Accounting for Interim Analyses in the Assessment of Program Expected Net Present Value

  • Lee KaiserEmail author
  • Jeffrey Helterbrand
  • Hal Barron


The calculation of the expected net present value of a clinical development program involves the integration of future outcomes with their timing, cash flows, and associated probabilities, both subjective and relative frequency. As applied to clinical trials, the outcomes are the clinical trial results, typically timed at the completion of the trial. When the trial design includes interim analyses with the possibility of early trial stopping with an efficacy or futility conclusion, we show that the expected net present value of the program can be meaningfully influenced through the inclusion of early stopping times and probabilities in the expected net present value calculations. This approach can be applied prior to the start of the trial, and it can also be applied during the course of the trial. In the latter case, given thai interim analyses have been passed, the trial-stopping probabilities change, with resulting effects on the expected net present value. In both cases, the improved expected net present value figures should lead to better portfolio decisions.

Key Words

Interim analysis Expected net present value Portfolio management 


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  1. 1.
    Bode-Greuel KM, Greuel JM. Determining the value of drug development candidates and technology platforms. J Commercial Biotechnol. 2005;11(2):155–170.CrossRefGoogle Scholar
  2. 2.
    Steven SE. Focused portfolio measures to sup-port decision making throughout the pipeline. Drug Inf J. 2002:36:623–630.CrossRefGoogle Scholar
  3. 3.
    Rosati N. Decision analysis and drug develop-ment portfolio management: uncovering the real options value of your projects. Expert Rev Pharma-coeconomics Outcomes Res. 2002:2 (2): 179–187.CrossRefGoogle Scholar
  4. 4.
    US Food and Drug Administration. Guidance for Clinical Trial Sponsors: Establishment and Operation of Clinical Trial Data Monitoring Committees. Available at Accessed February 28, 2008.
  5. 5.
    Ellenberg SS, Fleming TR, DeMets DL. Data Monitoring Committees in Clinical Trials. West Sussex, England: Wiley; 2002.CrossRefGoogle Scholar
  6. 6.
    Statistical Programs for Clinical Trials: ld98.exe. Available at Accessed February 28, 2008.
  7. 7.
    Insightful: S+SeqTrial®. Available at: Accessed February 28, 2008.
  8. 8.
    Cytel Statistical Software and Services; East®. Available at: Accessed February 28. 2008.
  9. 9.
    Lan GKK, DeMets DL. Discrete sequential boundaries for clinical trials. Biometrika. 1983; 70(3):659–663.CrossRefGoogle Scholar
  10. 10.
    Sandler A, Gray R, Perry MC. et al. Paclitaxel-carboplatin alone or with bevacizumab for non-small-cell lung cancer. N Engl J Med. 2006: 355:2542–2550.CrossRefGoogle Scholar
  11. 11.
    Romond EH, Perez EA, Bryant J. et al. Trastuzumab plus adjuvant chemotherapy for operable HER2-positive breast cancer. N Engl J Med. 2005;353:1673–1684.CrossRefGoogle Scholar
  12. 12.
    ICH E1A. The Extent of Population Exposure to Assess Clinical Safety: For Drugs Intended for Longterm Treatment of Non-Life-Threatening Conditions. Available at: Accessed February 28, 2008.Google Scholar

Copyright information

© Drug Information Association, Inc 2008

Authors and Affiliations

  1. 1.Statistical Methods and Research, Genentech, Inc.South San FranciscoUSA

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