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

מבחן דירוג לוג-קוקס של מנטל-קוקס הוא שיטה סטטיסטית לא פרמטרית להשוואת עקומות התפלגות הישרדות בין שתי קבוצות.
הוא משמש בדרך כלל במחקר קליני כדי להעריך את יעילות הטיפול לאורך זמן ולהנחות מחקרים נוספים.
לדוגמה, חוקרים יכולים להשתמש במבחן זה כדי לקבוע הבדלים מובהקים סטטיסטית בין עקומות ההישרדות של קבוצה אחת העוברת טיפול חדשני לבין קבוצה אחרת העוברת את טיפול הביקורת.
מבחן מנטל-קוקס מחשב הבדלים בין אירועים נצפים וצפויים בין קבוצות מבלי להניח התפלגות זמן הישרדות ספציפית. הוא אידיאלי לניתוח נתונים מצונזרים שבהם לא כל הנבדקים עשויים לחוות את האירוע המעניין, כמו מוות או הישנות מחלה.
מגבלתו נעוצה במהימנותו בהנחת הסיכונים הפרופורציונליים, המניחה יחסי סיכון קבועים לאורך זמן ועשויה להיות נכונה רק לעיתים. תוצאות בדיקה זו עלולות להטעות אם הנחת הסיכון המידתי מופרת.
זה נכון במיוחד עבור מחקרים עם מדגם קטן או שיעורי צנזורה גבוהים.

מבחן לוג-רנק של מנטל-קוקס

The Mantel-Cox Log-Rank Test

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מבחן לוג-רנק של מנטל-קוקס הוא שיטה סטטיסטית נפוצה בהשוואת התפלגויות הישרדות בין שתי קבוצות. המבחן בודק אם יש הבדל מובהק סטטיסטית בזמני הישרדות בין הקבוצות, מבלי להניח התפלגות מסוימת עבור נתוני הישרדות, ולכן מדובר במבחן לא פרמטרי. גמישות זו הופכת את מבחן הלוג-רנק לכלי בעל ערך במיוחד במחקר רפואי ובתחומים אחרים שבהם הזמן לאירוע, כמו מוות או הישנות של מחלה, הוא נושא מרכזי. מבחן זה נמצא בשימוש נרחב בניסויים קליניים ובמחקרים אפידמיולוגיים להערכת אם טיפול חדש משפר הישרדות בהשוואה לטיפול רגיל או קבוצת ביקורת.

אחד היתרונות של מבחן הלוג-רנק הוא היכולת להתמודד עם נתונים שנחסמו (censored data), כלומר כאשר האירוע המעניין לא התרחש עבור חלק מהנבדקים עד לסיום המחקר. תכונה זו מבטיחה שהמבחן יוכל לכלול את כל המידע הזמין, גם כאשר זמני הישרדות מלאים לא זמינים עבור כל המשתתפים. בנוסף, כיוון שהוא לא מסתמך על הנחה של התפלגות נורמלית של זמני הישרדות, המבחן מתאים למגוון רחב של נתוני הישרדות.

עם זאת, למבחן הלוג-רנק יש גם מגבלות. הוא מניח כי יחס הסיכון בין הקבוצות נשאר פרופורציונלי וקבוע לאורך זמן, הנחה שעשויה לא להתממש תמיד. הפרות של הנחה זו עשויות להוביל לתוצאות מטעיות. בנוסף, המבחן דורש מספר מספיק של אירועים על מנת להניב תוצאות אמינות, מה שהופך אותו לפחות יעיל במחקרים עם מדגמים קטנים או שיעור חסימה גבוה. במקרים כאלה, ייתכן ששיטות חלופיות כמו מודל הסיכונים הפרופורציונליים של קוקס יהיו מתאימות יותר.

למרות פשטותו, מבחן הלוג-רנק של מנטל-קוקס מספק דרך יעילה וישירה להעריך את השפעת הטיפולים השונים על הישרדות. הוא לוקח בחשבון את הזמן והחוזק של כל אירוע, מה שמאפשר לחוקרים להסיק מסקנות משמעותיות על יעילות ההתערבויות. למרות מגבלותיו, ההתאמה שלו לנתונים חסומים והיכולת לעבוד עם מגוון נתונים הופכות אותו לכלי חשוב בניתוח הישרדות.

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מבחן דירוג לוג-קוקס של מנטל-קוקס הוא שיטה סטטיסטית לא פרמטרית להשוואת עקומות התפלגות הישרדות בין שתי קבוצות.
זה בדרך כלל מועסק ...

Education

JoVE Core

Statistics

סטטיסטיקה

Unit 1

יחידה 1

Survival Analysis

ניתוח הישרדות

KEY TERMS AND CONCEPTS

מונחים ומושגי מפתח

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

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introduction to survival analysis

Time-to-Event Data and Censoring

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Kaplan-Meier Survival Curve Basics

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Life Table Method for Survival Rates

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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,...

comparing the survival analysis of two or more groups

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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...

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

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...

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Hazard Ratio in Clinical Trials

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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

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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

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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

Log-Rank Test for Comparing Survival Curves

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