How can data mining be used by pharmaceutical companies, CROs and marketers in lieu of conducting a costly clinical trial
How can data mining be used by pharmaceutical companies, CROs and marketers in lieu of conducting a costly clinical trial
Instructions:
Peer Responses:
- Start with a formal salutation; use your peer’s proper name
- Do not start with something like “Hey I really loved your post!” or “I agree completely!”
- Lead with (1) sentence restating the peer’s main point and express agreement/disagreement with a specific part of their point
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- Do Not: simply repeat the points you made in your initial posts – peer responses need to include fresh content and references
- References: a minimum of (1) (with APA 7th Edition formatting)
- Length: approximately (2) full paragraphs
Kimberly
Question 1
Data mining while expensive to get started can help companies in the long run. Being able to use data mining in lieu of recruiting patients helps speed up the process. According to Loftus (2019), the article stated that if data mining was not used, they would have to enroll at least 50% more patients in the trial to just have a full control arm. With data mining, you are not having to pay subject stipends, etc. so you are saving money in the long haul. Data mining is also useful in identifying different patterns. While researchers are conducting the studies on new products and medications data mining can be on going in the background and ready for then the researchers want to look at and compare that information to. The article also stated that the FDA had approved the use of Ibrance in men with information from prior clinical trials in women (Loftus, 2019). This was able to be looked at and determined by data mining instead of conducting another clinical trial in which would cost Pfizer more money.
Question 2
One pro of data mining is that it can potentially help shorten the length on some clinical trials, leading to medications being brought to the market quicker. Using mined data while unlikely to be used to the initial approval of a new drug, it will help with other trials such as a new use of an already approved drug (Loftus, 2019). Being able to use data mining helps to take out the time that it takes to find the patients needed for clinical trials. Finding the right people for trials can be time consuming and thus sometimes the need for an extension to be able to gather the needed data. If data mining is used, this illuminates the need to find patients who fit the criteria and getting them to participate. Which leads me to, in this case, subject participation is less likely to stop the study from being completed. Because while some people aren’t concerned with participating in clinical trials, there are some perceptions that clinical trials use people as guinea pigs and that leads people to being skeptical and the researchers having a harder time finding subjects. Being able to use data that has already been identified and found speeds this process along, and more time can be spent on other areas of the study. Data mining also allows for the creation of patterns found over time (Lombardo, 2017).
A con of data mining could be that the potential data is very outdated. Data mining while it does give you information from studies previously performed may not give you the most up to date information to compare to. And while some of those patients’ data can be used, in the case of something like genetic markers, or cancerous cells, it would not give you the most recent data on these items. Another con of data mining is that the start up costs can be expensive and not all companies will have the funds to be able to pay for the startup (Lombardo, 2017). You also need to have someone that knows what to look for and how to run the system and be there to answer any questions if needed (Lombardo, 2017). You also must take into consideration the security of the data and making sure that it stays safe and secure and that access to the system is limited to only those that are using it. As part of the study startup system to be able to mine for data, you should have a virus protector, etc. on the system which can get costly to protect all parts (Lombardo, 2017).
References:
Loftus, P. (2019, December 23). Drugmakers turn to data mining to avoid expensive, lengthy drug trials. The Wall Street Journal. Retrieved January 31, 2022, from https://www.wsj.com/articles/drugmakers-turn-to-da…
Lombardo, C. (2017, January 15). Pros and cons of Data Mining. Vision Launch Media. Retrieved January 31, 2022, from https://visionlaunch.com/pros-and-cons-of-data-min…
Melissa
Question 1: How can data mining be used by pharmaceutical companies, CROs and marketers in lieu of conducting a costly clinical trial
Data mining is a technique used to utilize existing available data for analysis. The article discusses that pharma companies will analyze current medical record data. For example, they may pull a specific group of individuals all prescribed the same medication and review for outcomes and overall health improvement. “Instead of finding trial subjects, companies simply mine hospital and doctor files for cases where patients already took a drug in routine medical care, looking for changes in blood pressure, tumor size and other readings to see if the medicine is helping or causing a side effect” (Loftus, 2019). The thought is that time and cost related to a full clinical trial would be reduced.
With the advancement of technology, pharma companies are developing new ways to accelerate new therapies for patients. “Data mining, which is knowledge discovery from large sets of data, helps pharma firms to discover patterns in improving the quality of drug discovery and delivery methods” (Ranjan, 2009). Companies may use data on a spectrum. Data may be fully utilized to attempt to bring about new therapies. Data may also be used for marketing purposes to see where healthcare and treatment needs are. The data can be used for submission to FDA to advance treatments. The key to data mining is that the use and analysis of data is only beneficial is the quality of data is present.
Question 2: 1 pro/1 con of data mining
For data mining, a pro is related to the time frame required for clinical trials. “This real world evidence is sometimes used in lieu of a clinical trial’s control arm, to compare outcomes for past patients who got a standard treatment against people who are taking a new drug in a clinical trial” (Loftus, 2019). By analyzing current data, participants may have access to new therapies sooner for currently unapproved indications. By utilizing currently available data, you are also decreasing the amount of risk of new therapy exposure to participants. This can improve healthcare treatment and outcomes.
For data mining, a con is the removal of standard clinical trials. In the article, it discusses the fact that data mining cannot fully replace the standard path of a clinical trial. Medical record data is not captured in the same rigor as a clinical trial. This can impact safety information capturing and side effects. “Some doctors worry about forsaking clinical trials, which are carefully designed and conducted in patients to get a sound read on a drug’s safety and efficacy” (Loftus, 2019). Within data mining, there may not be enough relevant data to fully analyze for beneficial impact. Data mining can be beneficial depending on the type of situation.
References
Loftus, P. (2019, December 23). Drugmakers turn to data mining to avoid expensive, lengthy drug trials. The Wall Street Journal. Retrieved February 11, 2022, from https://www.wsj.com/articles/drugmakers-turn-to-da…
Ranjan, J. (2009). Data Mining in Pharma Sector: Benefits. International journal of health care quality assurance. Retrieved February 11, 2022, from https://pubmed.ncbi.nlm.nih.gov/19284173/
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Subject: Health & Medical
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