Lazar Ann A, Gansky Stuart A, Halstead Donald D, Slajs Anthony, Weintraub Jane A
The University of California, San Francisco (UCSF) School of Dentistry, Division of Oral Epidemiology and Dental Public Health, & School of Medicine, Division of Biostatistics, USA.
The University of California, San Francisco (UCSF) School of Dentistry, Division of Oral Epidemiology and Dental Public Health, USA.
J Dent Oral Craniofac Epidemiol. 2013 Oct 1;1(3):19-33.
Because each patient's baseline (pre-treatment) characteristics differ (e.g., age, sex, socioeconomic status, ethnicity/race, biomarkers), treatments do not work the same for every patient-some can even cause detrimental effects. To improve patient care, it is critical to identify such heterogeneity of treatment effects. But the standard analytic approach dichotomizes baseline characteristics (low vs. high) which often leads to a loss of critical patient-care information and power to detect heterogeneity, as the results may depend strongly on the cut-points chosen. A more powerful analytic approach is to analyze baseline characteristics (i.e., covariates) measured on a continuous scale that retains all of the information available for the covariate.
In this article, we show how the Johnson-Neyman (J-N) method can be used to identify the prognostic and predictive value of baseline covariates measured on a continuous scale - findings that often cannot be determined using the traditional dichotomized approach. As an example, we used the J-N method to explore treatment effects for varying levels of the biomarker salivary mutans streptococci (MS) in a randomized clinical prevention trial comparing fluoride varnish with no fluoride varnish for 376 initially caries-free high-risk children, all of whom received oral health counseling.
The J-N analysis showed that children with higher baseline MS values who were randomized to receive fluoride varnish had the poorest dental caries prognosis and may have benefitted most from the preventive agent.
Such methods are likely to be an important tool in the field of personalized oral health care.
由于每位患者的基线（治疗前）特征各不相同（例如年龄、性别、社会经济地位、种族/民族、生物标志物），治疗方法对每个患者的效果并不相同——有些治疗甚至可能产生有害影响。为改善患者护理，识别这种治疗效果的异质性至关重要。但标准的分析方法会将基线特征进行二分法划分（低 vs. 高），这往往会导致关键的患者护理信息丢失以及检测异质性的能力丧失，因为结果可能强烈依赖于所选择的切点。一种更强大的分析方法是分析以连续尺度测量的基线特征（即协变量），这样可以保留协变量的所有可用信息。
在本文中，我们展示了如何使用约翰逊 - 奈曼（J - N）方法来识别以连续尺度测量的基线协变量的预后和预测价值——这些结果通常无法使用传统的二分法来确定。作为一个例子，我们在一项随机临床预防试验中使用J - N方法，探讨生物标志物唾液变形链球菌（MS）不同水平的治疗效果，该试验比较了376名最初无龋的高危儿童使用氟化物清漆与不使用氟化物清漆的情况，所有儿童均接受了口腔健康咨询。
J - N分析表明，基线MS值较高且被随机分配接受氟化物清漆的儿童龋齿预后最差，并且可能从预防剂中获益最多。
此类方法可能会成为个性化口腔保健领域的一项重要工具。
