Solved – Can Kaplan Meier method be used to estimate left-censored data

I have a set of 1000 datapoints of measured concentrations that may include up to 300 values which are censored (below the detection limit that the lab could reliably measure). The range of detection limit values vary, such as <2, <3, <7 etc. My data is neither normal or log-normal, and I've used non-parametric tests to analyze it so far.

The information in the environmental-research literature about the use of kaplan-meier, or ROS estimators for real left-censored data primarily only compares overall statistics (i.e. mean, median, std. dev) between these different estimator methods.

I would like to use KM to generate individual values for those of my results which are below the detection limit. To date I've relied on whatever stats software may be available to me, but I cannot find this option presently.

Edit: What are the steps to generate values for the left-censored data (using KM)?

Is there a programs/software that I could apply to my dataset and then impute values based on KM? Ultimately I am interested in using these generated values for further multivariate analysis of my dataset, and thus need new individual values (as opposed to overall mean, median etc).

Any comments would be helpful. Thank you.

Yes it is possible to use the Kaplan method to estimate left-censored data. The Wiki article is actually pretty decent Check it out

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