Real-world examples of ANOVA examples in clinical trials

Before definitions and formulas, it helps to see how ANOVA actually appears in published and planned trials. Here are a few situations that statisticians would immediately recognize as examples of ANOVA examples in clinical trials:

• A three-arm diabetes trial comparing mean HbA1c after 24 weeks across placebo, low-dose drug, and high-dose drug.
• A vaccine study comparing mean antibody titers among four candidate formulations.
• A depression trial comparing change in PHQ‑9 scores across three therapies: SSRI, CBT, and combined SSRI+CBT.

In all of these, the question is the same: are the mean outcomes across groups different beyond what we’d expect from random variation? That’s exactly what an ANOVA F‑test is built to answer.

Parallel-group drug trials: classic example of ANOVA in action

One of the cleanest examples of ANOVA examples in clinical trials is the standard parallel-group randomized controlled trial with more than two treatment arms.

Imagine a Phase II hypertension trial with four arms:

• Placebo
• Drug A – low dose
• Drug A – medium dose
• Drug A – high dose

The primary endpoint is change in systolic blood pressure (SBP) at 12 weeks. The analysis plan says:

“Mean change in SBP at 12 weeks will be compared across treatment arms using one-way ANOVA, followed by pairwise comparisons with multiplicity adjustment if the overall F-test is significant.”

How ANOVA is used here

• Factor: Treatment group (4 levels)
• Outcome: Continuous change in SBP
• Model: One-way ANOVA with treatment as a fixed effect
• Interpretation: If the F-test p-value is small, investigators conclude that at least one dose differs from the others in mean SBP reduction.

This is one of the best examples of ANOVA in a setting where linear model assumptions—independent subjects, roughly normal residuals, and similar variances—are reasonably plausible.

For context on hypertension outcomes and typical trial endpoints, see the NIH’s resources on blood pressure management: https://www.nhlbi.nih.gov/health/high-blood-pressure.

Dose–response studies: examples include graded ANOVA comparisons

Dose-finding work provides another rich example of ANOVA examples in clinical trials. Consider a Phase II oncology trial exploring a new oral agent at five dose levels. The endpoint might be tumor size reduction (percentage change from baseline) at 8 weeks.

Here, ANOVA helps answer:

• Do mean tumor shrinkage values differ across the five doses?
• Is there evidence that higher doses outperform lower doses on average?

Investigators might:

• Run a one-way ANOVA across the five dose groups.
• If significant, examine trend tests (e.g., linear contrast) to assess increasing benefit with dose.
• Use the ANOVA framework to estimate group means and confidence intervals for each dose.

In practice, dose–response modeling often uses more advanced methods (e.g., Emax models), but the initial question—“are the means different across doses?”—is a textbook example of ANOVA used in early-phase trials.

Vaccine immunogenicity trials: real examples of ANOVA with transformed outcomes

Vaccine trials frequently provide real examples of ANOVA examples in clinical trials, especially when comparing multiple formulations or schedules.

Suppose a vaccine immunogenicity study has three arms:

• Standard formulation
• New adjuvanted formulation
• Higher-dose formulation

The endpoint is geometric mean titer (GMT) of antibodies at Day 28. Because raw titers are skewed, analysts often log-transform them and then apply ANOVA.

Analysis steps:

• Take log of antibody titers.
• Fit a one-way ANOVA with treatment arm as the factor.
• Test whether mean log titers differ across groups.
• Back-transform estimated group means to report GMTs.

This combination of log transformation plus ANOVA is common in vaccine research, including influenza, COVID‑19, and RSV vaccine trials. For background on immunogenicity endpoints and GMTs, the CDC’s vaccine pages are a solid reference: https://www.cdc.gov/vaccines/terms/glossary.html.

Mental health trials: examples of ANOVA with repeated measures

Many mental health studies collect repeated measures of symptom scores over time. That opens the door to repeated-measures ANOVA or mixed models.

Consider a depression trial with three treatment groups:

• SSRI
• Cognitive behavioral therapy (CBT)
• Combined SSRI+CBT

PHQ‑9 scores are recorded at baseline, Week 4, Week 8, and Week 12. A traditional analysis might use repeated-measures ANOVA with:

• Within-subject factor: Time (4 levels)
• Between-subject factor: Treatment group (3 levels)

This allows investigators to test:

• Overall differences in mean PHQ‑9 across treatment groups.
• Changes over time in mean scores.
• Whether the time-by-treatment interaction is significant (i.e., do groups improve at different rates?).

In modern practice, many teams prefer linear mixed-effects models because they handle missing data and complex correlation structures better. But conceptually, these are extensions of the same ANOVA framework—still one of the best examples of ANOVA thinking applied to clinical trial longitudinal data.

For clinical context on depression measures like PHQ‑9, see the Mayo Clinic’s depression overview: https://www.mayoclinic.org/diseases-conditions/depression/symptoms-causes/syc-20356007.

Factorial designs: examples of ANOVA examples in clinical trials with interactions

Factorial trials are underrated as examples of ANOVA examples in clinical trials, because they naturally produce main effects and interactions—the bread and butter of ANOVA.

Imagine a 2×2 factorial weight-loss trial:

• Factor A: Medication (Drug vs Placebo)
• Factor B: Lifestyle program (Intensive vs Standard)

There are four groups total, and the endpoint is change in body weight at 6 months.

A two-way ANOVA would estimate:

• Main effect of medication (average difference between Drug and Placebo across both lifestyle programs).
• Main effect of lifestyle program (average difference between Intensive and Standard across both medication conditions).
• Interaction between medication and lifestyle program (does the effect of the drug depend on lifestyle intensity?).

If the interaction term is significant, it suggests that the combination of drug and intensive lifestyle has a different impact than we’d predict by simply adding their separate effects. This kind of interaction analysis is one of the best examples of ANOVA’s strength in multi-factor clinical trial designs.

Crossover trials: example of ANOVA with subject as a factor

Crossover studies give another example of ANOVA examples in clinical trials, especially in early-phase pharmacokinetic (PK) work.

Suppose a 2×2 crossover bioequivalence trial:

• Treatment A: Reference formulation
• Treatment B: Test formulation
• Sequence 1: A then B
• Sequence 2: B then A

Each subject receives both formulations, separated by a washout period. The outcome might be log-transformed AUC (area under the concentration–time curve).

A standard ANOVA model for bioequivalence includes:

• Fixed effects for sequence, period, and treatment.
• A random effect for subject nested within sequence.

The treatment effect estimate and its confidence interval are used to assess bioequivalence. While the design is more complex than a basic one-way ANOVA, the underlying logic is the same: partition variability into components (between treatments, between subjects, etc.) and test whether mean responses differ.

FDA guidance documents on bioequivalence describe this ANOVA-style analysis in detail: https://www.fda.gov/drugs/guidances-drugs.

Multi-center trials: examples include site effects in ANOVA-style models

Large modern trials often randomize participants across many centers or countries. That creates another example of ANOVA examples in clinical trials: handling center effects.

Imagine a cardiovascular outcomes trial with:

• Two treatment arms (New drug vs Standard of care).
• 150 clinical sites.

Even though the primary analysis might be a time-to-event model (e.g., Cox regression), secondary continuous endpoints—like change in LDL cholesterol—are often analyzed using ANOVA-style linear models with:

• Treatment as a fixed effect.
• Center (site) as a random effect.

Statistically, this is a mixed-effects ANOVA model. It helps account for systematic differences between sites (e.g., different patient populations, slightly different measurement practices) while focusing on the average treatment effect across all centers.

This is a good reminder that, in 2024–2025, many clinical trial analyses use linear mixed models that are, under the hood, ANOVA generalizations.

Safety and tolerability: examples of ANOVA for lab and biomarker outcomes

Safety monitoring also provides real examples of ANOVA examples in clinical trials. Consider:

• Comparing mean change in liver enzymes (ALT, AST) across three dose groups.
• Evaluating mean QTc interval prolongation across placebo, low-dose, and high-dose arms.
• Assessing average change in inflammatory biomarkers (e.g., CRP) with and without a particular biologic therapy.

These continuous lab and biomarker outcomes often get analyzed using ANOVA or ANCOVA (ANOVA with baseline as a covariate). Even if the primary endpoint is binary or time-to-event, safety and tolerability sections of trial reports are full of ANOVA-style comparisons of means.

Modern trends (2024–2025): where ANOVA fits now

If you skim recent trial publications, you’ll notice a few trends that shape how ANOVA is used today:

• Mixed models are everywhere. For repeated measures and multi-center data, linear mixed-effects models have largely replaced classical repeated-measures ANOVA. Conceptually, though, they’re still ANOVA-style models for mean differences.
• ANCOVA is often preferred. Trials commonly adjust for baseline values and key covariates, using ANCOVA rather than a pure one-way ANOVA. This improves precision but doesn’t change the core idea: comparing adjusted group means.
• Multiplicity control is more explicit. With many secondary endpoints and subgroup analyses, ANOVA sits inside broader multiple-testing strategies (e.g., hierarchical testing, FDR control).
• Regulatory expectations are clearer. FDA and EMA guidance documents explicitly describe analysis of continuous outcomes using ANOVA or ANCOVA within prespecified statistical analysis plans.

So while you might not always see the word “ANOVA” in the abstract, the underlying method—partitioning variance and testing mean differences across groups—is still a workhorse in modern trial statistics.

Practical tips for interpreting ANOVA results in trial papers

When you read clinical trial reports that use ANOVA or ANCOVA, focus on a few key points:

• Check the endpoint type. ANOVA is appropriate for continuous outcomes that are reasonably symmetric after any needed transformation.
• Look for model details. Good papers specify whether they used one-way ANOVA, two-way ANOVA, repeated-measures ANOVA, or mixed models.
• Watch assumptions. If group sizes are small or variances look very different, authors should at least mention checks or sensitivity analyses.
• Pay attention to effect sizes, not just p-values. Mean differences and confidence intervals tell you whether the effect is clinically meaningful.

Keeping these in mind will help you interpret real examples of ANOVA examples in clinical trials without getting lost in the technical language.

FAQ: common questions about ANOVA in clinical research

Q1. What is an example of ANOVA in a randomized clinical trial?
A classic example of ANOVA in a randomized clinical trial is a three-arm study comparing mean change in blood pressure across placebo, low-dose, and high-dose drug groups. A one-way ANOVA tests whether the average blood pressure reduction differs among the three arms.

Q2. When should ANOVA be used instead of a t-test in clinical trials?
Use ANOVA when you are comparing mean outcomes across three or more groups (e.g., multiple doses, several treatment combinations). A t-test only compares two groups at a time; ANOVA tests all groups together and maintains the overall Type I error rate.

Q3. Are repeated-measures ANOVA and mixed models both valid examples of ANOVA examples in clinical trials?
Yes. Repeated-measures ANOVA is one specific framework for handling multiple time points per subject. Linear mixed-effects models generalize that idea, allowing more flexible correlation structures and handling of missing data. Both approaches are used in real clinical trials to assess mean changes over time.

Q4. Do regulatory agencies accept ANOVA-based analyses?
Yes. FDA and EMA guidance documents routinely describe ANOVA or ANCOVA for continuous outcomes like blood pressure, cholesterol, HbA1c, and PK parameters. What matters is that the model is prespecified, assumptions are checked, and interpretation is clinically relevant.

Q5. What are examples of endpoints where ANOVA is not appropriate?
ANOVA is not appropriate for binary endpoints (e.g., response vs no response), counts with strong skew (e.g., number of seizures), or time-to-event outcomes (e.g., time to hospitalization). Those typically use logistic regression, Poisson/negative binomial models, or survival analysis methods instead.

In short, the best examples of ANOVA examples in clinical trials are hiding in plain sight: multi-arm dose studies, vaccine immunogenicity trials, mental health longitudinal designs, crossover bioequivalence work, and large multicenter trials analyzing continuous outcomes. Once you recognize the pattern—comparing mean values across groups under a linear model framework—you’ll start seeing ANOVA everywhere in the clinical research literature.