Name: life table method for survival rates
Uploaded: 2025-01-09T15:14:16.000Z
Duration: 1 min 20 s
Channel: JoVE

생존 분석은 사망 또는 치료 실패와 같은 사건이 발생할 때까지의 시간을 평가합니다. 보험계리학에서 차용한 보험수리적 또는 생명표 접근법은 생존 데이터를 분석합니다.
임상 연구 시나리오에서 참가자는 고위험 수술 후 첫 해와 같은 중요한 기간에 집중하기 위해 추적 관찰 기간이 다른 그룹으로 나뉩니다.
보험계리법은 사전 결정된 간격으로 생존율을 계산하며, 후속 조치에서 실패했거나 다른 원인으로 사망한 참가자를 포함한 모든 참가자의 데이터를 완전히 활용합니다.
1년 생존율에 미치는 영향을 이해하기 위해 1년 생존율은 총 참가자에서 사망자 수를 빼고 원래 숫자로 나누어 도출합니다.
2년 동안 관찰된 환자의 하위 집합만이 2년 생존을 추정하기 위해 고려되며, 첫 해에 생존한 후 두 번째 해에 계속 생존한 환자에 초점을 맞춥니다.
이 방법은 일관되고, 장기적인 추세를 식별하는 데 도움이 되며, 치료 또는 중재의 효과를 평가하여 의료 연구의 결과를 이해하기 위한 강력한 도구를 제공합니다.

보험 수학적 접근 방식

Actuarial Approach

원래 생명 보험 위험 평가를 위해 개발된 통계적 방법인 보험 수학적 접근 방식은 임상 및 인구 연구에서 생존율을 계산하는 데 널리 사용됩니다. 이 방법은 추적 조사에서 탈락한 참가자나 연구와 관련 없는 원인으로 사망한 참가자를 고려하여 생존 확률을 보다 정확하게 나타냅니다.

초기 사망률이 높은 고위험 수술의 예를 생각해 보겠습니다. 중요한 첫해에 초점을 맞춘 2년 임상 연구가 수행됩니다. 참가자는 두 그룹으로 나뉩니다. 한 그룹은 1년 동안 추적 조사하고 다른 그룹은 2년 동안 추적 조사합니다. 생존율은 연구 기간을 간격(일반적으로 1년)으로 나누는 보험 수학적(또는 생명표) 방법을 사용하여 추정합니다.

예를 들어, 첫 번째 그룹은 1,000명의 환자로 시작하여 그 중 240명이 첫해에 사망한다고 가정합니다. 두 번째 그룹도 1,000명의 환자로 시작하여 첫해에 200명, 두 번째 해에 16명이 사망합니다. 1년 생존율은 초기 코호트 크기에서 총 사망자 수를 빼고 그 결과를 시작 인구로 나누어 계산합니다.

1년 생존율 = (2000-240-200)/2000=0.78 또는 78%

이 접근 방식은 수년에 걸친 암 치료의 효능을 평가하는 것과 같은 장기적 결과에 대한 연구에도 적용할 수 있습니다. 보험 수학적 방법은 추적 조사에서 탈락하거나 관련 없는 원인으로 사망한 환자를 수용하여 장기적 효과에 대한 강력한 분석을 허용합니다.

2년 생존율의 경우 계산이 더 미묘해집니다. 조건부 확률을 사용하여 전체 2년 동안 관찰되고 첫해를 살아남은 개인만 고려합니다. 2년 생존율은 1년 생존율을 초과해서는 안 되며, 1년 생존 확률에 2년 생존 확률을 곱하여 결정하며, 1년 생존을 조건으로 합니다.

2년 생존율=0.78×0.98=0.7644 또는 76.44%

이 정교한 방법은 강력하지만 불완전한 추적 자료와 정확한 사망자 기록과 같은 과제에 직면합니다. 이러한 한계에도 불구하고, 개별 참가자를 추적하는 것이 비현실적인 대규모 인구 연구에서 특히 효과적입니다. 검열 및 간격 기반 생존 확률을 고려함으로써 보험 수학적 접근 방식은 다양한 연구 맥락에서 생존 분석을 위한 신뢰할 수 있는 프레임워크를 제공합니다.

생존 분석은 사망 또는 치료 실패와 같은 사건이 발생할 때까지의 시간을 평가합니다. 보험계리사에서 채택된 보험계리사 또는 생명표 접근법 ...

Education

교육

JoVE Core

Statistics

통계학

Unit 1

제1단원

Survival Analysis

생존 분석

KEY TERMS AND CONCEPTS

주요 용어 및 개념

life table method for survival rates

The life table method, also called the actuarial approach, is used to estimate survival rates in clinical and population studies. It began in life insurance, where risk over time must be measured carefully. In health research, it gives a clearer picture of survival when some participants are lost to follow-up or die from causes unrelated to the study.

This method works by dividing the study period into set intervals, usually one year. That makes it useful for tracking outcomes over time, especially in studies with early deaths after a high-risk surgical procedure. In one example, a two-year study follows two groups. One group is observed for one year, and the other is observed for two years.

The one-year survival rate is found from the starting group size and the number of deaths during that year. In the example, 1,000 patients begin in each group. The first group has 240 deaths in the first year, and the second group has 200 deaths in the first year. Using the actuarial calculation, the one-year survival rate is 78%.

The same method can also be used for long-term studies, such as testing how well a cancer treatment works over several years. It accounts for patients who are not followed for the full study or who die from unrelated causes. That makes the survival estimate more realistic when individual tracking is difficult.

Two-year survival uses conditional probability, which means the second year is calculated only for people who survived the first year and were observed for the full two years. In the example, the second-year survival probability is multiplied by the first-year survival probability. The result is 0.78 times 0.98, or 76.44%.

A two-year survival rate should not be higher than the one-year rate. The method is powerful, but it depends on complete follow-up and accurate death records. Even with those limits, it is especially useful in large population studies where following each person closely is not practical.

Life Table Method for Survival Rates

introduction to survival analysis

Time-to-Event Data and Censoring

Survival analysis is a statistical method for time-to-event data. The event can be death, disease relapse, system failure, or recovery. It helps estimate survival time, which is the time until a specific event occurs, and it also shows what factors may affect that time. A key idea in survival analysis is censoring. Censoring happens when the event of interest has not been observed for some people during the study period. Because the data are incomplete in those cases, survival analysis uses...

life tables

Life Tables and Survival Patterns

A life table is a statistical tool that shows mortality and survival patterns in a population. It organizes data across age intervals so students can see how likely a group is to survive or die at each stage of life. The table gives a clear snapshot of population dynamics over time. Life tables are used in demography, public health, actuarial science, and ecology. They help scientists study life expectancy, plan health interventions, estimate insurance risk, and compare how species survive in...

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Kaplan-Meier Survival Curve Basics

Kaplan-Meier survival curves show how the chance of staying event-free changes over time. They are used to track survival in a population and to compare groups in medicine, public health, and reliability engineering. The Kaplan-Meier estimator is the most common way to build these curves. It is a non-parametric method, which means it does not assume a fixed pattern for the data. The result is a stepwise line that drops each time an event happens, such as death, disease recurrence, or...

actuarial approach

The life table method, also called the actuarial approach, is used to estimate survival rates in clinical and population studies. It began in life insurance, where risk over time must be measured carefully. In health research, it gives a clearer picture of survival when some participants are lost to follow-up or die from causes unrelated to the study. This method works by dividing the study period into set intervals, usually one year. That makes it useful for tracking outcomes over time,...

kaplan-meier approach

Survival Curves with Kaplan-Meier Data

The Kaplan-Meier estimator is a non-parametric method for estimating a survival function from time-to-event data. It is commonly used in medical research to track how many patients survive for a set time after treatment. It is also important in clinical trials, epidemiological studies, and reliability engineering. This method helps researchers study survival probabilities over time, even when some data are incomplete. That incomplete data is called censored data, which means the exact event...

assumptions of survival analysis

Survival Analysis Assumptions in Studies

Survival analysis studies the time until an event happens. The event may be death in a living organism or failure in a mechanical system. These models are used in medicine, biology, engineering, and public health to study time-to-event data. Careful study design matters because the method depends on several key assumptions. One common feature of survival times is positive skewness. This means events often happen early, while fewer cases occur later. Survival data also often include censoring,...

comparing the survival analysis of two or more groups

comparing-the-survival-analysis-of-two-or-more-groups

Kaplan-Meier Curves and Hazard Ratios

Kaplan-Meier curves and hazard ratios help researchers compare survival time between groups. Survival analysis looks at the time until an event happens, such as death, disease recurrence, or recovery. It is a key tool in medical research. A major strength of survival analysis is that it can handle censored data. Censored data means the event has not happened for some participants by the end of the study, or it has not been observed. This makes survival analysis different from standard...

the mantel-cox log-rank test

Log-Rank Test for Comparing Survival Curves

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applications of life tables

Life Tables in Public Policy and Science

Life tables are used in many fields to study mortality and survival rates. They give a numerical way to examine how long people or living things are likely to survive. This makes them useful for public health, business planning, and scientific research. In demography and public health, life tables help experts study population changes and predict future trends. They can show patterns such as an aging population or the effect of a health policy. In Nigeria, health officials used survival rates...

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Cancer Survival Curves and Risk Factors

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hazard rate

Modeling Event Risk Over Time

Hazard rate is a statistical measure that describes how event risk changes over time. It is also called the hazard function or failure rate. In survival analysis, it shows the instantaneous rate at which an event occurs, but only after the subject has not had the event yet. From a probability view, hazard rate gives the chance that a subject will have the event in a very small time interval. That chance is conditional on surviving to the start of that interval. From a frequency view, it can...

hazard ratio

Hazard Ratio in Clinical Trials

The hazard ratio is a way to compare event risk in clinical trials. It is often used to study events such as death or disease recurrence in two groups over time. The two groups are usually a treatment group and a control group. A hazard ratio compares hazard rates, which are the instantaneous risk of an event happening at a given moment. It shows how the risk in one group relates to the risk in the other group. In a cancer drug study, for example, the hazard ratio can show how a new treatment...

truncation in survival analysis

Left and Right Truncation in Survival Data

Truncation in survival analysis describes data loss caused by the timing of an event or by when a subject can enter a study. It can happen in two forms: left truncation and right truncation. In both cases, some individuals are left out of the dataset before analysis begins. Left truncation happens when people who had the event before a set time are not included in the study. This often occurs with delayed entry, which means a person is only observed if they survive until the entry point. For...

censoring survival data

Types of Censoring in Survival Analysis

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survival tree

Survival Tree in Time-to-Event Analysis

A survival tree is a non-parametric tool in survival analysis that links predictor variables, or covariates, to survival time. Survival time is the time until an event of interest occurs. It is useful when some data are censored, which means the event has not happened for some people by the end of the study or the exact event time is unknown. Building a survival tree starts with a dataset that includes covariates, survival time, and a censoring indicator for each subject. The data must be...

parametric survival analysis: weibull and exponential methods

parametric-survival-analysis-weibull-and-exponential-methods

Modeling Survival Time with Weibull and Exponential

Parametric survival analysis models survival time by assuming a specific probability distribution for when an event occurs. The Weibull and exponential distributions are two common choices because they are flexible and relatively straightforward to use. The Weibull distribution is a flexible model in parametric survival analysis. It can describe both increasing and decreasing hazard rates, depending on its shape parameter beta. When beta is greater than 1, the hazard rate rises over time. That...

key terms and concepts

Learn how the life table method, or actuarial approach, estimates one-year and two-year survival rates while accounting for follow-up losses and unrelated deaths.

Life Table Method for Survival Rates | JoVE K12

statistics

JoVE Core Statistics introduces the concepts and application of the collection, analysis, presentation, and interpretation of data to students. These animated lessons provide an understanding of statistics beneficial to students from a wide range of disciplines. Additionally, the scientist-in-action videos demonstrate the application of related concepts in classical and original research experiments performed in today's laboratories worldwide

Life Table Method for Survival Rates

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