Beydoun May A, Kaufman Jay S, Ibrahim Joseph, Satia Jessie A, Heiss Gerardo
1Center for Human Nutrition, Department of International Health, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA.
BMC Med Res Methodol. 2007 Sep 14;7:41. doi: 10.1186/1471-2288-7-41.
We aimed at assessing the degree of measurement error in essential fatty acid intakes from a food frequency questionnaire and the impact of correcting for such an error on precision and bias of odds ratios in logistic models. To assess these impacts, and for illustrative purposes, alternative approaches and methods were used with the binary outcome of cognitive decline in verbal fluency.
Using the Atherosclerosis Risk in Communities (ARIC) study, we conducted a sensitivity analysis. The error-prone exposure - visit 1 fatty acid intake (1987-89) - was available for 7,814 subjects 50 years or older at baseline with complete data on cognitive decline between visits 2 (1990-92) and 4 (1996-98). Our binary outcome of interest was clinically significant decline in verbal fluency. Point estimates and 95% confidence intervals were compared between naive and measurement-error adjusted odds ratios of decline with every SD increase in fatty acid intake as % of energy. Two approaches were explored for adjustment: (A) External validation against biomarkers (plasma fatty acids in cholesteryl esters and phospholipids) and (B) Internal repeat measurements at visits 2 and 3. The main difference between the two is that Approach B makes a stronger assumption regarding lack of error correlations in the structural model. Additionally, we compared results from regression calibration (RCAL) to those from simulation extrapolation (SIMEX). Finally, using structural equations modeling, we estimated attenuation factors associated with each dietary exposure to assess degree of measurement error in a bivariate scenario for regression calibration of logistic regression model.
Attenuation factors for Approach A were smaller than B, suggesting a larger amount of measurement error in the dietary exposure. Replicate measures (Approach B) unlike concentration biomarkers (Approach A) may lead to imprecise odds ratios due to larger standard errors. Using SIMEX rather than RCAL models tends to preserve precision of odds ratios. We found in many cases that bias in naïve odds ratios was towards the null. RCAL tended to correct for a larger amount of effect bias than SIMEX, particularly for Approach A.
我们旨在评估通过食物频率问卷得出的必需脂肪酸摄入量的测量误差程度，以及校正此类误差对逻辑模型中比值比的精度和偏差的影响。为了评估这些影响，并用于说明目的，我们使用了替代方法和手段，以言语流畅性方面认知能力下降的二元结果进行分析。
利用社区动脉粥样硬化风险（ARIC）研究，我们进行了一项敏感性分析。易出错的暴露因素——访视1时的脂肪酸摄入量（1987 - 1989年）——可用于7814名50岁及以上的受试者，这些受试者在基线时拥有关于访视2（1990 - 1992年）和访视4（1996 - 1998年）之间认知能力下降的完整数据。我们感兴趣的二元结果是言语流畅性出现具有临床意义的下降。比较了随着脂肪酸摄入量占能量的百分比每增加一个标准差，朴素比值比和经测量误差调整后的下降比值比之间的点估计值和95%置信区间。探索了两种调整方法：（A）与生物标志物（胆固醇酯和磷脂中的血浆脂肪酸）进行外部验证，以及（B）在访视2和访视3时进行内部重复测量。两者的主要区别在于，方法B在结构模型中对缺乏误差相关性做出了更强的假设。此外，我们将回归校准（RCAL）的结果与模拟外推（SIMEX）的结果进行了比较。最后，使用结构方程模型，我们估计了与每种饮食暴露相关的衰减因子，以评估在逻辑回归模型回归校准的双变量情况下的测量误差程度。
方法A的衰减因子小于方法B，这表明饮食暴露中的测量误差量更大。与浓度生物标志物（方法A）不同，重复测量（方法B）可能由于标准误差较大而导致比值比不精确。使用SIMEX而非RCAL模型倾向于保持比值比的精度。我们发现在许多情况下，朴素比值比的偏差倾向于无效值。RCAL往往比SIMEX校正更大程度的效应偏差，特别是对于方法A。
