Name: log-rank test for comparing survival curves
Uploaded: 2025-01-09T15:14:16.000Z
Duration: 1 min 19 s
Channel: JoVE

Mantel-Cox log-rank testi, iki grup arasındaki sağkalım dağılım eğrilerini karşılaştırmak için parametrik olmayan bir istatistiksel yöntemdir.
Tipik olarak, zaman içinde tedavi etkinliğini değerlendirmek ve daha sonraki çalışmalara rehberlik etmek için klinik araştırmalarda kullanılır.
Örneğin, araştırmacılar bu testi, yeni bir tedavi gören bir grup ile kontrol tedavisi gören başka bir grubun sağkalım eğrileri arasındaki istatistiksel olarak anlamlı farklılıkları belirlemek için kullanabilirler.
Mantel-Cox testi, belirli bir hayatta kalma süresi dağılımı varsaymadan gruplar arasında gözlemlenen ve beklenen olaylar arasındaki farkları hesaplar. Ölüm veya hastalığın nüksetmesi gibi tüm deneklerin ilgilenilen olayı yaşayamayacağı sansürlü verileri analiz etmek için idealdir.
Sınırlaması, zaman içinde sabit tehlike oranlarını ortaya koyan ve yalnızca bazen doğru olabilen orantılı tehlikeler varsayımı üzerindeki güvenilirliğinde yatmaktadır. Oransal tehlike varsayımının ihlal edilmesi durumunda bu testin sonuçları yanıltıcı olabilir.
Bu, özellikle küçük örneklem boyutlarına veya yüksek sansür oranlarına sahip çalışmalar için geçerlidir.

mantel-cox log-sıra testi

The Mantel-Cox Log-Rank Test

Mantel-Cox log-sıra testi, iki grubun sağkalım dağılımlarını karşılaştırmak için yaygın olarak kullanılan bir istatistiksel yöntemdir. Sağkalım verileri için belirli bir dağılım varsaymadan gruplar arasında sağkalım sürelerinde istatistiksel olarak anlamlı bir fark olup olmadığını test eder ve bu da onu parametrik olmayan bir test yapar. Bu esneklik, log-sıra testini özellikle tıbbi araştırmalarda ve ölüm veya hastalığın tekrarlaması gibi bir olayın zamanlamasının ilgi çekici olduğu diğer alanlarda değerli kılar. Genellikle klinik çalışmalarda ve epidemiyolojik çalışmalarda, yeni bir tedavinin kontrol veya standart tedaviye kıyasla sağkalımı iyileştirip iyileştirmediğini değerlendirmek için uygulanır. Log-sıra testinin güçlü yönlerinden biri, ilgi çekici olayın çalışmanın sonunda bazı denekler için gözlemlenmemesi durumunda oluşan sansürlü verileri işleme yeteneğidir. Bu özellik, her katılımcı için tam sağkalım süreleri mevcut olmasa bile testin mevcut tüm bilgileri içerebilmesini sağlar. Ek olarak, normal dağılımlı sağkalım süreleri varsayımına dayanmadığı için, test geniş bir sağkalım verisi yelpazesi için oldukça uygundur. Ancak, log-sıra testinin sınırlamaları vardır. Gruplar arasındaki tehlike oranlarının zamanla orantılı ve sabit kaldığını varsayar; bu her zaman geçerli olmayabilecek bir durumdur. Bu varsayımın ihlali yanıltıcı sonuçlara yol açabilir. Dahası, test güvenilir bulgular üretmek için yeterli sayıda olay gerektirir ve bu da onu küçük örneklem büyüklükleri veya yüksek sansür oranları olan çalışmalarda daha az etkili hale getirir. Bu gibi durumlarda, Cox orantılı tehlikeler modeli gibi alternatif yöntemler daha uygun olabilir. Mantel-Cox log-sıra testi, basitliğine rağmen, farklı tedavilerin sağkalım üzerindeki etkisini değerlendirmek için güçlü ve basit bir yol sağlar. Olayların hem zamanlamasını hem de sıklığını hesaba katarak araştırmacıların müdahalelerin etkinliği hakkında anlamlı sonuçlar çıkarmasını sağlar. Belirli sınırlamaları olmasına rağmen, uyarlanabilirliği ve sansürlü verilerle çalışma yeteneği onu sağkalım analizinde önemli bir araç haline getirir.

Mantel-Cox Log-Sıra Testi

Mantel-Cox log-rank testi, iki grup arasındaki sağkalım dağılım eğrilerini karşılaştırmak için parametrik olmayan bir istatistiksel yöntemdir.
Genellikle kullanılır ...

Education

JoVE Core

Statistics

İstatistik

Unit 1

Ünite 1

Survival Analysis

Sağkalım Analizi

KEY TERMS AND CONCEPTS

TEMEL TERİMLER VE KAVRAMLAR

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 clinical trials and epidemiological studies.

A key strength of the test is that it can handle censored data. Censored data means the event has not been observed for some subjects by the end of the study. The test can still use that information, even when complete survival times are not available for every participant.

The test also does not rely on normally distributed survival times. That makes it suitable for many kinds of survival data. However, it assumes that the hazard ratios between groups stay proportional and constant over time, and that may not always be true.

If this assumption is violated, the results can be misleading. The test also needs enough events to give reliable findings. It is less effective when sample sizes are small or censoring is high, so a Cox proportional hazards model may be a better choice in those cases. Even with these limits, the Mantel-Cox log-rank test remains a simple and powerful tool for judging how treatments affect survival.

Log-Rank Test for Comparing Survival Curves

log-rank-test-for-comparing-survival-curves

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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...

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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,...

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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

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...

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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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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...

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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...

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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...

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Types of Censoring in Survival Analysis

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Survival Tree in Time-to-Event Analysis

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Modeling Survival Time with Weibull and Exponential

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key terms and concepts

Learn how the Mantel-Cox log-rank test compares survival curves, handles censored data, and helps assess treatment effects in survival analysis.

Log-Rank Test for Comparing Survival Curves | JoVE K12

statistics

JoVE Core İstatistikleri öğrencilere verilerin toplanması, analiz edilmesi, sunulması ve yorumlanması kavramlarını ve uygulamalarını tanıtır. Bu animasyonlu dersler, çok çeşitli disiplinlerden öğrencilere faydalı olacak istatistik anlayışı sağlar. Ek olarak, eylem halindeki bilim insanı videoları, dünya çapındaki günümüz laboratuvarlarında gerçekleştirilen klasik ve orijinal araştırma deneylerinde ilgili kavramların uygulanmasını göstermektedir.

Log-Rank Test for Comparing Survival Curves

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