Item | Recommendation | |
---|---|---|
Title and abstract | 1 | (a) Not essential in the title, but the fact that the study is hierarchical and the analyses are multilevel must be mentioned in the abstract (S) |
(b) The abstract should mention the various levels considered in the analyses and whether random intercepts only or also random slopes were modeled (N) | ||
Introduction | ||
Background/rationale | 2 | Provide rationale for the study design being hierarchical and for the analyses being multilevel (E) |
Objectives | 3 | Mention at what level the dependent and independent variables are taken (S) |
Methods | ||
Study design | 4 | (a) Provide the multilevel diagram for the study (S) |
(b) Justify level of the analyses (N) | ||
Population | 5 | (a) Provide the number of members of each level, the eligibility criteria, and the sources and methods of selection/sampling of the members (N) |
(b) If a repeated measures design, provide description of methods of follow-up, and spacing of time points (N) | ||
(c) Describe missingness patterns and imbalances in members across levels (E) | ||
Variables/ data structure | 6 | (a) Write out the multilevel model equation including the random effects – this may be provided in an Appendix (N) |
(b) Mention the variables used and from what level (N) | ||
Study size | 7 | (a) Provide details of the sample size calculation, and mention relevant variance partition coefficients (VPC) or intraclass correlation coefficients (ICC) and variance inflation factors (VIF) for each level (N) |
(b) Provide justification for ICCs from previous studies – literature or pilot studies (E) | ||
Statistical methods | 8 | (a) Describe all statistical methods, descriptive and inferential, detailing how the correlation in the data was dealt with (E) |
(b) Mention estimation procedure utilized (e.g. restricted maximum likelihood) (S) | ||
(c) Present variance components or VPCs/ICCs for ‘null’ model and for final model (S) | ||
(d) Justify variables considered in the initial model and justify the ones included in the final model (N) | ||
(e) Justify choice of random or fixed intercepts and random or fixed slopes for variables in the final model, along with correlation structure among the random effects (N) | ||
Results | ||
Participants | 9 | (a) Report the number of individuals from each level in the final model, since missing data may affect the original numbers (N) |
(b) Present a flow diagram (S) | ||
Descriptive data | 10 | (a) Indicate number of participants with missing data for each variable of interest, by level (S) |
(b) Identify the level when presenting graphs and tables (E) | ||
(c) Adjust the variances even in descriptive univariate or bivariate analyses (N) | ||
Modeling results | 11 | (a) Present the model equation and estimates – maybe in Appendix (S) |
(b) Present a summary table with estimates of fixed effects, VPCs/ICCs for null model, intermediate models (if any) and final model (N) | ||
(c) Present model goodness of fit statistics (N) | ||
Other analyses | 12 | Report other analyses and if multilevel, provide similar information as above (S) |