Related Experiment Video
Updated: May 19, 2026
10:46
A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
Published on: December 9, 2015
Design considerations for case series models with exposure onset measurement error.
Sandra M Mohammed1, Lorien S Dalrymple, Damla Sentürk
1Division of Biostatistics, Department of Public Health Sciences, University of California, Davis, CA 95616, U.S.A.
Statistics in Medicine
|August 23, 2012
Summary
This study introduces a new method for sample size calculations in the measurement error case series model, crucial for analyzing event incidence after exposures like infections. It also provides web tools to aid researchers in designing case series studies.
Area of Science:
- Epidemiology
- Biostatistics
- Statistical modeling
Background:
- The case series model estimates relative incidence of events post-exposure, requiring only cases and controls for time-invariant confounders.
- The measurement error case series model addresses challenges with imprecise exposure timing data, common in epidemiological studies.
Purpose of the Study:
- To propose a novel method for power and sample size determination specifically for the measurement error case series model.
- To evaluate the accuracy of the proposed sample size formulas through simulation studies.
- To quantify the impact of exposure onset measurement error on statistical power.
Main Methods:
- Development of power/sample size formulas for the measurement error case series model.
- Extensive simulation studies to validate the accuracy of the proposed formulas.
- Comparison of power loss due to measurement error versus precise exposure timing.
Main Results:
- The proposed sample size formulas demonstrate accuracy in simulation studies.
- Quantification of the relative loss of power attributable to exposure onset measurement error.
- Development of publicly available web-based tools for sample size determination.
Conclusions:
- The developed method and tools facilitate robust sample size determination for case series studies with potential exposure measurement error.
- Accurate sample size calculation is essential for the valid application of the measurement error case series model in epidemiological research.
- The tools support researchers in designing efficient studies, accounting for data imperfections.
Related Concept Videos
01:25Strategies for Assessing and Addressing Confounding
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.
Confounding can be addressed at both the design phase of a study and through analytical methods after data...
Confounding can be addressed at both the design phase of a study and through analytical methods after data...
01:20Study Designs in Epidemiology
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 case-control studies.
Observational studies are those where the researcher does not intervene but rather observes natural variations. They include cross-sectional, cohort, and case-control studies.
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.
01:29Bias 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:
01:26Introduction to Epidemiology
Epidemiology, known as the cornerstone of public health, involves studying the distribution and determinants of health-related events in defined populations and applying these insights to control health issues. This is essential for understanding how diseases spread, identifying populations at greater risk, and implementing measures to control or prevent outbreaks. Epidemiology addresses not only infectious diseases but also non-communicable conditions like cancer and cardiovascular disease,...
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...