CoxTimeVaryingFitter() raising `ZeroDivisionError: float division by 0`

Hi there,

I’m looking to fit a simple time-varying model using CoxTimeVaryingFitter() using a cumulative sum of an event occurring. However, every attempt at using the .fit() method results in a ZeroDivisionError: float division by zero. I’ve attached a sample XLSX of what a few of the observations I’m using look like: gh_issue.xlsx.

The code for fitting looks like this:

import numpy as np
import pandas as pd
from lifelines import CoxTimeVaryingFitter

df_fit = pd.read_excel("gh_issue.xlsx")
ctv = CoxTimeVaryingFitter()
ctv.fit(df_fit, id_col="ID", event_col="Event", start_col="Start", stop_col="Stop", show_progress=True)

The error appears to be with weighted_average = weight_count / tied_death_counts when calling self._get_gradients(). Is this intended behavior? If so, what is it signifying to the user?

Cheers, Indiana

[indiana-nikel]

Looking at events that occur exactly at the time of death think this would depend on what the event is. If it’s a perfect correlation with “death” (heart rate = 0, clicks the ‘Delete Account’ button in a SaaS example), then we would have convergence problem (I ran into this in a similar but unrelated piece of work using the R package survival). Otherwise, that event might be a clear indicator of why patient/customer would die/leave.

Does this question pertain to covariates changing over time or would it include categorical information to discard as well?

This also raises the question of instantaneous events at the beginning of a timeline as well. In my example, I’ve added 1 so that there is no instantaneous events at t=0, all starting events should start at t=1. Would this suggest that it makes sense to do the same for a death event?

[CamDavidsonPilon]

I need to think more about the following question:

suppose we are recording a patient’s measurements over time. One day, as we walk into the room, and patient dies (say at time t). A moment later, at time t+1, we look at the subject’s measurements, and record them.

i) Are the data record at time t+1 “allowed” in the inference? ii) What if we had looked at measurements at time t instead? t-1?

It seems silly to discard this row, but there are very valid cases where it makes sense to. Ex: if the measurement is heart rate, then at t+1 heart rate =0, and our inference will strongly suggest that “0 heart rate => death”, which is reverse causality.

What I’m trying to determine is if I should keep observations that land on exactly the time of death, or should they be discarded (implicitly or explicitly by the user).