When extracting data from the electronic database, we excluded all missing data, including missing data on accompanying symptoms.
In this case study, the automation of data processing and geoprocessing of projects data was complicated by a myriad of data quality issues; including missing data, inaccurate data, asymmetric data granularity, and semantic interoperability issues.
Also, there were some database limitations, including missing data on biological and clinical variables (e.g., albumin level or performance status) meaning that calculations on the prognostic value of these variables may be affected.
However, there are several important challenges which present themselves when using this data for research, including missing data, reporting/recording problems, limited access to data, the time lag between data collection and release, and the limited number of variables collected (see Hampton 2016; Zeng et al. 2016).
Including missing data concerning infertility treatment as having no infertility treatment did not change the results [adjusted OR (95%CI) for insemination and ICSI/IVF treatment 3.13 (1.32–7.41) and 0.75 (0.23–2.52), respectively].
aNot including missing data.
All data were double-entered into a database and systematically reviewed to minimize errors and include missing data.
The types of noise artifacts and corruptions that these signals suffer include, missing data, physical activities, muscle artifacts (MA), electro-magnetic interference (EM), and baseline wander (BW [1, 5].
Collection of this missing information is immediately sought to complete the record; hence few records include missing data.
Hence, we included missing data in our response but still could not attribute a positive effect to auxiliary variables.
