Key result
Using a sample selection bias model on observational health checkup data, taking blood pressure medicines was associated with a 7.9 mmHg decrease in systolic blood pressure, though simpler methods yielded contradictory results.
Why the study?
Does taking blood pressure control medicines reduce systolic blood pressure in an observational cohort of employees and their families?
Observational (n=113,979)
Does taking blood pressure control medicines reduce systolic blood pressure in an observational cohort of employees and their families?
Observational health checkup data, when analyzed with appropriate statistical methods to correct for endogeneity and sample selection bias, can estimate the blood pressure-lowering effects of medications as an alternative to randomized controlled trials.
Background: Many people take medicines to control high blood pressure (BP), or hypertension. Randomized clinical trials (RCT) are usually used for the evaluation of effects of medicines. However, RCT have some serious problems. Data and Methods: We evaluated the effects of BP medicines in Japan using a dataset containing 113,979 cases. We employed four statistical methods in the analysis. First, we simply compared the systolic blood pressure (SBP) of individuals with and without BP medicines. We then used a regression model with a dummy variable, representing taking medicines or not. We replaced the dummy variable by its expected value, and estimated the regression model again. Finally, we selected individuals who had both taken and not taken medicines at different times. The effect of sample selection was also considered in the estimation. Results: For the simple comparison, SBP with BP medicines was 11 mmHg higher than without medicines. In the next regression analysis, SBP with BP medicines was still 5 mmHg higher. When the dummy variable was replaced by its expected value, SBP with medicines decreased by 7 mmHg. For individuals taking medicines at some times and not at others, SBP decreased by 9 and 8 mmHg in models with and without a sample bias correction, respectively. Conclusion: The methods eliminated some problems of RCT and might be attractive. However, we obtained contradictory conclusions depending on the statistical methods employed, despite using the identical dataset. Statistical methods must be selected carefully to obtain a reliable evaluation. Limitations: The dataset was observatory, and the sample period was only 3 years.
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Nawata et al. (2018) conducted an observational in Hypertension (n=113,979). Blood pressure control medicines vs. No blood pressure medicines was evaluated on Systolic blood pressure (SBP) (MD -7.94 mmHg, p=<0.01). Using a sample selection bias model on observational health checkup data, taking blood pressure medicines was associated with a 7.9 mmHg decrease in systolic blood pressure, though simpler methods yielded contradictory results.
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