Table 2 Frequency of survival (Cox, cause-specific, or Fine and Gray) and logistic regression models used to investigate risk factors of time-to-event outcomes
Outcome investigated | na | Regression model mentioned in the paper | n (%)b |
|---|---|---|---|
Exactly known time-to-event | 307 | Â | Â |
 All-cause death | 132 | Cox model | 108 (81.8) |
Cause-specific model | 10 (7.6) | ||
Fine and Gray model | 10 (7.6) | ||
Logistic model | 4 (3.0) | ||
 Cardiovascular death | 31 | Cox model | 29 (93.6) |
Cause-specific model | 1 (3.2) | ||
Fine and Gray model | 1 (3.2) | ||
 Cardiovascular event | 23 | Cox model | 23 (100.0) |
 Initiation of kidney replacement therapy or death due to kidney failure | 83 | Cox model | 65 (78.3) |
Fine and Gray model | 10 (12.1) | ||
Cause-specific model | 7 (8.4) | ||
Logistic model | 1 (1.2) | ||
 Initiation of kidney replacement therapy or death (whichever comes first) | 38 | Cox model | 38 (100.0) |
Interval-censored time-to-event | 45 | Â | Â |
 Absolute or relative change in renal function higher than a specific value as compared to baseline value, based on | 23 | Cox model | 7 (30.4) |
Fine and Gray model | 1 (4.4) | ||
  - GFR (n = 19) | Logistic model | 15 (65.2) | |
  - creatinine clearance (n = 3) | |||
  - proteinuria (n = 1) | |||
 Transition to a specific stage of disease, based on | 13 | Cox model | 9 (69.2) |
  - GFR (n = 9) | Logistic model | 4 (30.8) | |
  - proteinuria (n = 4) | |||
 Doubling of creatinine (serum or clearance) | 8 | Cox model | 7 (87.5) |
Logistic model | 1 (12.5) | ||
 Composite of | 1 | Cox model | 1 (100.0) |
  - decline in 30% of creatinine clearance | |||
  - increase in proteinuria > 3.5 g/d | |||
Composite of exact and interval-censored time-to- events | 43 | Cox model | 35 (81.4) |
Fine and Gray model | 3 (7.0) | ||
Cause-specific model | 3 (7.0) | ||
Logistic model | 2 (4.6) |