Primer: using decision analysis to improve clinical decision making in urology

被引:10
作者
Elkin, Elena B.
Vickers, Andrew J.
Kattan, Michael W.
机构
[1] Mem Sloan Kettering Canc Ctr, Dept Epidemiol & Biostat, Hlth Outcomes Res Grp, New York, NY 10021 USA
[2] Cleveland Clin, Dept Quantitat Hlth Sci, Cleveland, OH 44106 USA
来源
NATURE CLINICAL PRACTICE UROLOGY | 2006年 / 3卷 / 08期
关键词
decision analysis; Markov model; prostate cancer; urology; utility;
D O I
10.1038/ncpuro0556
中图分类号
R5 [内科学]; R69 [泌尿科学(泌尿生殖系疾病)];
学科分类号
1002 ; 100201 ;
摘要
Many clinical decisions in urology involve uncertainty about the course of disease or the effectiveness of treatment. Many decisions also involve trade-offs; for example, an improvement in patient survival at the cost of an increased risk of treatment-related adverse effects. Decision analysis is a formal, quantitative method for systematically comparing the benefits and harms of alternative clinical strategies under circumstances of uncertainty. The basic steps in performing a decision analysis are to define the clinical scenario or problem, identify the clinical strategies to be considered in the decision, enumerate all of the important sequelae of each strategy and their associated probabilities, define the outcome of interest, and assign a value to each possible outcome. Health outcomes can be defined in a number of ways, including quality-adjusted survival. A key aspect of decision analysis is allowing the values of particular health outcomes to vary from patient to patient, depending on individual preferences. Decision analysis has already been used to assess a variety of prevention, screening and treatment decisions in urology, and there is much potential for its future application. Greater incorporation of decision-analytic techniques into urology research and clinical practice might improve decision making, and thereby improve patient outcomes.
引用
收藏
页码:439 / 448
页数:10
相关论文
共 50 条
  • [41] A Framework for Clinicians to Improve the Decision-Making Process in Return to Sport
    Yung, Kate K.
    Ardern, Clare L.
    Serpiello, Fabio R.
    Robertson, Sam
    SPORTS MEDICINE-OPEN, 2022, 8 (01)
  • [42] Optimal multivariate financial decision making
    Bernard, C.
    Aquino, L. De Gennaro
    Vanduffel, S.
    EUROPEAN JOURNAL OF OPERATIONAL RESEARCH, 2023, 307 (01) : 468 - 483
  • [43] What is a clinical decision analysis study
    Aleem, Ilyas S.
    Schemitsch, Emil H.
    Hanson, Beate P.
    INDIAN JOURNAL OF ORTHOPAEDICS, 2008, 42 (02) : 137 - 139
  • [44] Collaborative Decision Making
    Owen, Daniel
    DECISION ANALYSIS, 2015, 12 (01) : 29 - 45
  • [45] Decision Making and Cancer
    Reyna, Valerie F.
    Nelson, Wendy L.
    Han, Paul K.
    Pignone, Michael P.
    AMERICAN PSYCHOLOGIST, 2015, 70 (02) : 105 - 118
  • [46] Artificial Intelligence and Mechanistic Modeling for Clinical Decision Making in Oncology
    Benzekry, Sebastien
    CLINICAL PHARMACOLOGY & THERAPEUTICS, 2020, 108 (03) : 471 - 486
  • [47] Factors Guiding Clinical Decision-Making in Genitourinary Oncology
    Wosny, Marie
    Aeppli, Stefanie
    Fischer, Stefanie
    Peres, Tobias
    Rothermundt, Christian
    Hastings, Janna
    CANCER MEDICINE, 2024, 13 (20):
  • [48] Clinical judgment versus decision analysis for managing device advisories
    Amin, Mitesh S.
    Wood, Mark A.
    Shepard, Richard K.
    Kalahasty, Gautham
    Ellenbogen, Kenneth A.
    PACE-PACING AND CLINICAL ELECTROPHYSIOLOGY, 2008, 31 (10): : 1236 - 1240
  • [49] Clinical Decision Analysis and Markov Modeling for Surgeons An Introductory Overview
    Hogendoorn, Wouter
    Moll, Frans L.
    Sumpio, Bauer E.
    Hunink, M. G. Myriam
    ANNALS OF SURGERY, 2016, 264 (02) : 268 - 274
  • [50] Decision analysis: Theories - Methods in clinical development and healthcare strategy
    Varlan, E
    LePaillier, R
    THERAPIE, 1996, 51 (02): : 117 - 122