TY - JOUR
T1 - Role of Markov Modeling Approaches to Understand the Impact of Infertility Treatments
AU - Rao, Arni S.R.Srinivasa
AU - Diamond, Michael P.
N1 - Funding Information:
The author(s) disclosed receipt of the following financial support for the research, and/or publication of this article: M.P.D. is supported by NIH/Eunice Kennedy Shriver National Institute of Child Health and Human Development (U10 HD39005).
Publisher Copyright:
© The Author(s) 2017.
PY - 2017/11/1
Y1 - 2017/11/1
N2 - 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.
AB - 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.
KW - infertility data
KW - modeling
KW - probability of conception
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U2 - 10.1177/1933719117692448
DO - 10.1177/1933719117692448
M3 - Article
C2 - 29017436
AN - SCOPUS:85031306184
SN - 1933-7191
VL - 24
SP - 1538
EP - 1543
JO - Reproductive Sciences
JF - Reproductive Sciences
IS - 11
ER -