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

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

407
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
407
Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

1.3K
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...
1.3K
Distributions to Estimate Population Parameter01:26

Distributions to Estimate Population Parameter

5.7K
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...
5.7K
One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

1.4K
This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
On...
1.4K
Kaplan-Meier Approach01:24

Kaplan-Meier Approach

743
The Kaplan-Meier estimator is a non-parametric method used to estimate the survival function from time-to-event data. In medical research, it is frequently employed to measure the proportion of patients surviving for a certain period after treatment. This estimator is fundamental in analyzing time-to-event data, making it indispensable in clinical trials, epidemiological studies, and reliability engineering. By estimating survival probabilities, researchers can evaluate treatment effectiveness,...
743
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

335
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...
335

You might also read

Related Articles

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

Sort by
Same author

Errata to: "lmtp: An R Package for Estimating the Causal Effects of Modified Treatment Policies".

Observational studies·2026
Same author

Beneficial effects of the rapid vs. standard procedure for injection naltrexone initiation operate through increased adjunctive medication use.

Drug and alcohol dependence·2026
Same author

µCT scanning effects on aDNA and a multi-step workflow for archaeological petrous portions.

PloS one·2026
Same author

Recanting Twins: Addressing Intermediate Confounding in Mediation Analysis.

Statistics in medicine·2026
Same author

Constructing targeted minimum loss/maximum likelihood estimators: a simple illustration to build intuition.

American journal of epidemiology·2025
Same author

Polarimetric feature analysis of Mueller matrices for brain tumor image segmentation.

Optics express·2025

Related Experiment Video

Updated: Apr 6, 2026

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

3.8K

Targeted Maximum Likelihood Estimation using Exponential Families.

Iván Díaz, Michael Rosenblum

    The International Journal of Biostatistics
    |July 22, 2015
    PubMed
    Summary
    This summary is machine-generated.

    Targeted maximum likelihood estimation (TMLE) now offers a general implementation using exponential families, eliminating the need for complex "clever-covariates." This approach enhances TMLE

    More Related Videos

    A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
    10:46

    A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

    Published on: December 9, 2015

    11.2K
    A Workflow for Lipid Nanoparticle LNP Formulation Optimization using Designed Mixture-Process Experiments and Self-Validated Ensemble Models SVEM
    13:54

    A Workflow for Lipid Nanoparticle LNP Formulation Optimization using Designed Mixture-Process Experiments and Self-Validated Ensemble Models SVEM

    Published on: August 18, 2023

    6.3K

    Related Experiment Videos

    Last Updated: Apr 6, 2026

    Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
    04:35

    Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

    Published on: July 3, 2020

    3.8K
    A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
    10:46

    A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

    Published on: December 9, 2015

    11.2K
    A Workflow for Lipid Nanoparticle LNP Formulation Optimization using Designed Mixture-Process Experiments and Self-Validated Ensemble Models SVEM
    13:54

    A Workflow for Lipid Nanoparticle LNP Formulation Optimization using Designed Mixture-Process Experiments and Self-Validated Ensemble Models SVEM

    Published on: August 18, 2023

    6.3K

    Area of Science:

    • Statistics
    • Biostatistics
    • Epidemiology

    Background:

    • Targeted maximum likelihood estimation (TMLE) is a powerful statistical framework for parameter estimation in complex models.
    • Traditional TMLE implementations often rely on deriving specific 'clever-covariates,' which can be challenging and problem-dependent.
    • The absence of a clever-covariate can limit the applicability of TMLE in certain statistical and causal inference scenarios.

    Purpose of the Study:

    • To introduce a general TMLE implementation that circumvents the need for clever-covariates.
    • To demonstrate the utility of this new approach for estimating smooth parameters in nonparametric models.
    • To provide a computationally efficient and broadly applicable TMLE methodology.

    Main Methods:

    • Developed a general TMLE framework utilizing exponential families.
    • This method involves iterative estimation within an exponential family, leveraging convex optimization.
    • Applied the method to estimate the mean of a missing outcome, a median regression parameter, and a continuous exposure's causal effect.

    Main Results:

    • The exponential family-based TMLE successfully estimates various parameters without requiring clever-covariates.
    • Computational efficiency is achieved through convex optimization inherent in exponential family estimation.
    • Simulation studies indicated that the choice of parametric submodel significantly influences finite-sample performance.

    Conclusions:

    • The proposed general TMLE implementation broadens the applicability of TMLE, especially in causal inference.
    • This approach offers a computationally advantageous alternative to traditional clever-covariate methods.
    • Careful consideration of parametric submodel selection is crucial for optimal finite-sample TMLE performance.