Clinical Intelligence Analytics - Sponsor Trends

Sponsor Trends Dashboard

In the last post here, we gained some insights into top performing sponsors and overall trend. In this post, we will look further deep into how the sponsor participation has changed in last few years. The Sponsor Trends Dashboard (figure 6.1) can answer some interesting questions and see what's going on in the clinical trials industry.

Figure 6.1

Here, we will try to analyze the insights gained from Sponsor Trends Dashboard in Figure 6.1 above. 
1. What type of study Sponsors have the highest or lowest share in study registration, and how has that changed over the last few years?
The chart in Quadrant 2 tells us about the share of studies by different types of sponsors. Clearly, Universities have the highest share and has maintained a steep rise in the last few years. The share has increased from 40% in 2005 to 50% in last 2 years. These Universities could be public or private, funded or not-funded but we do not have that information available as of now. 
Hospitals have also performed well in registering the studies. The share of studies registered by Hospitals have almost doubled since 2005. On the other hand, the share of Industry sponsored studies has declined consistently and it has reduced to just 17% now which was 37% in 2008. The share does not indicate if the segment has really grown or declined. The share of one segment, Universities for example,  could rise because other segments have declined. To see the trend, read the next question below.

2. Which type of sponsors have shown growth in study activity?
The chart in Quadrant 1 shows how the registered studies by different categories or types of sponsors have grown over the period.
The studies registered by Universities have grown at a continuous and rapid pace. Until 2008, the Industry segment and Universities were overlapping but after that the Universities completely outpaced the Industry Sponsors. The growth in studies registered by Industry sponsors has remained almost flat but Hospitals maintained a steady growth until 2016 after which the growth is negligible. 

3. How has the total numbers of sponsors changed over the past few years?
In the above 2 charts in Quadrants 1 and 2, we looked at the trend of the studies registered by sponsors but now in this chart on quadrant 3, we will look at the trend of actual number of sponsors.
The overall trend shows that the total number of primary sponsors who registered their studies has grown consistently. All 3 important categories, Universities, Industry Sponsors and Hospitals have grown consistently. All 3 types of sponsors have increased almost 2 times of 2008 levels.

4. Has the participation of sponsors from industry has increased?
The chart in quadrant 4 shows the participation trend over the period. Industry participation means that either the study is sponsored by a sponsor from industry or at least one of the collaborators is an industry sponsor. 
The chart shows that the industry participation has declined and reduced to just 22% from upper 40s in 2008. Between 2005 and 2008, the share of studies having industry participation remained in upper 40s but started declining consistently after 2008. However, it is not clear from this chart if the studies with industry participation has decreased or the studies without industry participation has increased. To figure that out, let's take a look at the trend chart that shows the number of studies with or without industry participation over the period of time.

Figure 6.2

The trend in chart (figure 6.2) shows that the number of studies where there is industry participation has remained nearly constant but the studies without any industry participation has increased from 6K levels in 2005 to 24K in 2018, which is four times growth.
As a food for thought, how do you think clinical research industry can increase the collaboration for the larger benefit to the whole community?
Do post your questions and comments.
Keep thinking!

Source data extracted from: https://aact.ctti-clinicaltrials.org 

Clinical Intelligence Analytics - Primary Sponsor

Primary Sponsor Dashboard

Primary Sponsor dashboard (Figure 5.1) provides us insights into the study activities by primary or lead study sponsors. Primary sponsor is an important stakeholder in the clinical trials that has the primary responsibility of initiating, study design and study conduct. In simple terms, we can say that the primary sponsor is the owner of the clinical study. The sponsor can be an individual, a company or an institution, and they can be from industry (commercial) like pharmaceutical or Biotech companies or public non-industry (non-commercial) institutions like government or research institutions. 

 In this dashboard we will look at various aspects of study activities of sponsors.

Figure 5.1

1. How many sponsors have registered clinical trials in the US? How many of them are from the Industry (Commercial)? How many studies have they registered? For how many studies did the sponsors from commercial sector have posted the results for the studies?
The tiles on the top provides few sponsors related summary metrics to answer some of the questions mentioned above.
There are a total of 313,345 studies registered by 28,068 lead or primary sponsors in the US till date. Out of 28,068 sponsors, 8,717 sponsors are from commercial sector with 81,169 studies registered and the remaining 19,351 sponsors are from non-commercial sector with 232,176 studies registered. 
There are 4,545 sponsors with at least one study result posted and 1,680 of them are from Industry. 

2. What percentage of studies were registered by sponsors from industry as compared to the non-industry? 
About 26% of studies were sponsored from the industry or commercial sector. A small percentage 3.31% and 1.16% are contributed by NIH (National Institute of Health) and US government respectively. The raw data did not provide further classification into the non-industry sector. The data is transformed to figure out if the non-industry sponsor is a Hospital or University/Institute/School. The shows that Universities have been a major source of sponsored studies with almost 45% share and Hospitals having 9% share.

3. What's the growth in number of studies sponsored by study as compared to the non-industry sector over the last few years?
Except 2008 and 2014, the number of studies registered by Industry sponsors have largely remained stable around 5,500 mark. In contrast, the studies registered by non-industry sponsors have grown rapidly. Notice the size of the steps in the chart. Also notice the increasing gap between the two lines.

4. What percentage of registered studies have some participation from industry?
The pie chart named Industry Participation shows the share of studies where either the primary sponsor is from industry or at least one of of the collaborators is from industry. About 33% of the studies has some participation from industry.
 
5. Who are the top performing industry sponsors?
The tabular chart shows the primary sponsors from industry sector ranked based on the number of studies registered. The chart also display metrics like number of countries they have recruited patients, number of recruiting facilities, studies in completed state, studies where the recruitment has not yet started, studies that are currently recruiting and studies where results are posted. The success ratio compares the studies that were registered minus the studies that have not yet started or are in progress with the completed studies.
GlaxoSmithKline(GSK) is the top performer with 3351 studies and 91% success ratio followed by Pfizer and Novartis. Pfizer has recruited patients in 105 countries. Sanofi is another sponsor that recruited patients in 107 countries. 

6. Who are the top performing sponsors from non-commercial sector?
National Institute of Health Clinical Center, National Cancer Institute and M.D Anderson Cancer Institute are top 3 performers. The non-industry sponsors have recruited patients in fewer countries as compared to the top sponsors from commercial sectors.
The dashboards can answer many other questions by simply slicing and dicing the data by different dimensions.
If you get any questions in your mind that you want to share, please post them in comments and I will try to address them.
Till next time.  

Clinical Intelligence Analytics - Study

Study Dashboard

In study dashboard (Figure 3.1), we will look at certain aspects of study at aggregated level as well as at a study level. 

Figure 3.1

The study dashboard will try to answer following questions:

1. What is the average study completion duration for sponsors from Industry and non-Industry?

For all types of studies (All), the sponsors from Industry completed the studies in about 1.9 years. In comparison to that, sponsors from non-industry took almost 3 years to complete the study.

The observational studies (Obs) took longer to complete. The Industry sponsors with an average of 2.3 years performed fairly better than the non-industry sponsors with an average of 3.2 years.

For interventional type of studies (Int), the average study took 1.8 years for Industry sponsor as compared to 2.9 years for non-industry sponsors.

We may further want to look at the study duration by the phase of the study. Phase 3 studies are large scale and complex in nature and hence, it should take longer to complete when compared with phase 1 and phase 2 studies. Let's see what we find. Only interventional studies go through the drug development phases. If you take a look at Avg Study Duration by Phase chart, Phase 2 studies took longest among all the study phases with an average of 3.3 years. Phase 3 studies took an average of 2.9 years to complete where as phase 4 studies took 2.4 years. Early phase 1 studies took longer than the phase 1 studies.

2. What is the share of sponsors from industry and non-industry in interventional or observational studies?

Almost 80% of studies were interventional studies. 56% of 80% which 70% of total interventional studies were sponsored by non-industry sponsors. The industry sponsors have greater share in interventional studies as compared to its share in observational studies.

3. What percentage of studies were completed between 0 to 3 years or between 8 to 10 years?

40% of the studies were completed between 1 to 3 years. Around 29% studies were completed in less than 1 year. 

4. Which studies took the longest to complete?

There are 44 studies (0.02%) that took more than 30 years to complete. The study that took longest was sponsored by Johnson & Johnson to evaluate the efficacy of oral Levofloxacin in the treatment of chronic Bronchitis. This study took 63 years to complete starting in 1931 and completing in 1994 and has enrolled 367 patients. 

5. At study level, how many medical conditions a particular study is conducted?

See the tabular report to view the number of enrollment, medical conditions and the number of study sites and countries of subject recruitment.

6. In how many countries and facilities did a study recruited patients?

See the tabular report.

The dashboard will show the description of the selected study.

Clinical Intelligence Analytics - Trends by Study Attributes

In the last post, we looked at the growth trends of studies registered, initiated, completed and posted results over the period of last 20 years. We looked at yearly trends and then drilled down at quarterly and monthly trends and compared the growth in current year with the previous year.
In this post, we will see the growth trends of registered studies by study attributes like study type, study phase, Drug/Device and DMC flag over the period. I have filtered out the studies where the study attributes were not specified.

Figure 2.4

This dashboard (Figure 2.4) is an extension of growth trends:
1. Study Submission Trend by Study Type-
The chart shows the trend of studies submitted for interventional and observational types.
The share of interventional studies have decreased. In early 2000's, there were around 90-93% interventional studies and 7-10% observational studies. In last few years, the share of observational studies have increased to 21%.

2.  Study Submission Trend by FDA oversight-
The chart shows the trend of studies by the oversight of the FDA, if the study is for FDA regulated drug or a device. 
Until 2008, the share of the studies for FDA regulated devices was less than 10% which has increased to around 25% now.

3. Study Submission Trend by Study Phase-
 Phase 3 studies are important studies since sponsors apply for FDA approval after that. 
Study type NA are the studies that do not have a phase of a study, and I guess it is mainly for the device related studies. These studies share have increased significantly over the period and I think its because the device related studies share have increased as we observed in the previous chart.
There is a slight drop in the share of phase 1 studies. The share of Phase 2 studies in green has decreased from 43% in 2003 to 12% in 2018. Phase 3 studies have also followed the same trend with the share reducing to just 7% in 2018 from 25% in 2005. Phase 4 are post marketing studies and the share has reduced from 16% in 2005 to 7% in 2018.

4. Study Submission Trend by DMC-
The DMC flag tells if the study has a Data Monitoring Committee appointed or not. There were many studies that did not mention if they have DMC or not. As I mentioned in the beginning, the chart is the representation of only studies that has the data and others were filtered out.
The share of DMC appointed studies increased in the first few years and then start to drop until 2008 after which it picked up again a bit and maintaining the 40% level until 2013 but falling down a bit to 34% in 2018.
Feel free to share your thoughts.
See you soon in the next post.
  

Clinical Intelligence Analytics - Insights

Clinical Trial studies a new drug or device before it is brought to the market. The new therapy is tested on human subjects to evaluate its safety and efficacy.
Sponsors or investigators of certain clinical trials are required by U.S. law to register their trials on and submit summary results to ClinicalTrials.gov website. 
While working for a CRO as a Business Intelligence and Data warehouse Engineer, I gained some basic knowledge about the Clinical Research. I got so much interested that I decided to study Bio-sciences Management and Analytics subjects in my graduate MBA program. Unfortunately, my curiosity and the desire to learn more about Bio-sciences did not end there. 
I am passionate about creating insights out of data and I always try to unravel the layers of my curiosity by diving deep into the data. So, I decided to get the data from ClinicalTrials.gov and create Clinical Intelligence Analytics for myself. Here is the link in case you want to download it too.: https://aact.ctti-clinicaltrials.org/
The objective of creating this application is to share the insights with the community so that they know more about the things happening in this space. I would be really happy if this application could be of any benefit to patients, physicians,sponsors and other partners of the ecosystem. There are few online websites that helps patients find the recruiting study. However, I did not find any easy way that can help patients find out more about particular studies or sponsors or investigators in the past so that they can make informed decisions.
I used Talend software to get the data and used Qlikview BI to do data preparation and to create analytic dashboards. I have created more than a dozen dashboards so far and creating more as we go along. 
I would appreciate to leave a comment if you read the posts and find it useful. 
Since I do not want to make the posts boring for readers by putting a lot of information in one single post, I would be posting multiple posts in a series in coming days and update the link below in this post.

1. Clinical Summary Dashboard
2. Growth Trends Dashboard

Here are few links that would help in understanding basic terminology and basic information on ClinicalTrials.gov:
Common Terms
Trends Charts

Tableau - Implementation Challenges and Best Practices

Hi All,

I thought of sharing my leanings and experiences with Tableau so far.

This post will describe some of the challenges you could face while implementing Tableau BI in your enterprise from architectural standpoint.

If you are evaluating BI tools or planning to start implementation, you will definitely benefit from this post. I would be highlighting some of the best practices that you can include in your list.

Tableau is flexible when it comes to playing with and analyzing your data. It gives you complete freedom to choose and connect to your data source, and quickly start building those nice Viz (reports or charts or dashboards).
You can do pretty much everything to join the data sources in a SQL, put filters to restrict your data. If you are a data analyst, you can build some really compelling data visualizations or charts in a very short span of time.
Now you show those nice visualizations with your team or department and they too get very exited.

Till here it was all cool stuff. The challenges starts from here.

1. Do I don't need a Datawarehouse star schemas.?
Datawarehouse star schema contains Fact and dimensions that gives you enormous benefits in simplifying your implementation. You won't believe how it can benefit in terms of performance, scalability and maintenance.
Some may argue that Tableau doesn't need any kind of warehouse or these fact and dimensions star schemas.
Well, if you are really a very small enterprise then you may not need it but otherwise if you have good amount of data and have various source systems and applications, then do not build your BI without a datawarehouse. Or sometimes, your organisation has a warehouse but as a data analyst you may be tempted to NOT use it.
Since Tableau does not have any centralized metadata layer, users are free to create their SQL the way they want. This freedom proves costlier in long term strategy.
Developers build their SQL's on top of OLTP or normalized data structures and the result is you have highly complex SQL's with large numbers of joins giving you poor performance.
Very soon you will have hundreds of those complex SQL's with lots of duplicate data/information where one SQL may differ from another SQL slightly. It's not so easy to debug those complex SQL's to make any additions or alterations. Now you understand how difficult it would be to maintain those SQL's.
Star schema reduces those joins and makes your SQL very simple, and of course the performance is way better.
Tableau can extract the data in extract mode and improves the performance to some extent but do not just ignore the other benefits.For some reason in future if you need to make your application in Live mode then you may need to completely redesign it. Such reasons could be more frequent data refresh or implementing row level security for which you need to have Live connection for your Tableau application.

2. Temptation to put ALMOST everything in one Tableau Workbook:
When you start creating an application, you start with small dataset providing answers to very limited or few business questions. This is what tableau is built for.

Slowly when more and more people starts looking at it, they start asking for more and more information. This is when we start adding new data sets, joins, transformations and conditions. And our application starts growing from all angles.
It becomes more complex, performance goes down and it becomes difficult to  scale.
If we take a break here and plan things, we can do it in much better way.
Once we realize that our application is growing, think of going to point no 1 above of creating/extending the dimensional model.
You need recreate your application using a dimensional model. If you think about this early, you will reduce the amount of rework you would have to do.
The ideal design would be to do all the data analysis/discovery using your source systems structures (assuming you do not have a warehouse or the required information is not present in a warehouse at all).
Utilize all the freedom Tableau provides here. But once you start thinking of making it available for mass consumption by enterprise users, design the required subject areas (Facts and dimensions) or extend the existing ones.
Build your application now using these subjects areas. Your application would be simple, fast, scalable and easy to maintain. Since the new SQL would be using less joins, fewer calculations and aggregations, it would be fast and easier to read.
You can now imagine the benefits. If you need more data elements or metrics, simply keep adding them to your subject areas.
This will enable you to extend or scale your application to a greater extent BUT this does not mean you can still put almost everything in one workbook.
Definitely there is some more work here but I am sure you would appreciate the benefits it would bring in the long run.

3. I Still want to put almost everything in one Workbook:
You may be wondering if I am against that. Well I am not.
There are many instances where we need to have information to be displayed on our dashboards side by side that may be coming from different subject areas or Stars but there are certain things we need to consider and remember.
Since Tableau does not have a Semantic layer (aka Common Enterprise Reporting Layer), we need to have all the tables added to that one workbook as Data sources.
Here the grain of the data plays an important role. If the grain of the data is same then all can fit in one data source/SQL.
But if the grain of the two data sources are different and there is a need to have an interaction between these data sources then the real trouble starts.
When I say interaction between these two data sources, I mean to say that we need to pass common filters between them or need to show the data coming from these two data sources into one Viz/report.
When we need to have an interaction, we need to have a join between these two data sources. Tableau allows joins across data sources or perform blending but it may prove to be very costly in terms of performance and stability.
You would be surprised that even if individual queries have sub second response time, after applying the join the response time may be in minutes.
If your individual queries have limited or small data, it may work for you in some cases.
Better always test it out. Even Tableau experts suggest to avoid using the blending.

4. OK. what is the Solution then:
I know its frustrating when we talk about limitations only. Here it is also important to understand why such limitations when Tableau is such a nice tool?.
Well, Tableau is a tool for data discovery. Quickly go grab your data and starts visualizing it. Maps are inbuilt and required no configuration like in many other tools. But once we have built those nice dashboards we need to make it available for the enterprise users. Tableau can do certain things here but its not made for that. Now you are trying to make it do something that some enterprise BI reporting tools such as Oracle OBIEE or Business Objects or Cognos are just made for that. These tools can do some data discovery but not the way Tableau does, similarly Tableau can do some dashboarding but not the way they do it.
Here I am not comparing Tableau with them since they are not comparable and have totally different use case and technologies.

5. What else can I do to?
All right. Here is the solution.
We need to design our Tableau workbooks and dashboards intelligently keeping in mind the limitations.
Think of having a common landing page workbook with hyperlinks to all the other applications. Think of having some very common filters on your landing page. So your first workbook have just dimension data for those filters.
Now you can also think of making one or more of these filters mandatory meaning users need to have a filter value selected in order to go to a specific workbook/dashboard.
This would help in cases when your workbooks/dashboards have tons of data and you want to avoid just showing all of that data and slow down your application.
Now, you can build your simplified workbooks based on individual common subject areas and link them to your landing page.
Since Tableau allows to pass the filters between workbooks, you can pass the common filters from one workbook to another.
There may be certain cases when we want to have a dashboard/report having data from 2 different data sources and in those cases you can consider blending. I know I said Tableau experts suggest to avoid it.
See if blending works fine for you else think of creating a physical table in database combining the two sets of data having different grains.
This table will have data at both the grains and some indicator column will tell the row has data for which grain. you will find any example on the web for such cases since this issue is not specific to Tableau but common to data warehouse.

6. THAT'S IT?
Well I guess So until something comes to my mind. Please post your comments and questions, and share your thoughts and experiences.

Thanks for reading.
Manohar Rana

Gartner BI Magic Quadrant 2009

Yesterday just got a chance to look at Gartner's Magic Quadrant for Business Intelligence 2009.
I am really happy to see Qlikview almost touching the Leaders Quadrant Boundary and may be by next year Qlikview will join the leaders club.
So that's a good news for people who have invested in this technology.
This would be really interesting to watch how Qlikview will compete with the leaders. The strong points mentioned by Gartner are OK but I love to look at the weak points because thats the only area where you need to put more efforts and will eventually decide on your success.
1. Lack of statistical and predictive modelling: Thats the key area where Qlikview needs to improve heavily to be able to compete with other leaders. At present, Gartner named some of the competitors as Tibco and some other small products and while doing that it says it is behind them as well. If Qliktech fails to address this quickly before getting into leaders quadrant, it will become very difficult to move forward or maintain its position.
2. The fear of Qliktech being getting acquired will have some impact on the prospects. The prospects will get more cautious and may look for other options which are more certain and safe.
Prospects do not want to suffer from the change in policies, product names, re-architecturing etc and want to play safe wich is fair enough.
3. Gartner feels Qlikview still requires more examples of Large BI deployments and stressed on saying that it has not moved further in this area as compared to last year. I feel this will remain a challenge until Qlikview make some improvements in the architecture to deploy on large environment.
4. The last point may be very dangerous for Qliktech. People who were involved in large deployments understand the importance of Metadata management. making quick reports and good reporting capabilities are good but metadata management is the second pillar on which the deployment stands. If Qliktech fails to address this soon, it will definately be very difficult to get large deployment examples.

You may also want to read Qlikview vs Others which has some discussions on the pros and cons.

This was about Qlikview, the tool which I personally love.
The other interesting things which Gartner mentioned is inclusion of some open source BI tools like Jaspersoft and Pentaho. I hear a lot about Pehtaho and would love to include a review for this in my blog soon but before that I would like to try my hands on that or read some technology information whitepapers.
Now with the inclusion of open source BI, these tools will get some acknowledgement and people will have a choice to look at them as well.
Other open source which I am hearing a lot is Jaspersoft.

Another thing which Gartners mentioned is the SaaS(Software as a Service) BI tools. This may be good for products based on some properitery technology to store the data which has a potential risk of migrating the complete application if customer chooses to shift to a new technology or if the vendor plans to de support or does not provide a way to integrate with other technologies. In that case, customer has absolutely no choice other than to competely rebuild the entire application on different platform which I think will not be an easy and economical task.
I really dont have any idea how SAAS vendors make sure customers investment is not affected if anything of such sort happens. If someone can provide an insight would be helpful.
The new names which I never heard are Pivotlink, Lucid Era and Oco.
to be continued....