Siangphoe Umaporn, Wheeler David C
Department of Biostatistics, Virginia Commonwealth University, Richmond, VA, USA.
Cancer Inform. 2015 Apr 29;14(Suppl 2):107-16. doi: 10.4137/CIN.S17300. eCollection 2015.
Generalized additive models (GAMs) with bivariate smoothing functions have been applied to estimate spatial variation in risk for many types of cancers. Only a handful of studies have evaluated the performance of smoothing functions applied in GAMs with regard to different geographical areas of elevated risk and different risk levels. This study evaluates the ability of different smoothing functions to detect overall spatial variation of risk and elevated risk in diverse geographical areas at various risk levels using a simulation study. We created five scenarios with different true risk area shapes (circle, triangle, linear) in a square study region. We applied four different smoothing functions in the GAMs, including two types of thin plate regression splines (TPRS) and two versions of locally weighted scatterplot smoothing (loess). We tested the null hypothesis of constant risk and detected areas of elevated risk using analysis of deviance with permutation methods and assessed the performance of the smoothing methods based on the spatial detection rate, sensitivity, accuracy, precision, power, and false-positive rate. The results showed that all methods had a higher sensitivity and a consistently moderate-to-high accuracy rate when the true disease risk was higher. The models generally performed better in detecting elevated risk areas than detecting overall spatial variation. One of the loess methods had the highest precision in detecting overall spatial variation across scenarios and outperformed the other methods in detecting a linear elevated risk area. The TPRS methods outperformed loess in detecting elevated risk in two circular areas.
具有双变量平滑函数的广义相加模型（GAMs）已被应用于估计多种癌症风险的空间变异。仅有少数研究评估了GAMs中应用的平滑函数在不同高风险地理区域和不同风险水平下的性能。本研究通过模拟研究评估不同平滑函数在不同风险水平下检测不同地理区域风险总体空间变异和高风险的能力。我们在一个方形研究区域内创建了五种具有不同真实风险区域形状（圆形、三角形、线性）的情景。我们在GAMs中应用了四种不同的平滑函数，包括两种薄板回归样条（TPRS）和两个版本的局部加权散点图平滑（loess）。我们使用排列方法通过偏差分析检验风险恒定的原假设并检测高风险区域，并基于空间检测率、灵敏度、准确性、精确性、功效和假阳性率评估平滑方法的性能。结果表明，当真实疾病风险较高时，所有方法都具有较高的灵敏度和始终适中到较高的准确率。这些模型在检测高风险区域方面通常比检测总体空间变异表现更好。其中一种loess方法在跨情景检测总体空间变异方面具有最高的精确性，并且在检测线性高风险区域方面优于其他方法。TPRS方法在检测两个圆形区域的高风险方面优于loess。
