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- Reilev, M, et al.
(författare)
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Methodology of the brodalumab assessment of hazards: a multicentre observational safety (BRAHMS) study
- 2023
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Ingår i: BMJ open. - : BMJ. - 2044-6055. ; 13:2, s. e066057-
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Tidskriftsartikel (refereegranskat)abstract
- Safe and effective pharmacological treatment is of paramount importance for treating severe psoriasis. Brodalumab, a monoclonal antibody against interleukin (IL) 17 receptor A, was granted marketing authorisation in the EU in 2017. The European Medicines Agency requested a postauthorisation safety study of brodalumab to address potential safety issues raised during drug development regarding major adverse cardiovascular events, suicidal conduct, cancer and serious infections.Methods and analysisBRodalumab Assessment of Hazards: A Multinational Safety is a multicentre observational safety study of brodalumab running from 2017 to 2029 using population-based healthcare databases from Denmark, Sweden, Norway, Netherlands, Germany and three different centres in Italy. A distributed database network approach is used, such that only aggregate data are exchanged between sites.Two types of designs are used: a case-time-control design to study acute effects of transient treatment and a variation of the new user active comparator design to study the effects of transient or chronic treatment. As comparators, inhibitors of TNF-α, inhibitors of IL-12 and IL-23, and other inhibitors of cytokine IL-17A are included.In the self-controlled case-time-control design, the risk of developing the outcome of interest during periods of brodalumab use is compared within individuals to the risk in periods without use.In the active comparator cohort design, new users of brodalumab are identified and matched to new users of active comparators. Potential baseline confounders are adjusted for by using propensity score modelling. For outcomes that potentially require large cumulative exposure, an adapted active comparator design has been developed.Ethics and disseminationThe study is approved by relevant authorities in Denmark, Norway, Sweden, the Netherlands, Germany and Italy in line with the relevant legislation at each site. Data confidentiality is secured by the distributed network approach. Results will be published in peer-reviewed journals.Trial registration numberEUPAS30280.
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| 12. |
- Reilev, M, et al.
(författare)
-
Methodology of the brodalumab assessment of hazards: a multicentre observational safety (BRAHMS) study
- 2023
-
Ingår i: BMJ open. - : BMJ. - 2044-6055. ; 13:2, s. e066057-
-
Tidskriftsartikel (refereegranskat)abstract
- Safe and effective pharmacological treatment is of paramount importance for treating severe psoriasis. Brodalumab, a monoclonal antibody against interleukin (IL) 17 receptor A, was granted marketing authorisation in the EU in 2017. The European Medicines Agency requested a postauthorisation safety study of brodalumab to address potential safety issues raised during drug development regarding major adverse cardiovascular events, suicidal conduct, cancer and serious infections.Methods and analysisBRodalumab Assessment of Hazards: A Multinational Safety is a multicentre observational safety study of brodalumab running from 2017 to 2029 using population-based healthcare databases from Denmark, Sweden, Norway, Netherlands, Germany and three different centres in Italy. A distributed database network approach is used, such that only aggregate data are exchanged between sites.Two types of designs are used: a case-time-control design to study acute effects of transient treatment and a variation of the new user active comparator design to study the effects of transient or chronic treatment. As comparators, inhibitors of TNF-α, inhibitors of IL-12 and IL-23, and other inhibitors of cytokine IL-17A are included.In the self-controlled case-time-control design, the risk of developing the outcome of interest during periods of brodalumab use is compared within individuals to the risk in periods without use.In the active comparator cohort design, new users of brodalumab are identified and matched to new users of active comparators. Potential baseline confounders are adjusted for by using propensity score modelling. For outcomes that potentially require large cumulative exposure, an adapted active comparator design has been developed.Ethics and disseminationThe study is approved by relevant authorities in Denmark, Norway, Sweden, the Netherlands, Germany and Italy in line with the relevant legislation at each site. Data confidentiality is secured by the distributed network approach. Results will be published in peer-reviewed journals.Trial registration numberEUPAS30280.
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| 13. |
- Romagnoni, A, et al.
(författare)
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Comparative performances of machine learning methods for classifying Crohn Disease patients using genome-wide genotyping data
- 2019
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Ingår i: Scientific reports. - : Springer Science and Business Media LLC. - 2045-2322. ; 9:1, s. 10351-
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Tidskriftsartikel (refereegranskat)abstract
- Crohn Disease (CD) is a complex genetic disorder for which more than 140 genes have been identified using genome wide association studies (GWAS). However, the genetic architecture of the trait remains largely unknown. The recent development of machine learning (ML) approaches incited us to apply them to classify healthy and diseased people according to their genomic information. The Immunochip dataset containing 18,227 CD patients and 34,050 healthy controls enrolled and genotyped by the international Inflammatory Bowel Disease genetic consortium (IIBDGC) has been re-analyzed using a set of ML methods: penalized logistic regression (LR), gradient boosted trees (GBT) and artificial neural networks (NN). The main score used to compare the methods was the Area Under the ROC Curve (AUC) statistics. The impact of quality control (QC), imputing and coding methods on LR results showed that QC methods and imputation of missing genotypes may artificially increase the scores. At the opposite, neither the patient/control ratio nor marker preselection or coding strategies significantly affected the results. LR methods, including Lasso, Ridge and ElasticNet provided similar results with a maximum AUC of 0.80. GBT methods like XGBoost, LightGBM and CatBoost, together with dense NN with one or more hidden layers, provided similar AUC values, suggesting limited epistatic effects in the genetic architecture of the trait. ML methods detected near all the genetic variants previously identified by GWAS among the best predictors plus additional predictors with lower effects. The robustness and complementarity of the different methods are also studied. Compared to LR, non-linear models such as GBT or NN may provide robust complementary approaches to identify and classify genetic markers.
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