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Longitudinal Prospective Cohort Studies: A Research Method Primer

观察研究 队列 队列研究 人口 临床研究设计 因果关系(物理学) 自然史 研究设计 医学 心理学 混淆 计量经济学 统计 人口学 临床试验 环境卫生 数学 内科学 量子力学 社会学 物理
作者
Jasmine Zouhair
出处
期刊:Undergraduate Research in Natural and Clinical Science and Technology (URNCST) Journal 卷期号:7 (3): 1-5 被引量:1
标识
DOI:10.26685/urncst.399
摘要

Introduction: A cohort study is a nonexperimental study design used to investigate the outcomes of a particular risk factor. They help researchers understand the prevalence, distribution, and correlation of variables in a population and function by following participants over a period of time, typically years. Utility: The results of well-designed observational studies are comparable to those of randomized controlled trials. Among some of the strengths of cohort studies is the ability to measure incidence rates, and to allow for a wide range of variables to be examined. In addition, one can examine disease progression and natural history because of their longitudinal design characteristic. Specifically, they are advantageous for rare exposures since subjects are chosen based on exposure status and can be monitored throughout the study for any changes caused by said exposure. Although they can infer a relationship between variables, they do not confirm causality. Challenges: One of the greatest challenges posed by cohort studies is the considerable amount of time and funding required to conduct them as they require large samples. Other challenges include, but are not limited to, maintaining follow-ups and accounting for withdrawals, and minimal control over the variables that are being studied Limitations: Variables may be measured incorrectly or inconsistently, resulting in information bias. For diseases with extensive latency periods, this study strategy is ineffective and cannot be used to establish causation between variables because the disease may have not completely manifested in the time it takes to conduct the study. Another significant limitation of this design is the sources of bias that could jeopardize the reliability of the study as a result of faulty measurement, an unrepresentative sample, or the differing impact of other factors on the association of interest.

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