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Your search keyword '"Nigar Sekercioglu"' showing total 2 results
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2 results on '"Nigar Sekercioglu"'

1. Dialysis Initiation and All-Cause Mortality Among Incident Adult Patients With Advanced CKD: A Meta-analysis With Bias AnalysisPlain-Language Summary

Author

Rui Fu, Nigar Sekercioglu, Maya B. Mathur, Rachel Couban, and Peter C. Coyte

Subjects
Dialysis, conservative management, survival, mortality, unmeasured confounding, treatment effects, Diseases of the genitourinary system. Urology, RC870-923
Abstract

Rationale & Objectives: Due to unmeasured confounding, observational studies have limitations when assessing whether dialysis initiation reduces mortality compared with conservative therapy among adults with advanced chronic kidney disease (CKD). We addressed this issue in this meta-analysis. Study Design: Meta-analysis with bias analysis for unmeasured confounding. Setting & Study Population: Adults with stage 4 or 5 CKD who had initiated dialysis or conservative treatment. Selection Criteria for Studies: Prospective or retrospective cohort studies comparing survival of dialysis versus conservatively managed patients were searched on MEDLINE and Embase from January 2009 to March 20, 2019. Data Extraction: HRs of all-cause mortality associated with dialysis initiation compared with conservative treatment. Analytical Approach: We pooled HRs using a random-effects model. We estimated the percentage of effect sizes more protective than HRs of 0.80 and severity of unmeasured confounding that could reduce this percentage to only 10%. Subgroup analysis was performed for studies with only older patients (aged ≥ 65 years). Results: 12 studies were included that involved 16,609 dialysis patients and 3,691 conservatively managed patients. A random-effects model suggested that dialysis initiation was associated with a mean mortality HR of 0.47 (95% CI, 0.34-0.64), in which 92% (95% CI, 50%-100%) of the true effects were more protective than HRs of 0.80. To reduce the percentage of HRs

Published
2021
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2. GRADE concept paper 2: Concepts for Judging Certainty on the Calibration of Prognostic Models in a Body of Validation Studies

Author

Ana C. Alba, Marialena Trivella, Nigar Sekercioglu, Carlos Canelo, Pavel S Roshanov, Holger J. Schünemann, Gordon H. Guyatt, Zachary Munn, Farid Foroutan, Alfonso Iorio, Nina Kreuzberger, Nicole Skoetz, Romina Brignardello-Petersen, and Richard D Riley

Subjects
Prognostic factor, Epidemiology, Calibration (statistics), Computer science, media_common.quotation_subject, probability, Inference, forecasting, male, systematic review, reassurance, Selection (linguistics), Econometrics, Humans, Point estimation, human, outcome assessment, Prognostic models, media_common, Probability, adult, article, risk assessment, R735, Certainty, calibration, Prognosis, R1, clinical practice, Systematic review, female, validation study, Calibration, Forecasting, meta analysis
Abstract

Background: Prognostic models combine several prognostic factors to provide an estimate of the likelihood (or risk) of future events in individual patients, conditional on their prognostic factor values. A fundamental part of evaluating prognostic models is undertaking studies to determine whether their predictive performance, such as calibration and discrimination, is reproduced across settings. Systematic reviews and meta-analyses of studies evaluating prognostic models’ performance are a necessary step for selection of models for clinical practice and for testing the underlying assumption that their use will improve outcomes, including patient's reassurance and optimal future planning. Methods: In this paper, we highlight key concepts in evaluating the certainty of evidence regarding the calibration of prognostic models. Results and Conclusion: Four concepts are key to evaluating the certainty of evidence on prognostic models’ performance regarding calibration. The first concept is that the inference regarding calibration may take one of two forms: deciding whether one is rating certainty that a model's performance is satisfactory or, instead, unsatisfactory, in either case defining the threshold for satisfactory (or unsatisfactory) model performance. Second, inconsistency is the critical GRADE domain to deciding whether we are rating certainty in the model performance being satisfactory or unsatisfactory. Third, depending on whether one is rating certainty in satisfactory or unsatisfactory performance, different patterns of inconsistency of results across studies will inform ratings of certainty of evidence. Fourth, exploring the distribution of point estimates of observed to expected ratio across individual studies, and its determinants, will bear on the need for and direction of future research. © 2021

Published
2021

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