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Assumptions of Survival Analysis

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.

Random Error

Random Error

Random or indeterminate errors originate from various uncontrollable variables, such as variations in environmental conditions, instrument imperfections, or the inherent variability of the phenomena being measured. Usually, these errors cannot be predicted, estimated, or characterized because their direction and magnitude often vary in magnitude and direction even during consecutive measurements. As a result, they are difficult to eliminate. However, the aggregate effect of these errors can be...

Random and Systematic Errors

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 Errors

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...

Censoring Survival Data

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...

Uncertainty in Measurement: Accuracy and Precision

Uncertainty in Measurement: Accuracy and Precision

Scientists typically make repeated measurements of a quantity to ensure the quality of their findings and to evaluate both the precision and the accuracy of their results. Measurements are said to be precise if they yield very similar results when repeated in the same manner. A measurement is considered accurate if it yields a result that is very close to the true or the accepted value. Precise values agree with each other; accurate values agree with a true value.

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Related Experiment Video

Updated: May 22, 2026

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

Avoidable deaths and random variation in patients' survival.

K Seppä¹, T Hakulinen, E Läärä

¹Finnish Cancer Registry, Institute for Statistical and Epidemiological Cancer Research, Pieni Roobertinkatu 9, FI-00130, Helsinki, Finland. karri.seppa@cancer.fi

British Journal of Cancer

|April 26, 2012

Summary

Quantifying random error in avoidable cancer deaths is crucial. This study developed methods to estimate these errors, revealing substantial variations in colon cancer mortality across regions.

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Last Updated: May 22, 2026

Establishing a Competing Risk Regression Nomogram Model for Survival Data

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Published on: October 23, 2020

Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index

Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

Area of Science:

Oncology
Biostatistics
Public Health

Background:

Previous studies on avoidable cancer deaths overlooked random error.
Accurate estimation of cancer mortality requires accounting for statistical uncertainty.

Purpose of the Study:

To develop and apply statistical methods for estimating confidence intervals (CIs) for avoidable cancer deaths.
To quantify the impact of random error on estimates of avoidable colon cancer deaths.

Main Methods:

Developed methods to calculate valid confidence intervals for numbers of avoidable deaths.
Estimated excess mortality rates for colon cancer patients (2000-2007) in Finnish regions using relative survival regression.
Modeled numbers of avoidable deaths under different scenarios.

Main Results:

Out of 10,772 patients, 4,139 died from colon cancer and 1,335 from other causes within 5 years.
An estimated 146 avoidable deaths (95% CI 3-290) were identified if all patients achieved the survival rate of the best-performing region.

Conclusions:

Random error in avoidable death counts is often substantial.
Statistical methods can quantify this error using realistic margins.
Accurate assessment of cancer control effectiveness requires considering random error.