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

Censoring Survival Data01:09

Censoring Survival Data

Survival analysis is a statistical method used to analyze time-to-event data, often employed in fields such as medicine, engineering, and social sciences. One of the key challenges in survival analysis is dealing with incomplete data, a phenomenon known as "censoring." Censoring occurs when the event of interest (such as death, relapse, or system failure) has not occurred for some individuals by the end of the study period or is otherwise unobservable, and it might have many different reasons...
Systematic Error: Methodological and Sampling Errors01:15

Systematic Error: Methodological and Sampling Errors

In the case of systematic errors, the sources can be identified, and the errors can be subsequently minimized by addressing these sources. According to the source, systematic errors can be divided into sampling, instrumental, methodological, and personal errors.
Sampling errors originate from improper sampling methods or the wrong sample population. These errors can be minimized by refining the sampling strategy. Defective instruments or faulty calibrations are the sources of instrumental...
Random and Systematic Errors01:20

Random and Systematic Errors

Scientists always try their best to record measurements with the utmost accuracy and precision. However, sometimes errors do occur. These errors can be random or systematic. Random errors are observed due to the inconsistency or fluctuation in the measurement process, or variations in the quantity itself that is being measured. Such errors fluctuate from being greater than or less than the true value in repeated measurements. Consider a scientist measuring the length of an earthworm using a...
Random and Systematic Errors01:20

Random and Systematic Errors

Scientists always try their best to record measurements with the utmost accuracy and precision. However, sometimes errors do occur. These errors can be random or systematic. Random errors are observed due to the inconsistency or fluctuation in the measurement process, or variations in the quantity itself that is being measured. Such errors fluctuate from being greater than or less than the true value in repeated measurements. Consider a scientist measuring the length of an earthworm using a...
Sampling Plans01:23

Sampling Plans

Sampling is a crucial step in analytical chemistry, allowing researchers to collect representative data from a large population. Common sampling methods include random, judgmental, systematic, stratified, and cluster sampling.
Random sampling is a method where each member of the population has an equal chance of being selected for the sample. It involves selecting individuals randomly, often using random number generators or lottery-type methods. For example, when analyzing the properties of a...
Data Reporting and Recording01:24

Data Reporting and Recording

Reporting and recording are crucial in data documentation. The timely, thorough, and accurate documentation of facts is essential when recording patient data. Failure to record findings during an assessment or interpretation of a problem will result in loss of information and make the patient document unreliable. The reader is left with general impressions if the information is not specific. A recording is documenting data of the individual's health information in a traceable, secure, and...

You might also read

Related Articles

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

Sort by
Same author

Biomarker affliction classes contribute additively to observed dementia severity and prospective conversion risk.

Journal of Alzheimer's disease : JAD·2026
Same author

Chemical Intolerance Is Associated with Autism Spectrum and Attention Deficit Disorders: A Five-Country Cross-National Replication Analysis.

Journal of xenobiotics·2026
Same author

Affliction class moderates the impact of neurodegeneration: Implications for A/T/N.

Journal of Alzheimer's disease : JAD·2025
Same author

Affliction class moderates the dementing impact of amyloidopathy.

Neuropsychology·2025
Same author

Multi-ancestry genome-wide meta-analysis of 56,241 individuals identifies known and novel cross-population and ancestry-specific associations as novel risk loci for Alzheimer's disease.

Genome biology·2025
Same author

Effects of the COVID-19 pandemic on individuals with chemical intolerance.

Family medicine and community health·2025
Same journal

Utilization Patterns Among Heterogeneous Subgroups of Homebound Older Adults: A Latent Class Analysis.

Journal of the American Geriatrics Society·2026
Same journal

Off-Label Initiation of Gabapentin and Valproic Acid Among Long-Stay Nursing Home Residents With and Without Dementia.

Journal of the American Geriatrics Society·2026
Same journal

The DOAC-FRAIL Study-Same Dose, Different Story: Prevalence of Deviant Direct Oral Anticoagulant Levels in Nursing Home Residents.

Journal of the American Geriatrics Society·2026
Same journal

Practice Environment and Job Outcomes Among Primary Care Nurse Practitioners Caring for Patients With Dementia.

Journal of the American Geriatrics Society·2026
Same journal

Anticholinergic Medication Use in Veterans Affairs Long-Term Care Residents: Clinical Patterns and Opportunities for Deprescribing.

Journal of the American Geriatrics Society·2026
Same journal

Perioperative Transfusion Trigger Score Versus Restrictive Transfusion in Older Non-Cardiac Surgery Patients: A Multicenter Randomized Controlled Trial.

Journal of the American Geriatrics Society·2026
See all related articles

Related Experiment Video

Updated: Jun 7, 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

Missing data? Plan on it!

Raymond F Palmer1, Donald R Royall

  • 1Department of Family and Community Medicine, University of Texas Health Science Center, San Antonio, Texas 78284, USA. palmerr@uthscsa.edu

Journal of the American Geriatrics Society
|October 30, 2010
PubMed
Summary
This summary is machine-generated.

Longitudinal studies track age-related changes effectively. Modern methods for handling missing data improve accuracy and enable purposeful missing data designs to enhance research efficiency.

More Related Videos

Databases to Efficiently Manage Medium Sized, Low Velocity, Multidimensional Data in Tissue Engineering
09:43

Databases to Efficiently Manage Medium Sized, Low Velocity, Multidimensional Data in Tissue Engineering

Published on: November 22, 2019

Using Continuous Data Tracking Technology to Study Exercise Adherence in Pulmonary Rehabilitation
09:42

Using Continuous Data Tracking Technology to Study Exercise Adherence in Pulmonary Rehabilitation

Published on: November 8, 2013

Related Experiment Videos

Last Updated: Jun 7, 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

Databases to Efficiently Manage Medium Sized, Low Velocity, Multidimensional Data in Tissue Engineering
09:43

Databases to Efficiently Manage Medium Sized, Low Velocity, Multidimensional Data in Tissue Engineering

Published on: November 22, 2019

Using Continuous Data Tracking Technology to Study Exercise Adherence in Pulmonary Rehabilitation
09:42

Using Continuous Data Tracking Technology to Study Exercise Adherence in Pulmonary Rehabilitation

Published on: November 8, 2013

Area of Science:

  • Gerontology
  • Biostatistics
  • Longitudinal Research Methodology

Background:

  • Longitudinal study designs are crucial for understanding age-related functional changes.
  • Established methods exist for managing missing data in longitudinal research.
  • Modern techniques address issues like power loss and biased estimates caused by missing data.

Purpose of the Study:

  • To describe advanced statistical methods for handling missing data in longitudinal studies.
  • To introduce the concept and benefits of purposefully planning missing data in research designs.
  • To highlight how these methods can optimize research outcomes and resource allocation.

Main Methods:

  • Growth curve analysis for longitudinal data.
  • Statistical measurement models for complex data structures.
  • Strategies for purposeful missing data design in research.

Main Results:

  • Modern missing data methods effectively mitigate problems like reduced statistical power and biased parameter estimates.
  • Purposeful planning of missing data can lead to reduced participant burden.
  • Improved data quality, enhanced statistical power, and cost management are achievable through strategic missing data design.

Conclusions:

  • Advanced statistical methodologies like growth curve analysis and measurement models are vital for robust longitudinal research.
  • Purposefully incorporating missing data into research designs offers significant advantages in efficiency and data integrity.
  • These approaches represent the state-of-the-art in addressing missing data challenges in studies of aging and functional change.