Michael W. Kattan, PhD

Department Chair
The Dr. Keyhan and Dr. Jafar Mobasseri Endowed Chair for Innovations in Cancer Research
(Joint appointment with Department of Urology in the GUKI)

Lerner Research Institute
9500 Euclid Avenue
Cleveland, Ohio 44195
Phone: (216) 444-0584
Fax: (216) 445-7659

My general research interest lies in medical decision making. More specificlally, my research is focused on the development, validation, and use of prediction models. Most of these models are available online, and designed for physician use, at http://rcalc.ccf.org/. I am also interested in quality of life assessment to support medical decision making (such as utility assessment), decision analysis, cost-effectiveness analysis, and comparative effectiveness.

Here are some pages you might want to check out:

  1. My official Cleveland Clinic page
  2. My list of publications
  3. My Google Scholar page
  4. My ResearchGate page
  5. Researchers with the highest h-indices

Janine  Bauman  BSN
Janine Bauman BSN
Clinical Research Nurse
Phone:(216) 444-6526
Xinge (Kathy) Ji  MS
Xinge (Kathy) Ji MS
Statistical Programmer I
Phone:(216) 444-9913
Jian  Jin  MS
Jian Jin MS
Data Scientist II
Phone:(216) 444-8371
Alex  Milinovich  BA
Alex Milinovich BA
Lead Systems Analyst
Phone:(216) 444-9931
David  Sugano  DrPH
David Sugano DrPH
Contract Staff
Phone:(216) 952-3871
Changhong  Yu  MS
Changhong Yu MS
Sr. Biostatistician NE
Alexander  Zajichek  MS
Alexander Zajichek MS
Phone:(216) 444-0489

Inspired by this, I am particularly interested in 6 main areas:

A. Prediction Communication and Interpretation

  1. As cancer survivors, we like to think we both needed the treatment we received and were cured by it, but that is hard to prove.
  2. Here's an example of how patients should be counseled: a table of tailored predictions of benefits and harms crossed by treatment options.
  3. It is useless and confusing to put confidence intervals around a predicted probability.
  4. You must apply a statistical prediction model to achieve informed consent.
  5. What is a real nomogram anyway?
  6. My definition of comparative effectiveness.
  7. Too often we diagnose patients based on some arbitrary cutoff.  Let's stop doing that and recognize risk is on a continuum.
  8. Don't just look at the p-value when judging a new marker.
  9. Cancer staging systems need to go away.
  10. Before you ask, "Doc, what are my chances?", read this.
  11. Here is how we make our online risk calculators.

B. Prediction Model Development

  1. Here are the requirements for having a statistical prediction model endorsed by the American Joint Commission on Cancer.
  2. Here is how we process data from Epic to make it research ready.
  3. In the TRIPOD group, we came up with a checklist of what should be reported in a paper presenting a prediction model.
  4. Making a prediction model when there is a time-varying covariate.
  5. Propensity scores do not improve the accuracy of statistical prediction models.
  6. Here's how to make a competing risks regression nomogram. Detailed R code is  here.
  7. Machine learning approaches usually lose

C. Prediction Model Assessment

  1. Here's a decent way to compare two rival prediction tools that both predict on an ordinal scale.
  2. How to make a calibration plot for a prediction model in the presence of competing risks.
  3. How to estimate a time-dependent concordance index.
  4. This is why you can't compare two prediction models tested on separate datasets. The figure is updated here.
  5. A guide to the many metrics for assessing prediction models.
  6. How to calculate a p-value when comparing actual outcomes to predicted ones in a single arm trial.
  7. How to determine the area under the ROC curve for a binary diagnostic test.
  8. The concordance index is not proper.  Use the Index of Predictive Accuracy (IPA) instead.

D. Predictions Doctors Make

  1. The wisdom of crowds of doctors: averaging their individual predictions improves accuracy over the individuals themselves.
  2. Predicted probabilities coming from doctors are

E. Decision Analysis Limitations

  1. Rather than running a decision analysis at the bedside, apply a nomogram instead -- much easier and same answer.
  2. The method used to measure utilities affects the decision analytic recommendation.
  3. Unfortunately you probably have to measure individual patient utilities to run a decision analysis on someone.

F. Utility Assessment

  1. Stop multiplying health state utilities to get the utility of the combined health state.
  2. Why utilities are more helpful than traditional health-related quality of life measures with respect to medical decision making.
  3. The layout of the time trade-off is problematic.
  4. How to measure standard gamble on paper.