<abstract xmlns="http://www.w3.org/1999/xhtml"><p>Even a small percentage of missing data can cause serious problems with analysis, reducing the statistical power of a study and leading to wrong conclusions being drawn. In the case of monitoring a woman’s monthly cycle, missing entries can appear even in a woman experienced in fertility awareness methods. Due to the fact that in a system of controlling a woman’s fertility, it is the most important to predict the day of ovulation and, ultimately, to determine the fertile window as much precisely as possible, much attention should be paid to the quality of the used data. This paper presents the results of handling missing observations as far as predicting the time during the cycle when a woman can become pregnant is concerned. Data taken from a multinational European study of daily fecundability was used to learn the quantitative part of the variety of a higher-order dynamic Bayesian network modeling a woman’s monthly cycle. The main goal of this paper is to examine whether omitting observations has an influence on the model’s reliability. The accuracy of comparison was examined based on two measures: the average percentage length of the infertile time during the monthly cycle and average percentage of days inside the fertile window classified as infertile.</p></abstract>

Even a small percentage of missing data can cause serious problems with analysis, reducing the statistical power of a study and leading to wrong conclusions being drawn. In the case of monitoring a woman’s monthly cycle, missing entries can appear even in a woman experienced in fertility awareness methods. Due to the fact that in a system of controlling a woman’s fertility, it is the most important to predict the day of ovulation and, ultimately, to determine the fertile window as much precisely as possible, much attention should be paid to the quality of the used data. This paper presents the results of handling missing observations as far as predicting the time during the cycle when a woman can become pregnant is concerned. Data taken from a multinational European study of daily fecundability was used to learn the quantitative part of the variety of a higher-order dynamic Bayesian network modeling a woman’s monthly cycle. The main goal of this paper is to examine whether omitting observations has an influence on the model’s reliability. The accuracy of comparison was examined based on two measures: the average percentage length of the infertile time during the monthly cycle and average percentage of days inside the fertile window classified as infertile.

Handling Missing Entries in Monitoring a Woman’s Monthly Cycle and Controlling Fertility

Studies in Logic, Grammar and Rhetoric

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Even a small percentage of missing data can cause serious problems with analysis, reducing the statistical power of a study and leading to wrong conclusions...

Handling Missing Entries in Monitoring a Woman’s Monthly Cycle and Controlling Fertility

Published Online: Mar 16, 2019

Page range: 75 - 90

DOI: https://doi.org/10.2478/slgr-2018-0042

© 2018 Anna Łupińska-Dubicka, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.