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!!!
R and Python
There are times when we not only look at the descriptive analysis but also want to make future predictions based on the past trends. We will look at techniques that we can use to predict the number of studies submitted or registered in future years.
We will see how we can use some of the libraries like pandas, statsmodels and matplotlib in python.
the python code is available on my github repository here:
https://github.com/kalehdoo/clintrials/blob/master/ctrials_1.py
I will also try to explain the steps and procedures to perform the analysis later.
Here is the final outcome:
Model Summary:
Studies submitted predicted:
For 2019 : 31,022
For 2020 : 32,479
The data used for the regression is from 2005-2018.
There is still some time left for 2019 to complete so I will come back next year to compare the 2019 actuals with the predicted numbers here. The actuals for 2019 is 19,990 (data until Aug 2019) which was posted in one of the previous posts here.
I have also shown how to do regression using R programming, and also how to interpret the results. The link below has complete code and the analytics:
http://rpubs.com/kalehdoo/sponsor_analytics
Source data extracted from: https://aact.ctti-clinicaltrials.org
Study Activity Dashboard
In this post, we will try to understand the clinical study activities across the globe. We will gather some inputs like population, GDP and health spending as % of GDP. Then we compare different countries by their involvement in clinical studies. The clinical study activity is based on the clinical site in that country for a particular study. It is important to keep in mind that those clinical sites or facilities may or may not have enrolled any participants. Also, the demographics data is for the year 2017 and we are considering all the clinical studies registered in the USA clinicaltrials.gov as of Aug 2019. Keeping all that in mind, we will try to get a sense of overall study activity and compare them for different countries. We will also look at the study activity from region level. So, just sit back and relax.
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| Figure 1.1 |
Figure 1.1 shows study activity per 100 K population of a country. Denmark tops the list with the highest number of studies per 100 K population. This is not the complete list and I have tried to display maximum I could fit in a picture. You would notice that there are countries with very small population and hence they have got a high activity per capita. Also, there are few countries with very large populations and have got a low ratio.
In figure 1.2 above, the countries are categorized under geographic region. It also shows the percentage share of the population and number of studies. The dashboard allows to drill down on a region and see the details by country.
We will look at the study activity based on the GDP and health spending as % of GDP of countries in next post.
Keep thinking till next time.
