Evaluation of Blood Pressure Control Medicines Using Health and Medical Checkup Data in Japan: Alternative Methods for Randomized Controlled Trials

Abstract

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.

An Analysis of Blood Pressure Situations in Japan Using the Large-Scale Medical Checkup Dataset

“…So, we cannot directly use Antihypertensive (taking antihypertensive drugs: 1; 0: otherwise) dummy because of the endogeneity problem (actually, if we directly use Antihypertensive in the regression equation, its estimate becomes + 4.80 mmHg). Nawata, Sekizawa and Kimura [34] suggested a method to take the expected value of the variable to solve the endogeneity problem, the following model evaluating the effect of Antihypertensive. (Model 1C):…”

“…They found that the "white coat effect" was significant and suggested that the careful payments for upward errors of BP measurements were necessary. Nawata, Sekizawa and Kimura [34] pointed out the problems of the previous BP studies. They are: since the participants and doctors (or researchers) can easily know that the groups (treated or controlled) that they are belonging, the double blinded randomized clinical trials are impossible for the BP studies; trials with positive results are more likely to be published than those with negative or questionable results; researchers themselves might not have strong incentives to publish when the expected results are not obtained; in many studies sponsored by the pharmaceutical companies, biases toward the sponsor's products and conflicts of interest (CI) might occur; and practices are often terminated in the early stages for various reasons especially when expected results are not obtained (termination or endpoint biases).…”

An Analysis of the Effects of Blood Pressure and Antihypertensive Drugs on Heart Disease

“…However, questions have arisen about the results of previous studies, as pointed out by Nawata, Sekizawa, and Kimura [47]. Biases such as publication, conflict of interest, and termination (or endpoint) biases might exist in these studies.…”

“…(1: with anamnesis; 0: otherwise) and Trend represents the time trend. For details, see Model 3 of Nawata, Sakizawa, and Kimura [47].…”

Empirical Studies of Effects of High Blood Pressure on Medical Costs and Heart Disease: Is the 2017 ACC/AHA Guideline Supported by Enough Evidence?

“…The selection criteria of 61 studies used in the meta-analysis were not clarified. As pointed out by Nawata Sekizawa and Kimura [13], biases such as publication [14] [15] biases and conflicts of interest [16] might have existed, and the results could have been affected by such biases even if all studies were proper RCTs. Moreover, BP levels were strongly affected by various factors such as the age, gender, health conditions, and lifestyles of the individuals.…”