Related Experiment Video
Updated: May 14, 2026

04:57
Establishing a Competing Risk Regression Nomogram Model for Survival Data
Published on: October 23, 2020
IUD Survival and Its Determinants; a Historical Cohort Study
T Aghamolaei1, Sh Zare, Sh Zare
1Dept. of Health Services, School of Public Health, Hormozgan University of Medical Sciences, Bandar Abbas, Iran.
Journal of Research in Health Sciences
|January 25, 2013
Summary
Intrauterine device (IUD) continuation rates decline over time, with side effects being the primary reason for discontinuation. Counseling and pregnancy desire significantly impact IUD survival.
Area of Science:
- Reproductive Health
- Contraception Research
- Public Health
Background:
- Intrauterine devices (IUDs) are a leading reversible contraception method.
- Understanding IUD survival and discontinuation is crucial for effective family planning.
Purpose of the Study:
- To determine the continuation rate of IUD use.
- To identify reasons for early IUD discontinuation in Bandar Abbas, Iran.
Main Methods:
- A historical cohort study analyzed 400 women's records (March 2002-February 2004).
- Data collection involved health center records and subject interviews.
- Life tables, Kaplan-Meier, log-rank test, and Cox regression were used for analysis.
Main Results:
- IUD continuation rates at 48 months were 50%.
- Counseling and desire for pregnancy were linked to higher continuation rates (P<0.03).
- Key discontinuation reasons included side effects, pregnancy desire, health concerns, expulsion, and dissatisfaction.
Conclusions:
- Informed counseling on IUD side effects is essential before and during use.
- Addressing side effects can improve IUD continuation rates.
Related Concept Videos
Assumptions of Survival Analysis
Survival models analyze the time until one or more events occur, such as death in biological organisms or failure in mechanical systems. These models are widely used across fields like medicine, biology, engineering, and public health to study time-to-event phenomena. To ensure accurate results, survival analysis relies on key assumptions and careful study design.
Cancer Survival Analysis
Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...
Comparing the Survival Analysis of Two or More Groups
Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and Cox...
Longitudinal Studies
Longitudinal studies are also widely used in other medical and social science fields. For instance, in cardiovascular research, they can monitor patients' health over decades to identify risk factors for heart disease, such as high cholesterol or smoking, and evaluate the long-term effectiveness of preventive measures. Similarly, in mental health studies, researchers might follow individuals from adolescence into adulthood to understand the development and progression of conditions like...
Introduction To Survival Analysis
Survival analysis is a statistical method used to study time-to-event data, where the "event" might represent outcomes like death, disease relapse, system failure, or recovery. A unique feature of survival data is censoring, which occurs when the event of interest has not been observed for some individuals during the study period. This requires specialized techniques to handle incomplete data effectively.
The primary goal of survival analysis is to estimate survival time—the time until a...
The primary goal of survival analysis is to estimate survival time—the time until a...
Study Designs in Epidemiology
Epidemiological study designs are fundamental tools for investigating the distribution, determinants, and control of health conditions in populations. They help researchers understand the relationships between exposures and outcomes, and they broadly fall into two categories: "observational" and "experimental" studies.
Observational studies are those where the researcher does not intervene but rather observes natural variations. They include cross-sectional, cohort, and case-control studies.
Observational studies are those where the researcher does not intervene but rather observes natural variations. They include cross-sectional, cohort, and case-control studies.