Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Quantifying and Rejecting Outliers: The Grubbs Test01:02

Quantifying and Rejecting Outliers: The Grubbs Test

Sometimes, a data set can have a recorded numerical observation that greatly  deviates from the rest of the data. Assuming that the data is normally distributed, a statistical method called the Grubbs test can be used to determine whether the observation is truly an outlier.  To perform a two-tailed Grubbs test, first, calculate the absolute difference between the outlier and the mean. Then, calculate the ratio between this difference and the standard deviation of the sample. This number is...
Survival Tree01:19

Survival Tree

Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a survival tree begins...
Residuals and Least-Squares Property01:11

Residuals and Least-Squares Property

The vertical distance between the actual value of y and the estimated value of y. In other words, it measures the vertical distance between the actual data point and the predicted point on the line
If the observed data point lies above the line, the residual is positive, and the line underestimates the actual data value for y. If the observed data point lies below the line, the residual is negative, and the line overestimates the actual data value for y.
The process of fitting the best-fit...
Prediction Intervals01:03

Prediction Intervals

The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y. 
The...
Truncation in Survival Analysis01:09

Truncation 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.
Testing a Claim about Population Proportion01:24

Testing a Claim about Population Proportion

A complete procedure for testing a claim about a population proportion is provided here.
There are two methods of testing a claim about a population proportion: (1) Using the sample proportion from the data where a binomial distribution is approximated to the normal distribution and (2) Using the binomial probabilities calculated from the data.
The first method uses normal distribution as an approximation to the binomial distribution. The requirements are as follows: sample size is large...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

When to Adjust for Multiple Testing: A Unifying Guiding Principle.

Biometrical journal. Biometrische Zeitschrift·2026
Same author

Donor bone marrow together with recipient regulatory T cells induces chimerism without irradiation in kidney transplantation.

Science translational medicine·2026
Same author

Using routinely collected data for research purposes: challenges and mitigation strategies.

BMJ (Clinical research ed.)·2026
Same author

First attempt success rate of intraosseous access in preterm infants and neonates: a systematic review.

Resuscitation plus·2026
Same author

Plasma aldosterone is low in patients hospitalized with COVID-19 and not associated with changes in serum potassium levels: <i>post hoc</i> observational analyses of clinical trial data.

Frontiers in endocrinology·2025
Same author

TRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods: a Korean translation.

Ewha medical journal·2025
Same journal

Optimal Weighted Tests for Replication Studies and the 'Two-Trials Rule' With Multiple Hypotheses.

Statistics in medicine·2026
Same journal

Identifiable Copula-Double-Cox Models: A Fully Parametric Framework for Dependent Right-Censored Survival Data.

Statistics in medicine·2026
Same journal

Moving From Individualized Risk-Based Prevention to Benefit-Based Prevention: Estimating Individualized Life-Years Gained From Prevention Services as a Basis for Eligibility.

Statistics in medicine·2026
Same journal

A Mixture of Distributed Lag Non-Linear Models to Account for Spatially Heterogeneous Exposure-Lag-Response Associations.

Statistics in medicine·2026
Same journal

Practical Considerations for Gaussian Process Modeling for Causal Inference in Quasi-Experimental Studies With Panel Data.

Statistics in medicine·2026
Same journal

Covariate Adjustment for Wilcoxon Two Sample Statistic and Test.

Statistics in medicine·2026
See all related articles

Related Experiment Video

Updated: Jun 15, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

Bias-reduced and separation-proof conditional logistic regression with small or sparse data sets.

Georg Heinze1, Rainer Puhr

  • 1Section of Clinical Biometrics, Core Unit of Medical Statistics and Informatics, Medical University of Vienna, Spitalgasse 23, Vienna A-1090, Austria. georg.heinze@meduniwien.ac.at

Statistics in Medicine
|March 10, 2010
PubMed
Summary
This summary is machine-generated.

Conditional penalized likelihood (CFL) offers improved bias correction for stratified binary data analysis. This method provides nearly unbiased estimates and better confidence interval coverage compared to conditional maximum likelihood (CML) and LogXact.

Related Experiment Videos

Last Updated: Jun 15, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

Area of Science:

  • Biostatistics
  • Statistical Modeling

Background:

  • Conditional logistic regression is standard for stratified binary outcomes.
  • Conditional maximum likelihood (CML) can yield infinite estimates and monotone likelihood, particularly in small samples or sparse data.
  • Existing software like LogXact offers improvements but may still exhibit bias.

Purpose of the Study:

  • To introduce and evaluate a novel penalized conditional likelihood (CFL) method for stratified binary data analysis.
  • To compare the performance of CFL against CML and LogXact in terms of bias, confidence interval coverage, and statistical power.
  • To provide an accessible SAS program for implementing the CFL method.

Main Methods:

  • Development of a penalized conditional likelihood (CFL) approach, inspired by Firth's bias correction method.
  • Application and comparison of CFL, CML, and LogXact using an animal experiment and a lung cancer case-control study.
  • A small-sample simulation study to assess the statistical properties of the estimators.

Main Results:

  • CFL demonstrated nearly unbiased log odds ratio estimates in simulations, outperforming biased CML and slightly biased LogXact estimates.
  • CFL-based confidence intervals and tests showed close-to-nominal coverage rates and the highest power among compared methods.
  • CFL effectively addressed issues of infinite estimates and monotone likelihood encountered with CML.

Conclusions:

  • Conditional penalized likelihood (CFL) is proposed as a robust and attractive solution for stratified binary data analysis.
  • CFL offers superior performance over CML and LogXact, especially when monotone likelihood is present.
  • The availability of a SAS program facilitates the adoption of CFL in biostatistical practice.