Related Experiment Video
Updated: Jul 6, 2025

An R-Based Landscape Validation of a Competing Risk Model
Published on: September 16, 2022
Evaluating bias with loss to follow-up in a community-based cohort: empirical investigation from the CARRS Study
Dimple Kondal1, Ashish Awasthi2,3, Shivani Anil Patel4
1Centre for Chronic Disease Control, New Delhi, India dimple@ccdcindia.org.
Background:
Retention of participants is a challenge in community-based longitudinal cohort studies. We aim to evaluate the factors associated with loss to follow-up and estimate attrition bias.
Methods:
Data are from an ongoing cohort study, Center for cArdiometabolic Risk Reduction in South Asia (CARRS) in India (Delhi and Chennai). Multinomial logistic regression analysis was used to identify sociodemographic factors associated with partial (at least one follow-up) or no follow-up (loss to follow-up). We also examined the impact of participant attrition on the magnitude of observed associations using relative ORs (RORs) of hypertension and diabetes (prevalent cases) with baseline sociodemographic factors.
Results:
There were 12 270 CARRS cohort members enrolled in Chennai and Delhi at baseline in 2010, and subsequently six follow-ups were conducted between 2011 and 2022. The median follow-up time was 9.5 years (IQR: 9.3-9.8) and 1048 deaths occurred. Approximately 3.1% of participants had no follow-up after the baseline visit. Younger (relative risk ratio (RRR): 1.14; 1.04 to 1.24), unmarried participants (RRR: 1.75; 1.45 to 2.11) and those with low household assets (RRR: 1.63; 1.44 to 1.85) had higher odds of being lost to follow-up. The RORs of sociodemographic factors with diabetes and hypertension did not statistically differ between baseline and sixth follow-up, suggesting minimal potential for bias in inference at follow-up.
Conclusion:
In this representative cohort of urban Indians, we found low attrition and minimal bias due to the loss to follow-up. Our cohort's inconsistent participation bias shows our retention strategies like open communication, providing health profiles, etc have potential benefits.
More Related Videos
07:31Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack
Published on: May 15, 2020
06:55Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
Published on: January 8, 2020
Related Concept Videos
Longitudinal Research
Assumptions of Survival Analysis
Actuarial Approach
Consider the example of a high-risk surgical procedure with significant early-stage mortality. A two-year clinical study is conducted,...
Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches
Non-controlled studies, commonly employed for initial exploration, lack a control group, rendering them susceptible to biases and external influences. In contrast,...
Kaplan-Meier Approach
Bias in Epidemiological Studies