This page provides an independent statistical analysis and educational interpretation of publicly reported results. ClinicalTrials.gov provides the official trial registry record. Trial-specific numerical results on this page are limited to the ClinicalTrials.gov record.
1. Trial at a Glance
CAN-COVID was a randomized, double-blind, parallel-group phase 3 trial evaluating canakinumab versus placebo in participants with cytokine release syndrome in COVID-19-induced pneumonia. The registry reports an enrollment of 454 participants, two treatment arms, one registered primary endpoint, and formal statistical analyses using logistic regression.
| Feature | CAN-COVID |
|---|---|
| Trial name | CAN-COVID |
| NCT identifier | NCT04362813 |
| Phase | Phase 3 |
| Condition | Cytokine Release Syndrome (CRS) in Patients With COVID-19-induced Pneumonia |
| Allocation | Randomized |
| Design model | Parallel |
| Masking | Double |
| Primary purpose | Treatment |
| Enrollment | 454 |
| Lead sponsor | Novartis Pharmaceuticals |
| Sponsor type | Industry |
| Trial status | Completed |
| Start | 2020-04-30 |
| Primary completion | 2020-09-16 |
2. Clinical Question
The central statistical question was whether participants randomized to canakinumab had higher odds than participants randomized to placebo of surviving without requiring invasive mechanical ventilation from Day 3 to Day 29, using the registry's prespecified binary primary endpoint.
Population
Participants with cytokine release syndrome in COVID-19-induced pneumonia.
Intervention
Canakinumab.
Comparator
Placebo.
Primary question
Does canakinumab increase the odds of surviving without requiring invasive mechanical ventilation from Day 3 to Day 29?
3. Trial Design
Canakinumab
- Drug intervention
- Randomized treatment assignment
- Included in the primary comparison against placebo
Placebo
- Drug comparator
- Randomized treatment assignment
- Reference group for the reported odds ratio
4. Endpoint and Assessment Framework
| Endpoint | Time frame | Type | Registry definition |
|---|---|---|---|
| Participants Who Survived Without Requiring Invasive Mechanical Ventilation From Day 3 to Day 29, Primary Analysis | Day 3 to Day 29 | Binary | Number of responders who survived without requiring invasive mechanical ventilation from Day 3 to Day 29. An early dropout without requiring invasive mechanical ventilation is considered as a responder if discharged from hospital with 9-point ordinal scale ≤1 or with last 9-point ordinal scale on or after Day 15 better than baseline. |
The registry describes the primary endpoint as a binary outcome. In practical terms, each participant contributes to a responder/non-responder classification according to the endpoint definition rather than contributing a continuous measurement.
The registry's endpoint definition also specifies how certain early dropouts are classified, using hospital discharge status or the participant's last available 9-point ordinal scale assessment.
5. Analysis Population and Covariate Adjustment
The primary analysis population was defined as randomized participants with at least one assessment of the 9-point ordinal scale between Day 3 and Day 29. The registry specifies that the ordinal scale ranges from 0, representing uninfected, to 8, representing death.
| Analysis component | Registry specification |
|---|---|
| Population | Randomized participants with at least one 9-point ordinal scale assessment between Day 3 and Day 29 |
| Groups compared | Canakinumab vs placebo |
| Regression method | Logistic regression |
| Effect measure | Odds ratio |
| Hypothesis type | Superiority |
| Adjustment variables | Treatment, region (North America vs Europe), and baseline 9-point ordinal scale (≤4, ≥5) |
This distinction between the randomized population and the specific analysis population is important. The registry does not define the primary analysis simply as all 454 enrolled participants. Instead, the posted primary analysis uses randomized participants who had at least one qualifying ordinal-scale assessment during the specified Day 3 to Day 29 period.
6. Primary Results
The registry reports a formal statistical analysis for the primary binary endpoint using logistic regression adjusted for treatment, region, and baseline 9-point ordinal scale.
Odds ratio for the primary endpoint
95% CI: 0.76–2.54 · P = 0.2874
Two-sided superiority analysis; canakinumab versus placebo.
| Primary endpoint | Canakinumab vs placebo |
|---|---|
| Effect measure | Odds ratio |
| Estimate | 1.39 |
| 95% confidence interval | 0.76–2.54 |
| P-value | 0.2874 |
| Model | Logistic regression |
| Adjustment | Treatment, region, and baseline 9-point ordinal scale |
An odds ratio of 1.39 means that the estimated odds of meeting the primary responder definition were 1.39 times as high in the canakinumab group as in the placebo group, based on the fitted logistic-regression model. Expressed as a simple relative comparison of odds, 1.39 corresponds to estimated odds that are 39% higher in the canakinumab group.
The odds ratio does not mean that 39% more participants survived, that the probability of response was 39% higher, or that an individual participant's probability of avoiding invasive mechanical ventilation increased by 39%. Odds and probabilities are different quantities, particularly when the event is not rare.
The 95% confidence interval of 0.76–2.54 describes uncertainty around the estimated odds ratio under the statistical model and sampling framework. Because the interval includes 1, the data are compatible with both a lower and a higher odds of the primary outcome under the range represented by this interval.
The p-value of 0.2874 is a measure of compatibility between the observed data and the null hypothesis used for the statistical test. It is not a measure of effect size, clinical importance, or the probability that the treatment works. A p-value also does not tell us the probability that the null hypothesis is true.
The interpretation is further conditioned on the analysis population and the prespecified definition of the binary endpoint. Because logistic regression was adjusted for region and baseline ordinal-scale category, the reported odds ratio is a model-based adjusted estimate rather than a simple unadjusted ratio of two observed response proportions.
7. Why Logistic Regression Was Used
The primary endpoint is binary: each participant is classified according to whether the specified responder definition was met. Logistic regression is a standard method for modeling a binary outcome while simultaneously estimating the association between treatment and the outcome and adjusting for prespecified covariates.
The treatment coefficient is transformed to an odds ratio. The actual fitted coefficients are not reported in the ClinicalTrials.gov record; the registry reports the adjusted odds ratio of 1.39.
For this trial, the adjustment variables were treatment, region, and baseline 9-point ordinal scale category. This structure allows the treatment comparison to account for the specified baseline and regional factors rather than relying only on a crude two-group comparison.
8. What an Odds Ratio of 1.39 Means
An odds ratio compares odds, not probabilities. If the odds of response in the placebo group were represented by a value \(O\), an odds ratio of 1.39 would correspond to estimated odds of \(1.39O\) in the canakinumab group, under the fitted model.
What it says
The fitted model estimated higher odds of meeting the primary responder definition with canakinumab than with placebo, with an odds ratio of 1.39.
What it does not say
It does not directly state the difference in responder probabilities between the two groups.
Why the CI matters
The 95% CI of 0.76–2.54 spans 1, so the estimate is uncertain enough to include both directions of an odds-ratio comparison around the null value.
Why the p-value matters differently
The p-value of 0.2874 addresses the statistical test; it should not be used as a substitute for the effect estimate or its confidence interval.
9. Secondary Endpoint Result: COVID-19-related Death
The registry also reports a formal analysis for the secondary endpoint COVID-19-related Death After Study Treatment, assessed at 29 days.
Odds ratio for COVID-19-related death
95% CI: 0.30–1.50 · P = 0.3303
Two-sided superiority analysis; canakinumab versus placebo.
| Secondary endpoint | Reported analysis |
|---|---|
| Outcome | COVID-19-related Death After Study Treatment |
| Time frame | 29 days |
| Effect measure | Odds ratio |
| Estimate | 0.67 |
| 95% confidence interval | 0.30–1.50 |
| P-value | 0.3303 |
| Model | Logistic regression |
| Adjustment | Treatment, region (North America vs Europe), and baseline 9-point ordinal scale (≤4, ≥5) |
An odds ratio of 0.67 corresponds to estimated odds of COVID-19-related death that were 33% lower in the canakinumab group than in the placebo group, under the fitted logistic-regression model. This is a relative comparison of odds, not a 33-percentage-point reduction in mortality probability.
The 95% CI of 0.30–1.50 is wide relative to the point estimate and includes 1. The interval therefore includes values compatible with lower odds as well as values compatible with higher odds under the model.
The p-value of 0.3303 does not measure the magnitude of the estimated effect. It describes the statistical evidence against the null hypothesis in the specified analysis and should be considered together with the odds ratio and confidence interval.
The analysis population was defined according to the intent-to-treat principle, including all randomized participants who did not receive any dose, with an additional registry-specified rule excluding early dropouts if their last available 9-point ordinal scale was greater than 1, including non-available cases as described in the registry analysis record.
10. Primary and Secondary Analysis Side by Side
| Feature | Primary endpoint | Secondary endpoint |
|---|---|---|
| Outcome | Survived without requiring invasive mechanical ventilation from Day 3 to Day 29 | COVID-19-related death after study treatment |
| Time frame | Day 3 to Day 29 | 29 days |
| Endpoint type | Binary | Binary |
| Method | Logistic regression | Logistic regression |
| Effect measure | Odds ratio | Odds ratio |
| Estimate | 1.39 | 0.67 |
| 95% CI | 0.76–2.54 | 0.30–1.50 |
| P-value | 0.2874 | 0.3303 |
The two estimates point in different directions numerically, but their confidence intervals are broad and both include the null odds ratio of 1. The registry's reported analyses therefore need to be read as estimates with uncertainty rather than as definitive evidence based on the point estimates alone.
11. Intention-to-Treat Analysis
The secondary mortality analysis explicitly states that the analysis population included all randomized participants including those who did not receive any dose, as per intent-to-treat principle, subject to the registry's specified early-dropout rule.
The intention-to-treat principle preserves the connection between the analysis and the original randomized treatment assignment. This is particularly important in randomized trials because post-randomization treatment changes, discontinuation, or nonreceipt of treatment can otherwise create differences between the groups that were not generated by the original randomization.
12. The Role of the 9-point Ordinal Scale
The primary analysis uses the 9-point ordinal scale in two related ways: the primary endpoint definition depends on ordinal-scale assessments, and baseline ordinal-scale category is included as an adjustment variable in the logistic regression.
| Use of the ordinal scale | Role in the analysis |
|---|---|
| Primary endpoint | Helps determine whether an early dropout meets the registry's responder definition. |
| Baseline adjustment | Baseline scale is categorized as ≤4 versus ≥5 and included in the logistic-regression adjustment. |
| Analysis population | Participants needed at least one ordinal-scale assessment between Day 3 and Day 29 for the primary analysis. |
| Scale range reported by registry | 0 = uninfected; 8 = death. |
This is an example of why endpoint definition and statistical model cannot be separated. The outcome is binary, but its classification incorporates information from an ordinal clinical scale, and the baseline value of that same scale is included as a covariate.
13. Confidence Intervals and Statistical Uncertainty
A confidence interval is more informative than a point estimate alone because it shows the range of parameter values that remain compatible with the data under the specified inferential framework.
Primary OR: 1.39
The point estimate is above 1, corresponding to higher estimated odds with canakinumab.
Primary 95% CI: 0.76–2.54
The interval crosses 1, so the null odds ratio is within the reported uncertainty interval.
Secondary OR: 0.67
The point estimate is below 1, corresponding to lower estimated odds of COVID-19-related death.
Secondary 95% CI: 0.30–1.50
The interval also crosses 1, so the uncertainty includes the null odds ratio.
It is important not to interpret the width of a confidence interval as a probability statement about where the true effect lies for this particular trial. Instead, the interval quantifies uncertainty associated with the estimated parameter under the statistical framework used to construct it.
14. P-values: What They Tell Us and What They Do Not
The primary analysis reported a two-sided p-value of 0.2874. The secondary COVID-19-related death analysis reported a two-sided p-value of 0.3303.
The p-value evaluates evidence against the null hypothesis within the specified model and testing framework. It does not measure the size of the observed odds ratio and does not provide the probability that the null hypothesis is true.
A small p-value would not automatically imply a large or clinically important treatment effect, and a larger p-value does not prove that treatment groups are identical. The confidence interval and effect estimate provide essential complementary information.
15. Statistical Methods Explained
Why was logistic regression used?
Because the primary endpoint is binary. Logistic regression is designed for outcomes that can be represented as two categories and allows treatment effects to be estimated while adjusting for specified covariates. Here, the registry reports adjustment for region and baseline 9-point ordinal scale in addition to treatment.
What does an odds ratio of 1.39 mean?
It means that the model estimated the odds of meeting the primary responder definition to be 1.39 times as high with canakinumab as with placebo. It does not mean that the probability of response was 39 percentage points higher.
Why is an odds ratio of 1 the null value?
An odds ratio of 1 means that the estimated odds are the same in the two groups. Values above 1 indicate higher odds in the numerator group, while values below 1 indicate lower odds.
Why adjust for baseline ordinal scale?
The registry specifies baseline 9-point ordinal scale category as an adjustment factor, with categories ≤4 and ≥5. Including this prespecified baseline factor can account for differences in baseline clinical status represented by the ordinal scale when estimating the treatment association.
Why does the confidence interval matter more than the point estimate alone?
The point estimate is only one estimate of the treatment association. The confidence interval shows how uncertain that estimate is under the model and data. For the primary analysis, the interval of 0.76–2.54 includes 1, indicating substantial uncertainty about the direction and magnitude of the odds ratio.
Why is the p-value not a measure of effect size?
The p-value addresses the statistical evidence against a null hypothesis; it does not quantify how large the treatment effect is. Effect size is represented here by the odds ratio, while the confidence interval describes uncertainty around that estimate.
Why does the analysis population matter?
The statistical interpretation applies to the participants included in the specified analysis population. For the primary endpoint, this means randomized participants with at least one qualifying ordinal-scale assessment between Day 3 and Day 29. A treatment estimate cannot automatically be generalized to participants who were not represented in the analysis.
16. Blinding and Randomization as Statistical Protection
Two design features are especially important for interpreting the reported regression estimates: randomization and double masking.
17. Safety Results
The ClinicalTrials.gov record reports serious adverse events by treatment arm as the number affected divided by the number at risk.
| Safety measure | Canakinumab | Placebo |
|---|---|---|
| Serious adverse events | 47/225 | 53/223 |
The ClinicalTrials.gov record reports serious adverse events by arm but do not provide a formal statistical comparison for this safety measure. The counts should therefore be treated as descriptive safety information rather than as a reported hypothesis test.
18. What the Registry Results Do Not Establish
The posted statistical analyses are informative, but several conclusions would require evidence that is not contained in the ClinicalTrials.gov record.
- They do not establish the probability of individual patient benefit. An odds ratio is a group-level statistical measure and cannot be converted directly into an individual patient's probability of benefit.
- They do not establish a treatment effect from the p-value alone. The primary p-value is 0.2874 and the secondary p-value is 0.3303; neither should be treated as a measure of effect magnitude.
- They do not provide an absolute treatment difference for the primary endpoint. The registry-reported statistical analysis reports the adjusted odds ratio and its confidence interval, not the treatment-group response percentages.
- They do not establish effect modification across subgroups. The statistical analyses posted on ClinicalTrials.gov do not report subgroup estimates or interaction tests.
- They do not provide a survival-analysis result. Although the trial concerns survival without invasive mechanical ventilation as part of a binary endpoint, the posted primary analysis is logistic regression, not a Kaplan-Meier or Cox analysis.
- They do not provide a non-inferiority conclusion. The analyses posted on ClinicalTrials.gov specify superiority, and no non-inferiority margin is reported.
- They do not provide a crossover analysis. No crossover is reported in the ClinicalTrials.gov record.
- They do not provide a Bayesian analysis. The reported statistical method is logistic regression, with odds ratio as the effect measure.
19. Limitations
- Primary analysis population: the primary analysis is restricted to randomized participants with at least one qualifying ordinal-scale assessment between Day 3 and Day 29. This is not identical to simply analyzing every enrolled participant.
- Binary endpoint construction: the primary endpoint compresses clinical information into a responder/non-responder outcome. That makes the endpoint straightforward to analyze with logistic regression but means the odds ratio summarizes the binary classification rather than every possible trajectory on the 9-point ordinal scale.
- Confidence-interval width: the primary 95% CI of 0.76–2.54 and secondary 95% CI of 0.30–1.50 indicate considerable uncertainty around the respective point estimates.
- Adjustment model: the reported estimates depend on the logistic-regression model adjusted for treatment, region, and baseline ordinal-scale category.
- Safety analysis: the registry-reported serious-adverse-event results are descriptive counts by arm; no formal comparison is reported in the statistical analyses posted on ClinicalTrials.gov.
- Limited posted analyses: the ClinicalTrials.gov record contains two formal statistical analyses: one primary endpoint analysis and one secondary endpoint analysis. Additional outcome measures are reported in the registry profile, but no additional formal statistical analyses were reported here.
- Endpoint-specific interpretation: the primary endpoint and COVID-19-related death endpoint use different analysis-population specifications. Their estimates should therefore not be treated as if they came from an identical analysis set without checking the underlying registry definitions.
20. Why This Trial Matters Statistically
CAN-COVID is a useful teaching example because it demonstrates how a randomized clinical trial can use a binary clinical endpoint rather than a continuous measurement or a conventional time-to-event endpoint, and then use logistic regression to adjust the treatment comparison for prespecified baseline and regional factors.
| Concept | How it appears in CAN-COVID |
|---|---|
| Randomization | Participants were randomized to canakinumab or placebo. |
| Blinding | The trial was double masked. |
| Parallel design | The registry identifies a parallel-group design. |
| Binary endpoint | The primary outcome classifies participants according to survival without requiring invasive mechanical ventilation from Day 3 to Day 29. |
| Logistic regression | The primary and reported secondary analyses used logistic regression. |
| Odds ratio | The treatment effect was summarized using an odds ratio. |
| Confidence interval | Both reported effect estimates include two-sided 95% confidence intervals. |
| P-value | Both formal analyses report two-sided p-values under a superiority hypothesis. |
| Covariate adjustment | The logistic model was adjusted for treatment, region, and baseline 9-point ordinal scale. |
| Intention-to-treat | The secondary COVID-19-related death analysis explicitly invokes the intent-to-treat principle. |
21. Statistical Interpretation of the Primary Result
The primary odds ratio of 1.39 is above the null value of 1. On the fitted model scale, the estimated odds of meeting the primary responder definition were higher with canakinumab than with placebo.
The corresponding 95% CI of 0.76–2.54 is relatively broad and crosses the null value. The point estimate therefore should not be interpreted without the uncertainty interval.
The reported two-sided p-value of 0.2874 is not a measure of the size of the observed association. It should be considered alongside the odds ratio, confidence interval, endpoint definition, and analysis population.
The estimate is an adjusted odds ratio from logistic regression incorporating treatment, region, and baseline 9-point ordinal scale category. It is therefore not simply the ratio of two raw response percentages.
The statistical result describes the observed randomized-group comparison for the specified endpoint. It does not by itself establish how an individual participant would respond or establish a treatment recommendation.
22. Primary vs Secondary Evidence
Primary endpoint
Survival without requiring invasive mechanical ventilation from Day 3 to Day 29. Adjusted OR 1.39; 95% CI 0.76–2.54; P = 0.2874.
Secondary endpoint
COVID-19-related death after study treatment at 29 days. Adjusted OR 0.67; 95% CI 0.30–1.50; P = 0.3303.
The two estimates should not be collapsed into a single summary statistic. They represent different binary outcomes and, importantly, have different analysis-population specifications in the ClinicalTrials.gov record.
23. Trial Timeline
Trial start
The registry lists 2020-04-30 as the study start date.
Primary completion
The registry lists 2020-09-16 as the primary completion date.
Registry status
the ClinicalTrials.gov record identifies CAN-COVID as completed and reports results on ClinicalTrials.gov.
24. What Would Normally Be Reported for a Binary Endpoint?
For a binary clinical endpoint, a complete statistical presentation often benefits from showing both the underlying event proportions and the model-based effect estimate. The registry analysis reports the adjusted odds ratio, confidence interval, and p-value, but the ClinicalTrials.gov record does not give the treatment-specific responder counts or responder percentages for the primary endpoint.
| Component | Available in the ClinicalTrials.gov record | Interpretive role |
|---|---|---|
| Binary endpoint definition | Yes | Defines exactly what counts as a responder. |
| Analysis population | Yes | Defines which randomized participants enter the primary analysis. |
| Regression method | Yes | Specifies the statistical model. |
| Adjusted odds ratio | Yes: 1.39 | Summarizes the modeled treatment association. |
| 95% CI | Yes: 0.76–2.54 | Quantifies uncertainty around the estimated odds ratio. |
| P-value | Yes: 0.2874 | Reports the statistical test result. |
| Raw responder count by arm | Not reported | Would show the observed number of responders in each group. |
| Raw responder percentage by arm | Not reported | Would provide an absolute probability-scale description. |
This distinction is important because an adjusted odds ratio and an absolute response rate answer different questions. The odds ratio describes the modeled relative association on the odds scale, whereas response percentages would describe the observed absolute frequency of the binary outcome.
25. Statistical Methods Summary
| Method | Role in CAN-COVID |
|---|---|
| Randomization | Creates the randomized treatment comparison between canakinumab and placebo. |
| Double masking | Reduces potential influence of treatment knowledge on trial conduct and assessment. |
| Binary endpoint analysis | Primary endpoint is analyzed as a responder/non-responder outcome. |
| Logistic regression | Models the probability of the binary outcome while adjusting for specified covariates. |
| Odds ratio | Primary effect measure; compares modeled odds between canakinumab and placebo. |
| 95% confidence interval | Quantifies uncertainty around the odds-ratio estimate. |
| Two-sided testing | Both registry-reported formal analyses report two-sided confidence intervals and p-values. |
| Superiority testing | Both formal analyses are identified as superiority analyses. |
| Intent-to-treat principle | Explicitly identified for the secondary COVID-19-related death analysis. |
26. Related Tutorials
Learn more about the methods used in this trial:
27. Related Calculators
28. Sources
- ClinicalTrials.gov: NCT04362813 — CAN-COVID.
- Linked publication: PubMed PMID 34367680.
- Linked publication: PubMed PMID 34283183.
Continue through Clinical Biostats
Build from the statistical concepts in this trial with focused tutorials, statistical calculators, and additional clinical-trial analyses.
29. Record Summary
CAN-COVID provides a clear example of a randomized phase 3 trial in which a clinically meaningful binary endpoint was analyzed using logistic regression. The primary endpoint was survival without requiring invasive mechanical ventilation from Day 3 to Day 29, with early-dropout rules incorporating hospital discharge and the 9-point ordinal scale. The reported adjusted odds ratio was 1.39, with a two-sided 95% confidence interval of 0.76–2.54 and a p-value of 0.2874. The registry also reports a secondary analysis of COVID-19-related death at 29 days, with an adjusted odds ratio of 0.67, 95% confidence interval 0.30–1.50, and p-value 0.3303.
The most useful statistical reading of these results is not to focus on any single number. The odds ratio describes the estimated treatment association, the confidence interval describes uncertainty around that estimate, and the p-value describes the statistical test under the specified model. Together with the randomized, double-blind design, the endpoint definitions, the analysis populations, and the covariate adjustment, these elements form the statistical framework for interpreting the posted CAN-COVID results.