Name: hazard ratio in clinical trials
Uploaded: 2025-01-09T15:14:17.000Z
Duration: 1 min 12 s
Channel: JoVE

위험 비율은 두 그룹 간의 주어진 시점에서 발생하는 이벤트의 위험 또는 위험을 비교합니다.
λ(t)를 시간 t의 위험률로 간주하면, 그 순간에 시간 t가 감소할 때까지 개체군의 비율이 얼마나 빨리 생존할 것인지를 측정합니다.
다른 말로 하면, t시간까지 살아 있는 사람들 중에서 죽을 사람들의 비율이 될 수 있다.
이제 대조군 A와 실험군 B의 위험률은 여기에서 각각 λA(t) 및 λB(t)로 표현할 수 있습니다. 따라서 λB(t)와 λA(t)의 비율은 θ(t)로 표시되는 위험 비율입니다.
위험비가 1 미만이면 실험 그룹에서 사망과 같은 사건이 발생할 확률이 낮다는 것을 의미하며, 실험 치료를 선호합니다.
위험비가 1 이상이면 대조군 치료를 선호하는 실험군의 사망 가능성이 더 높다는 것을 나타냅니다.
마지막으로, 위험비 1은 대조군 치료와 실험적 치료 모두에서 사망 가능성이 동일하다는 것을 의미합니다.

위험 비율

Hazard Ratio

위험 비율(HR)은 임상 시험에서 두 그룹 간의 사망이나 질병 재발과 같은 사건 위험을 시간 경과에 따라 비교하는 데 널리 사용되는 척도입니다. 치료 그룹과 대조군 간의 위험 비율(사건이 발생할 즉각적인 위험)을 반영합니다. 이 척도는 두 그룹 간의 사건 위험이 어떻게 다른지 평가하여 치료의 상대적 효과에 대한 귀중한 통찰력을 제공합니다.

예를 들어, 표준 요법과 비교하여 새로운 항암제를 평가하는 임상 시험에서 위험 비율은 암 재발이나 생존과 같은 결과에 대한 약물의 영향을 정량화합니다. 위험 비율이 1 미만이면 치료 그룹의 사건 위험이 낮아 새로운 치료가 유익할 수 있음을 나타냅니다. 반대로 위험 비율이 1보다 크면 치료 그룹의 위험이 높아 대조군 개입이 유리함을 의미합니다. 위험 비율이 정확히 1이면 그룹 간에 위험 차이가 없음을 나타냅니다.

임상 연구에서 위험 비율은 연구 종료 시의 발생뿐만 아니라 사건의 타이밍을 고려하기 때문에 특히 가치가 있습니다. 이는 생존 분석에서 특히 유용한데, 여기서 사건이 얼마나 빨리 발생하는지 이해하는 것이 치료 효과를 평가하는 데 중요합니다. HR은 연구자가 치료가 효과적인지 여부뿐만 아니라 결과에 영향을 미치는 속도도 결정하는 데 도움이 됩니다.

종양학을 넘어 위험 비율은 심장학, 역학 및 공중 보건을 포함한 다양한 분야에 적용됩니다. 다양한 상태에서 개입, 약물 및 치료 전략의 효능을 평가하는 데 사용됩니다. 위험 비율은 시간 경과에 따른 위험의 동적 측정을 제공함으로써 의학 연구를 발전시키고 환자 치료에서 증거 기반 의사 결정을 지원하는 데 중요한 역할을 합니다.

위험 비율은 두 그룹 간의 주어진 시점에서 발생하는 이벤트의 위험 또는 위험을 비교합니다.
λ(t)를 에서 위험률로 간주합니다.

Education

교육

JoVE Core

Statistics

통계학

Unit 1

제1단원

Survival Analysis

생존 분석

KEY TERMS AND CONCEPTS

주요 용어 및 개념

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 affects cancer recurrence or survival compared with standard therapy.

A hazard ratio below 1 means the treatment group has a lower risk of the event. That result suggests the new treatment may help. A hazard ratio above 1 means the treatment group has a higher risk, which favors the control intervention. A hazard ratio of 1 means there is no difference in risk between the groups.

This measure is especially useful in survival analysis because it includes timing, not just whether an event happened by the end of the study. Researchers can use it to see both if a treatment works and how quickly it affects outcomes. Hazard ratio is used in many areas of medical research, including oncology, cardiology, epidemiology, and public health. It helps scientists evaluate drugs, interventions, and treatment strategies and supports evidence-based decisions in patient care.

Hazard Ratio in Clinical Trials

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...

survival curves

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

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,...

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

The Mantel-Cox log-rank test compares survival curves from two groups. It checks whether the groups have a statistically significant difference in survival time. This makes it useful when the timing of an event matters, such as death or disease recurrence. The log-rank test is non-parametric. That means it does not assume a specific distribution for the survival data. It is also useful in medical research, where it often helps compare a new treatment with a control or standard treatment in...

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...

cancer survival analysis

Cancer Survival Curves and Risk Factors

Cancer survival analysis measures how long it takes from diagnosis or the start of treatment to a specific outcome, such as remission or death. It helps researchers judge treatment effectiveness and understand what affects patient outcomes. These results also support clinical decisions and prognostic evaluations. Survival data are often skewed and not normally distributed. They also include censored cases, where the full survival time is unknown because the study ends, a patient is lost to...

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

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

Survival analysis studies time-to-event data, which tracks how long it takes for an outcome to happen. It is used in medicine, engineering, and social science. A common problem in this type of data is censoring, which means the full event time is not observed for every person or item. Censoring can happen for several reasons and in different patterns. The most common kind is right censoring. This happens when the event has not occurred by the end of the study or when a participant is lost to...

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 hazard ratio compares event risk over time in clinical trials and why values below, above, or equal to 1 matter in medical research.

Hazard Ratio in Clinical Trials | 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

Hazard Ratio in Clinical Trials

Description

Transcript