insurers often make funding decisions based on health gains from randomised controlled trials. 

These decisions are inherently uncertain because health gains in trials may not translate to practice owing to differences in the population, treatment use and setting. 

Post-market analysis of real world data can provide additional evidence but estimates from standard matching methods may be biased when unobserved characteristics explain whether a patient is treated and their outcomes. 

The authors propose a new untreated matching approach that can reduce this bias.

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Michael Wonder

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Michael Wonder