ReviewClinical journal of the American Society of Nephrology : CJASN2017
Statistical Methods for Cohort Studies of CKD: Prediction Modeling.
Review in Clinical journal of the American Society of Nephrology : CJASN, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
16 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Methodological quality of cohort study on rheumatic diseases in China: A systematic review.PloS one · 2020Pooled it
- Trial
- Effect of healthy lifestyle on renal dysfunction risk: interactions with genetic risk.Clinical kidney journal · 2025Article
- The Impact of Primary Renal Diagnosis on Prognosis and the Varying Predictive Power of Albuminuria in the NURTuRE-CKD Study.American journal of nephrology · 2025Article
- Lipid parameters, adipose tissue distribution and prognosis prediction in chronic kidney Disease patients.Lipids in health and disease · 2024Article
- A pre-transplantation risk assessment tool for graft survival in Dutch pediatric kidney recipients.Clinical kidney journal · 2023Article
- Article
- Adherence to multidisciplinary care in a prospective chronic kidney disease cohort is associated with better outcomes.PloS one · 2022Article
- OMICS in Chronic Kidney Disease: Focus on Prognosis and Prediction.International journal of molecular sciences · 2021Review
- Predicting mortality in hemodialysis patients using machine learning analysis.Clinical kidney journal · 2021Article
- Contribution of Predictive and Prognostic Biomarkers to Clinical Research on Chronic Kidney Disease.International journal of molecular sciences · 2020Review
- The Role of Prognostic and Predictive Biomarkers for Assessing Cardiovascular Risk in Chronic Kidney Disease Patients.BioMed research international · 2020Review
- Identification of young adults at risk of an accelerated loss of kidney function in an area affected by Mesoamerican nephropathy.BMC nephrology · 2019Article
- Hematuria as a risk factor for progression of chronic kidney disease and death: findings from the Chronic Renal Insufficiency Cohort (CRIC) Study.BMC nephrology · 2018Article
- Prediction Model and Risk Stratification Tool for Survival in Patients With CKD.Kidney international reports · 2018Article
- Integrated Digital Health System Tools to Support Decision Making and Treatment Preparation in CKD: The PREPARE NOW Study.Kidney medicineArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
13 authors.
Funding
Abstract
Prediction models are often developed in and applied to CKD populations. These models can be used to inform patients and clinicians about the potential risks of disease development or progression. With increasing availability of large datasets from CKD cohorts, there is opportunity to develop better prediction models that will lead to more informed treatment decisions. It is important that prediction modeling be done using appropriate statistical methods to achieve the highest accuracy, while avoiding overfitting and poor calibration. In this paper, we review prediction modeling methods in general from model building to assessing model performance as well as the application to new patient populations. Throughout, the methods are illustrated using data from the Chronic Renal Insufficiency Cohort Study.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.