ArticlePloS one2014
Should we use standard survival models or the illness-death model for interval-censored data to investigate risk factors of chronic kidney disease progression?
Article in PloS one, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed, 9 citations in OpenAlex.
- Combined effects of blood pressure and glucose status on the risk of chronic kidney disease.Hypertension research : official journal of the Japanese Society of Hypertension · 2024Article
- Performance of Cox regression models for composite time-to-event endpoints with component-wise censoring in randomized trials.Clinical trials (London, England) · 2023Article
- Understanding International Variations in Kidney Failure Incidence and Initiation of Replacement Therapy.Kidney international reports · 2022Article
- Factors associated with renal function state transitions: A population-based community survey in Taiwan.Frontiers in public health · 2022Article
- Association of moderate and vigorous physical activity with incidence of type 2 diabetes and subsequent mortality: 27 year follow-up of the Whitehall II study.Diabetologia · 2020Article
- The Non-Dialysis Chronic Renal Insufficiency study (ND-CRIS): an open prospective hospital-based cohort study in France.BMC nephrology · 2016Article
- Estimating Chronic Kidney Disease Stage Transitions from Irregular Electronic Health Record Data Using an Expectation-Maximization Framework.MDM policy & practiceArticle
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundIn studies investigating risk factors of chronic kidney disease (CKD) progression, one may be interested in estimating factors effects on both a fall of glomerular filtration rate (GFR) below a specific level (i.e., a CKD stage) and death. Such studies have to account for the fact that GFR is measured at intermittent visit only, which implies that progression to the stage of interest is unknown for patients who die before being observed at that stage. Our objective was to compare the results of an illness-death model that handles this uncertainty, with frequently used survival models.
methodsThis study included 1,519 patients from the NephroTest cohort with CKD stages 1-4 at baseline (69% males, 59±15 years, median protein/creatinine ratio [PCR] 27.4 mg/mmol) and subsequent annual measures of GFR (follow-up time 4.3±2.7 years). Each model was used to estimate the effects of sex, age, PCR, and GFR at baseline on the hazards of progression to CKD stage 5 (GFR<15 mL/min/1.73 m2, n = 282 observed) and death (n = 168).
resultsFor progression to stage 5, there were only minor differences between results from the different models. The differences between results were higher for the hazard of death before or after progression. Our results also suggest that previous findings on the effect of age on end-stage renal disease are more likely due to a strong impact of age on death than to an effect on progression. The probabilities of progression were systematically under-estimated with the survival model as compared with the illness-death model.
conclusionsThis study illustrates the advantages of the illness-death model for accurately estimating the effects of risk factors on the hazard of progression and death, and probabilities of progression. It avoids the need to choose arbitrary time-to-event and time-to-censoring, while accounting for both interval censoring and competition by death, using a single analytical model.
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