Sponsor Key Performance Indicators - Collaboration

Sponsor Performance KPI - Collaboration

In part 1 of the sponsor KPI's, we learned that the number of studies completed and number of studies for which the results were posted are two important indicators to gauge the performance of a sponsor. In this series we will see other metrics related to lead times and collaboration, and also try to dig deeper into why collaboration is important.

Clinical research is a capital intensive business with very high lead times and very low success rate, which makes it one of the riskier businesses. Clinical trials requires high investments in financial capital as well as in human capital. The lead time (time taken for completing all 3 phases of study before the drug gets approval from FDA) is in the range of 8-13 years. Historically, the average time to complete a study phase has been around 2.62 years. However, phase 1 trials are shorter and takes around 1.84 years as compared to phase 2 and phase 3 studies. The longer a study goes, higher would be the costs associated. Hence, it is in the interest of sponsors and end consumers (patients) to reduce the lead time in order to reduce the prices of patented drugs. 

In order to understand how can the lead time be reduced, we first need to understand the factors that affect the duration of a study. Sponsors and CRO's have developed various methods to increase operational efficiencies based on their long experiences in designing and conducting a study. There are possibilities for improvements there, however, I will not discuss them here. 

The availability of sufficient patients(subjects) to conduct a trial is an important factor. The process of recruiting is time-consuming. Broadly, there are two ways sponsors find patients. 

Firstly, sponsors advertise through various channels to find patients. Social media like facebook, and other digital channels like blogs and websites have picked up in advertising for recruiting clinical trials. Sponsors/CRO's most often pay some compensation for patient's time and travel. This compensation vary from $500 to $9000. 
The second method of recruiting patients is through physician networks. Sponsors/CRO's have physicians in their network that have a database of their patients. 
Out of many who show interest in participating in a trial, many patients fail to clear the screening process and become ineligible to enroll. 
Despite all the efforts from sponsors, they are not able to enroll enough patients. Insufficient enrollment forces sponsors to close many sites(facility like a hospital or physician clinic). 
Finally, insufficient sites results in discontinuation of the study.
The competition to recruit patients has increased with many sponsors competing with other sponsors recruiting for the same patient profile. Also, the sponsors have different competencies. For example, commercial industry sponsors have better operational efficiencies and global network(below figure shows that industry sponsors are doing more global studies) where as non-industry sponsors like hospitals have strong patient base but limited to a local geographical location. 

Therefore, it makes sense for sponsors to collaborate more with other sponsors and leverage their strengths for a win-win situation. 
For example, for a local hospital or research university conducting a study on diabetes in Raleigh, it make sense to collaborate with other non-industry sponsors in other cities to increase the number of study sites that increases diversity in the patient population which ultimately increases the quality of entire study. 
For the same local hospital, collaborating with industry sponsors having a global network and expertise in conducting trials in different countries, can not only help them expand their patient base but also leverage their expertise in conducting a global study with high operational efficiency.
There is a greater need for sponsors to collaborate instead of competing for recruiting patients. They should come together and explore possibilities of combining studies together or sharing detailed data they have obtained in previous trials. 
Now we know why collaboration for sponsors is important, let's see how the industry and non-industry sponsors have been collaborating.
I have created an interactive sponsor dashboard for sponsors who have completed at least 10 studies, mainly to eliminate very small sponsors and to reduce the data. Hover over the data points to see details.
On KPI-1 tab in the dashboard, you would see chart that show the collaboration as shown in the figure below:

  
Few observations are that only some bigger industry sponsors are collaborating more with non-industry. The non-industry sponsors have collaborated with non-industry sponsors more as compared to industry sponsors.
Till next time!       

Data Preparation using R

Data Analytics - Data Preparation using R

Data preparation is an important step in data science. Before you can start analysis, we need to ingest the required data and then clean and transform it so that it becomes more easy to perform analytics.

R is an open source and powerful software having reliable packages and libraries to perform data manipulation tasks.

I have downloaded the clinical trials data from ACCT website and used that to show data preparation steps. You can reference the complete code published here in a R Markdown document:

http://rpubs.com/kalehdoo/Data_Preparation

Let's walk through the code and understand how to do it. You may want to open the link mentioned above in a separate tab since I will not mention the code here again and will simply refer to the document. 

You would need to first download the data files from ACCT website download tab and unzip them into a folder in your local directory.

Installing the required packages is the first step. 'dplyr' is an important library package to perform data manipulation and 'lubridate' provides important functions to perform operations with date columns.

Save the directory paths into variables. Notice that the '\' in the path are replaced with '/'. 

read.csv function is used to read the pipe delimited data files into dataframes. Another option is to use read.table function can also be used but I found read.csv works better for me. I encountered errors reading records but read.csv worked without any issues for the same data set.

studies table has lots of columns that I did not require for the analysis, so I created a subset with the required columns only. 

subset.data.frame function allows to select the required columns using 'select' parameter. You can also provide any data filter using 'subset' parameter specifying the filter condition if you want a subset of rows.

I need to create new formula columns into the subset of studies created. mutate function helps to do that.

Another function which I found helpful in doing a wildcard search is 'grepl' function.

summarise function is helpful in creating aggregations.

Hope you will find this useful.

Sponsor Key Performance Indicators - Part 1

Sponsor Performance KPI - Part 1

Sponsors are the key stakeholders in clinical trials. It is important to measure the performance of sponsors to understand the trends in the industry and market. Metrics such as studies registered, studies completed, study results posted, collaboration and study completion duration can be key performance indicators from study conduct perspective. We can gain competitive intelligence about competitor sponsors or find opportunities to collaborate with potential partners. The possibilities are endless. 
Let's see who were top Industry sponsors in terms of studies completed in 2018 ranked by number of completed studies. 

There are 4,017 studies completed by industry sponsors in 2018. Novartis is leading the board with 114 studies completed in 2018, followed by GSK and Pfizer with 96 and 92 studies respectively. The chart also shows the number of results posted by those sponsors in 2018. There may be studies completed in 2018 for which the results are not posted yet, but I am not aware of any requirements for posting results. Another metric is the ration of completed studies to the studies with posted results. There should be a linear relationship since more completed studies would mean more posted results. To just verify that, let's create a scatter plot. There is a strong linear relationship. The R-square value is pretty high. See the plot below with a fitted line. It may be interesting to look at sponsors with very low ratio.
  

Now, let's take a look at what's going on with non-industry sponsors in the same year 2018.

NIH, Mayo clinic and Duke University are the top 3 performers. 

The studies completed numbers are comparable  but the posted results and the ratio is very low when compared to sponsors from industry segment. The slope is 0.13 as compared to 0.3 for industry sponsors. So, we see that industry sponsors are posting more results. 

   
We can see that the industry sponsors(blue) have higher posted results for same levels of completed studies as compared to the non-industry sponsors(red).
I am really trying to think why non-industry sponsors have low results postings than industry sponsors. 
Every study is required to submit the results, generally no later than 12 months of completion.
The clinicaltrials.gov explains what all is included in the results and also mentions few valid reasons when the results are not submitted:
https://clinicaltrials.gov/ct2/about-site/results#DisplayOfResults 

Good news!!! I have recreated sponsor analytics using flexdashboards and plotly R so that you all can interact instead of viewing static jpeg images. The charts are very interactive and you can view individual data points. However, in the analysis, I have only included sponsors that have completed at least 10 studies, to reduce the number of data points as well as the skewness caused by them since there were a large numbers of sponsors within that range. I hope you would enjoy that.
Link to Dashboard:
http://rpubs.com/kalehdoo/SponsorDashboard

Summary -There are a total of 2234 sponsors who have completed at least 10 studies in the past out of which there are 647 (29%) sponsors from Industry and the remaining 1587 (71%) from non-industry.
Non-industry sponsors are further classified into Academic and Hospital based on their names (this may not be 100% correct and you may notice some sponsors classified incorrectly).
Keep reading!!!