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Related Concept Videos

Clinical Trials01:16

Clinical Trials

11.2K
Clinical trials are prospective experimental studies conducted on humans to determine the safety and efficacy of treatments, drugs, diet methods, and medical devices. Using statistics in clinical trials enables researchers to derive reasonable and accurate conclusions from the collected data, allowing them to make wise decisions in uncertain situations. In medical research, statistical methods are crucial for preventing errors and bias.
There are four phases in a clinical trial. A phase one...
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Clinical Trials: Overview01:11

Clinical Trials: Overview

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Clinical development focuses on how the drug will interact with the human body and encompasses four key phases of clinical trials, each serving a specific purpose in assessing the safety and effectiveness of new drugs. These phases overlap and build upon one another. Phase I involves a small group of healthy volunteers (typically 20-80 individuals) or, in cases where significant toxicity is expected, patients with the targeted disease, such as cancer or AIDS. The volunteers are tested for...
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Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches01:23

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.
Non-controlled studies, commonly employed for initial exploration, lack a control group, rendering them susceptible to biases and external influences. In contrast,...
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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.
Observational studies are those where the researcher does not intervene but rather observes natural variations. They include cross-sectional, cohort, and...
1.6K
Bioequivalence Experimental Study Designs: Completely Randomized and Randomized Block Designs01:20

Bioequivalence Experimental Study Designs: Completely Randomized and Randomized Block Designs

374
Bioequivalence experimental study designs are crucial methodologies used in evaluating and comparing the bioavailability of different drug products. These designs are categorized into various types: completely randomized, randomized block, repeated measures, cross and carry-over, and Latin square designs.Completely randomized designs involve randomly allocating treatments to all subjects participating in the experiment. This allocation is achieved by assigning unique random numbers to subjects...
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Statistical Software for Data Analysis and Clinical Trials01:12

Statistical Software for Data Analysis and Clinical Trials

1.9K
Statistical software is pivotal in data analysis and clinical trials by providing tools to analyze data, draw conclusions, and make predictions. These software packages range from simple data management applications to complex analytical platforms, supporting various statistical tests, models, and simulation techniques. Their significance lies in their ability to handle vast amounts of data with precision and efficiency, enabling researchers to validate hypotheses, identify trends, and make...
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Big data: Are large prospective randomized trials obsolete in the future?

Clifford A Hudis1

  • 1Memorial Sloan Kettering Cancer Center, United States; Weill Cornell Medical College, United States.

Breast (Edinburgh, Scotland)
|August 11, 2015
PubMed
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Big data analytics offers a new way to understand and improve global cancer care by refining clinical guidelines and identifying knowledge gaps. This approach complements traditional randomized clinical trials.

Keywords:
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Area of Science:

  • Oncology
  • Health Informatics
  • Data Science

Background:

  • Traditional evidence development in cancer care relies heavily on randomized clinical trials (RCTs).
  • RCTs have limitations in reflecting real-world cancer care practices globally.
  • There is a growing need for complementary methods to enhance understanding and improvement of cancer care.

Purpose of the Study:

  • To review the historical approach to evidence development in cancer care.
  • To discuss the limitations of traditional methods like randomized clinical trials.
  • To explore the complementary role of big data analytics in improving cancer care.

Main Methods:

  • Review of historical evidence development methodologies in oncology.
  • Analysis of limitations inherent in randomized clinical trials.
  • Exploration of big data analytics applications in cancer care.

Main Results:

  • Randomized clinical trials, while foundational, have inherent limitations.
  • Big data analytics presents a complementary approach to address these limitations.
  • Big data can enhance understanding of global cancer care practices.

Conclusions:

  • Big data analytics provides a valuable opportunity to augment traditional research methods.
  • Improved understanding of global cancer care can be achieved through big data.
  • Refinement of clinical guidelines and identification of knowledge gaps are key benefits.