Andrew Grygiel10.11.11
Take a Phase II oncology study, with lung cancer as the indication being studied. Clinical operations, working with R&D finance, prepares a detailed study budget that turns out to be approximately $10 million.
The study is completed 420 days later. R&D finance compares the actual study cost against the study budget, and finds it cost $1.1 million more than planned — enough to fund an entire Phase I study.
An aberration? Hardly. This represents a typical scenario for nearly half (45%) of the life sciences professionals who responded to a recent industry survey on study budgeting practices and metrics (see sidebar). Charged with forecasting and budgeting clinical studies at biopharmaceutical and medical device companies, these professionals reported that their typical variance — from forecast to actual costs — for clinical studies was at least 11%, and was often greater. And an astonishing one in five stated their cost variance was 16% or more.
Note that this variance can easily be negative as well, something which is equally disturbing to management. Overestimating the cost of clinical studies means that funds are not being allocated efficiently, leading to wasted resources and a higher opportunity cost.
Perhaps not surprisingly, respondents also demonstrated a marked lack of confidence in the accuracy of their study budgets, with only 21% stating they were “highly confident” in their budget forecasts. The remainder were either “somewhat confident” (69%) or “not confident” (10%).
Do large companies (defined in the survey as having at least $1 billion in annual revenue) fare better? The answer is yes — but not by much. Of the respondents from large companies, 40% reported a budget variance of at least 11%, with several reporting variances of 20% or greater. And those that were “highly confident” in their budget forecasting rose just two percentage points, to 23%.
Mid-sized companies ($100 million to $1 billion in annual revenue) tracked closely with large companies when it came to variances from plan to actual costs; however only 19% of respondents from mid-sized companies were “highly confident” in their budgeting accuracy. And of the respondents from small companies (less than $100 million in annual revenue), 50% reported a budget variance of at least 11%, while 20% were “highly confident” in their budget forecasts.
Not Only Inaccurate, but Inefficient as Well
Another notable gap uncovered by the survey was in planning efficiency. Only 16% of all respondents were able to create a ballpark budget for a clinical study in less than a week, with half of respondents requiring three weeks or more. Large companies actually fared worse in planning efficiency, with only 9% able to prepare a ballpark study budget in under one week. Small companies, as might be expected, were more nimble: 24% reported being able to create a ballpark budget in less than a week.
This inability to conduct study budgeting activities in a timely fashion extended to the review process, with 65% of respondents requiring five weeks or more to complete the budget review and revision cycle for a single study. And almost one-third of respondents (31%) required at least nine weeks to do so. Again, small companies moved faster: 46% were able to conduct the review and revision cycle in fewer than five weeks, while only 23% of large companies could make the same claim.
What about companies budgeting multiple studies? It usually falls to R&D finance to roll up individual study budgets into a budget portfolio in order to look at the bigger picture. According to the survey this is a time-consuming process, with 62% of all respondents requiring at least three weeks to build their portfolios. The difference between small and large companies showed itself once again, with 51% of small companies able to build their portfolios in less than three weeks, while just 26% of large companies could.
Why Is Study Budgeting so Difficult?
A key finding from the survey points to one possible explanation for this industry struggle to forecast clinical study costs accurately and efficiently. According to the survey, the life sciences industry still lags far behind other industries in the use of purpose-built forecasting and budgeting software.
When asked their primary tool used for clinical study forecasting and budgeting, 57% of respondents pointed to Microsoft Excel. This is in stark contrast to other industries, such as manufacturing or construction, which have long faced cost and resource pressures and which utilize planning software built around their industry’s unique requirements. In fact, the use of planning spreadsheets uncovered by the survey is at a level last seen in other industries 10 or 15 years ago.
The results of the survey clearly show that the life sciences industry continues to experience difficulty accurately predicting spending for its most costly area: clinical development. Biopharmaceutical and medical device companies of all sizes are struggling to create accurate clinical study budgets in a timely manner, and cost variances from forecast to actual for clinical studies remain high — in spite of the ongoing industry push for greater efficiency. The wide use of spreadsheets rather than purpose-built software bears the brunt of the burden for these failings.
There’s no need for this. I personally know companies that are able to forecast studies with 95%-99% accuracy from forecast to actual costs. One clinical program manager for a mid-sized sponsor company told me he was able to deliver a <1% cost variance on a $130 million clinical portfolio by combining good business practices with the right tools (which did not include Excel). Another person, a director of clinical outsourcing for a small biotech, is able to build detailed, accurate study budgets and generate RFPs in a matter of hours — strengthening her negotiating position with CROs and cutting contract closure time from two months to two weeks. So we know it can be done.
The incentive to be more efficient in study forecasting and budgeting is increasing along with the well-known pressures on the life sciences industry. If the industry is truly serious about improving efficiency in clinical development, study sponsors will need to do more to improve the methods they use.
The survey was conducted at arm’s length by a third-party firm on behalf of ClearTrial. There was no contact between ClearTrial and the survey respondents.
Quick Facts
• Survey conducted between May 18 and June 1, 2011
• 187 complete responses
• 115 biopharmaceutical and medical devices companies represented
• Cross-section of small, mid-sized, and large sponsor companies
• US, European, and Japanese companies represented
• Primary functional areas: clinical development; clinical operations; finance; outsourcing
Complete survey results can be downloaded from http://info.cleartrial.com/budgeting-survey.html.
Andrew Grygiel is chief marketing officer at ClearTrial, a developer of clinical trial operations software. He can be reached at agrygiel@cleartrial.com.
The study is completed 420 days later. R&D finance compares the actual study cost against the study budget, and finds it cost $1.1 million more than planned — enough to fund an entire Phase I study.
An aberration? Hardly. This represents a typical scenario for nearly half (45%) of the life sciences professionals who responded to a recent industry survey on study budgeting practices and metrics (see sidebar). Charged with forecasting and budgeting clinical studies at biopharmaceutical and medical device companies, these professionals reported that their typical variance — from forecast to actual costs — for clinical studies was at least 11%, and was often greater. And an astonishing one in five stated their cost variance was 16% or more.
Note that this variance can easily be negative as well, something which is equally disturbing to management. Overestimating the cost of clinical studies means that funds are not being allocated efficiently, leading to wasted resources and a higher opportunity cost.
Perhaps not surprisingly, respondents also demonstrated a marked lack of confidence in the accuracy of their study budgets, with only 21% stating they were “highly confident” in their budget forecasts. The remainder were either “somewhat confident” (69%) or “not confident” (10%).
Do large companies (defined in the survey as having at least $1 billion in annual revenue) fare better? The answer is yes — but not by much. Of the respondents from large companies, 40% reported a budget variance of at least 11%, with several reporting variances of 20% or greater. And those that were “highly confident” in their budget forecasting rose just two percentage points, to 23%.
Mid-sized companies ($100 million to $1 billion in annual revenue) tracked closely with large companies when it came to variances from plan to actual costs; however only 19% of respondents from mid-sized companies were “highly confident” in their budgeting accuracy. And of the respondents from small companies (less than $100 million in annual revenue), 50% reported a budget variance of at least 11%, while 20% were “highly confident” in their budget forecasts.
Not Only Inaccurate, but Inefficient as Well
Another notable gap uncovered by the survey was in planning efficiency. Only 16% of all respondents were able to create a ballpark budget for a clinical study in less than a week, with half of respondents requiring three weeks or more. Large companies actually fared worse in planning efficiency, with only 9% able to prepare a ballpark study budget in under one week. Small companies, as might be expected, were more nimble: 24% reported being able to create a ballpark budget in less than a week.
This inability to conduct study budgeting activities in a timely fashion extended to the review process, with 65% of respondents requiring five weeks or more to complete the budget review and revision cycle for a single study. And almost one-third of respondents (31%) required at least nine weeks to do so. Again, small companies moved faster: 46% were able to conduct the review and revision cycle in fewer than five weeks, while only 23% of large companies could make the same claim.
What about companies budgeting multiple studies? It usually falls to R&D finance to roll up individual study budgets into a budget portfolio in order to look at the bigger picture. According to the survey this is a time-consuming process, with 62% of all respondents requiring at least three weeks to build their portfolios. The difference between small and large companies showed itself once again, with 51% of small companies able to build their portfolios in less than three weeks, while just 26% of large companies could.
Why Is Study Budgeting so Difficult?
A key finding from the survey points to one possible explanation for this industry struggle to forecast clinical study costs accurately and efficiently. According to the survey, the life sciences industry still lags far behind other industries in the use of purpose-built forecasting and budgeting software.
When asked their primary tool used for clinical study forecasting and budgeting, 57% of respondents pointed to Microsoft Excel. This is in stark contrast to other industries, such as manufacturing or construction, which have long faced cost and resource pressures and which utilize planning software built around their industry’s unique requirements. In fact, the use of planning spreadsheets uncovered by the survey is at a level last seen in other industries 10 or 15 years ago.
The results of the survey clearly show that the life sciences industry continues to experience difficulty accurately predicting spending for its most costly area: clinical development. Biopharmaceutical and medical device companies of all sizes are struggling to create accurate clinical study budgets in a timely manner, and cost variances from forecast to actual for clinical studies remain high — in spite of the ongoing industry push for greater efficiency. The wide use of spreadsheets rather than purpose-built software bears the brunt of the burden for these failings.
There’s no need for this. I personally know companies that are able to forecast studies with 95%-99% accuracy from forecast to actual costs. One clinical program manager for a mid-sized sponsor company told me he was able to deliver a <1% cost variance on a $130 million clinical portfolio by combining good business practices with the right tools (which did not include Excel). Another person, a director of clinical outsourcing for a small biotech, is able to build detailed, accurate study budgets and generate RFPs in a matter of hours — strengthening her negotiating position with CROs and cutting contract closure time from two months to two weeks. So we know it can be done.
The incentive to be more efficient in study forecasting and budgeting is increasing along with the well-known pressures on the life sciences industry. If the industry is truly serious about improving efficiency in clinical development, study sponsors will need to do more to improve the methods they use.
The survey was conducted at arm’s length by a third-party firm on behalf of ClearTrial. There was no contact between ClearTrial and the survey respondents.
Quick Facts
• Survey conducted between May 18 and June 1, 2011
• 187 complete responses
• 115 biopharmaceutical and medical devices companies represented
• Cross-section of small, mid-sized, and large sponsor companies
• US, European, and Japanese companies represented
• Primary functional areas: clinical development; clinical operations; finance; outsourcing
Complete survey results can be downloaded from http://info.cleartrial.com/budgeting-survey.html.
Andrew Grygiel is chief marketing officer at ClearTrial, a developer of clinical trial operations software. He can be reached at agrygiel@cleartrial.com.
