I'm dabbling in survival analysis, applied to cars. I created a survival graph based on age using Kaplan-Meier, in lifelines
(python library). However, guidance on how to apply that graph to inference seems a bit thin (or I'm dense ;-) ) so I would like to ask you to validate my current calculation of how many cars will still be on the road next year, given my data, collected about cars for the last few years. The Kaplan-Meier graph describes the survival of a car related to its age. I interpret that as describing the average 'walk of life' of a car.
My calculation for the amount of cars surviving until next year is : I have a population of cars on the road now. so for their current age, they have survived with probability 1. Now, their chance to reach next year, using the survival graph based on age, is (I think) : value[next year]/value[this year]. then I sum up this ratio for all cars currently still on the road, and that gives me the amount of cars expected to be on the road next year.
Please let me know if this is a valid way to estimate the amount of cars still on the road next year, given their age. Or what calculation you would apply.
For bonus points : I'm adding up survival probabilities of individual cars (or age-groups if you want) with different confidence intervals. is there any way to calculate the overall confidence interval for the total of surviving cars ? I now take the maximum interval encountered, as a sort of worst-case guess.
Initially I just looked up the numbers in the graph for [age+1] and summed them over all cars, but (fortunately) that gave me far too low survival estimates, which got me thinking. I'm doing the calculation as a review of a report that included historical numbers. that gave me some indication, so I'm thinking I'm on the right track, but I felt the same way the previous time.