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Related Concept Videos

Prediction Intervals01:03

Prediction Intervals

3.4K
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. 
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Structural Joints: Synovial Joints01:16

Structural Joints: Synovial Joints

7.0K
Synovial joints are the most common type of joint in the body. A key structural characteristic for a synovial joint is the presence of a joint cavity. This fluid-filled space is where the articulating surfaces of the bones contact each other. Also, unlike fibrous or cartilaginous joints, the articulating bone surfaces at a synovial joint are not directly connected to each other with fibrous connective tissue or cartilage. This gives the bones of a synovial joint the ability to move smoothly...
7.0K
Structural Joints: Fibrous Joints01:03

Structural Joints: Fibrous Joints

3.8K
Fibrous joints are a type of joint where the bones are connected by fibrous connective tissue. These joints provide stability and minimal to no movement between the articulating bones. There are three types of fibrous joints.
Suture
All the bones of the skull, except for the mandible, are joined to each other by a fibrous joint called a suture. The fibrous connective tissue found at a suture strongly unites the adjacent skull bones and thus helps to protect the brain and form the face. In...
3.8K
Structural Joints: Cartilaginous Joints01:17

Structural Joints: Cartilaginous Joints

4.1K
As the name indicates, at a cartilaginous joint, the adjacent bones are united by cartilage, a tough but flexible type of connective tissue. Unlike synovial joints, these types of joints lack a joint cavity and involve bones joined together by either hyaline cartilage or fibrocartilage.
There are two types of cartilaginous joints:
Synchondrosis
A synchondrosis ("joined by cartilage") is a cartilaginous joint where bones are connected by hyaline cartilage. Synchondrosis may be temporary...
4.1K
Joints01:26

Joints

35.8K
Joints, also called articulations or articular surfaces, are points at which ligaments or other tissues connect adjacent bones. Joints permit movement and stability, and can be classified based on their structure or function.
Structural joint classifications are based on the material that makes up the joint as well as whether or not the joint contains a space between the bones. Joints are structurally classified as fibrous, cartilaginous, or synovial.
Fibrous Joints Are Immovable
The bones of a...
35.8K
Confidence Intervals01:21

Confidence Intervals

10.8K
An unbiased point estimate is often insufficient to predict a population estimate, such as population mean or population proportion. In this scenario, a confidence interval is used. A confidence interval is an estimate similar to a  sample proportion. However, unlike the point estimate which is a single value, the confidence interval  contains a range of values. These values have lower and upper limits, known as confidence limits, and can be designated as L1 and L2, respectively.
A...
10.8K

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Related Experiment Video

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Optimizing personalized screening intervals for clinical biomarkers using extended joint models.

Nobuhle Nokubonga Mchunu1,2,3, Henry Mwambi2, Tarylee Reddy1,2

  • 1Biostatistics Research Unit, South African Medical Research Council, Durban, South Africa.

Journal of Applied Statistics
|February 6, 2026
PubMed
Summary

This study enhances HIV and TB patient care with advanced joint modeling for personalized screening schedules. Optimized models predict future events, improving outcomes for co-infected individuals.

Keywords:
62H9962N0162P1092B15CD4 countcross validationmultivariate joint modelspersonalized screening intervalssuper learning

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Area of Science:

  • Biostatistics
  • Epidemiology
  • Infectious Diseases

Background:

  • HIV and TB co-infection presents complex data challenges for patient management.
  • Existing models may lack flexibility for longitudinal outcomes and personalized risk assessment.

Purpose of the Study:

  • To develop and validate advanced joint models for personalized screening schedules in HIV/TB co-infected patients.
  • To identify optimal predictors of mortality using longitudinal health data.

Main Methods:

  • Utilized multivariate joint models incorporating censored longitudinal outcomes (CD4 count, viral load).
  • Employed super learning techniques for model selection, validated by Integrated Brier Score and Expected Predictive Cross-Entropy (EPCE).
  • Integrated predictive models with a risk-based screening strategy.

Main Results:

  • The multivariate joint model, parameterizing area under CD4 and viral load profiles, was identified as optimal.
  • These parameters strongly predicted mortality in event-free subjects.
  • Personalized screening intervals were recommended: 10.3 months for stable patients, with tiered intervals (3.5, 6.2, 12 months) for deteriorating health.

Conclusions:

  • The developed joint modeling approach offers a flexible and accurate method for personalized patient management in HIV/TB co-infection.
  • Findings support refined clinical protocols and advance personalized medicine for co-infected individuals.
  • The methodology is adaptable and has potential applications in other biomarker studies and clinical trials.