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Basic Research Design:
Published in Lynne M. Bianchi, Research during Medical Residency, 2022
Lynne M. Bianchi, Luke J. Rosielle, Justin Puller, Kristin Juhasz
Retrospective cohort studies, like other retrospective studies, are relatively fast and inexpensive to complete.
Cancer Epidemiology
Published in Trevor F. Cox, Medical Statistics for Cancer Studies, 2022
Cohort studies are forward looking, where individuals are selected to be members of one or more cohorts, the selection being based on risk exposure and demographic variables. The cohort(s) are followed up for a certain period of time, with data collected on cancer outcomes and other variables. Data is often collected at various timepoints during the course of the study. This is a prospective cohort study which may take years to complete. Another type of cohort study is an historical cohort study or sometimes called a retrospective cohort study, where the data are obtained from historical records, appropriate individuals being selected for the cohort(s), again based on risk and demographic variables. Their records are then traced forward to see how the cancer outcome variables performed up to a particular time point. It is as if the researcher travels back in time and then conducts a prospective cohort study, eventually arriving back to the present.
Understanding research
Published in Geraldine Lee-Treweek, Tom Heller, Hilary MacQueen, Julie Stone, Sue Spurr, Complementary and Alternative Medicine: Structures and Safeguards, 2020
Hilary MacQueen, Sheena Murdoch, Andrew Vickers
Epidemiologists try to discover the causes of disease by looking at the whole population rather than individuals. Some of the most important health-related discoveries were made by epidemiological research: for instance, the links between smoking and lung cancer and cholera and dirty drinking water. The two most common types of study are the cohort study (sometimes called a prospective study) and the case–control study (or retrospective study). The cohort study involves two groups, one of which has been exposed to something that is thought to affect health (for example, second-hand or passive smoking or asbestos). The two groups are then followed to see whether they develop a particular disease (such as lung cancer). The case-control study uses the reverse approach: a group of people who have a particular disease (such as heart disease or lung cancer) are asked about previous exposures (for example, whether they smoke) and their answers are compared with those of a group of people who do not have the disease. It is important to realise that epidemiological studies can only reveal links between events and not causes.
The Florida Cohort study: methodology, initial findings and lessons learned from a multisite cohort of people living with HIV in Florida
Published in AIDS Care, 2021
Gladys E. Ibañez, Zhi Zhou, Christa L. Cook, Tania A. Slade, Charurut Somboonwit, Jaime Morano, Jeffrey Harman, Kendall Bryant, Nicole Ennis Whitehead, Babette Brumback, Angel B. Algarin, Emma C. Spencer, Robert L. Cook
Prior to this study’s initiation, there had been no large cohort studies focusing on PLWH in Florida. Cohort studies allow researchers to understand what happens to persons over time, and to identify strategies to improve health outcomes. The Florida Cohort research team, comprised of investigators and clinicians from several Florida universities and the Florida Department of Health (FDOH), sought to create a cohort that would span the entire state of Florida and reflect the unique demographic characteristics of PLWH. The Florida Cohort recruited primarily from public health settings, in order to engage a population which typically has less access to healthcare. Employing a socio-ecological conceptual model, the research team hypothesized that variables affecting the HIV care continuum would reflect the individual (e.g., attitudes, substance use, mental health), society (e.g., social support, stigma), and healthcare system (e.g., insurance, access to care, types of provider). Alcohol consumption was a focus of the cohort, because it is common in PLWH and correlates with HIV care (Vagenas et al., 2015).
Should we overcome the resistance to bioelectrical impedance in heart failure?
Published in Expert Review of Medical Devices, 2020
Stephen J. Hankinson, Charles H. Williams, Van-Khue Ton, Stephen S. Gottlieb, Charles C. Hong
Noninvasive BI methods that measure salt and fluid retention may prove to be a useful tool for population-based HF research. Population-based cohort studies allow researchers to prospectively follow and longitudinally make observations regarding exposure-disease relationships in the general population. The advent of big data and high-powered statistical software enabled the creation of mega-cohorts, which are databases with several hundred thousands of participants that help identify the genetic, epigenetic, proteomic, and environmental variants that influence disease [68]. Furthermore, mega-cohorts provide the opportunity to evaluate countless scientific hypotheses for translational research and discover the complex interaction of environmental, behavioral, and genetic components of cardiovascular disease [69,70]. Using the UK Biobank, Lindholm et al. demonstrated that a model combining leg BI, self-reported history of myocardial infarction, age, and sex accurately predicted future HF hospitalization [58]. Considering its availability in the UK Biobank, segmental BI and whole-body BI may have potential as a noninvasive, quantitative HF variable for population-based research to investigate the genetic, epigenetic, proteomic, and environmental variants that influence HF.
Dietary Fat Intake and Risk of Ovarian Cancer: A Systematic Review and Dose–Response Meta-Analysis of Observational Studies
Published in Nutrition and Cancer, 2019
Alireza Sadeghi, Sakineh Shab-Bidar, Mohammad Parohan, Kurosh Djafarian
In this meta-analysis, studies were included if they met the following criteria: (i) observational studies with case-control or nested case-control or cohort or cross-sectional design, (ii) participants enrolled were adults (over 18 years), (iii) evaluated the association between intake of dietary fat (total fat, animal fat, saturated fat, unsaturated fat, trans fat, and cholesterol) and the risk of ovarian cancer, (iv) reported odds ratio (ORs), relative risks (RRs) or hazard ratios (HRs) along with 95% or presented available data to calculate these parameters. Review articles, commentaries, case reports, animal studies, research notes, letters and non-English studies were excluded. If duplicate records drew from the same population, the bigger sample size with higher quality was considered for inclusion in the current systematic review and meta-analysis.