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Cohort Studies

Marching Forward
Authors: Marcello Tonelli; Ravi Thadhani;
Abstract

Because of their well-documented advantages, randomized controlled trials (RCTs) represent the gold standard for testing hypotheses in medical research. However, RCTs are not ideal for addressing certain research questions ( e.g. , the risk for renal insufficiency associated with alcohol intake). In addition, conducting a properly designed RCT often requires substantial time and resources. For these reasons, including barriers of cost and need for rapid answers, the majority of clinical research studies in the renal literature use an observational design. Although there clearly is a great need for more RCTs that are conducted in populations with kidney disease, rigorous observational studies are extremely valuable and in many circumstances yield similar results as rigorous RCTs (1). Furthermore, although RCTs are considered the gold standard for examining the efficacy of a therapy, studies of prognosis often are addressed best by cohort studies. In this first of a multipart series dedicated to reviewing clinical research methods in nephrology, after providing a brief overview of observational studies, we focus on one of the major subtypes of observational designs: The cohort study. A glossary of common terms that are used in this and subsequent sections of this series is included in Table 1. Details on other methods ( e.g. , case-control studies, RCTs) will be the subject of future reviews. View this table: Table 1. Glossary The simplest form of observational study is the case report or case series, which describes the clinical course of individuals with a particular condition or diagnosis. Such studies often highlight a single clinical condition and at times even suggest a potential biologic mechanism. In a case series, the clinician gains an appreciation of the breadth of abnormalities ( e.g. , range of proteinuria in patients with HIV-associated nephropathy) that may characterize a single disease process. Although these studies may attempt to identify factors or treatments that …

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Keywords

Biomedical Research, Confounding Factors, Epidemiologic, Kaplan-Meier Estimate, Risk Assessment, Cohort Studies, Epidemiologic Studies, Bias, Nephrology, Risk Factors, Data Interpretation, Statistical, Humans, Kidney Failure, Chronic

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    citations
    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    57
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Top 10%
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Top 10%
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Top 10%
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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
57
Top 10%
Top 10%
Top 10%
bronze