Flexible Sample Size Considerations Using Information-Based Interim Monitoring


At the design phase of a clinical trial the total number of participants needed to detect a clinically important treatment difference with sufficient precision frequently depends on nuisance parameters such as variance, baseline response rate, or regression coefficients other than the main effect. In practical applications, nuisance parameter values are often unreliable guesses founded on little or no available past history. Sample size calculations based on these initial guesses may, therefore, lead to under- or over-powered studies. In this paper, we argue that the precision with which a treatment effect is estimated is directly related to the statistical information in the data. In general, statistical information is a complicated function of sample size and nuisance parameters. However, the amount of information necessary to answer the scientific question concerning treatment difference is easily calculated a priori and applies to almost any statistical model for a large variety of endpoints. It is thus possible to be flexible on sample size but rather continue collecting data until we have achieved the desired information. Such a strategy is well suited to being adopted in conjunction with a group sequential clinical trial where the data are monitored routinely anyway. We present several scenarios and examples of how group sequential information-based design and monitoring can be carried out and demonstrate through simulations that this type of strategy will indeed give us the desired operating characteristics.

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  1. 1.

    Kim K, Tsiatis AA. Study duration for clinical trials with survival response and early stopping rule. Biometrics. 1990;46:81–92.

    CAS  Article  Google Scholar 

  2. 2.

    Scharfstein DO, Tsiatis AA. The use of simulation and bootstrap in information-based group sequential studies. Stat Med. 1998;17:75–87.

    CAS  Article  Google Scholar 

  3. 3.

    Scharfstein DO, Tsiatis AA, Robins JM. Semiparametric efficiency and its implication on the design and analysis of group-sequential studies. J Am Stat Assoc. 1997;92:1342–1350.

    Article  Google Scholar 

  4. 4.

    EaSt: Software for design and interim monitoring of group sequential clinical trials. Cambridge, MA: Cytel Software Corporation; 2000.

  5. 5.

    Wang SK, Tsiatis AA. Approximately optimal one-parameter boundaries for group sequential trials. Biometrics. 1987;43:193–199.

    CAS  Article  Google Scholar 

  6. 6.

    O’Brien PC, Fleming TR. A multiple testing procedure for clinical trials. Biometrics. 1979;35:549–556.

    Article  Google Scholar 

  7. 7.

    Pocock SJ. Group sequential methods in the design and analysis of clinical trials. Biometrika. 1977;64:191–199.

    Article  Google Scholar 

  8. 8.

    Lan KKG, DeMets DL. Discrete sequential boundaries for clinical trials. Biometrika. 1983;70:659–663.

    Article  Google Scholar 

  9. 9.

    Armitage P, McPherson CK, Rowe BC. Repeated significance tests on accumulating data. J R Stat Soc A. 1969;132:232–244.

    Google Scholar 

  10. 10.

    Self SG, Mauritsen RH, Ohara J. Power calculation for likelihood ratio tests in generalized linear models. Biometrics. 1992;48:31–39.

    Article  Google Scholar 

  11. 11.

    Facey KM. A sequential procedure for a Phase II efficacy trial in hypercholesterolemia. Control Clinical Trials. 1992;13:122–133.

    CAS  Article  Google Scholar 

  12. 12.

    Haybittle JL. Repeated assessment of results in clinical trials of cancer treatment. Br J Radiology. 1971;44: 793–797.

    CAS  Article  Google Scholar 

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Correspondence to Cyrus R. Mehta.

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Mehta, C.R., Tsiatis, A.A. Flexible Sample Size Considerations Using Information-Based Interim Monitoring. Ther Innov Regul Sci 35, 1095–1112 (2001). https://doi.org/10.1177/009286150103500407

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Key Words

  • Sample size reestimation
  • Information-based design and monitoring
  • Adaptive design