Navigating Missing Data in Clinical Research: Concepts, Pitfalls, and Best Practices [Multiple imputation, MI]

Mayta
May 30
4 min read

Introduction

Missing data is an inevitable reality in clinical research. From incomplete medical records to patient dropouts in follow-up studies, missing information can arise for numerous reasons. Yet, how researchers handle this issue can drastically impact the validity, reliability, and interpretability of their findings. Mishandled missing data may not only reduce statistical power but also introduce bias that distorts the conclusions drawn from a study.

This article outlines the nature of missing data, its potential consequences, frequent errors in its management, and principled goals for handling it effectively.

Why Missing Data Matters

Impact on Precision and Validity

Reduced Statistical Power: Missing values shrink the effective sample size, limiting the ability to detect real effects.
Bias Risk: If the data are not missing at random, inferences based on available data alone may be misleading.

Imagine conducting a trial on drug adherence, where patients with the poorest compliance—and hence poorest outcomes—are also the most likely to have missing data. An analysis based only on the remaining participants would paint an overly optimistic picture of treatment effect.

Informative vs Non-informative Missingness

Non-informative Missingness: Occurs when the missing parts contain redundant or low-impact data. Analytically, this is less concerning.
Informative Missingness: More dangerous. These gaps contain information not captured elsewhere. Losing them means losing crucial context, leading to skewed interpretations.

An analogy: consider a complex puzzle. If edge pieces are missing, the picture might still be recognizable. But if the centerpieces that define the subject’s face are gone, the entire meaning of the image collapses.

How Missing Data Leads to Bias

To understand bias from missing data, consider this breakdown:

A study has patients grouped by medication compliance: poor, average, and good.
Each group has associated average outcomes: poor = -1, average = 0, good = +1.
In the full dataset, these balance to a neutral overall effect (e.g., net = 0).

However:

If data from the “poor compliance” group are disproportionately missing, the remaining average will shift upwards—misleadingly suggesting treatment benefit.

This example shows how selective data loss can mask adverse effects or falsely amplify positive ones.

Common Pitfalls in Handling Missing Data

1. Ignoring Missingness Entirely

Some researchers wrongly equate “handling” with “removing.” The most common method—listwise deletion—simply excludes records with any missing value. This often:

Distorts the sample
Reduces statistical power
Introduces selection bias

As highlighted by a widely cited review, this practice can lead to erroneous biological or clinical interpretations.

2. Concealed Exclusions

A frequent trap in reporting is excluding incomplete data early and then claiming the dataset has “no missing values.” This illusion of completeness hides a biased selection process. It is especially misleading when presented in publications or flow diagrams without transparency about exclusions.

3. Overlooking Partial Completeness

In multivariable models, even if only a few variables are missing per patient, combining them can drastically reduce the usable sample. For example:

A study with 1,000 participants aims to model 3 predictors.
Only 300 participants have complete data on all 3.
Without realizing it, the final model uses only 30% of the dataset.

Best Practices for Transparent and Effective Management

1. Preserve the Full Domain During Preprocessing

During cohort construction, avoid removing cases due to missingness until after data are described and handling strategies are chosen. Present:

Frequency and percentage of missingness per variable
Rationale for exclusion, if any
Chosen strategy for handling missingness

2. Use Transparent Flow Diagrams

Well-structured diagrams should:

Show the total eligible population
Indicate reasons for exclusions
Distinguish between missing data due to study design vs random loss

3. Handle Missing Data at the Analysis Stage, Not Screening Stage

Delaying deletion ensures:

Accurate depiction of study completeness
Valid decisions on imputation vs exclusion
More robust models based on full observed data structures

Goals of Missing Data Imputation

What to Aim For

Minimize Bias: Retain representativeness of the original sample.
Maximize Use of Available Information: Leverage partial data without wholesale deletion.
Produce Valid Measures of Uncertainty: Reflect genuine variability, not artificial precision.

What NOT to Aim For

Guessing the “True Value”: Imputation is about estimates, not recovering exact truths.
Chasing Statistical Significance: Imputation should clarify, not manipulate, inference.

Consider this example:

A complete dataset shows a mean difference of +50 (CI: +40 to +60).
A complete-case-only analysis (excluding missing data) yields a lower mean difference of +30 (CI: +5 to +55)—less accurate and less precise.
An appropriate imputation restores the original estimate (e.g., +50) with a narrower but still valid confidence interval (e.g., +45 to +55), illustrating better bias control and uncertainty quantification.

Conclusion

Missing data is not just a technical nuisance—it is a source of potential bias, misinterpretation, and weakened conclusions. Yet with thoughtful planning and principled handling, its dangers can be mitigated. Clinical researchers must recognize the informative potential of what is missing and adopt strategies that enhance—not obscure—the integrity of their findings.

Key Takeaways

Missing data affects both power and validity.
Ignoring missingness or removing incomplete records can introduce serious bias.
Imputation is not about replacing true values but about generating valid inferences.
Transparent handling requires reporting missingness and applying sound analytical strategies.
Your dataset’s completeness is a design artifact—honest flow diagrams and tables reveal the true analytical sample.