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

Kaplan-Meier Approach01:24

Kaplan-Meier Approach

634
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,...
634
Analysis of Population Pharmacokinetic Data01:12

Analysis of Population Pharmacokinetic Data

822
Analysis of population pharmacokinetic data involves studying the behavior of drugs within diverse populations to understand their pharmacokinetic parameters. Traditional pharmacokinetic methods typically involve collecting samples from a few individuals and estimating these parameters. While these methods are commonly used, they have limitations in capturing the variability in drug response among individuals or heterogeneous populations. Population pharmacokinetics is employed to address these...
822
Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

1.1K
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...
1.1K
Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data01:16

Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data

514
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,...
514
Dosage Regimens: Partial Pharmacokinetic Parameters01:01

Dosage Regimens: Partial Pharmacokinetic Parameters

193
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...
193
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

285
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...
285

You might also read

Related Articles

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

Sort by
Same author

Temperature-dependent photoluminescence of cadmium-free Cu-Zn-In-S quantum dot thin films as temperature probes.

Dalton transactions (Cambridge, England : 2003)·2015
Same author

Overexpressed CISD2 has prognostic value in human gastric cancer and promotes gastric cancer cell proliferation and tumorigenesis via AKT signaling pathway.

Oncotarget·2015
Same author

PBOV1 correlates with progression of ovarian cancer and inhibits proliferation of ovarian cancer cells.

Oncology reports·2015
Same author

A novel insight in exploring the positive end expiratory pressure for sustained ventilation after lung recruitment in a porcine model of acute respiratory distress syndrome.

International journal of clinical and experimental medicine·2015
Same author

Advanced Chronic Obstructive Pulmonary Disease: Innovative and Integrated Management Approaches.

Chinese medical journal·2015
Same author

Polyploidy Enhances F1 Pollen Sterility Loci Interactions That Increase Meiosis Abnormalities and Pollen Sterility in Autotetraploid Rice.

Plant physiology·2015

Related Experiment Video

Updated: Feb 17, 2026

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
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

15.4K

New semiparametric method for predicting high-cost patients.

Adam Maidman1, Lan Wang1

  • 1School of Statistics, University of Minnesota, Minneapolis, Minnesota 55455, U.S.A.

Biometrics
|December 12, 2017
PubMed
Summary

This study introduces a novel semiparametric method for predicting high medical expenditure, improving upon traditional classification techniques. The new approach offers a more robust and informative prediction of patient healthcare costs.

Keywords:
Conditional quantileExpenditure predictionHigh-cost patientPartially linear additive modelSemiparametric regressionUpper tail

More Related Videos

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack
07:31

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

Published on: May 15, 2020

8.2K
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

8.1K

Related Experiment Videos

Last Updated: Feb 17, 2026

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
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

15.4K
Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack
07:31

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

Published on: May 15, 2020

8.2K
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

8.1K

Area of Science:

  • Biostatistics
  • Health Economics
  • Data Science

Background:

  • High medical expenditure prediction is crucial for healthcare management.
  • Current methods often involve artificial data dichotomization, potentially losing information.
  • Existing classification methods may lack robustness and fail to capture nonlinear effects.

Purpose of the Study:

  • To develop a semiparametric procedure for predicting high medical expenditure.
  • To offer a more robust and informative alternative to artificial dichotomization.
  • To enhance prediction accuracy by incorporating nonlinear covariate effects.

Main Methods:

  • Proposed a new semiparametric prediction rule for upper-tail response distribution.
  • Developed a method that avoids artificial dichotomization of the response variable.
  • Utilized an R package (plaqr) for implementation and simulation studies.

Main Results:

  • The semiparametric method demonstrates improved performance in simulations.
  • The procedure effectively predicts high medical expenditure without parametric assumptions.
  • The method provides prediction intervals, offering more comprehensive future response information.

Conclusions:

  • The novel semiparametric procedure offers a robust and efficient approach to predicting high medical expenditure.
  • This method improves upon traditional classification by leveraging full data information and handling nonlinearities.
  • The plaqr package facilitates the application of this advanced statistical technique in healthcare analytics.