Related Experiment Video
Updated: Aug 11, 2026
09:35
Dispersion of Nanomaterials in Aqueous Media: Towards Protocol Optimization
Published on: December 25, 2017
Dispersion tests and adjustments for survival and case-control studies
1Centers for Disease Control and Prevention, Division of Diabetes Translation, Atlanta, GA 30341-3724.
Statistics in Medicine
|September 30, 1993
Summary
Discrepancies between empirical and nominal variance can mislead statistical inferences. This study introduces a test and robust method to adjust standard errors in survival and case-control analyses for accurate dispersion assessment.
Area of Science:
- Biostatistics
- Statistical Modeling
- Epidemiological Research
Background:
- Empirical variance discrepancies can lead to misleading inferences in statistical analyses.
- Standard errors require adjustment when observed data deviates from assumed probability models.
Purpose of the Study:
- To introduce a statistical test for assessing departures from nominal dispersion.
- To provide a robust procedure for adjusting standard errors in survival analysis and case-control studies.
Main Methods:
- Development of a specific test to detect deviations in dispersion.
- Implementation of a robust standard error adjustment procedure.
Main Results:
- The proposed test effectively identifies departures from nominal dispersion.
- The robust adjustment procedure yields more reliable standard errors.
Conclusions:
- Accurate dispersion assessment is crucial for valid inferences in survival and case-control studies.
- The presented methods improve the reliability of statistical analyses when dispersion is non-nominal.
Related Concept Videos
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...
01:18Survival Curves
Survival curves are graphical representations that depict the survival experience of a population over time, offering an intuitive way to track the proportion of individuals who remain event-free at each time point. These curves are widely used in fields such as medicine, public health, and reliability engineering to visualize and compare survival probabilities across different groups or conditions.
The Kaplan-Meier estimator is the most common method for constructing survival curves. This...
The Kaplan-Meier estimator is the most common method for constructing survival curves. This...
01:15Assumptions of Survival Analysis
Survival models analyze the time until one or more events occur, such as death in biological organisms or failure in mechanical systems. These models are widely used across fields like medicine, biology, engineering, and public health to study time-to-event phenomena. To ensure accurate results, survival analysis relies on key assumptions and careful study design.
01:20Comparing the Survival Analysis of Two or More Groups
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 Cox...
01:19The Mantel-Cox Log-Rank Test
The Mantel-Cox log-rank test is a widely used statistical method for comparing the survival distributions of two groups. It tests whether a statistically significant difference exists in survival times between the groups without assuming a specific distribution for the survival data, making it a non-parametric test. This flexibility makes the log-rank test particularly valuable in medical research and other fields where the timing of an event, such as death or disease recurrence, is of interest.
01:09Censoring Survival Data
Survival analysis is a statistical method used to analyze time-to-event data, often employed in fields such as medicine, engineering, and social sciences. One of the key challenges in survival analysis is dealing with incomplete data, a phenomenon known as "censoring." Censoring occurs when the event of interest (such as death, relapse, or system failure) has not occurred for some individuals by the end of the study period or is otherwise unobservable, and it might have many different reasons...