TY - JOUR
T1 - Clinical epidemiological analysis of cohort studies investigating the pathogenesis of kidney disease
AU - Tanaka, Shigeru
AU - Nakano, Toshiaki
AU - Tsuruya, Kazuhiko
AU - Kitazono, Takanari
N1 - Funding Information:
This study was supported by the Kidney Foundation (H19 JKFB 07-13, H20 JKFB 08-8, H23 JKFB 11-11) and the Japan Dialysis Outcome Research Foundation (H19-076-02, H20-003). The funders of this study had no role in study design; collection, analysis, and interpretation of data; writing the report; or the decision to submit the report for publication.
Publisher Copyright:
© 2021, The Author(s).
PY - 2022/1
Y1 - 2022/1
N2 - In recent years, large cohort studies of patients with chronic kidney disease (CKD) have been established all over the world. These studies have attempted to analyze the pathogenesis of CKD using a large body of published evidence. The design of cohort studies is characterized by the measurement of the exposure prior to the occurrence of the outcome, which has the advantage of clarifying the temporal relationship between predictors and outcomes and estimating the strength of the causal relationship between predictors and multiple outcomes. Recent advances in biostatistical analysis methods, such as propensity scores and risk prediction models, are facilitating causal inference using higher quality evidence with greater precision in observational studies. In this review, we will discuss clinical epidemiological research of kidney disease based on the analysis of observational cohort data sets, with a focus on our previous studies.
AB - In recent years, large cohort studies of patients with chronic kidney disease (CKD) have been established all over the world. These studies have attempted to analyze the pathogenesis of CKD using a large body of published evidence. The design of cohort studies is characterized by the measurement of the exposure prior to the occurrence of the outcome, which has the advantage of clarifying the temporal relationship between predictors and outcomes and estimating the strength of the causal relationship between predictors and multiple outcomes. Recent advances in biostatistical analysis methods, such as propensity scores and risk prediction models, are facilitating causal inference using higher quality evidence with greater precision in observational studies. In this review, we will discuss clinical epidemiological research of kidney disease based on the analysis of observational cohort data sets, with a focus on our previous studies.
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U2 - 10.1007/s10157-021-02121-9
DO - 10.1007/s10157-021-02121-9
M3 - Review article
C2 - 34374903
AN - SCOPUS:85112181657
VL - 26
JO - Clinical and Experimental Nephrology
JF - Clinical and Experimental Nephrology
SN - 1342-1751
IS - 1
ER -