Table 1 Results in tabular form

Data harmonization

measurement & definition of variables

11 (37.9%)

10 (34.5%)

8 (27.6%)

N = 29

differences in measurement & definitions

10 (34.5%)

11 (37.9%)

8 (27.6%)

N = 29

harmonization/standardization efforts

20 (68.9%)

8 (27.6%)

1 (3.4%)

N = 29

Accounting for missing data

missing data within and across studies

11 (37.9%)

14 (48.3%)

4 (13.8%)

N = 29

reasons/mechanisms of missingness

1 (3.4%)

0 (0%)

28 (96.6%)

N = 29

how accounted for missing data

19 (65.5%)

3 (10.3%)

7 (24.1%)

N = 29

Imputation Model

Variables included in imputation model

5 (50%)

1 (10%)

4 (40%)

N = 10

rationale for variables in imputation model

0 (0%)

0 (0%)

10 (100%)

N = 10

accounted for heterogeneity in imputation model

4 (40%)

0 (0%)

6 (60%)

N = 10

Pooling

assumptions to pool data

0 (0%)

0 (0%)

29 (100%)

N = 29

testing any assumptions to pool data?

0 (0%)

0 (0%)

29 (100%)

N = 29

one-step or two-step

8 (27.6%)

0 (0%)

21 (72.4%)

N = 29

Causality

causal methods

3 (10.3%) ^a

0 (0%)

26 (89.7%)

N = 29

justification of methods

2 (6.9%)

3 (10.3%)

24 (82.8%)

N = 29

state assumptions of analysis methods

4 (13.8%)

0 (0%)

25 (86.2%)

N = 29

tested testable assumptions

3 (10.3%)

0 (0%)

26 (89.7%)

N = 29

evaluation of untestable assumptions

2 (6.9%)

0 (0%)

27 (93.1%)

N = 29

investigated the heterogeneity of results

13 (44.8%)

5 (17.2%)

11 (37.9%)

N = 29

the generalizability of results

10 (34.5%)

3 (10.3%)

16 (55.2%)

N = 29

Sensitivity analyses

23 (79.3%)

0 (0%)

6 (20.7%)

N = 29

Confounder Control

how they controlled for clustering

24 (82.8%)

0 (0%)

5 (17.2%)

N = 29

labelling of covariates as confounders or mediators

15 (51.7%)

5 (17.2%)

9 (31%)

N = 29

how covariates were selected

15 (51.7%)

1 (3.4%)

13 (44.8%)

N = 29