Related Experiment Video
Updated: May 28, 2026
06:55
Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index
Published on: January 8, 2020
Semiparametric inference of proportional odds model based on randomly truncated data
1Biometry and Mathematical Statistics Branch, Division of Epidemiology, Statistics and Prevention Research, National Institute of Child Health and Human Development, NIH, DHHS, 6100 Executive Boulevard, Rockville, MD 20878, USA.
Journal of Statistical Planning and Inference
|October 7, 2011
Summary
This study introduces new methods for estimating proportional odds models using randomly truncated data. The research provides consistent estimators for regression coefficients, validated through simulations and an AIDS data illustration.
Area of Science:
- Statistics
- Biostatistics
Background:
- Proportional odds models are widely used for ordinal outcomes.
- Estimating these models with randomly truncated data presents unique challenges.
- Existing methods may lack robustness or efficiency under truncation.
Purpose of the Study:
- To develop and evaluate novel estimators for the proportional odds model with randomly truncated data.
- To investigate the asymptotic and finite sample properties of the proposed estimators.
- To demonstrate the practical application of the method using real-world data.
Main Methods:
- Proposed a class of minimum distance estimators.
- Utilized a weighted empirical odds function for estimation.
- Investigated asymptotic properties including consistency and limiting distribution.
- Conducted simulation studies for finite sample performance evaluation.
Main Results:
- The proposed estimators demonstrate desirable asymptotic properties under mild conditions.
- Simulation results indicate competitive finite sample performance compared to existing methods.
- The method is successfully applied to analyze a known AIDS dataset.
Conclusions:
- The developed minimum distance estimators are effective for proportional odds models with randomly truncated data.
- The study provides a robust statistical framework for analyzing such data.
- The findings have implications for statistical modeling in fields with truncated data, such as survival analysis and epidemiology.
Related Concept Videos
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...
01:14Parametric Survival Analysis: Weibull and Exponential Methods
Parametric survival analysis models survival data by assuming a specific probability distribution for the time until an event occurs. The Weibull and exponential distributions are two of the most commonly used methods in this context, due to their versatility and relatively straightforward application.
Weibull Distribution
The Weibull distribution is a flexible model used in parametric survival analysis. It can handle both increasing and decreasing hazard rates, depending on its shape parameter...
Weibull Distribution
The Weibull distribution is a flexible model used in parametric survival analysis. It can handle both increasing and decreasing hazard rates, depending on its shape parameter...
01:09Truncation in Survival Analysis
Truncation in survival analysis refers to the exclusion of individuals or events from the dataset based on specific criteria related to the time of the event. This exclusion can happen in two primary forms: left truncation and right truncation.
Left truncation occurs when individuals who experienced the event of interest before a certain time are not included in the study. This is often due to a "delayed entry" into the study where only those who survive until a certain entry point are observed.
Left truncation occurs when individuals who experienced the event of interest before a certain time are not included in the study. This is often due to a "delayed entry" into the study where only those who survive until a certain entry point are observed.
01:24Bootstrapping
The term "bootstrap" originated in the 19th century as a metaphor for self-improvement or achieving something independently, without external assistance. This concept extends to statistical bootstrapping, a self-contained method for estimating population parameters through resampling, even though it can be computationally intensive. Developed by the American statistician Dr. Bradley Efron in 1979, bootstrapping provides a robust way to perform inference when the original sample size is small or...
01:16Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data
Statistical inference techniques, paramount in hypothesis testing, differentiate into two broad categories: parametric and nonparametric statistics.
Parametric statistics, as the name suggests, assumes that data follow a specific distribution, often a normal distribution. This assumption enables robust hypothesis testing and estimation. Parametric methods, like the Student's t-test or Goodness-of-fit test, are frequently employed in biostatistics due to their robustness. For instance, comparing...
Parametric statistics, as the name suggests, assumes that data follow a specific distribution, often a normal distribution. This assumption enables robust hypothesis testing and estimation. Parametric methods, like the Student's t-test or Goodness-of-fit test, are frequently employed in biostatistics due to their robustness. For instance, comparing...
01:26Distributions to Estimate Population Parameter
The accurate values of population parameters such as population proportion, population mean, and population standard deviation (or variance) are usually unknown. These are fixed values that can only be estimated from the data collected from the samples. The estimates of each of these parameters are sample proportion, the sample mean, and sample standard deviation (or variance). To obtain the values of these sample statistics, data are required that have particular distribution and central...