Drug Safety

, Volume 36, Supplement 1, pp 195–204

The Impact of Drug and Outcome Prevalence on the Feasibility and Performance of Analytical Methods for a Risk Identification and Analysis System

  • Christian G. Reich
  • Patrick B. Ryan
  • Marc A. Suchard
Original Research Article

DOI: 10.1007/s40264-013-0112-0

Cite this article as:
Reich, C.G., Ryan, P.B. & Suchard, M.A. Drug Saf (2013) 36(Suppl 1): 195. doi:10.1007/s40264-013-0112-0



A systematic risk identification system has the potential to study all marketed drugs. However, the rates of drug exposure and outcome occurrences in observational databases, the database size and the desired risk detection threshold determine the power and therefore limit the feasibility of the application of appropriate analytical methods. Drugs vary dramatically for these parameters because of their prevalence of indication, cost, time on the market, payer formularies, market pressures and clinical guidelines.


Evaluate (i) the feasibility of a risk identification system based on commercially available observational databases, (ii) the range of drugs that can be studied for certain outcomes, (iii) the influence of underpowered drug-outcome pairs on the performance of analytical methods estimating the strength of their association and (iv) the time required from the introduction of a new drug to accumulate sufficient data for signal detection.


As part of the Observational Medical Outcomes Partnership experiment, we used data from commercially available observational databases and calculated the minimal detectable relative risk of all pairs of marketed drugs and eight health outcomes of interest. We then studied an array of analytical methods for their ability to distinguish between pre-determined positive and negative drug-outcome test pairs. The positive controls contained active ingredients with evidence of a positive association with the outcome, and the negative controls had no such evidence. As a performance measure we used the area under the receiver operator characteristics curve (AUC). We compared the AUC of methods using all test pairs or only pairs sufficiently powered for detection of a relative risk of 1.25. Finally, we studied all drugs introduced to the market in 2003–2008 and determined the time required to achieve the same minimal detectable relative risk threshold.


The performance of methods improved after restricting them to fully powered drug-outcome pairs. The availability of drug-outcome pairs with sufficient power to detect a relative risk of 1.25 varies enormously among outcomes. Depending on the market uptake, drugs can generate relevant signals in the first month after approval, or never reach sufficient power.


The incidence of drugs and important outcomes determines sample size and method performance in estimating drug-outcome associations. Careful consideration is therefore necessary to choose databases and outcome definitions, particularly for newly introduced drugs.

Copyright information

© Springer International Publishing Switzerland 2013

Authors and Affiliations

  • Christian G. Reich
    • 1
    • 5
  • Patrick B. Ryan
    • 2
    • 5
  • Marc A. Suchard
    • 3
    • 4
    • 5
  1. 1.AstraZeneca PLCWalthamUSA
  2. 2.Janssen Research and Development LLCTitusvilleUSA
  3. 3.Departments of Biomathematics, and of Human Genetics, David Geffen School of Medicine at UCLAUniversity of CaliforniaLos AngelesUSA
  4. 4.Department of Biostatistics, UCLA Fielding School of Public HealthUniversity of CaliforniaLos AngelesUSA
  5. 5.Observational Medical Outcomes Partnership, Foundation for the National Institutes of HealthBethesdaUSA

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