Related Experiment Video
Updated: Aug 2, 2026
08:20
Using Microarrays to Interrogate Microenvironmental Impact on Cellular Phenotypes in Cancer
Published on: May 21, 2019
Covariate adjustment in the analysis of microarray data from clinical studies
Debashis Ghosh1, Arul M Chinnaiyan
1Department of Biostatistics, School of Public Health, University of Michigan, Room M4057, 1420 Washington Heights, Ann Arbor, MI 48109-2029, USA. ghoshd@umich.edu
Functional & Integrative Genomics
|September 21, 2004
Summary
This study introduces two methods for analyzing gene expression data in clinical oncology settings, emphasizing the need to adjust for confounding factors. These approaches improve the selection of differentially expressed genes for validation in non-randomized studies.
Area of Science:
- Bioinformatics
- Genomics
- Clinical Oncology
Background:
- Gene expression data analysis is crucial in clinical settings, particularly in oncology.
- Adjusting for confounders and prognostic factors is essential for accurate gene selection.
- Non-randomized clinical studies present unique challenges for data analysis.
Purpose of the Study:
- To present two novel approaches for analyzing gene expression data in non-randomized clinical settings.
- To highlight the importance of covariate adjustment in identifying differentially expressed genes.
- To improve the selection of genes for follow-up validation studies.
Main Methods:
- Extension of Significance Analysis of Microarrays (SAM) to incorporate covariates.
- Development of a novel covariate-adjusted regression modeling approach using Receiver Operating Characteristic (ROC) curves.
- Application of methods to prostate cancer molecular profiling data.
Main Results:
- The proposed methods effectively account for confounding factors in gene expression analysis.
- Improved selection of differentially expressed genes is achieved through covariate adjustment.
- Demonstration of the utility of ROC curve-based regression for gene expression analysis.
Conclusions:
- Adjusting for confounders and prognostic factors is vital for reliable gene expression analysis in clinical research.
- The developed methods offer robust solutions for analyzing microarray data in non-randomized settings.
- These approaches enhance the identification of significant genes for further clinical validation.
Related Concept Videos
01:15Study Design in Statistics
A study design is a set of techniques that allow a researcher to collect and analyze data from different variables defined for a specific research problem. Statistics is commonly for effective study design and more robust experiments,
Does aspirin reduce the risk of heart attacks? Is one brand of fertilizer more effective at growing roses than another? Is fatigue as dangerous to a driver as the influence of alcohol? Questions like these are answered using randomized experiments with proper...
Does aspirin reduce the risk of heart attacks? Is one brand of fertilizer more effective at growing roses than another? Is fatigue as dangerous to a driver as the influence of alcohol? Questions like these are answered using randomized experiments with proper...
01:23Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches
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, controlled...
Non-controlled studies, commonly employed for initial exploration, lack a control group, rendering them susceptible to biases and external influences. In contrast, controlled...
01:27Confounding in Epidemiological Studies
Confounding in statistical epidemiology represents a pivotal challenge, referring to the distortion in the perceived relationship between an exposure and an outcome due to the presence of a third variable, known as a confounder. This variable is associated with both the exposure and the outcome but is not a direct link in their causal chain. Its presence can lead to erroneous interpretations of the exposure's effect, either exaggerating or underestimating the true association. This phenomenon...
01:29Bias in Epidemiological Studies
Biases can arise at various stages of research, from study design and data collection to analysis and interpretation. Recognizing and addressing these biases is essential to ensure the validity and reliability of epidemiological findings.Broadly speaking, biases in epidemiology fall into three main categories: selection bias, information bias, and confounding. A more detailed description of possible biases is:
01:25Statistical Methods for Analyzing Epidemiological Data
Epidemiological data primarily involves information on specific populations' occurrence, distribution, and determinants of health and diseases. This data is crucial for understanding disease patterns and impacts, aiding public health decision-making and disease prevention strategies. The analysis of epidemiological data employs various statistical methods to interpret health-related data effectively. Here are some commonly used methods:
01:12Statistical Software for Data Analysis and Clinical Trials
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...