Medical Articles Involving Multivariable Modeling to Critique
Students should divide into groups each containing 3 or 4 persons. Each group should select two of the following articles to read and critique. Define group membership and claim articles by inserting text at the bottom of this page. Each group will be asked to present each of its critiques to the class on 26 Feb 04. Describe the study and its goals, the information base on which the model was developed, and discuss good and bad aspects of the analysis and how the results were presented. The instructor will be able to project tables and graphics in the articles in class. Groups may want to bring handouts or slides of other material they wish to present.
Barquet, Domingo, Cayla et al: Prognostic factors in meningococcal disease. JAMA 1997;278:491-496. Paper copies will be made available on request.
Budde, Haude, Hopp et al: A prognostic computer model to predict individual outcome in interventional cardiology. Eur Heart J 1997:18:1611-1619. Paper copies will be made available on request.
Kirby, Eng, Dantler et al: Neural network prediction of obstructive sleep apnea from clinical criteria. CHEST 1999;116:409-415. Paper copies will be made available on request.
Sarkisian, Liu, Gutierrez et al: Modifiable risk factors predict functionable decline among older women: a prospectively validated clinical prediction tool. J Am Geriatr Soc 48:170-178,2000. Paper copies will be made available on request.
-- FrankHarrell - 18 Feb 2004
Hiroyuki Kobayashi, Catherine Brown, Emily Garland, Patrice Joseph. 1. Predicting ten-year survival of patients with primary cutaneous melanoma. 2. Prediction of Survival for Older Hospitalized Patients: The HELP Survival Model.
Heidi Smith, Seema Basi, Mary Taylor, Pratik Pandharipande. 1. Prediction of Pulmonary Embolism extent by clinical findings. 2. The stroke prognostic instrument II.
Charles Leys, Todd Rice, Matt Dzurik. 1. Preoperative prediction model of outcome after cholecystectomy for symptomatic gallstones. 2. A prognostic model that makes quantitative estimates of probability of relapse for breast cancer patients