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Conditional power as an aid in making interim decisions in observational studies

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Abstract

Conditional power combines the findings of a partially completed study with assumptions about the future. The goal is to estimate the probability that the eventual study result will be incompatible with a criterion value, such as acceptable risk or the null hypothesis. Some history and motivation for conditional power calculations are provided, with examples illustrating the application to drug safety studies. This is an expository article suggesting that conditional power, which is well-established in clinical trials research, also has application to observational studies. The utility may be highest in regulatory settings where resources are limited and interim decisions have to be made accurately in the shortest possible time.

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Notes

  1. The “B” refers to statisticians’ use of the idea of Brownian motion, in which particles in a solution are continuously displaced from their previous position by random collisions. The statistical version is a number series generated by progressive summation, in which each new value to be added to the sum has distributional properties that are independent of the preceding increments.

  2. Assuming a null-hypothesis slope of Θ0 = 0 for the remainder of the study, the expected value of B1 is 4.49, which exceeds the criterion value of 1.96 by 2.53. The exceedance probability is 99.4%.

  3. The lower values with the exact conditional power reflect the fact that the size of the rejection region is typically smaller than its nominal value for an exact significance test with discrete data.

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Acknowledgements

Robert Glynn, Susan Gruber, Martin Kulldorff and Sebastian Schneeweiss, as well as members of the Division of Pharmacoepidemiology and Pharmacoeconomics at the Brigham and Women’s Hospital in Boston, provided important critiques of the material presented here. AMW is an employee of World Health Information Science Consultants, LLC.

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    Alexander Muir Walker

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Walker, A.M. Conditional power as an aid in making interim decisions in observational studies. Eur J Epidemiol 33, 777–784 (2018). https://doi.org/10.1007/s10654-018-0413-9

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