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

Random Sampling Method01:09

Random Sampling Method

15.5K
Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. Data are the result of sampling from a population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest. Among the various sampling methods used by...
15.5K
Sampling Plans01:23

Sampling Plans

1.2K
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...
1.2K
Randomized Experiments01:13

Randomized Experiments

9.2K
The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
Simple randomization
Simple...
9.2K
Convenience Sampling Method00:55

Convenience Sampling Method

11.9K
Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. Data are the result of sampling from a population. The sampling method ensures that samples are drawn without bias and accurately represent the population.
Convenience sampling is a non-random method of sample selection; this method selects individuals that are easily accessible and may result in biased data. For example, a marketing...
11.9K
Systematic Sampling Method01:17

Systematic Sampling Method

13.7K
Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. Data are the result of sampling from a population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
Systematic sampling is one of the simplest methods...
13.7K
Cluster Sampling Method01:20

Cluster Sampling Method

15.4K
Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
15.4K

You might also read

Related Articles

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

Sort by
Same author

Trends in frequency of HIV viral load and CD4 cell count monitoring among Asian cohort of adults with HIV: an analysis of the TREAT Asia HIV Observational Database, 2003-2018.

medRxiv : the preprint server for health sciences·2026
Same author

Lingering sex and age disparities in dolutegravir uptake among adults with HIV: a multicountry observational cohort study.

BMJ global health·2026
Same author

Kidney dysfunction in adults living with HIV and HBV: a 10-year retrospective cohort study across seven Asia-Pacific countries.

AIDS research and therapy·2025
Same author

Virological outcomes and treatment retention in North Vietnam amidst transition to social insurance-based HIV services and dolutegravir-based regimens.

Scientific reports·2025
Same author

Low Cholesterol Associated With TB in People Living With HIV in an Asia-Pacific Cohort.

Journal of acquired immune deficiency syndromes (1999)·2025
Same author

Pregnancy Outcomes After HIV Diagnosis in Women Living With HIV in Japan, South Korea and Hong Kong Special Administrative Region: A Brief Report.

The Pediatric infectious disease journal·2025

Related Experiment Video

Updated: Mar 13, 2026

E-Patient Counseling Trial E-PACO: Computer Based Education versus Nurse Counseling for Patients to Prepare for Colonoscopy
06:28

E-Patient Counseling Trial E-PACO: Computer Based Education versus Nurse Counseling for Patients to Prepare for Colonoscopy

Published on: August 1, 2019

9.3K

A pseudo-random patient sampling method evaluated.

Nicole L De La Mata1, Mi-Young Ahn2, Nagalingeswaran Kumarasamy3

  • 1The Kirby Institute, UNSW Australia, Wallace Wurth Building, Sydney, NSW 2052, Australia.

Journal of Clinical Epidemiology
|October 25, 2016
PubMed
Summary

This study compared two human immunodeficiency virus (HIV) cohorts, finding that a pseudo-random sample can represent the entire HIV-positive patient population for certain outcomes. However, viral load results may differ due to incomplete data.

Keywords:
AsiaCohortHIVObservational dataPatient samplingSelection bias

More Related Videos

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

16.4K
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

Related Experiment Videos

Last Updated: Mar 13, 2026

E-Patient Counseling Trial E-PACO: Computer Based Education versus Nurse Counseling for Patients to Prepare for Colonoscopy
06:28

E-Patient Counseling Trial E-PACO: Computer Based Education versus Nurse Counseling for Patients to Prepare for Colonoscopy

Published on: August 1, 2019

9.3K
Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

16.4K
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

Area of Science:

  • Clinical Medicine
  • Epidemiology
  • Public Health

Background:

  • The TREAT Asia HIV Observational database (TAHOD) utilizes a pseudo-random patient sample.
  • The TREAT Asia HIV Observational database-Low Intensity Transfer (TAHOD-LITE) includes all patients from eight sites in seven Asian countries.
  • Comparing these cohorts is crucial for understanding the generalizability of findings from sampled HIV populations.

Purpose of the Study:

  • To determine if a pseudo-random sample accurately represents a complete HIV-positive patient cohort.
  • To compare demographics, CD4 counts, and viral load testing between a sampled and a complete HIV cohort.
  • To identify risk factors for CD4 count response, viral load suppression, and survival in both cohorts.

Main Methods:

  • Comparison of patient demographics, CD4 counts, and HIV viral load testing rates between the TAHOD (pseudo-random sample) and TAHOD-LITE (complete cohort) databases.
  • Determination of risk factors associated with CD4 count response, HIV viral load suppression (<400 copies/mL), and survival.
  • Statistical analysis to assess consistency and differences between the two cohorts.

Main Results:

  • Patient demographics, CD4 counts, and viral load testing rates were broadly similar between the 2,318 TAHOD patients and 14,714 TAHOD-LITE patients.
  • CD4 count response and all-cause mortality showed consistency with similar risk factors across both cohorts.
  • HIV viral load response appeared superior in the TAHOD cohort, with differing risk factors possibly due to subset testing.

Conclusions:

  • Analysis of risk factors for completely ascertained endpoints from the pseudo-random TAHOD sample can be generalized to the larger TAHOD-LITE population.
  • Significant variations in results can occur with smaller or pseudo-random samples, especially if data are not missing at random (e.g., viral load).
  • Empirical evidence supports the generalizability of findings from sampled HIV cohorts for specific endpoints, with caveats for data completeness.