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...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.展开更多
文摘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.
