Literature DB >> 17868735

Decision analysis and Markov modeling in urology.

Michael H Hsieh1, Maxwell V Meng.   

Abstract

PURPOSE: The process of decision making in medicine has become increasingly complex. This has developed as the result of increasing amounts of data, often without direct information or answers regarding a specific clinical problem. The use of mathematical models has grown and they are commonly used in all areas. We describe and discuss the application of decision analysis and Markov modeling in urology.
MATERIALS AND METHODS: We define decision analysis and Markov models, providing a background and primer to educate the urologist. In addition, we performed a complete MEDLINE database search for all decision analyses in all disciplines of urology, serving as a reference summarizing the current status of the literature.
RESULTS: The review provides urologists with the ability to critically evaluate studies involving decision analysis and Markov models. We identified 107 publications using decision analysis or Markov modeling in urology. A total of 36 studies used Markov models, whereas the remainder used standard decision analytical models. All areas of urology, including oncology, pediatrics, andrology, endourology, reconstruction, transplantation and erectile dysfunction, were represented.
CONCLUSIONS: Decision analysis and Markov modeling are widely used approaches in the urological literature. Understanding the fundamentals of these tools is critical to the practicing urologist.

Entities:  

Mesh:

Year:  2007        PMID: 17868735     DOI: 10.1016/j.juro.2007.07.006

Source DB:  PubMed          Journal:  J Urol        ISSN: 0022-5347            Impact factor:   7.450


  5 in total

1.  The value of newborn urinary proteome analysis in the evaluation and management of ureteropelvic junction obstruction: a cost-effectiveness study.

Authors:  Hrair-George O Mesrobian
Journal:  World J Urol       Date:  2008-11-26       Impact factor: 4.226

Review 2.  The use of clinical utility assessments in early clinical development.

Authors:  Anis A Khan; Itay Perlstein; Rajesh Krishna
Journal:  AAPS J       Date:  2009-01-16       Impact factor: 4.009

Review 3.  Quality assessment of economic analyses in pediatric urology.

Authors:  Paul J Kokorowski; Jonathan C Routh; Caleb P Nelson
Journal:  Urology       Date:  2013-02       Impact factor: 2.649

4.  When is pharmacogenetic testing for antidepressant response ready for the clinic? A cost-effectiveness analysis based on data from the STAR*D study.

Authors:  Roy H Perlis; Amanda Patrick; Jordan W Smoller; Philip S Wang
Journal:  Neuropsychopharmacology       Date:  2009-06-03       Impact factor: 7.853

5.  Comparative quality-adjusted survival analysis between radiation therapy alone and radiation with androgen deprivation therapy in patients with locally advanced prostate cancer: a secondary analysis of Radiation Therapy Oncology Group 85-31 with novel decision analysis methods.

Authors:  Soyeon Ahn; Minjung Lee; Chang Wook Jeong
Journal:  Prostate Int       Date:  2018-02-02
  5 in total

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