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](https://www.hsph.harvard.edu/miguel-hernan/causal-inference-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          0         0          0
a    0          66        0          0
*b*  0          0         1          0
c    0          0         0          1
c    1          30        0          1
*d*  0          0         0          1
e    0          0         0          0
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.