# How would you describe Kaplan-Meier curves?

## How would you describe Kaplan-Meier curves?

The Kaplan Meier Curve is an estimator used to estimate the survival function. The Kaplan Meier Curve is the visual representation of this function that shows the probability of an event at a respective time interval.

**Does Kaplan-Meier assume proportional hazards?**

Kaplan–Meier provides a method for estimating the survival curve, the log rank test provides a statistical comparison of two groups, and Cox’s proportional hazards model allows additional covariates to be included. Both of the latter two methods assume that the hazard ratio comparing two groups is constant over time.

**Which assumption do we have to make about censoring if we want to use standard methods of survival analysis?**

Kaplan-Meier (Product Limit) Approach Appropriate use of the Kaplan-Meier approach rests on the assumption that censoring is independent of the likelihood of developing the event of interest and that survival probabilities are comparable in participants who are recruited early and later into the study.

### What does it mean when Kaplan-Meier curves cross?

If the Kaplan-Meier survival curves cross then this is clear departure from proportional hazards, and the log rank test should not be used. This can happen, for example, in a two drug trial for cancer, if one drug is very toxic initially but produces more long term cures.

**How do you read a Kaplan-Meier table?**

The Kaplan-Meier plot can be interpreted as follow: The horizontal axis (x-axis) represents time in days, and the vertical axis (y-axis) shows the probability of surviving or the proportion of people surviving. The lines represent survival curves of the two groups. A vertical drop in the curves indicates an event.

**What are the assumptions of Kaplan-Meier?**

Kaplan-Meier estimator has a few assumptions: the survival probability is the same for censored and uncensored subjects; the likelihood of the occurrence of the event is the same for the participants enrolled early and late; the probability of censoring is the same for different groups; finally, the event is assumed to …

## How do you deal with right censoring?

Dealing with Right Censored Data

- Cut off the end of the sample period earlier so as to minimize the amount of censored data.
- Use up to the minute data which would include censored observations, but somehow estimate a stand in measurement or otherwise weight them differently.

**How do you read survival curves?**

The lines represent survival curves of the two groups. A vertical drop in the curves indicates an event. The vertical tick mark on the curves means that a patient was censored at this time. At time zero, the survival probability is 1.0 (or 100% of the participants are alive).

**How is the Kaplan Meier method used in statistics?**

Kaplan-Meier using SPSS Statistics. Introduction. The Kaplan-Meier method (Kaplan & Meier, 1958), also known as the “product-limit method”, is a nonparametric method used to estimate the probability of survival past given time points (i.e., it calculates a survival distribution).

### What are the assumptions in the Kaplan Meier survival curve?

The Kaplan-Meier survival curve is defined as the probability of surviving in a given length of time while considering time in many small intervals.[3] There are three assumptions used in this analysis. Firstly, we assume that at any time patients who are censored have the same survival prospects as those who continue to be followed.

**How is Kaplan Meier used in Ayurveda research?**

Such situations are common in Ayurveda research when two interventions are used and outcome assessed as survival of patients. So Kaplan-Meier method is a useful method that may play a significant role in generating evidence-based information on survival time.

**What do you need for a Kaplan Meier estimator?**

To generate a Kaplan–Meier estimator, at least two pieces of data are required for each patient (or each subject): the status at last observation (event occurrence or right-censored), and the time to event (or time to censoring).