16th Applied Statistics 2019
International Conference
September 22 - 25, 2019
Ribno (Bled), Slovenia

Past conferences:

2018 2017 2016 2015
2013 2012 2011 2010 2009
2008 2007 2006 2005 2004
 before 2004


  Important dates

      June 1

  Acceptance note
     July 1
  Reduced early
  payment fee
     July 15



Cancer risk model requirements for counseling, prevention and early detection

Mitchell H. Gail
Biostatistics Branch, Division of Cancer Epidemiology and Genetics
National Cancer Institute, NIH, HHS
Bethesda, MD 20892-7244

“Cancer risk model” refers to a model that predicts the absolute risk of developing cancer over a defined age interval, which is reduced by competing causes of mortality. The usefulness of a risk model depends not only on the properties of the model, but also on the available interventions and the intended applications. For most applications the model should be “well calibrated”, namely should correctly predict the probability that a person with given characteristics develops cancer in a given age interval. In addition to good calibration, model performance is often characterized by its “discriminatory accuracy”, which is the probability that a randomly selected person who develops cancer has a higher risk than a randomly selected person who does not. Although this general criterion has some value, it is preferable to consider the benefits and losses associated with specific applications of the risk model to assess its usefulness. Some applications require well calibrated models but do not require high discriminatory accuracy. Examples include: providing general perspective on risk in counseling; deciding whether a woman in her forties might benefit from mammographic screening; assessing the burden of disease from specific risk factors in a population and gauging the potential population benefit of reducing exposure to that risk factor; and sample size calculations for designing prevention trials.

Some applications require high discriminatory accuracy. An ideal preventive intervention would be so safe that it could be applied broadly without the need for risk models. However, if the preventive intervention has serious adverse effects, it should be given only to the subset of the population at high enough risk that the expected benefit from prevention of the main outcome outweighs the expected losses from adverse events. Such applications require risk models with high discriminatory accuracy for high public health impact if the targeted subset is small. Models with intermediate discriminatory accuracy can contribute to other applications, such as risk-based allocation of preventive resources, improved screening recommendations compared to age-based screening, and weighing risks and benefits of preventive interventions for individuals.

I shall discuss the usefulness of available and foreseeable models for breast cancer in such applications, taking into account likely improvements from including mammographic density and polygenic risk scores in breast cancer risk models.

 Applied Statistics 2019      http://conferences.nib.si/AS2019                                e-mail: info.AS@nib.si