Clinical Data Analysis Guide: From Hospital Records to Research Results

You have exported an Excel file from your hospital information system. It contains hundreds of patient records with admission data, lab values, and follow-up outcomes. The column headers read HbA1c, SBP, DBP, eGFR — some cells are empty, some date formats are inconsistent — and you are not sure where to begin.

This is the reality for many clinical researchers starting a new project. Getting data out of the EMR is not the hard part. The hard part is turning those raw records into a publishable clinical paper.

This article walks through the full pipeline, from data export to final analysis output.

Step 1: Export and inspect your data

Clinical data typically comes from hospital information systems (HIS), electronic medical records (EMR), clinical databases, or data capture platforms like REDCap. Most systems support export in Excel or CSV format.

Once you have your file, check the following:

• Does each row represent one patient (or one encounter)?
• Are column names clear? Are they standard abbreviations (ALT, AST, WBC) or system-generated codes?
• Are there summary rows, header comments, or merged cells mixed into the data?
• Are date formats consistent (some may be 2024-01-15, others 20240115 or 01/15/2024)?
• Does the file contain patient identifiers that need to be de-identified?

Understanding your data structure is the foundation for everything that follows. If the data comes from a longitudinal study (multiple records per patient), confirm whether it is in wide format (one column per visit) or long format (one row per visit).

Step 2: Clean the data

Raw clinical data exports are rarely analysis-ready. Common cleaning tasks include:

• Handling missing values: Distinguish between "not tested" and "result lost" — the former may have clinical significance, the latter is a data quality issue. For key variables with high missingness (e.g., >20%), consider excluding the variable or using multiple imputation
• Standardizing coding: The same diagnosis may appear as "Type 2 diabetes," "T2DM," or "type 2 DM" — these need to be unified
• Handling outliers: A systolic blood pressure of 300 mmHg or age of -5 years is clearly a data entry error and needs verification or exclusion
• Standardizing date formats: Convert all dates to a consistent YYYY-MM-DD format
• De-identification: Remove names, national IDs, medical record numbers, and other identifiable information
• Deriving variables: Calculate BMI from height and weight, length of stay from admission and discharge dates, survival time from surgery date and last follow-up date

This step often takes longer than running the statistical analysis itself, but data quality determines the credibility of all downstream results.

Step 3: Baseline characteristics table

Table 1 in virtually every clinical paper is the baseline characteristics table, presenting demographic and clinical features by group.

Standard formatting for baseline tables:

• Categorical variables (sex, smoking status, comorbidities): Report frequency and percentage. Compare groups using chi-square test or Fisher exact test
• Normally distributed continuous variables (age, BMI): Report mean ± standard deviation. Compare using independent samples t-test or ANOVA
• Skewed continuous variables (length of stay, certain lab values): Report median (interquartile range). Compare using Mann-Whitney U test or Kruskal-Wallis test

The baseline table is not just a sample description — it also shows reviewers whether there are imbalances in confounding factors between groups, which directly affects the choice of downstream analysis strategy.

Step 4: Choose statistical methods

The choice of statistical method in clinical data analysis depends on your study design and outcome variable type:

Group comparisons

• Continuous outcome + two groups: Independent samples t-test (normal) or Mann-Whitney U test (non-normal)
• Continuous outcome + multiple groups: ANOVA (normal) or Kruskal-Wallis test (non-normal)
• Categorical outcome: Chi-square test or Fisher exact test

Multivariable analysis

• Continuous outcome: Multiple linear regression
• Binary outcome (e.g., complication yes/no): Logistic regression
• Survival outcome (e.g., progression-free survival): Cox proportional hazards regression
• Count outcome (e.g., number of hospital days): Poisson regression or negative binomial regression

Diagnostic and predictive evaluation

• Diagnostic accuracy: ROC curve and AUC
• Prediction model calibration: Hosmer-Lemeshow test, calibration curves

Survival analysis

• Survival curves: Kaplan-Meier method
• Between-group survival differences: Log-rank test
• Multivariable survival analysis: Cox regression

Each method has assumptions. Logistic regression requires adequate sample size (typically at least 10–20 events per predictor). Cox regression requires the proportional hazards assumption to hold. Running analyses without checking these assumptions is a common reason papers get sent back by reviewers.

Step 5: Interpret and report

Statistical output is numbers. A paper needs clinical conclusions. You need to translate statistical results into clinical language:

• Report effect sizes and confidence intervals, not just p-values. "The complication rate was 12.3% in the treatment group vs. 23.1% in the control group (OR = 0.47, 95% CI: 0.28–0.79, p = 0.004)" is far more informative than "p < 0.05, statistically significant"
• Tables should follow journal standards: typically three-line tables, with continuous variables reported as mean ± SD or median (IQR), and categorical variables as n (%)
• Choose the right chart type: KM curves for survival data, ROC curves for diagnostic evaluation, forest plots or bar charts for group comparisons
• Multivariable regression results are usually presented as forest plots showing OR/HR values with confidence intervals

This is where many researchers get stuck — they can run the analysis but struggle to write results in journal-ready language.

The manual workflow problem

If you are doing all of this in SPSS or R, you are probably switching between your statistical software and a Word document, manually formatting baseline tables, adjusting chart layouts one by one, and translating statistical output into manuscript text. A single dataset can easily take a week or more.

Clinical data is also more complex than survey data — continuous, categorical, time-to-event, and censoring variables are all mixed together — making the analysis pipeline more error-prone.

How Data2Paper fits into this workflow

Data2Paper supports the full clinical data analysis pipeline. Upload your Excel or CSV file, describe your research topic and grouping, and the system handles data cleaning, variable type detection, statistical method selection, analysis execution, and paper-section generation.

The system recognizes common clinical variable names (such as HbA1c, SBP, eGFR), automatically determines variable types, and selects appropriate statistical tests. Output includes properly formatted baseline tables, regression results, survival curves, ROC curves, and accompanying interpretation text — ready for journal submission.

For clinical researchers who want to focus on the clinical question rather than the mechanics of statistical software, this is a meaningful reduction in friction.

Upload your clinical data and start generating your paper →