Recommendation	Indicator definition	Examples “yes”	Examples “no”	Notes
A1. Maximise response rate (consider questionnaire design, mode of administration, reminders, incentives, participants' engagement, etc.)	Mention taking steps to maximise response rate	Reminder, incentives, home/hospital visit, multiple attempts,	Mention response was maximised for clinical outcome but not reported for cost‐effectiveness endpoints	Can be for overall trial data if implicit includes cost or effect data. Except if steps are clearly for non‐CE variables only (e.g., primary outcome only).
A2. Consider alternative data sources (e.g., routinely collected data)	Mention that considered missing data issues when choosing appropriate source, OR mention more than one source used for a CE data.	Use of electronic health records or administrative data, e.g., hospital episode statistics were used to supplement trial's data, for example, about hospital admissions post‐randomisation (which might be otherwise missing).	Using routine data as a primary source: e.g., resource use taken primarily from administrative/hospital records.
A3. Monitor cost‐effectiveness data completeness while trial ongoing	Mentioned monitoring data completeness while trial ongoing.	Data managers checked inconsistent and missing data (if not clear “while trial ongoing” but mention monitoring probably fine). Mention taking new steps to reduce MD (e.g., incentive) as realised lots of MD after trial started.	Mention data checks for inconsistencies, but no mention of checking missing data.	Can be for overall trial data. Except if monitoring clearly for non‐CE variables only (e.g., primary outcome only).
B1. Formulate realistic and accessible missing data assumption for the primary analysis (typically, but not necessarily, a form of the missing at random assumption)	Primary (base‐case) CEA based on reasonable missing data assumptions. (likely MAR, or alternative if well justified).	– Used MI for primary analysis ‐ well justified and clear alternative	– Hybrid method, except if clearly explain and justify underlying assumptions
B2. Use appropriate method valid under that assumption (typically, but not necessarily, multiple imputation or maximum likelihood)	Use appropriate analysis method.	– MI for primary analysis ‐Bayesian under MAR ‐ well justified and clear alternative	– Use unadjusted CCA when reporting data are MAR.
C1. Discuss with clinicians and investigators to formulate plausible departures from the primary missing data assumption		Conducted MNAR SA + mention elicitation.	Did not conduct MNAR SA
C2. Consider a broad range of assumptions, including missing not at random mechanisms		Conducted MNAR SA	Did not conduct MNAR SA
C3. Use appropriate method valid under these assumptions (typically, but not necessarily, pattern‐mixture models or reference‐based approach)		Conducted MNAR SA, and used an appropriate method (PMM, etc.).	Did not conduct MNAR SA
D1. Report number of participants with cost and outcome data, by arm and time‐point	Report number (or %) of complete or missing data. Split at least by effectiveness vs. cost, time point (when applicable), and arm		Reported missing data by endpoint and arm, but not by time point.	Do not have to be all at the same time (split by endpoint + time + arm), can be three separate table/texts.
D2. Report possible reasons for non‐response, and baseline predictors of missing values	Mention something about main reason for the missing data, OR Explore factors associated with it.	Comment on why missing data (e.g., “because patients were too ill”). Or explore baseline factors associated with missingness	No mention of reasons for MD in the CE section.	Have to be specific to the CE missing data, or clearly mentioning something like “reasons for MD are discussed in clinical analysis section …”
D3. Describe methods used, and underlying missing data assumptions	Clearly state the method used to address missing data, AND the underlying assumption.		No report of missing data assumption or method used
Draw overall conclusion in light of the different results and the plausibility of the respective assumptions	Conduct sensitivity analyses, and interpret results appropriately.	Did MNAR SA and appropriate conclusion.	– Did not conduct sensitivity analyses – Conducted sensitivity analyses, but no comment/conclusion – Did MI and CC and only say “results did not change/robust to missing data”