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Table 3 Frequency of standard linear, linear mixed, and generalized estimating equations regression models to investigate repeated measurements of renal function

From: Regression methods for investigating risk factors of chronic kidney disease outcomes: the state of the art

Outcome investigated

na

Regression model mentioned in the paper

n (%)b

All repeated measurements of renal function

48

  

 Repeated measurements of

36

Linear mixed model

22 (61.1)

  - GFR (n = 33)

 

Linear mixed model accounting for informative

8 (22.2)

  - Creatinine clearance (n = 2)

 

drop-out

 

  - Proteinuria (n = 1)

 

Linear GEE

4 (11.1)

  

Linear GEE accounting for informative drop-out

1 (2.8)

  

Latent class growth analysis

1 (2.8)

 Repeated measurements of

10

Linear mixed model

7 (70.0)

  - log GFR (n = 5)

 

Linear GEE

2 (20.0)

  - log creatinine (serum or clearance) (n = 2)

 

Latent class growth analysis

1 (10.0)

  - log proteinuria (n = 3)

 Absolute GFR change between each visit and baseline

1

Linear mixed model

1 (100.0)

 Relative GFR change each year

1

Linear GEE

1 (100.0)

A summary statistic for the change of renal function

45

  

 Individual slopec of

36

Linear model

36 (100.0)

  - GFR (n = 30)

  - Creatinine (serum or clearance) (n = 4)

  - UACR (n = 2)

 Absolute GFR change as compared to baseline

7

Linear model

7 (100.0)

 Relative GFR change as compared to baseline

1

Linear model

1 (100.0)

 Log of absolute proteinuria change as compared to baseline

1

Linear model

1 (100.0)

  1. Abbreviations: GFR glomerular filtration rate, GEE generalized estimating equations, UACR urine albumin-to-creatinine ratio.
  2. aNumber of occurrences the specific outcome was used. The total exceeds 304 because some papers investigated several types of outcomes.
  3. bNumber and percentage of occurrences the statistical method was used for each specific outcome.
  4. cSlope of a marker is a summary statistic derived from measurements of a patient.