Related Experiment Video
Updated: Oct 10, 2025
04:57
Establishing a Competing Risk Regression Nomogram Model for Survival Data
Published on: October 23, 2020
10.4K
Temporal trends in lung cancer survival: a population-based study
Lukas Löfling1,2, Shahram Bahmanyar2, Helle Kieler2,3
1Department of Research, Cancer Registry of Norway, Oslo, Norway.
Acta Oncologica (Stockholm, Sweden)
|December 10, 2021
Summary
Lung cancer survival rates in Sweden have significantly improved between 1995 and 2016. This positive trend in lung cancer survival was observed across various patient subgroups, indicating progress in treatment and management.
Area of Science:
- Oncology
- Public Health
- Epidemiology
Background:
- Lung cancer is a leading cause of cancer death globally and in Sweden.
- Survival rates for lung cancer have historically been low, typically 10-20% five-year survival.
- Recent decades have seen advancements in lung cancer diagnosis and treatment.
Purpose of the Study:
- To investigate temporal trends in lung cancer survival in Sweden.
- To analyze survival changes in subgroups based on sex, stage, histopathology, and smoking history.
- To assess changes in excess mortality over time.
Main Methods:
- Utilized population-based data from the Swedish National Lung Cancer Register (LCBaSe).
- Included 36,935 patients diagnosed with squamous cell carcinoma or adenocarcinoma between 1995 and 2016.
- Estimated one-, two-, and five-year relative survival and excess mortality.
Main Results:
- Overall one-, two-, and five-year survival increased from 38% to 53%, 21% to 37%, and 14% to 24%, respectively.
- Improved survival was noted in subgroups, including Stage III-IV disease, adenocarcinoma, and never-smokers.
- Excess mortality decreased significantly over the study period for all patient groups.
Conclusions:
- Lung cancer survival has demonstrably increased over time in Sweden.
- Significant survival improvements were observed even in advanced stages (Stage IV).
- Findings align with global trends of enhanced lung cancer patient outcomes.
More Related Videos
07:13Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
Published on: April 18, 2025
243
06:46Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery
Published on: September 27, 2024
418
Related Concept Videos
01:21Cancer Survival Analysis
478
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...
478
02:20Longitudinal Research
12.7K
Sometimes we want to see how people change over time, as in studies of human development and lifespan. When we test the same group of individuals repeatedly over an extended period of time, we are conducting longitudinal research. Longitudinal research is a research design in which data-gathering is administered repeatedly over an extended period of time. For example, we may survey a group of individuals about their dietary habits at age 20, retest them a decade later at age 30, and then again...
12.7K
01:25Statistical Methods for Analyzing Epidemiological Data
599
Epidemiological data primarily involves information on specific populations' occurrence, distribution, and determinants of health and diseases. This data is crucial for understanding disease patterns and impacts, aiding public health decision-making and disease prevention strategies. The analysis of epidemiological data employs various statistical methods to interpret health-related data effectively. Here are some commonly used methods:
599
01:18Introduction To Survival Analysis
430
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...
The primary goal of survival analysis is to estimate survival time—the time...
430
01:20Actuarial Approach
147
The actuarial approach, a statistical method originally developed for life insurance risk assessment, is widely used to calculate survival rates in clinical and population studies. This method accounts for participants lost to follow-up or those who die from causes unrelated to the study, ensuring a more accurate representation of survival probabilities.
Consider the example of a high-risk surgical procedure with significant early-stage mortality. A two-year clinical study is conducted,...
Consider the example of a high-risk surgical procedure with significant early-stage mortality. A two-year clinical study is conducted,...
147
01:20Comparing the Survival Analysis of Two or More Groups
336
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...
336