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1Repair Windows errors before they cause bigger problems2Scan for outdated or missing drivers - takes under a minute3Clear out junk files and repair common Windows errorsIf you have typed “How should I handle my missing data?” into a search engine, or seen the question in an r/statistics thread, the honest answer is a sequence of decisions rather than one method. No single approach is best across datasets. The method you choose is defensible only when its assumptions about why values are missing fit your study, and when you show how much your conclusions depend on those assumptions.
Step 1: Define the analysis before looking at the gaps
Write the analysis as a set of named variables and a target quantity. You need the outcome, the exposure or main predictor, the covariates, the estimand (the quantity the estimate is meant to represent), and the data structure. Missingness does not affect every part of the analysis equally. A gap in the outcome, in a predictor, in a covariate, or in one wave of repeated measurements can each change which methods are workable. A single percentage for the whole file hides the question that matters.
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- Outcome: the variable being modelled, when it is measured, and how many records lack it.
- Exposure or main predictor: counted separately, because a gap here can change the comparison itself.
- Covariates: the adjustment set, including any variable you would also use to explain missingness.
- Estimand: the population, the comparison, and the summary the analysis reports, such as a mean difference or a hazard ratio.
- Data structure: one measurement per record, repeated measures, or time-to-event data with planned follow-up.
Step 2: Describe where the values are missing and what the gaps have in common
Missing data can reduce power, introduce bias, increase uncertainty, and make the analysed sample less representative of the population you care about. Each of these risks grows or shrinks with the pattern of gaps, so describe the pattern before choosing a method. For each variable in the analysis, record the following:
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One free scan finds every outdated or missing driver and matches the right update for your exact hardware.Free scan · exact hardware match- How many values are missing, and how many records would drop out of a complete-case analysis.
- Whether gaps cluster. Do the same people lack several variables, or are the gaps scattered?
- When gaps occur over time. Do participants leave early, skip a single wave, or drop out after a particular event?
- Reasons recorded during collection or follow-up, such as refusal, unreachable contact details, instrument failure, or a skipped question.
- How records with a gap differ from complete records on the variables you do observe, such as age, site, or baseline score.
The last check does not identify the mechanism, but it shows which assumptions deserve scrutiny. If participants with missing follow-up scores had lower baseline scores, then complete cases differ from the original group on that variable, and a complete-case estimate may inherit that difference.
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Step 3: State the mechanism assumption explicitly
Every missing-data method rests on an assumption about how the gaps were produced. The standard labels describe those assumptions. They are not properties you can read off the dataset.
MCAR: missing completely at random
Missingness is unrelated to the variables in the analysis, including the value that is missing. This is a strong assumption and should be justified rather than assumed by default. A measurement lost to a random equipment fault, unconnected to the participant, is the kind of situation it describes.
MAR: missing at random
Systematic differences between missing and observed values can be explained by observed data that are included in the analysis process. The assumption is conditional. If the variables that drive missingness are in your model or imputation model, MAR can be a credible working assumption. If an important driver was never recorded, it is not.
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Systematic differences remain after the observed data are taken into account. Missingness depends on unobserved values or other unobserved causes. Consider a depression score that is more likely to be missing when a participant’s symptoms are severe at that visit. Even if every recorded covariate is included, the severity that drives the gap was never measured.
What the observed data can and cannot tell you
Observed predictors of missingness can challenge MCAR, because they show that missingness is related to something you measured. Beyond that, observed data generally cannot tell MAR from MNAR. The ENCEPP methodological guide, chapter 6, section 6.3, states it directly: “It is however not feasible to assess MAR versus MNAR based on the observed data.” Any test run on the observed records answers a question about those records, not about the values that are missing.
Write the assumption as a sentence you can defend. For example: “Among participants with the same baseline score and treatment arm, missing follow-up scores are unrelated to how severe the follow-up symptoms were.” A sentence like this can be examined by people who know the study, and it gives the sensitivity analysis in Step 5 something specific to vary.
Step 4: Compare candidate methods against the assumptions and the analysis
Each method below is defensible in settings that match its assumptions. Compare your shortlist on the same axes before choosing one. The table summarises the main options.
| Method | When it can make sense | Main cautions |
|---|---|---|
| Complete-case analysis (CCA) | When the records that are complete form a selection that leaves the target analysis unbiased under your stated assumptions. This can hold in some settings, including some in which a covariate is missing not at random. | Discards incomplete records, which lowers precision and power. Whether it biases the estimate depends on how selection into complete records relates to the outcome and the covariates. |
| Multiple imputation (MI) | Under MAR, with an imputation model that includes the analysis variables and useful auxiliary information. Creating several completed datasets lets uncertainty from imputation reach the final estimates. | Results depend on the imputation model and on the MAR assumption. If MAR is wrong, MI built on it can be biased. Leaving out an analysis variable from the imputation model can distort the estimate. |
| Likelihood and maximum likelihood | Particularly relevant to longitudinal outcomes, and to models that can use incomplete records under their assumptions. The NIH Research Methods Resources guidance lists maximum likelihood as an option for missing longitudinal outcomes. | State the model and the missingness assumptions. Confirm that the likelihood approach suits your estimand and data structure. |
| Weighting and inverse probability weighting (IPW) | When you can model the probability that each record is observed, using observed covariates. Weighting is among the principled approaches described in the ENCEPP guide and in the 2024 review by Roderick J. Little. | Needs a credible observation-probability model and enough overlap in covariates between observed and missing records. The variables and assumptions behind the weights must be reported. |
| MNAR-oriented models and sensitivity analysis | When missingness may depend on unobserved values, or when the mechanism remains uncertain. Pattern-mixture models and specialised MNAR models are examples. Compare their assumptions explicitly. | They add assumptions that subject-matter knowledge has to justify, because the observed data cannot supply them. |
Comparison axes to make explicit
For each method on your shortlist, write a short comparison along these axes:
Best Value
- Assumptions about the missingness mechanism.
- Compatibility with the estimand and the model.
- Ability to use incomplete records and auxiliary information.
- Risk of bias if the assumptions are wrong.
- Precision, and how uncertainty is represented.
- Sensitivity to alternative plausible mechanisms.
Auxiliary variables
An auxiliary variable is not part of the analysis model, but it helps explain missingness or predict the missing values. Including such variables in an imputation or weighting model can make MAR more credible, provided they are measured for the records with gaps. For each candidate, ask two questions: does it predict whether the value is missing, and does it predict the value itself? A variable that answers yes to either is worth considering.
Longitudinal outcomes
For missing longitudinal outcomes, the NIH guidance recommends considering maximum likelihood or multiple imputation methods that can condition on prior outcomes and baseline variables. In practice, your model should carry each participant’s earlier measurements and baseline characteristics, so that a missing later visit can be predicted from what was observed.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Step 5: Test how far the conclusion depends on the mechanism
When the mechanism is uncertain, the aim is not to find one correct model. It is to show whether the conclusion survives plausible alternatives. The NIH guidance is direct on this point: “If there is considerable uncertainty about the missing-data mechanism, investigators should consider a sensitivity analysis (Baker, 2019), which may include a worst-case scenario.” In its clinical-trial planning context, a worst-case scenario gives a bound on how far the result could move under the least favourable assumption.
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A useful sensitivity analysis does three things:
- It varies the assumed difference between missing and observed values across a plausible range, and reports the estimate at each point.
- It reports whether the conclusion changes direction, crosses a decision threshold, or only shifts in magnitude.
- It compares at least two methods with different model structures, such as multiple imputation and weighting, so the result does not rest on a single modelling choice.
Step 6: Report the choice and its limits
A reader should be able to rebuild your reasoning from the report. Include:
Quick Recap
- The missingness description: counts by variable, patterns over time, and recorded reasons.
- The stated mechanism assumptions, and why each is plausible in your setting.
- The model and the estimand, and any changes made to accommodate the missing-data method.
- The auxiliary variables used, and why they were chosen.
- For multiple imputation: the imputation model, the number of imputed datasets, and how the completed-data estimates were combined.
- For weighting: the variables in the observation-probability model, and how the weights were checked.
- The sensitivity results, including any scenario in which the conclusion changed.
- The limitations that the assumptions cannot resolve.
Mistakes to avoid when choosing a method
- Choosing by the missing percentage. The ENCEPP guide points to published discussion holding that the proportion missing should not guide the choice of a multiple imputation method.
- Treating multiple imputation as a default that always wins. The 2019 International Journal of Epidemiology article “Accounting for missing data in statistical analyses: multiple imputation is not always the answer” makes the same point in its title.
- Treating complete-case analysis as valid only under MCAR. Nor does a small missing share make it unbiased.
- Using mean substitution or last-observation-carried-forward as a general fix. The ENCEPP guide notes that simple methods can produce misleading inferences when their assumptions fail.
- Adding a missing-indicator category as an automatic solution. The ENCEPP guide warns that this can be invalid, including under MCAR.
Further reading
- Little and Rubin, Statistical Analysis with Missing Data. The ENCEPP guide names it as a useful reference. Check the current edition before buying.
- Roderick J. Little, “Missing Data Analysis,” Annual Review of Clinical Psychology, 2024, 20:149–173, available from Annual Reviews.
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