# Problem with Cox regression

Using drug dispensing data, I am trying to examine the association between proportion of days covered (PDC) by a drug and disease relapse. PDC is calculated by drug's supply divided by follow-up time. Events such as end of study and death are censored using Cox regression. Follow-up basically ends at disease relapse, death or end of study, whichever comes first.

Example of patient:

Date: 1 Jan 2010 (hospital discharge), dispensed 30 days of drug

Date: 1 Feb 2010, dispensed 60 days

Date: 1 May 2010, dispensed 60 days

Disease relapse: 1 Aug 2010 (end of follow-up)

Duration of follow-up: 210 days (from 1 Jan to 1 Aug 2010)

PDC = (30 + 60 + 60) / 210 = 0.714

With the PDC, I divided the patients into PDC >=80% and PDC < 80%, and examine the rate of disease relapse. The HR associated with a category of PDC is derived using exposure information concurrent to the observed outcomes.

My results showed that a higher PDC (>=80%) is associated with higher HR (risk) of disease relapse, which does not make sense. I examined my data closer and found that those with disease relapse, appeared to have shorter follow-up time, which also tends to result in higher PDC values. This may be possible as the PDC usually becomes more accurate over a longer period of time. But in patients with the disease relapse, the follow-up time is cut and hence their PDC becomes higher, relative to those without relapse.

Is there any way of handling this issue? I am not sure whether the PDC should be considered a time-varying covariate. Any help is appreciated. Thank you.

• Can you provide sample data? What does it mean 'the drug's supply divided by followup time'? Dec 11, 2017 at 22:39
• Also, when do the subjects enter the study? a certain date? based on some event? Dec 11, 2017 at 22:54
• I will add more details.
– Rey
Dec 12, 2017 at 2:09