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Related Concept Videos

Stratified Sampling Method01:16

Stratified Sampling Method

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Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
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Study Designs in Epidemiology01:20

Study Designs in Epidemiology

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Epidemiological study designs are fundamental tools for investigating the distribution, determinants, and control of health conditions in populations. They help researchers understand the relationships between exposures and outcomes, and they broadly fall into two categories: "observational" and "experimental" studies.
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Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches

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Biopharmaceutical studies constitute a vital field aiming to enhance drug delivery methods and refine therapeutic approaches, drawing upon diverse interdisciplinary knowledge. In research methodologies, the choice between controlled and non-controlled studies significantly influences the study's reliability and accuracy.
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Confounding is a critical issue in epidemiological studies, often leading to misleading conclusions about associations between exposures and outcomes. It occurs when the relationship between the exposure and the outcome is mixed with the effects of other factors that influence the outcome. Given that, addressing confounding is of high importance for drawing accurate inferences in research.
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Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and...
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The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
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Methods for Stratification and Validation Cohorts: A Scoping Review.

Teresa Torres Moral1,2,3, Albert Sanchez-Niubo1,4,5, Anna Monistrol-Mula1

  • 1Research and Development Unit, Parc Sanitari Sant Joan de Déu, 08830 Barcelona, Spain.

Journal of Personalized Medicine
|May 28, 2022
PubMed
Summary

Personalized medicine relies on large patient cohorts, but standardized methods for their design and management are lacking. This review highlights gaps in current practices, especially concerning data quality and sample size calculations for better reproducibility.

Keywords:
cohortspersonalized medicinesample sizestratification

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Area of Science:

  • Biomedical research
  • Personalized medicine
  • Clinical cohort studies

Background:

  • Personalized medicine necessitates large patient cohorts for effective stratification and validation.
  • Current practices for designing and managing these cohorts lack standardized methods and tools.
  • This deficiency impacts the reproducibility and robustness of personalized medicine research.

Purpose of the Study:

  • To conduct a scoping review of the state-of-the-art in methods and tools for designing and managing cohorts in personalized medicine.
  • To identify existing practices and highlight areas needing standardization.

Main Methods:

  • A comprehensive scoping review was performed using major scientific databases (PubMed, EMBASE, Web of Science, Psycinfo, Cochrane Library).
  • Searches focused on reviews concerning tools and methods for cohorts in cancer, stroke, and Alzheimer's disease (2005-April 2020).
  • PRISMA guidelines were followed for screening and inclusion of 50 reviews.

Main Results:

  • Most included reviews (25/50) detailed data generation methods, and (24/50) discussed data management and analysis tools.
  • Significant gaps were identified regarding data quality monitoring and requirements for associated clinical data.
  • A scarcity of information and standards was noted for critical aspects like sample size calculation.

Conclusions:

  • The current landscape of cohort design and management in personalized medicine reveals a need for standardized guidelines.
  • Addressing identified gaps in data quality, monitoring, and sample size calculation is crucial.
  • Developing comprehensive guidelines will enhance the reproducibility and robustness of future personalized medicine studies.