This page separates reported trial results from statistical interpretation. The numerical results presented here are taken from the ClinicalTrials.gov record. The registry provides the official trial record.
1. Trial at a Glance
DAPA-CKD was a randomized, parallel-group, quadruple-masked phase 3 treatment trial evaluating dapagliflozin versus placebo in patients with chronic kidney disease. The primary endpoint was a time-to-event composite incorporating sustained decline in estimated glomerular filtration rate, end-stage renal disease, cardiovascular death, or renal death.
| Feature | DAPA-CKD |
|---|---|
| Trial name | DAPA-CKD |
| Phase | Phase 3 |
| Therapeutic area | Nephrology |
| Condition | Chronic Kidney Disease |
| Design | Randomized, parallel-group |
| Masking | Quadruple |
| Allocation | Randomized |
| Primary purpose | Treatment |
| Enrollment | 4304 |
| Interventions | Dapagliflozin; placebo |
| Primary endpoint type | Time-to-event |
| Primary hypothesis | Superiority |
| Primary analysis | Cox proportional-hazards model |
| Lead sponsor | AstraZeneca |
| Study status | Completed |
| ClinicalTrials.gov | NCT03036150 |
2. Clinical Question
The central statistical question was whether treatment with dapagliflozin, compared with placebo, changed the time until the first occurrence of the registered composite primary endpoint in patients with chronic kidney disease.
Population
Patients with chronic kidney disease enrolled in the phase 3 DAPA-CKD trial.
Intervention
Dapagliflozin.
Comparator
Placebo.
Primary question
Does dapagliflozin produce a different time-to-event experience from placebo for the registered composite endpoint?
The trial's statistical hypothesis was explicitly one of superiority. That distinction matters: the objective was to estimate and test whether the randomized treatment groups differed, rather than to demonstrate that dapagliflozin was merely no worse than placebo within a prespecified non-inferiority margin.
3. Trial Design
Dapagliflozin
- Dapagliflozin was the active intervention.
- Patients were analyzed according to randomized treatment assignment for the reported efficacy analyses.
Placebo
- Placebo was the comparator intervention.
- Patients were analyzed according to randomized treatment assignment for the reported efficacy analyses.
4. Endpoints
| Endpoint | Registry definition / time frame | Type |
|---|---|---|
| Primary composite endpoint | Time to the First Occurrence of Any of the Components of the Composite: ≥50% Sustained Decline in eGFR or Reaching ESRD or CV Death or Renal Death. Up to 38.2 months. |
Time-to-event |
| Secondary renal composite | Time to the First Occurrence of Any of the Components of the Composite: ≥50% Sustained Decline in eGFR or Reaching ESRD or Renal Death. Up to 38.2 months. |
Time-to-event |
| Secondary cardiovascular composite | Time to the First Occurrence of Either of the Components of the Composite: CV Death or Hospitalization for Heart Failure. Up to 38.2 months. |
Time-to-event |
| Secondary mortality endpoint | Time to Death From Any Cause. Up to 38.2 months. |
Time-to-event |
The primary endpoint is a composite time-to-event endpoint. The registry definition combines several clinically distinct events into one time-to-first-event outcome. The statistical analysis therefore asks whether the time until the first qualifying component differs between treatment groups; it does not, by itself, establish that every component has the same treatment effect.
5. Statistical Methodology
Cox proportional-hazards model
The registry reports a Cox proportional-hazards regression model for the primary endpoint and each of the three reported secondary analyses. The effect measure was the hazard ratio.
For the primary endpoint, the Full Analysis Set was used. This included all patients who were randomized to study treatment, with patients analyzed according to their randomized treatment assignment. This analysis structure preserves the treatment contrast created by randomization.
The coefficient associated with treatment is transformed into a hazard ratio. In this setting, the reported HR compares the estimated hazard of experiencing the endpoint under dapagliflozin with that under placebo, conditional on the model structure.
Stratification
The primary and secondary Cox analyses were stratified by the randomization stratification of Type 2 Diabetes and urine albumin creatinine ratio. Stratification allows the baseline hazard structure to differ across these strata while retaining a common treatment-effect parameter in the reported model.
Covariate adjustment
The analyses also adjusted for baseline eGFR. This means the reported treatment comparison is not simply an unadjusted comparison of event times; baseline eGFR was incorporated into the regression analysis as a covariate.
Randomization
Randomization establishes the treatment comparison before outcomes occur. An analysis based on randomized assignment retains that central design feature.
Stratification
The Cox model was stratified using the reported randomization strata of Type 2 Diabetes and urine albumin creatinine ratio.
Covariate adjustment
Baseline eGFR was included as an adjustment variable in the reported Cox analyses.
Effect measure
The treatment effect was expressed as a hazard ratio rather than as a risk ratio or absolute risk difference.
Kaplan-Meier estimation and the Cox model
Kaplan-Meier estimation is a standard descriptive method for displaying time-to-event outcomes, while the Cox model provides a regression-based estimate of the relative hazard. The registry analysis specifically reports Cox regression; it does not identify Kaplan-Meier estimation as the formal inferential method in the posted statistical-analysis fields.
An HR below 1 indicates a lower estimated instantaneous event rate under dapagliflozin than under placebo within the fitted model. The HR is a relative time-to-event measure, not a direct measure of absolute risk reduction.
6. Analysis Population and Estimand Structure
The registry gives an unusually important detail about the analysis population: the Full Analysis Set was used for the primary endpoint, and it included all patients who were randomized to study treatment. Patients were analyzed according to their randomized treatment assignment.
| Element | Reported approach |
|---|---|
| Primary analysis population | Full Analysis Set |
| Population definition | All patients randomized to study treatment |
| Treatment assignment. | Analyzed according to randomized treatment assignment. |
| Primary comparison | Dapagliflozin vs placebo |
| Regression model | Cox proportional-hazards model |
| Stratification | Type 2 Diabetes and urine albumin creatinine ratio |
| Adjustment covariate | Baseline eGFR |
This is closely related to the intention-to-treat principle. The key statistical idea is that treatment assignment remains the defining exposure for the efficacy comparison. That reduces the risk that the estimated treatment effect becomes primarily a comparison of patients who remained on treatment versus patients who did not.
7. Primary Endpoint Result
The primary endpoint was analyzed through the Full Analysis Set using a stratified Cox proportional-hazards model adjusted for baseline eGFR. The comparison was dapagliflozin versus placebo, with a superiority hypothesis.
Hazard ratio for the primary composite endpoint
95% CI: 0.51–0.72 · P < 0.0001
Time frame: up to 38.2 months
| Primary endpoint | Dapagliflozin vs placebo | Analysis |
|---|---|---|
| Time to first occurrence of ≥50% sustained decline in eGFR, reaching ESRD, CV death, or renal death | HR 0.61 95% CI 0.51–0.72 P < 0.0001 |
Stratified Cox regression, adjusted for baseline eGFR |
An HR of 0.61 means that, under the reported Cox model, the estimated instantaneous rate of experiencing the first qualifying component of the composite endpoint was 0.61 times that under placebo. Expressed as a relative model-based interpretation, this corresponds to an estimated 39% lower hazard for the dapagliflozin group.
The HR does not mean that 39% of patients avoided the endpoint, that an individual patient's absolute risk was reduced by 39%, or that every component of the composite was reduced by exactly 39%. The endpoint is defined by the first occurrence of any qualifying component.
The 95% CI of 0.51–0.72 describes uncertainty around the estimated hazard ratio under the model and sampling framework. It is not an interval containing the treatment effect experienced by individual patients.
The very small P-value, P < 0.0001, addresses evidence against the null hypothesis in the reported superiority analysis. A P-value is not a measure of effect size and does not indicate the probability that the treatment hypothesis is true.
Because the estimate comes from a Cox proportional-hazards model, interpretation of a single HR should also be understood in the context of the model's proportional-hazards assumption. The registry result itself is a model-based summary of the time-to-event comparison rather than a direct statement about absolute event probabilities at every point in follow-up.
8. Secondary Endpoint Results
The registry reports three secondary time-to-event analyses. Each used the Full Analysis Set, randomized treatment assignment, Cox regression, the same reported stratification variables, and adjustment for baseline eGFR.
Renal composite
Hazard ratio for renal composite
95% CI: 0.45–0.68 · P < 0.0001
Time frame: up to 38.2 months
The endpoint was time to the first occurrence of any component of the composite of ≥50% sustained decline in eGFR, reaching ESRD, or renal death. An HR of 0.56 corresponds to an estimated 44% lower hazard under the fitted Cox model for dapagliflozin relative to placebo.
Cardiovascular death or hospitalization for heart failure
Hazard ratio for cardiovascular composite
95% CI: 0.55–0.92 · P = 0.0089
Time frame: up to 38.2 months
The endpoint was time to the first occurrence of either cardiovascular death or hospitalization for heart failure. The HR of 0.71 represents an estimated 29% lower hazard under the fitted model for dapagliflozin relative to placebo.
Death from any cause
Hazard ratio for all-cause mortality
95% CI: 0.53–0.88 · P = 0.0035
Time frame: up to 38.2 months
The endpoint was time to death from any cause. An HR of 0.69 corresponds to an estimated 31% lower hazard of death under the fitted model for dapagliflozin relative to placebo.
| Endpoint | HR | 95% CI | P-value |
|---|---|---|---|
| Primary composite: ≥50% sustained decline in eGFR, ESRD, CV death, or renal death | 0.61 | 0.51–0.72 | < 0.0001 |
| Renal composite: ≥50% sustained decline in eGFR, ESRD, or renal death | 0.56 | 0.45–0.68 | < 0.0001 |
| CV death or hospitalization for heart failure | 0.71 | 0.55–0.92 | 0.0089 |
| Death from any cause | 0.69 | 0.53–0.88 | 0.0035 |
9. How the Primary and Secondary Results Relate
The four reported analyses form a coherent time-to-event analysis set, but they do not answer identical questions. The primary composite combines renal and mortality events. The renal secondary endpoint removes cardiovascular death from that composite, the cardiovascular secondary endpoint focuses on cardiovascular death or hospitalization for heart failure, and the all-cause mortality endpoint considers death from any cause.
| Endpoint family | What it asks | HR |
|---|---|---|
| Primary composite | When does a patient first experience any of the four qualifying renal or mortality events? | 0.61 |
| Renal composite | When does a patient first experience a qualifying renal event? | 0.56 |
| CV death / heart failure hospitalization | When does a patient first experience either cardiovascular death or hospitalization for heart failure? | 0.71 |
| All-cause mortality | When does the patient die from any cause? | 0.69 |
The HRs should not be interpreted as interchangeable estimates of one underlying quantity. Each corresponds to a different endpoint definition. In particular, the primary composite HR should not be described as though it were simply an all-cause mortality HR.
10. Statistical Methods Explained
Why was a Cox proportional-hazards model used?
The primary endpoint is explicitly a time-to-event outcome. A Cox model is designed to compare event hazards while accounting for differing follow-up times and right censoring. Rather than reducing each participant to a simple yes/no event indicator, the analysis uses the timing of the event or censoring information.
What does a hazard ratio of 0.61 mean?
An HR of 0.61 means the estimated hazard under dapagliflozin was 0.61 times the hazard under placebo in the reported Cox model. The complementary interpretation is a 39% lower estimated hazard. This is not equivalent to saying that the probability of an event was 39% lower at every time point.
Why adjust for baseline eGFR?
Baseline eGFR was specified as a covariate in the reported analysis. Covariate adjustment incorporates information about baseline renal function into the regression model. It can improve statistical efficiency and account for baseline variation relevant to the outcome, while the treatment comparison remains anchored to randomized assignment.
Why stratify the Cox model?
The analysis was stratified by the randomization stratification of Type 2 Diabetes and urine albumin creatinine ratio. A stratified Cox model permits different baseline hazard functions across the strata without requiring separate treatment-effect estimates for every stratum.
Why is the Full Analysis Set important?
The registry states that the Full Analysis Set included all randomized patients and that patients were analyzed according to randomized treatment assignment. This is important because analyzing patients according to the treatment they were assigned to preserves the comparison generated by randomization more directly than restricting analysis to patients who remained exposed to treatment.
What does the 95% confidence interval tell us?
For the primary HR, the 95% CI is 0.51–0.72. It quantifies uncertainty around the estimated HR under the statistical model and sampling framework. A narrower interval generally indicates greater precision than a wider interval, but the interval should not be interpreted as a range of individual patient outcomes.
Why does the P-value not tell us the size of the effect?
The P-value addresses the compatibility of the observed data with the null hypothesis under the specified testing framework. It does not measure how clinically large the effect is. Effect size is communicated by the HR, while precision is communicated by the confidence interval. Both should be read alongside the endpoint definition and analysis population.
11. Composite Endpoints: An Important Statistical Distinction
The primary endpoint combines four components: ≥50% sustained decline in eGFR, reaching ESRD, cardiovascular death, and renal death. The statistical analysis records the time to the first occurrence of any component.
One analysis, multiple events
The primary HR summarizes time to the first qualifying component rather than estimating four independent treatment effects.
Component interpretation
A composite result should not automatically be interpreted as though every component contributes equally to the observed treatment effect.
Different clinical events
The components span renal deterioration and cardiovascular or renal mortality, so the endpoint represents a broad outcome construct.
Secondary endpoint context
The separately reported renal, cardiovascular, and all-cause mortality endpoints help distinguish the questions addressed by each analysis.
This distinction is especially important when communicating the primary result. Saying that the HR was 0.61 for the primary endpoint is precise. Saying that dapagliflozin "reduced kidney failure by 39%" would be a different claim and is not an accurate restatement of the registered primary endpoint.
12. Confidence Intervals and Precision
The confidence intervals posted on ClinicalTrials.gov for the four reported analyses are:
The intervals provide a useful reminder that an observed HR is an estimate, not a fixed biological constant. The primary estimate of 0.61 is accompanied by a 95% CI extending from 0.51 to 0.72. The corresponding secondary estimates also have uncertainty, with different interval widths reflecting the information available for each endpoint.
Read the result in three steps: endpoint first, then effect estimate, then uncertainty. For DAPA-CKD, "primary composite, HR 0.61, 95% CI 0.51–0.72" is substantially more informative than reporting the HR alone.
13. Statistical Interpretation of the P-values
| Endpoint | P-value | What it contributes |
|---|---|---|
| Primary composite | < 0.0001 | Evidence against the null hypothesis under the reported superiority analysis. |
| Renal composite | < 0.0001 | Evidence against the null hypothesis for the reported secondary analysis. |
| CV death or hospitalization for heart failure | 0.0089 | Evidence against the null hypothesis for the reported secondary analysis. |
| Death from any cause | 0.0035 | Evidence against the null hypothesis for the reported secondary analysis. |
These P-values should be kept distinct from the corresponding effect estimates. For example, the primary result contains both an HR of 0.61 and a P-value of < 0.0001. The former describes the estimated relative hazard; the latter describes the strength of evidence against the null hypothesis under the specified statistical test.
The ClinicalTrials.gov record identifies the hypothesis type as superiority, but do not provide enough information here to reconstruct a complete multiplicity framework for the family of primary and secondary tests. The P-values should therefore be reported exactly as posted rather than assigning them an additional hierarchy or adjusted interpretation that is not contained in the ClinicalTrials.gov record.
14. Safety Results
The ClinicalTrials.gov record reports serious adverse events by randomized treatment arm using affected participants divided by participants at risk.
| Safety measure | Dapagliflozin | Placebo |
|---|---|---|
| Serious adverse events, affected / at risk | 633 / 2149 | 729 / 2149 |
The reported figures indicate the number of participants affected by serious adverse events and the corresponding number at risk in each arm. These are counts by treatment arm, not a hazard ratio and not an efficacy endpoint.
Dapagliflozin
633 participants with serious adverse events among 2149 at risk.
Placebo
729 participants with serious adverse events among 2149 at risk.
Because the ClinicalTrials.gov record provides the affected and at-risk counts rather than a formal statistical comparison, the safety results should not be converted into a treatment-effect estimate or described as statistically different without an appropriate analysis.
15. Trial Timeline
Study start
The DAPA-CKD study began on February 2, 2017.
Randomized treatment comparison
The completed phase 3 trial used a randomized, parallel-group design with quadruple masking and two interventions: dapagliflozin and placebo.
Registered outcome window
The primary endpoint and the three reported secondary endpoints were evaluated over a time frame of up to 38.2 months.
Primary completion
The trial reached primary completion on June 12, 2020.
16. Why This Trial Matters Statistically
DAPA-CKD is a useful statistical teaching case because the registry record brings together several core concepts in survival analysis: randomized treatment assignment, a composite time-to-event endpoint, a Full Analysis Set, Cox proportional-hazards regression, stratification, covariate adjustment, hazard ratios, confidence intervals, and superiority testing.
| Concept | How it appears in DAPA-CKD |
|---|---|
| Randomization | Patients were randomized to dapagliflozin or placebo. |
| Parallel-group design | The trial used two parallel treatment arms. |
| Blinding | The registry reports quadruple masking. |
| Time-to-event endpoint | The primary endpoint was time to first occurrence of a composite event. |
| Composite endpoint | The primary endpoint combined four qualifying components. |
| Cox regression | The primary and secondary analyses used Cox regression. |
| Hazard ratio | Treatment effects were reported as HRs. |
| Confidence interval | All four statistical analyses registry-reported 95% CIs. |
| Covariate adjustment | Baseline eGFR was included in the reported models. |
| Stratified analysis | Models were stratified by Type 2 Diabetes and urine albumin creatinine ratio. |
| Full Analysis Set | All randomized patients were included in the reported efficacy analysis population. |
| Superiority testing | The registered hypothesis type was superiority. |
17. Important Limitations and Interpretation Issues
- Hazard ratio is not absolute risk: the HR summarizes a relative hazard under the Cox model and does not directly provide an absolute risk difference.
- Composite endpoint: the primary result applies to time to the first occurrence of any qualifying component. It should not automatically be interpreted as the treatment effect for each individual component.
- Proportional-hazards assumption: the Cox model is based on a proportional-hazards framework. A single HR can be an incomplete description if the relative hazards vary substantially over time.
- Different endpoints answer different questions: the renal composite, cardiovascular death or hospitalization for heart failure, and all-cause mortality endpoints should not be treated as interchangeable measures.
- Secondary analyses: the ClinicalTrials.gov record identifies three secondary analyses but do not provide a complete multiplicity-adjustment framework for interpreting their P-values as a family.
- Safety comparison: the registry-reported serious-adverse-event data are arm-level counts and do not include a formal statistical comparison.
- Registry-level information: the ClinicalTrials.gov record provides the reported model, population, estimates, confidence intervals, and P-values, but not the underlying participant-level event and censoring data needed for independent reconstruction of survival curves.
- Generalizability: the ClinicalTrials.gov record identifies chronic kidney disease as the condition studied, but do not provide the detailed eligibility and baseline-characteristic information needed to characterize applicability to particular patient subgroups.
18. What the Primary Hazard Ratio Does — and Does Not — Mean
The primary HR of 0.61 means that the estimated hazard of the first qualifying primary composite event was 0.61 times the placebo hazard under the reported Cox model. The corresponding relative interpretation is an estimated 39% lower hazard.
It does not mean that exactly 39% fewer patients experienced an event, that each individual patient had a 39% lower probability of an event, or that every component of the composite was reduced by 39%.
The 95% CI of 0.51–0.72 expresses uncertainty around the estimated HR. It should be read together with the endpoint definition, analysis population, stratification, and baseline-eGFR adjustment.
The P-value of < 0.0001 addresses statistical evidence against the null hypothesis in the reported superiority analysis. It is not a measure of the magnitude or clinical importance of the HR.
19. Clinical Interpretation vs Statistical Interpretation
Statistical interpretation
The primary Cox model estimated an HR of 0.61 with a 95% CI of 0.51–0.72 and P < 0.0001. The model was stratified by Type 2 Diabetes and urine albumin creatinine ratio and adjusted for baseline eGFR.
Clinical interpretation
The primary endpoint represents a broad composite of renal and mortality outcomes. Its HR describes the time-to-first-event comparison, while the separate secondary analyses provide distinct perspectives on renal, cardiovascular/heart-failure, and all-cause mortality outcomes.
Keeping these two levels separate is important. Statistical significance does not by itself determine clinical importance, and a hazard ratio cannot be interpreted properly without knowing precisely which event definition generated it.
20. Comparing Relative Effects Across the Reported Endpoints
The reported HRs all fall below 1, but each belongs to a different endpoint. A useful way to read them is descriptively rather than treating their numerical ordering as a ranking of clinical importance.
| Endpoint | HR | 95% CI | Model adjustment |
|---|---|---|---|
| Primary composite | 0.61 | 0.51–0.72 | Stratified by Type 2 Diabetes and urine albumin creatinine ratio; adjusted for baseline eGFR |
| Renal composite | 0.56 | 0.45–0.68 | Stratified by Type 2 Diabetes and urine albumin creatinine ratio; adjusted for baseline eGFR |
| CV death or hospitalization for heart failure | 0.71 | 0.55–0.92 | Stratified by Type 2 Diabetes and urine albumin creatinine ratio; adjusted for baseline eGFR |
| Death from any cause | 0.69 | 0.53–0.88 | Stratified by Type 2 Diabetes and urine albumin creatinine ratio; adjusted for baseline eGFR |
Notice that the confidence intervals overlap substantially across the reported estimates. Numerical differences between HRs should not be interpreted as evidence that one endpoint has a statistically different treatment effect from another. Formal comparisons would require an analysis specifically designed to test such differences.
21. A Worked Reading of the Primary Result
The outcome is time to the first occurrence of ≥50% sustained decline in eGFR, reaching ESRD, CV death, or renal death, over up to 38.2 months.
The Full Analysis Set was used. Patients were analyzed according to randomized treatment assignment. A stratified Cox proportional-hazards model was used, with stratification by Type 2 Diabetes and urine albumin creatinine ratio and adjustment for baseline eGFR.
The HR was 0.61. Under the model, the estimated hazard under dapagliflozin was 0.61 times the hazard under placebo.
The 95% CI was 0.51–0.72. This communicates uncertainty around the estimated HR.
The P-value was < 0.0001. This provides evidence against the null hypothesis under the reported superiority analysis, but it does not quantify the size of the treatment effect.
22. Related Tutorials
Learn more about the methods used in this trial:
23. Related Statistical Calculators
24. Sources
- ClinicalTrials.gov: NCT03036150 — DAPA-CKD.
- PubMed record reported with the trial data: PMID 41926219.
- PubMed record reported with the trial data: PMID 41128261.
- PubMed record reported with the trial data: PMID 40679863.
- PubMed record reported with the trial data: PMID 40658498.
- PubMed record reported with the trial data: PMID 40526444.
Continue through the Clinical Biostats statistical library
Explore the statistical concepts behind randomized trials, time-to-event endpoints, Cox regression, hazard ratios, confidence intervals, and clinical-trial analysis.
25. Record Summary
DAPA-CKD provides a clear example of how a randomized clinical trial can evaluate a composite renal and cardiovascular time-to-event outcome using Cox proportional-hazards regression. The registry analysis used the Full Analysis Set, analyzed patients according to randomized treatment assignment, stratified the Cox model by Type 2 Diabetes and urine albumin creatinine ratio, and adjusted for baseline eGFR.
The primary analysis reported an HR of 0.61 with a 95% CI of 0.51–0.72 and P < 0.0001 for time to the first occurrence of ≥50% sustained decline in eGFR, reaching ESRD, CV death, or renal death over up to 38.2 months. Three secondary analyses also reported HRs below 1: 0.56 for the renal composite, 0.71 for CV death or hospitalization for heart failure, and 0.69 for death from any cause.
The statistical lesson is broader than any individual number. Correct interpretation requires identifying the exact endpoint, preserving the randomized analysis population, understanding the Cox model and its assumptions, distinguishing hazard ratios from absolute risks, reading confidence intervals as measures of precision, and keeping P-values separate from effect size.