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

Regression Toward the Mean01:52

Regression Toward the Mean

Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when researchers try to extrapolate results...
Dosage Regimens: Partial Pharmacokinetic Parameters01:01

Dosage Regimens: Partial Pharmacokinetic Parameters

It is not uncommon for complete drug pharmacokinetic profiles to remain elusive in pharmacokinetics. This necessitates certain educated assumptions by pharmacokineticists to determine appropriate dosage regimens without comprehensive pharmacokinetic data from animal or human studies. One prevalent assumption is setting the bioavailability factor, denoted as F, to 1 or 100%. This assumption caters to the scenario where a drug doesn't achieve full systemic absorption, resulting in the patient...
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least squares (OLS)...
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.
Impact of Pharmacokinetic–Pharmacodynamic Models: Regulatory Decisions01:15

Impact of Pharmacokinetic–Pharmacodynamic Models: Regulatory Decisions

PK–PD modeling has significantly influenced FDA regulatory decisions, particularly drug approval, dosage optimization, and labeling. These models integrate pharmacokinetics (PK) and pharmacodynamics (PD) to predict drug behavior and effects, aiding in optimizing dosing regimens and enhancing the probability of clinical trial success.One notable example is Nesiritide (Natrecor®), a recombinant human brain natriuretic peptide for treating acute decompensated congestive heart failure (CHF).
Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric 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...

You might also read

Related Articles

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

Sort by
Same author

Effect of home visiting support on maternal psychosocial needs and postnatal depression: emulating a target trial.

BMJ mental health·2026
Same author

Beyond the Hazard Ratio: Causal Inference from Time-to-Event Data with Dependent Censoring, Confounding, and Competing Risks.

Journal of epidemiology·2026
Same author

Prognostic Impact of Renal Function on Outcomes After Physiology-Guided Coronary Revascularization: Insights From the J-PRIDE Registry.

Circulation. Cardiovascular interventions·2026
Same author

Prognostic Implications of Bleeding and Ischemic Complications in Acute Myocardial Infarction-Related Cardiogenic Shock Managed With Microaxial Flow Pump.

Circulation. Cardiovascular interventions·2026
Same author

Dynamic Borrowing With a Bias-Tolerance Cap in Augmented Randomized Controlled Trials.

Statistics in medicine·2026
Same author

Doubly robust g-estimation of structural nested cumulative survival time models with non-ignorable, non-monotone missing data in time-varying confounders.

Lifetime data analysis·2026
Same journal

Interpretable Bayesian Modeling for Multireader Multicase Studies: Addressing Overdispersion and Limited Sample Size in Diagnostic Enhancement Evaluation.

Statistics in medicine·2026
Same journal

Adaptive Sequential Multiple Hypotheses Testing for Concomitant Vaccine Safety Surveillance.

Statistics in medicine·2026
Same journal

Novel Distance Regression for Repeated Outcomes With Missing Data: Applications to Longitudinal and Crossover Studies of Microbiome Beta-Diversity.

Statistics in medicine·2026
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
See all related articles

Related Experiment Videos

Caveats on Using Firth's Penalization in the Model-Based Regression Standardization for Rare Diseases.

Sotaro Hashibe1, Wataru Hongo2,3, Tomohiro Shinozaki4,5

  • 1Statistics and Decision Sciences Japan, R&D, Janssen Pharmaceutical K.K., Tokyo, Japan.

Statistics in Medicine
|June 23, 2026
PubMed
Summary
This summary is machine-generated.

Firth's penalized likelihood method, used for rare disease analysis, can bias regression standardization. New corrections were proposed and validated, showing improved accuracy in estimating rare disease associations, such such as surgical site infections.

Keywords:
Firthbias reductioncausal effectmodel‐based standardizationseparation

Related Experiment Videos

Area of Science:

  • Epidemiology
  • Biostatistics
  • Medical Informatics

Background:

  • Model-based regression standardization (parametric g-formula) estimates marginal effects but struggles with rare diseases due to data separation.
  • Firth's penalized likelihood method addresses separation but can introduce bias in standardization by shrinking probabilities.
  • Surgical site infections (SSI) are a relevant rare disease for evaluating these statistical methods.

Purpose of the Study:

  • To examine the bias introduced by Firth's method in model-based regression standardization for rare diseases.
  • To propose and evaluate novel ad hoc corrections to mitigate Firth's method bias.
  • To assess the association between SSI and smoking status using the corrected methods.

Main Methods:

  • Empirical study on surgical site infections (SSI) in orthopedic surgery patients.
  • Application of Firth's penalized likelihood regression.
  • Development and simulation-based evaluation of two ad hoc corrections: intercept correction and added covariate.
  • Comparison with propensity score-based methods.

Main Results:

  • Firth's method, while resolving convergence issues, demonstrated bias in regression standardization, leading to discrepancies in event rates.
  • The proposed ad hoc corrections effectively mitigated the bias associated with Firth's method.
  • The corrected methods showed improved performance compared to standard propensity score approaches in simulations.
  • The final analysis identified an association between SSI and smoking status in orthopedic surgery patients.

Conclusions:

  • Firth's penalized likelihood method requires careful consideration when used for regression standardization in rare disease settings.
  • The proposed ad hoc corrections offer a viable approach to reduce bias and improve the accuracy of rare disease effect estimation.
  • These corrected methods can be reliably applied to investigate associations between rare diseases and risk factors, such as smoking and SSI.