Table 1.

Summary of recommendations or considerations from STROBE, ROBINS-I and Sterne et al. guidelines

Recommendation	Explanation	STROBE	ROBINS-I	Sterne et al.
Patient Selection
State eligibility criteria	State inclusion and exclusion criteria of study participants, including criteria concerning missing data	✓		✓
Report the number of individuals at each stage of the study	Give reasons for exclusion at each stage	✓
	Indicate the amount of individuals discarded due to missingness at each stage of the study	✓		✓
	Give consideration to selection bias introduced by exclusion criteria		✓
	May use a flowchart to summarise	✓
Modelling and Covariate Selection
Covariates	Detail whether included as continuous or categorical and, if relevant, detail how the quantitative covariate was categorised	✓	✓
	Consider departures from linearity for continuous covariates and state which transformation, if any, was used	✓	✓
State analysis model	make it clear which method will be used to model the data	✓	✓
Covariate Selection	describe the procedure used to reach the final model	✓	✓
	this includes, but is not restricted to, missing data imputation, transformation of covariates, interactions between covariates or inclusion of covariates for a priori reasons	✓	✓
Results	Provide unadjusted estimates and the final adjusted model	✓	✓
	State the number of participants included in unadjusted and adjusted analyses	✓
Missing Data
Report the number of participants with missing data	Report this for each covariate of interest or the number of complete data for the important covariates	✓		✓
	Give reasons for missing values	✓	✓	✓
	Investigate if there are key differences between those observed and those with missing data - this may be compared across exposure/intervention groups.		✓	✓
Missing data methods (general)
Which method was used to handle missing data?	State clearly the method used	✓	✓	✓
State any missing data assumptions that were made	Such as whether the data are MCAR, MAR or MNAR	✓	✓	✓
Sensitivity analysis	Should investigate robustness of findings	✓	✓
	Compare method with a complete-case analysis		✓
	If necessary, assess validity of methods if there are differences	✓	✓
	Assess plausibility of missing data assumptions		✓
Multiple Imputation
Give details of the imputation model	State the software used and key settings for imputation model			✓
	State the number of imputations used			✓
	State variables included in imputation model			✓
	State how non-normal or binary covariates were handled			✓
	Were interactions in analysis model included in imputation model?			✓
If a large fraction of data are imputed, compare observed and imputed values				✓
Missing data assumptions	Discuss if variables included in the imputation model make MAR assumption plausible			✓
Sensitivity analyses	Compare MI results with CC results			✓
	Investigate departures from MAR assumption			✓
	If necessary, suggest explanations for why there are differences in results across sensitivity analyses			✓