Dictionary

The Dictionary provides concise definitions of health economics terms, arranged alphabetically for quick reference. Use it to understand unfamiliar terminology or confirm the meaning of a specific term.

22 terms matching Missing Data

A

Available Case Analysis
A missing data method including all observations available for each specific calculation, potentially using different subsets across different parts of an analysis.

C

Completer Analysis
A missing data method including only participants who completed the entire study with no missing outcome data, excluding all others.
Copy Reference
A missing data method, often used after treatment discontinuation, assuming a patient's future outcomes follow the trajectory observed in the reference arm.

I

Imputation
A technique replacing missing data values with estimated substitutes, ranging from simple mean substitution to sophisticated multiple imputation approaches.

J

Jump to Reference
A missing data method assuming a patient's outcome trajectory immediately shifts to match the reference arm's from the point of treatment discontinuation.

L

Last Observation Carried Forward
A missing data method substituting a participant's most recent value for all subsequent missing time points, assuming their condition stayed unchanged.
Listwise Deletion
A missing data method excluding any observation with a missing value on any variable in an analysis, using only complete cases.

M

Missing at Random
A missing data classification indicating the probability of a value being missing depends on other observed variables, not the missing value itself.
Missing Completely at Random
A missing data classification indicating the probability of missingness is entirely unrelated to any observed or unobserved variables, the strongest assumption.
Missing Data
Information intended to be collected but absent for some individuals or variables, arising from dropout, non-response, or data entry errors.
Missing Data Handling
The overall process of identifying, characterising, and appropriately addressing missing values within a dataset before or during statistical analysis.
Missing Data Handling Qol
The statistical methods used to address incomplete responses in quality of life questionnaires, which commonly arise from patient dropout or survey fatigue.
Missing Data Methods
Statistical techniques, from simple complete case analysis to multiple imputation, used to address incomplete data, chosen based on the likely missingness mechanism.
Missing Not at Random
A missing data classification indicating the probability of missingness depends on the unobserved value itself, even after accounting for other variables.
Multiple Imputation
A missing data technique generating several plausible complete datasets with different estimated replacement values, then combining separate analyses of each.

P

Pairwise Deletion
A missing data method in which each specific calculation uses all observations with complete data for that particular pair of variables.
Pattern Mixture Model
A missing data modelling approach stratifying analysis by the observed pattern of missingness, rather than assuming one model applies to everyone.

R

Return to Baseline
A missing data method assuming a patient's outcome trajectory reverts to their original pre-treatment level from the point of discontinuation.

S

Selection Model
A missing data modelling approach explicitly modelling the process by which observations become missing, alongside the model for the outcome itself.
Sensitivity Analysis for Missing Data
Techniques assessing how much a study's conclusions might change under different, less favourable assumptions about missing data values.
Single Imputation
A missing data method replacing each missing value with a single estimated substitute, unlike multiple imputation, which generates several plausible values.

T

Tipping Point Analysis
A missing data sensitivity technique searching for the specific departure from a standard assumption that would be needed to overturn a study's conclusion.