Pdb16 - Compare Renal Functional Prservation Outcome of Sglt2 Inhibitor vs Dpp4 Inhibitor in Patients With Type 2 Diabetes: A Retrospective Cohort Study of Japanese Commercial Database With Advanced Analytics Approach

Zhou, Fang Liz; Watada, Hirotaka; Tajima, Yuki; Berthelot, Mathilde; Kang, Dian; Esnault, C.; Shuto, Yujin; Maegawa, Hiroshi; Koya, Daisuke

doi:10.1016/j.jval.2018.09.722

Cited by 2 publications

(3 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Q-Finder was applied in the field of diabetes to many other research questions, such as the detection of patient profiles that benefit the most of SGLT2i compared to DDP4i in terms of renal function preservation, using Electronic Health Record data ( Zhou et al, 2018 ; Zhou et al, 2019 ); the identification of profiles of patients who better control their blood sugar, using data from pooled observational studies ( Rollot, 2019 , “Reali project”); and the discovery of new predictors of diabetic ketoacidosis (DKA), a serious complication of type 1 diabetes, using data from a national diabetes registry ( Ibald-Mulli et al, 2019 ). Q-Finder was also successfully applied in the context of several other pathologies such as hypophosphatasia, using SNPs data ( Mornet et al, 2020 ), dry eye disease using prospective clinical trials data ( Amrane et al, 2015 ), and cancer using clinical data from RCTs ( Nabholtz, 2012 ; Dumontet et al, 2016 ; Dumontet et al, 2018 ; Alves et al, 2020 ) or transcriptomic data from a research cohort ( Adam et al, 2016 ).…”

Section: Discussionmentioning

confidence: 99%

“…For further details, an in-depth discussion of Q-Finder is also proposed in Section 14 of supplementary materials. This approach has been applied in several therapeutic areas, with published examples available ( Nabholtz, 2012 ; Eveno, 2014 ; Amrane et al, 2015 ; Adam et al, 2016 ; Dumontet et al, 2016 ; Gaston-Mathe, 2017 ; Dumontet et al, 2018 ; Rollot, 2019 ; Zhou et al, 2018 ; Ibald-Mulli, 2019 ; Zhou et al, 2019 ; Alves et al, 2020 ; Mornet, 2020 ).…”

Section: Q-finder’s Pipeline To Increase Credible Findings Generationmentioning

confidence: 99%

See 1 more Smart Citation

Q-Finder: An Algorithm for Credible Subgroup Discovery in Clinical Data Analysis — An Application to the International Diabetes Management Practice Study

Esnault¹,

Gadonna²,

Queyrel

et al. 2020

Front. Artif. Intell.

View full text Add to dashboard Cite

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. 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. 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). 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. 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. 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). This allows Q-Finder to directly target and assess subgroups on recommended credibility criteria. The top-k credible subgroups are then selected, while accounting for subgroups’ diversity and, possibly, clinical relevance. Those subgroups are tested on independent data to assess their consistency across databases, while preserving statistical power by limiting the number of tests. 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. We compared Q-Finder with state-of-the-art approaches from both Subgroup Identification and Knowledge Discovery in Databases literature. 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.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Q-finder’s Pipeline To Increase Credible Findings Generationmentioning

confidence: 99%

Q-Finder: An Algorithm for Credible Subgroup Discovery in Clinical Data Analysis — An Application to the International Diabetes Management Practice Study

Esnault¹,

Gadonna²,

Queyrel

et al. 2020

Front. Artif. Intell.

View full text Add to dashboard Cite

show abstract

“…For further details, an in-depth discussion of Q-Finder is also proposed in Section 14 of supplementary materials. This approach has been applied in several therapeutic areas, with published examples available (Nabholtz, 2012;Eveno, 2014;Amrane et al, 2015;Adam et al, 2016;Dumontet et al, 2016;Gaston-Mathe, 2017;Dumontet et al, 2018;Rollot, 2019;Zhou et al, 2018;Ibald-Mulli, 2019;Zhou et al, 2019;Alves et al, 2020;Mornet, 2020).…”

Section: Q-finder's Pipeline To Increase Credible Findings Generationmentioning

confidence: 99%

datasheet1.pdf

View full text Add to dashboard Cite

Pdb16 - Compare Renal Functional Prservation Outcome of Sglt2 Inhibitor vs Dpp4 Inhibitor in Patients With Type 2 Diabetes: A Retrospective Cohort Study of Japanese Commercial Database With Advanced Analytics Approach

Cited by 2 publications

References 0 publications

Q-Finder: An Algorithm for Credible Subgroup Discovery in Clinical Data Analysis — An Application to the International Diabetes Management Practice Study

Q-Finder: An Algorithm for Credible Subgroup Discovery in Clinical Data Analysis — An Application to the International Diabetes Management Practice Study

datasheet1.pdf

Contact Info

Product

Resources

About