I have data from a prospective study with two measurements per participant (baseline and follow-up). I am interested in whether a score obtained at baseline predicts disease development at follow-up, taking interval between baseline and follow-up into account (time interval differs for each participant).

Because some paticipants missed follow-up (they dropped out or deceased) my data is right-censored. Cox regression demonstrated my score as significant predictor, however, this analysis was performed on the non-censored sample resulting in selection bias.

I read about inverse probability weighting, f.e., in Hernán's and Robins' book. I wonder whether this technique is also applicable in my case of only one observation per censored participant.

If no, does anbyody have any advice to account for the selection bias in my sample?

edit:

The cox regression which I calculated was performed on a dataset including only non-censored participants, follow-up status (disease yes/no), and number of months from baseline to follow-up. Example of my data:

ID   disease   months   censored   score
a    0          66        0          0
c    1          30        0          1
e    0          45        0          0

coxph(Surv(months, disease)
          ~score,
           covariates,
          data = dat)

I was thinking right now whether in this case I can simply account for right-censoring by including censored participants?

ID   disease   months   censored   score
a    0          66        0          0
*b*  0          0         1          0
c    1          30        0          1
*d*  0          0         0          1
e    0          45        0          0

coxph(Surv(months, disease)
          ~score,
           covariates,
          data = dat)

... But as my goal is to predict whether particpants developed the disease at follow-up and not at baseline, I am unsure whether this analysis answers another statistical question than mine.