Related Experiment Video
Updated: Jul 13, 2026
10:46
A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
Published on: December 9, 2015
Regression analysis of a disease onset distribution using diagnosis data
Jessica G Young1, Nicholas P Jewell, Steven J Samuels
1Division of Biostatistics, School of Public Health, 140 Warren Hall 7360, University of California, Berkeley, CA 94720, USA. jgerald@berkeley.edu
Biometrics
|August 8, 2007
Summary
We evaluated statistical methods for estimating disease onset, comparing a two-step approach to existing maximum likelihood methods. The two-step estimator is simpler and more robust when assumptions about disease onset distributions fail.
Area of Science:
- Biostatistics
- Epidemiology
- Survival Analysis
Background:
- Estimating disease onset is complex, especially with right-censored diagnosis times and current status data.
- Existing maximum likelihood methods (Dunson and Baird, 2001) rely on a monotonicity assumption for diagnosis and onset distributions.
Purpose of the Study:
- To propose and evaluate a computationally simpler two-step estimator for disease onset.
- To compare the performance of the new estimator against existing methods, including one that ignores diagnosis data.
Main Methods:
- Developed a two-step estimator, extending the work of van der Laan et al. (1997).
- Conducted a simulation study comparing the two-step estimator, Dunson and Baird's method, and a standard current status analysis.
- Applied methods to a real-world study on uterine fibroids and dioxin exposure.
Main Results:
- The Dunson and Baird estimator performed better when the monotonicity assumption held.
- The two-step estimator was superior when the monotonicity assumption failed.
- The current status estimator showed minimal precision loss compared to the two-step method but requires more data.
Conclusions:
- The proposed two-step estimator offers a robust and computationally efficient alternative for disease onset estimation, particularly when distributional assumptions are violated.
- In the uterine fibroids and dioxin exposure study, the two-step and current status estimators found no significant association, with the two-step method providing the lowest variance estimate for relative hazard.
Related Concept Videos
01:18Steps in Outbreak Investigation
In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:
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:
01:18Introduction To Survival Analysis
Survival analysis is a statistical method used to study time-to-event data, where the "event" might represent outcomes like death, disease relapse, system failure, or recovery. A unique feature of survival data is censoring, which occurs when the event of interest has not been observed for some individuals during the study period. This requires specialized techniques to handle incomplete data effectively.
The primary goal of survival analysis is to estimate survival time—the time until a...
The primary goal of survival analysis is to estimate survival time—the time until a...
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...
01:10Documentation of Nursing Diagnosis
The nurse documents nursing diagnoses and enters them into the patient record. The identified patient's nursing diagnosis is either written out with a plan of care or entered into the electronic health record.
In some settings, data-driven computerized decision support systems are in place, allowing for more accurate nursing diagnoses. The database within one of these systems includes diagnostic labels defining characteristics, activities, and indicators for nursing. A nurse enters assessment...
In some settings, data-driven computerized decision support systems are in place, allowing for more accurate nursing diagnoses. The database within one of these systems includes diagnostic labels defining characteristics, activities, and indicators for nursing. A nurse enters assessment...
01:19Data: Types and Distribution
In biostatistics, data are the observations collected for analysis. There are two main types: parametric and non-parametric. Parametric data, which include continuous (e.g., weight) and discrete numerical data (e.g., number of tablets), assume a particular distribution pattern, often the normal distribution. Non-parametric data do not adhere to a specific distribution and typically comprise nominal (e.g., gender) and ordinal categorical data (e.g., pain scale ratings).
Distributions in...
Distributions in...