When Missing Data Is More Than Missing Values: Lessons from Longitudinal Clinical Research

In clinical and health research, conversations around “missing data” are often reduced to a single question:

How many values are missing?

But in practice, some of the most serious analytical problems have little to do with missing values themselves, and far more to do with how the dataset is organised and prepared for analysis.

A dataset can appear complete on the surface while still being fundamentally unsuitable for valid statistical modelling.

This is very common in longitudinal and repeated-measures research, where participants are assessed across multiple visits or timepoints over time.

In these studies, the validity of the analysis depends on the statistical model chosen, and on whether the dataset has been organized correctly from the beginning.

The Problem with Treating Data Cleaning as Administrative Work

One of the most underestimated stages of research is data preparation.

Data cleaning is often viewed as:

• removing duplicates
• correcting typos
• filling empty cells
• renaming variables

But in reality, data preparation is a methodological process.

Poor restructuring decisions can:

• distort repeated observations
• break participant-level tracking
• create artificial patterns
• invalidate statistical assumptions
• lead to incorrect conclusions

In many real-world projects, the issue is not simply “missing data.”
It is structural instability within the dataset itself.

When Dataset Structure Becomes the Real Problem

Longitudinal studies require data to reflect the repeated nature of the research design.

For example, if a participant attends:

• baseline
• 6-month follow-up
• 12-month follow-up

…the dataset must preserve the relationship between:

• participant identity
• timepoint
• repeated measurements
• outcome trajectories over time

However, many datasets are delivered in formats that make this impossible to analyse appropriately.

Common problems include:

• wide-format structures unsuitable for repeated measures modelling
• inconsistent row alignment
• duplicated participant records
• fill-down artefacts
• variables shifting across columns
• entire visits represented as partially empty rows
• missing timepoint indicators

At this stage, the issue is no longer “cleaning.”
The issue becomes whether the data architecture itself reflects the underlying study design.

Why Repeated Measures Require Different Statistical Thinking

One of the most common analytical mismatches in longitudinal research occurs when repeated observations are analysed as though they were independent.

But repeated measures violate a core assumption of many standard statistical methods:

observations from the same participant are correlated.

A participant’s measurements across time are not independent from one another. Their baseline values influence later outcomes.

This is why longitudinal studies often require approaches such as:

• Linear Mixed Models (LMMs)
• Generalized Estimating Equations (GEEs)

These methods explicitly account for:

• within-subject correlation
• repeated observations
• time-dependent variation
• missing follow-up data patterns

Without this structure, analyses can underestimate uncertainty, inflate significance, or produce misleading findings.

Importantly, these models only work correctly when the dataset itself has been properly organised.

Wide or Long Format: A Small Structural Difference with Major Consequences

One of the most important, yet overlooked, aspects of longitudinal analysis is dataset shape.

Wide Format

In wide datasets, repeated measurements are stored across multiple columns:

This format may appear intuitive for viewing data manually, but it becomes restrictive and error-prone for many repeated-measures analyses.

Long Format

In long datasets, each row represents a single observation at a specific timepoint:

This structure allows statistical models to properly recognise:

• repeated observations
• participant trajectories
• temporal relationships
• missing follow-up patterns

In many clinical projects, converting data from wide to long format is not a cosmetic change. It is a prerequisite for valid modelling.

Missingness Is Not Always Random

Another critical issue is understanding why data are missing.

Missing data are rarely neutral.

In clinical datasets, missingness may occur because:

• participants drop out after deterioration
• follow-up attendance declines over time
• certain tests are only conducted for high-risk patients
• adverse events interrupt measurement collection
• clinical workflows differ between sites

This means missingness can itself contain information.

A simple percentage of missing values is therefore insufficient.

Researchers must assess:

• whether missingness is random or systematic
• whether missingness differs by group or timepoint
• whether variables are structurally absent versus truly missing
• whether the study design itself contributed to incomplete observations

Without this assessment, statistical outputs may appear robust while being fundamentally biased.

What Reviewers and Statisticians Notice Immediately

In publication research, methodological weaknesses are often identified long before results are interpreted.

Experienced reviewers typically notice:

• analytical methods inconsistent with study design
• inappropriate handling of repeated measures
• unclear missing data strategies
• absent timepoint variables
• inconsistent participant counts
• implausible restructuring decisions
• unexplained exclusions
• endpoint definitions lacking clinical rationale

In many cases, these issues become barriers to publication, peer-review acceptance, or regulatory confidence.

The problem is rarely just statistical complexity.

More often, it is a failure to align:

study design → dataset structure → analytical method.

Analytical Validity Begins Before Modelling

There is increasing pressure within research environments to move quickly toward results:

• run the model
• generate the figures
• produce the tables

But robust analysis starts much earlier.

Before any modelling begins, researchers should ask:

• Does the dataset reflect the study design?
• Can repeated observations be tracked correctly?
• Are timepoints explicitly represented?
• Is the missingness pattern understood?
• Are outcomes clinically and methodologically coherent?
• Is the dataset analysis-ready?

Because ultimately:

sophisticated statistical models cannot compensate for structurally flawed data.

Final Reflection

In modern clinical and health research, methodological rigour depends as much on data architecture as it does on statistical sophistication.

Missing data should not be viewed merely as empty cells in a spreadsheet.

Sometimes the most important analytical question is not:

“How much data are missing?”

…but rather:

“Does this dataset actually represent the reality of the study it claims to measure?”

That distinction often determines whether findings become reliable evidence or misleading conclusions.