Real world study and its application in gastrointestinal stromal tumor
10.3760/cma.j.issn.1671-0274.2019.09.005
- VernacularTitle: 胃肠间质瘤领域的真实世界临床研究
- Author:
Zhaolun CAI
1
;
Yuan YIN
;
Bo ZHANG
Author Information
1. Department of Gastrointestinal Surgery, West China Hospital, Sichuan University, Chengdu 610041, China
- Publication Type:Journal Article
- Keywords:
Gastrointestinal stromal tumor;
Real world study;
Pragmatic randomized controlled trial;
Observational study;
Pharmacoeconomic
- From:
Chinese Journal of Gastrointestinal Surgery
2019;22(9):826-830
- CountryChina
- Language:Chinese
-
Abstract:
Gastrointestinal stromal tumor (GIST) is a relatively rare type of gastrointestinal tract tumors. Thus, it is difficult to perform randomized controlled trials (RCTs) of GIST in a single center, which are often plagued by a small number of participants. Real world study (RWS) is a complement to the evidence derived from traditional RCT. Emerging sources of real world data offer enormous opportunity for deeper understanding of why treatments work (or not) and for whom. Evidence generated from RWD can help clarify best use of treatments for individuals and populations, and care value. Thus, RWS of GIST has attracted much attention. RWS helps us better understand the diagnosis, treatment, prognosis and outcome prediction of GIST in clinical practice. GIST is often misdiagnosed as other tumor. A diagnostic test provides evidence on how well a test correctly identifies or rules out GIST. Therapeutic research aims to evaluate the effectiveness and/or safety of a therapy for GIST. Prognostic research aims to forecast the likely outcome of GIST, explore the factors affecting the outcome, and analyze quality of life. Predictive research aims to quantify the probability of identification or health outcome of GIST based on a set of predictors. Pharmacoeconomic data in real world can evaluate the cost-effectiveness of medicinal products for GISTs and serve as a guidance tool for optimal healthcare resource allocation. Attaching importance to data sourse and data quality, strenthening communicate with a qualified statistician, and the implementation of a standardized process are necessary for performing a high-quality RWS.
