Update posts/2024-09-03_university_undergraduate_report/university_un…

…dergraduate_report.Rmd

Co-authored-by: StefanThoma <[email protected]>

posts/2024-09-03_university_undergraduate_report/university_undergraduate_report.Rmd

@@ -32,7 +32,7 @@ Throughout the trainStats documentation, I have primarily used the `adoe_ophtha`
 
 The objective of the trainStats was not solely about investigating the data in detail and learning statistical theory, but instead  highlighting the application of fundamental mathematical theory to the pharmaceutical sector as a whole, and help users familiarise themseleves with ADaM datasets. My favourite element of the package was that the format of both synthetic ADaM datasets were incredibly similar to that of a true clinical trial ADaM for a study in Ophthalmology. 
 
-To further develop and improve the pharamverse package, I believe including more endpoints in the "adoe_ophtha" dataset would be invaluable for future application and statistical analyses. Often adoe datasets have several endpoints but the "adoe_ophtha" dataset only included 2 clinical parameters namely "Central Subfield Thickness" and "Diabetic Retinopathy Severity Scale". In addition, since the data is synthetic and randomly generated, the outputs had no significant correlations or trends from a statistical perspective in terms of disease progression or measures of central tendencies. Although, in this case, the emphasis was on understanding logic and reasoning whilst programming the statistical outputs, I experienced difficulties analysing the data quantitatively in my university report due to the high variation in data. Going forward, if there is a method to simulate the data less randomly, then that may be more useful for future analyses on pharamverse data. 
+To further develop and improve the pharamverse package, I believe including more endpoints in the "adoe_ophtha" dataset would be invaluable for future application and statistical analyses. Often adoe datasets have several endpoints but the "adoe_ophtha" dataset only included 2 clinical parameters namely "Central Subfield Thickness" and "Diabetic Retinopathy Severity Scale". In addition, since the data is synthetic and randomly generated, the outputs had no significant correlations or trends from a statistical perspective in terms of disease progression or measures of central tendencies. Although, in this case, the emphasis was on understanding logic and reasoning whilst programming the statistical outputs, I experienced difficulties analysing the data quantitatively in my university report due to the high variation in data. Going forward, if there is a method to simulate the data in a more realistic way, then that may be more useful for future analyses on pharamverse data. 
 
 Overall, my experience of using the pharamverse and pharmaverseadam package for the first time was excellent. The package was convenient to use in R Studio, and clearly formatted for multi-purpose use. I would definitely recommend using pharamverse to all users in the industry, who are required to produce a piece of project work or any analyses/summary for external use, or even those keen to publicly publish articles and papers in their areas within pharma to the wider community, in a safe and responsible manner regarding external use of data. I would like to thank Ross Farrugia for introducing me to the package, and especially Edoardo Mancini for talking me through the package and supporting me throughout the business project and university report.