Depending on the field of application, the algorithms considered differ in particular on the metrics used to qualify the groups of interest. There are a large number of methods for finding these subgroups that have been developed in different areas of research. Searching for subgroups of items with properties that differentiate them from others is a very general task in data analysis. The results demonstrate its ability to identify and support a short list of highly credible and diverse data-driven subgroups for both prognostic and predictive tasks. We compared Q-Finder with state-of-the-art approaches from both Subgroup Identification and Knowledge Discovery in Databases literature. To illustrate this algorithm, we applied it on the database of the International Diabetes Management Practice Study (IDMPS) to better understand the drivers of improved glycemic control and rate of episodes of hypoglycemia in type 2 diabetics patients. Those subgroups are tested on independent data to assess their consistency across databases, while preserving statistical power by limiting the number of tests. The top-k credible subgroups are then selected, while accounting for subgroups’ diversity and, possibly, clinical relevance. This allows Q-Finder to directly target and assess subgroups on recommended credibility criteria. It combines an exhaustive search with a cascade of filters based on metrics assessing key credibility criteria, including relative risk reduction assessment, adjustment on confounding factors, individual feature’s contribution to the subgroup’s effect, interaction tests for assessing between-subgroup treatment effect interactions and tests adjustment (multiple testing). In this paper, we present the Q-Finder algorithm that aims to generate statistically credible subgroups to answer clinical questions, such as finding drivers of natural disease progression or treatment response. However, both the limited consideration by standard SD algorithms of recommended criteria to define credible subgroups and the lack of statistical power of the findings after correcting for multiple testing hinder the generation of hypothesis and their acceptance by healthcare authorities and practitioners. Within the latter area, subgroup discovery (SD) data mining approach is widely used-particularly in precision medicine-to evaluate treatment effect across different groups of patients from various data sources (be it from clinical trials or real-world data). In randomized clinical trials (RCTs), confirmatory subgroup analyses focus on the assessment of drugs in predefined subgroups, while exploratory ones allow a posteriori the identification of subsets of patients who respond differently. Cannot detect your public IP Access to myQNAPcloud myQNAPcloud Link services.Addressing the heterogeneity of both the outcome of a disease and the treatment response to an intervention is a mandatory pathway for regulatory approval of medicines. Failed to access myQNAPcloud myQNAPcloud Link service. Accessed myQNAPcloud myQNAPcloud Link services. The user is not in the whitelist of the device. This device has been in your favorite device You have reached the maximum number of members. No mapped ports found! This device is not in your favorite list. The specified access code is not found for the device. The specified user is not found for the device. The device has not published any service.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |