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What is the worst case survival rate after 24 months assuming all patients censored ended up dying? What is the best case survival rate after 24 months?

What is the worst case survival rate after 24 months assuming all patients censored ended up dying? What is the best case survival rate after 24 months?

MATLAB: An Introduction with Applications
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
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What is the worst case survival rate after 24 months assuming all patients censored ended up dying? What is the best case survival rate after 24 months?

 

### Graph Explanation This graph illustrates the survival percentage over time. **X-Axis:** - Represents time intervals, numbered from 0 to 24. **Y-Axis:** - Represents the percentage of survival, ranging from 0.1 to 1.1. **Graph Line:** - The line begins at 100% survival (1.0) and gradually decreases as time progresses. - Key points of decrease occur at intervals: - Between 0 and 1, a slight drop occurs. - From 1 to 3, there is a notable decrease. - Between 14 and 17, another drop is observed. - Finally, between 20 and 21, there is a significant decrease. - After this, the percentage levels off and remains constant towards the end of the interval. This graph is typically used to represent a survival analysis or the effectiveness of a treatment/drug over time, showcasing how survival rates decline as the duration increases.
Transcribed Image Text:### Graph Explanation This graph illustrates the survival percentage over time. **X-Axis:** - Represents time intervals, numbered from 0 to 24. **Y-Axis:** - Represents the percentage of survival, ranging from 0.1 to 1.1. **Graph Line:** - The line begins at 100% survival (1.0) and gradually decreases as time progresses. - Key points of decrease occur at intervals: - Between 0 and 1, a slight drop occurs. - From 1 to 3, there is a notable decrease. - Between 14 and 17, another drop is observed. - Finally, between 20 and 21, there is a significant decrease. - After this, the percentage levels off and remains constant towards the end of the interval. This graph is typically used to represent a survival analysis or the effectiveness of a treatment/drug over time, showcasing how survival rates decline as the duration increases.
### Survival Data Table This dataset represents survival analysis, detailing the time until an event occurs, where the event is either a "death" (1) or data censoring (0). #### Columns: 1. **Year of Last Contact**: Indicates the last year the subject had contact or data was recorded. 2. **Time**: The time in years from the start of the observation until the event or censoring occurs. 3. **Event (1=death, 0=censored)**: - "1" denotes that the event (death) occurred. - "0" indicates that the data was censored, meaning the end point was not observed. 4. **Number at Risk**: The number of subjects still under observation and at risk at each time point. 5. **Number of Deaths**: Total number of deaths occurring at each time point. 6. **Number Censored**: Total number of subjects whose data was censored at each time point. 7. **Survival Function Estimate**: Probabilistic estimate of surviving past a given time point, calculated using the proportion of subjects surviving at each step. #### Explanation of Data: - At time 0, there are 20 subjects at risk, with an initial survival probability of 1. - The table captures a decrease in the number at risk as time progresses, with either deaths occurring or cases being censored. - As time increases, the survival probability generally decreases, indicating higher cumulative risk of death over time. This dataset is typical in medical studies and insurance to evaluate the effectiveness of treatments or to understand life expectancy. #### Graphical Representation: While the table itself provides quantitative data, typical graphical representations include survival curves known as Kaplan-Meier curves, which visually depict these survival probabilities over time.
Transcribed Image Text:### Survival Data Table This dataset represents survival analysis, detailing the time until an event occurs, where the event is either a "death" (1) or data censoring (0). #### Columns: 1. **Year of Last Contact**: Indicates the last year the subject had contact or data was recorded. 2. **Time**: The time in years from the start of the observation until the event or censoring occurs. 3. **Event (1=death, 0=censored)**: - "1" denotes that the event (death) occurred. - "0" indicates that the data was censored, meaning the end point was not observed. 4. **Number at Risk**: The number of subjects still under observation and at risk at each time point. 5. **Number of Deaths**: Total number of deaths occurring at each time point. 6. **Number Censored**: Total number of subjects whose data was censored at each time point. 7. **Survival Function Estimate**: Probabilistic estimate of surviving past a given time point, calculated using the proportion of subjects surviving at each step. #### Explanation of Data: - At time 0, there are 20 subjects at risk, with an initial survival probability of 1. - The table captures a decrease in the number at risk as time progresses, with either deaths occurring or cases being censored. - As time increases, the survival probability generally decreases, indicating higher cumulative risk of death over time. This dataset is typical in medical studies and insurance to evaluate the effectiveness of treatments or to understand life expectancy. #### Graphical Representation: While the table itself provides quantitative data, typical graphical representations include survival curves known as Kaplan-Meier curves, which visually depict these survival probabilities over time.
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