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Missing data

Missing data are observations that were intended or relevant to the analysis but are unavailable, and the reason they are missing can affect bias and precision.

What question does this help answer?

“Could incomplete data change the study population, reduce precision, or bias the estimated association?”

Why it matters

Dropping every participant with any missing value may waste information and can bias results when completeness is related to exposure, outcome, or other predictors.

How to think about it

The traditional labels MCAR, MAR, and MNAR describe assumptions about the missingness process: missing completely at random means whether a value is missing depends on nothing in the data; missing at random means it depends only on information that was observed; missing not at random means it depends on the unobserved value itself. In practice, researchers should focus on what predicts missingness, what information is available, how the substantive analysis works, and which missingness scenarios are scientifically plausible.

What each mechanism depends on

Three small diagrams of what missingness depends on. Missing completely at random: whether a value is missing depends on nothing in the data. Missing at random: it depends only on observed data. Missing not at random: it depends on the missing value itself.Completely at random: dependson nothingObservedValueMissing?At random: depends on observeddataObservedValueMissing?Not at random: depends on thevalue itselfObservedValueMissing?Observed: data that were recorded. Value: the measurement that may be missing. Missing?: whether itis. Each label is an assumption about what predicts missingness, chosen for a reason, not a testresult.
Conceptual diagram. It shows the structure of the idea, not data from any study.

How Mallard may use this concept

Mallard may flag missing data when the plan relies on EHR variables with incomplete measurement, longitudinal follow-up, survey nonresponse, or covariates that are not universally collected. It may recommend multiple imputation or sensitivity analyses.

Good practice

  • Report how much is missing for important variables.
  • Compare missingness patterns by key study characteristics.
  • Avoid automatic single-value or mean imputation.
  • Make the missing-data assumptions explicit.
  • Align the missing-data method with the substantive analysis.
  • Perform sensitivity analysis when departures from the main assumption are plausible.

Common mistake

“Less than 5% missing” is not automatically ignorable. Bias depends on why values are missing, not only how many are missing.

Clinical example

If follow-up lab testing occurs more often among sicker patients, simply analyzing patients with observed labs can select a systematically different subgroup.

Recent studies that used this
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Part of the learning path Plan an observational study from existing records (step 6 of 7).
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Sources & further reading
  1. Multiple imputation for missing data in epidemiological and clinical research: potential and pitfalls (opens in a new tab) · BMJ · doi 10.1136/bmj.b2393
  2. Missing Data in Clinical Research: A Tutorial on Multiple Imputation (opens in a new tab) · Can J Cardiol · doi 10.1016/j.cjca.2020.11.010
Last source review: 2026-09-05
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Mallard uses reputable methodological and reporting sources, but this material may contain errors or omit important context. Verify important methodological decisions with the cited sources and, when appropriate, a qualified research methodologist or statistician.