Rao Arni S R Srinivasa, Diamond Michael P
1 Biostatistics & Epidemiology, Medical College of Georgia, Augusta University, Augusta, GA, USA.
2 Section of Infectious Diseases, Department of Medicine, Medical College of Georgia, Augusta University, Augusta, GA, USA.
Reprod Sci. 2017 Nov;24(11):1538-1543. doi: 10.1177/1933719117692448. Epub 2017 Feb 12.
We are proposing to use Markov modeling type of analysis to understand data generated by treatments for infertility in women receiving ovarian stimulations. We describe the conceptual novelties, need for such an analysis, basics of the proposed methods, and theoretical constructions of various probabilities associated with practical level implementation of the Markov modeling procedures. This method can be adopted to infertility-related data visualizations whenever progression of outcome stages in infertility treatment is recorded. These methods if implemented should be able to enhance the understanding of treatment impacts of gonadotropins, clomiphene citrate, or an aromatase inhibitor at the beginning of treatment cycles of infertile women. This framework will be very useful for infertility treatment practitioners to compute the values of success rates of treatment for total population or population divided by demographic, clinical, and genetic factors. These methods can be continuously updated with newer data and translated into a mobile app to be used by clinical practitioners.
我们提议使用马尔可夫模型分析类型来理解接受卵巢刺激的不孕女性治疗所产生的数据。我们描述了概念上的新颖之处、进行此类分析的必要性、所提议方法的基础以及与马尔可夫建模程序实际层面实施相关的各种概率的理论构建。只要记录了不孕治疗结果阶段的进展情况，这种方法就可用于与不孕相关的数据可视化。如果实施这些方法，应该能够增强对促性腺激素、枸橼酸氯米芬或芳香化酶抑制剂在不孕女性治疗周期开始时治疗效果的理解。这个框架对于不孕治疗从业者计算总体人群或按人口统计学、临床和遗传因素划分的人群的治疗成功率值将非常有用。这些方法可以用更新的数据不断更新，并转化为移动应用程序供临床从业者使用。
