PURPOSE: We evaluated the reproducibility of a study characterizing newly-diagnosed multiple myeloma (MM) patients within an electronic health records (EHR) database using different analytic tools. METHODS: We reproduced the findings of a descriptive cohort study using an iterative two-phase approach. In Phase I, a common protocol and statistical analysis plan (SAP) were implemented by independent investigators using the Aetion Evidence Platform® (AEP), a rapid-cycle analytics tool, and SAS statistical software as a gold standard for statistical analyses. Using the UK Clinical Practice Research Datalink (CPRD) dataset, the study included patients newly diagnosed with MM within primary care setting and assessed baseline demographics, conditions, drug exposure, and laboratory procedures. Phase II incorporated analysis revisions based on our initial comparison of the Phase I findings. Reproducibility of findings was evaluate by calculating the match rate and absolute difference in prevalence between the SAS and AEP study results. RESULTS: Phase I yielded slightly discrepant results, prompting amendments to SAP to add more clarity to operational decisions. After detailed specification of data and operational choices, exact concordance was achieved for the number of eligible patients (N = 2646), demographics, comorbidities (i.e., osteopenia, osteoporosis, cardiovascular disease [CVD], and hypertension), bone pain, skeletal-related events, drug exposure, and laboratory investigations in the Phase II analyses. CONCLUSIONS: In this reproducibility study, a rapid-cycle analytics tool and traditional statistical software achieved near-exact findings after detailed specification of data and operational choices. Transparency and communication of the study design, operational and analytical choices between independent investigators were critical to achieve this reproducibility.
PURPOSE: We evaluated the reproducibility of a study characterizing newly-diagnosed multiple myeloma (MM) patients within an electronic health records (EHR) database using different analytic tools. METHODS: We reproduced the findings of a descriptive cohort study using an iterative two-phase approach. In Phase I, a common protocol and statistical analysis plan (SAP) were implemented by independent investigators using the Aetion Evidence Platform® (AEP), a rapid-cycle analytics tool, and SAS statistical software as a gold standard for statistical analyses. Using the UK Clinical Practice Research Datalink (CPRD) dataset, the study included patients newly diagnosed with MM within primary care setting and assessed baseline demographics, conditions, drug exposure, and laboratory procedures. Phase II incorporated analysis revisions based on our initial comparison of the Phase I findings. Reproducibility of findings was evaluate by calculating the match rate and absolute difference in prevalence between the SAS and AEP study results. RESULTS: Phase I yielded slightly discrepant results, prompting amendments to SAP to add more clarity to operational decisions. After detailed specification of data and operational choices, exact concordance was achieved for the number of eligible patients (N = 2646), demographics, comorbidities (i.e., osteopenia, osteoporosis, cardiovascular disease [CVD], and hypertension), bone pain, skeletal-related events, drug exposure, and laboratory investigations in the Phase II analyses. CONCLUSIONS: In this reproducibility study, a rapid-cycle analytics tool and traditional statistical software achieved near-exact findings after detailed specification of data and operational choices. Transparency and communication of the study design, operational and analytical choices between independent investigators were critical to achieve this reproducibility.
Authors: Olaf H Klungel; Xavier Kurz; Mark C H de Groot; Raymond G Schlienger; Stephanie Tcherny-Lessenot; Lamiae Grimaldi; Luisa Ibáñez; Rolf H H Groenwold; Robert F Reynolds Journal: Pharmacoepidemiol Drug Saf Date: 2016-03 Impact factor: 2.890
Authors: Sinéad M Langan; Sigrún Aj Schmidt; Kevin Wing; Vera Ehrenstein; Stuart G Nicholls; Kristian B Filion; Olaf Klungel; Irene Petersen; Henrik T Sorensen; William G Dixon; Astrid Guttmann; Katie Harron; Lars G Hemkens; David Moher; Sebastian Schneeweiss; Liam Smeeth; Miriam Sturkenboom; Erik von Elm; Shirley V Wang; Eric I Benchimol Journal: BMJ Date: 2018-11-14
Authors: Anouchka Seesaghur; Natalia Petruski-Ivleva; Victoria Banks; Jocelyn Ruoyi Wang; Pattra Mattox; Edwin Hoeben; Joe Maskell; David Neasham; Shannon L Reynolds; George Kafatos Journal: Pharmacoepidemiol Drug Saf Date: 2020-11-21 Impact factor: 2.890
Authors: Anouchka Seesaghur; Natalia Petruski-Ivleva; Victoria Louise Banks; Jocelyn Ruoyi Wang; Ali Abbasi; David Neasham; Karthik Ramasamy Journal: BMJ Open Date: 2021-10-06 Impact factor: 3.006
Authors: Anouchka Seesaghur; Natalia Petruski-Ivleva; Victoria Banks; Jocelyn Ruoyi Wang; Pattra Mattox; Edwin Hoeben; Joe Maskell; David Neasham; Shannon L Reynolds; George Kafatos Journal: Pharmacoepidemiol Drug Saf Date: 2020-11-21 Impact factor: 2.890