Balakrishnan Poojitha, Beaty Terri, Young J Hunter, Colantuoni Elizabeth, Matsushita Kunihiro
Department of Environmental Health Sciences, Columbia University School of Public Health, New York, New York, United States of America.
Department of Epidemiology, Johns Hopkins School of Public Health, Baltimore, Maryland, United States of America.
PLoS One. 2017 Jul 11;12(7):e0179234. doi: 10.1371/journal.pone.0179234. eCollection 2017.
Antihypertensive medications complicate studies of blood pressure (BP) natural history; BP if untreated ("underlying BP") needs to be estimated. Our objectives were to compare validity of five missing data imputation methods to estimate underlying BP and longitudinal associations of underlying BP and age. We simulated BP treatment in untreated hypertensive participants from Atherosclerosis Risk in Communities (ARIC) in visits 1-5 (1987-2013) using matched treated hypertensive participants. The underlying BP was imputed: #1, set as missing; #2, add 10 mmHg for systolic, 5 mmHg for diastolic; #3, add medication class-specific constant; #4, truncated normal regression; and #5, truncated normal regression including prior visit data. Longitudinal associations were estimated using linear mixed models of imputed underlying BP for simulated treated and measured BP for untreated participants. Method 3 was the best-performing for systolic BP; lowest relative bias (5.3% for intercept at age 50, 0% for age coefficient) and average deviation from expected (0.04 to -1.79). Method 2 performed best for diastolic BP; lowest relative bias (0.6% intercept at age 50, 33.3% age <60, 9.1% age 60+) and average deviation (-1.25 to -1.68). Methods 4 and 5 were comparable or slightly inferior. In conclusion, constant addition methods yielded valid and precise underlying BP and longitudinal associations.
抗高血压药物使血压（BP）自然史的研究变得复杂；需要对未治疗时的血压（“基础血压”）进行估计。我们的目标是比较五种缺失数据插补方法在估计基础血压方面的有效性，以及基础血压与年龄的纵向关联。我们利用匹配的已治疗高血压参与者，模拟了社区动脉粥样硬化风险研究（ARIC）中未治疗高血压参与者在第1 - 5次访视（1987 - 2013年）时的血压治疗情况。基础血压的插补方法如下：#1，设为缺失值；#2，收缩压加10 mmHg，舒张压加5 mmHg；#3，加上特定药物类别的常数；#4，截断正态回归；#5，包括上次访视数据的截断正态回归。使用插补的基础血压的线性混合模型对模拟治疗组进行纵向关联估计，对未治疗参与者使用测量的血压。方法3在收缩压方面表现最佳；相对偏差最低（50岁时截距为5.3%，年龄系数为0%），与预期的平均偏差为（0.04至 - 1.79）。方法2在舒张压方面表现最佳；相对偏差最低（50岁时截距为0.6%，年龄<60岁时为33.3%，年龄60岁及以上时为9.1%），平均偏差为（ - 1.25至 - 1.68）。方法4和5相当或稍差。总之，常数加法方法得出了有效且精确的基础血压及纵向关联。
