This page provides an independent statistical analysis and educational interpretation of publicly reported results. ClinicalTrials.gov provides the official trial registry record. Numerical trial results on this page are restricted to the ClinicalTrials.gov record.
1. Trial at a Glance
EMPEROR-Preserved was a randomized, parallel, double-blind phase 3 trial in heart failure. The registry reports 5988 enrolled participants and two study arms: 10 mg empagliflozin and placebo.
| Feature | EMPEROR-Preserved |
|---|---|
| Trial name | EMPEROR-Preserved |
| NCT identifier | NCT03057951 |
| Therapeutic area | Cardiology |
| Condition | Heart Failure |
| Phase | Phase 3 |
| Allocation | Randomized |
| Design model | Parallel |
| Masking | Double |
| Primary purpose | Treatment |
| Enrollment | 5988 |
| Interventions | Empagliflozin and placebo |
| Primary endpoint type | Time-to-event |
| Results posted | Yes |
| Outcome measures posted | 10 |
| Statistical analyses posted | 10 |
| Lead sponsor | Boehringer Ingelheim |
2. Clinical Question
The registered primary question concerns the time to first event of adjudicated cardiovascular death or adjudicated hospitalization for heart failure, comparing 10 mg empagliflozin with placebo from randomization through completion of the planned treatment phase.
Population
Patients enrolled in the phase 3 EMPEROR-Preserved trial for the condition of heart failure. The ClinicalTrials.gov record reports 5988 enrolled participants.
Intervention
10 mg empagliflozin.
Comparator
Placebo.
Primary question
How does 10 mg empagliflozin compare with placebo for the time to first adjudicated cardiovascular death or adjudicated hospitalization for heart failure?
3. Trial Design
Empagliflozin
- 10 mg empagliflozin
- Randomized treatment arm
- Compared with placebo
- Included in the Randomised Set for the registered efficacy analyses
Placebo
- Placebo
- Randomized comparator arm
- Compared with 10 mg empagliflozin
- Included in the Randomised Set for the registered efficacy analyses
4. Endpoints
The registry reports 10 outcome measures and 10 statistical analyses. The primary endpoint is a time-to-event composite; the secondary analyses span recurrent hospitalization, renal function, cardiovascular and all-cause mortality, diabetes onset, patient-reported outcome, and all-cause hospitalization.
| Role | Registered endpoint | Time frame | Endpoint type |
|---|---|---|---|
| Primary | Time to First Event of Adjudicated Cardiovascular (CV) Death or Adjudicated Hospitalisation for Heart Failure (HHF) | From randomization until completion of the planned treatment phase, up to 1403 days. | Time-to-event |
| Secondary | Occurrence of Adjudicated Hospitalisation for Heart Failure (HHF) (First and Recurrent) | From randomization until completion of the planned treatment phase, up to 1403 days. | Time-to-event |
| Secondary | eGFR (CKD-EPI) cr Slope of Change From Baseline | At baseline, week 4, 12, 32, 52, 76, 100, 124, 148, 172 and week 196, up to 1043 days. | Continuous |
| Secondary | Time to the First Event in the Composite Renal Endpoint: Chronic Dialysis, Renal Transplant, or Sustained Reduction in eGFR (CKD-EPI)cr | From randomization until completion of the planned treatment phase, up to 1403 days. | Time-to-event |
| Secondary | Time to First Adjudicated Hospitalisation for Heart Failure (HHF) | From randomization until completion of the planned treatment phase, up to 1403 days. | Time-to-event |
| Secondary | Time to Adjudicated Cardiovascular (CV) Death | From randomization until completion of the planned treatment phase, up to 1403 days. | Time-to-event |
| Secondary | Time to All-cause Mortality | From randomization until completion of the planned treatment phase, up to 1403 days. | Time-to-event |
| Secondary | Time to Onset of Diabetes Mellitus (DM) in Patients With Pre-DM | From randomization until completion of the planned treatment phase, to 1403 days. | Time-to-event |
| Secondary | Change From Baseline in Kansas City Cardiomyopathy Questionaire (KCCQ) Clinical Summary Score at Week 52 | At baseline and at week 12, week 32 and week 52. | Continuous |
| Secondary | Occurrence of All-cause Hospitalisation (First and Recurrent) | From randomization until completion of the planned treatment phase, up to 1403 days. | Time-to-event |
Primary endpoint definition
The registered primary endpoint is the Time to First Event of Adjudicated Cardiovascular (CV) Death or Adjudicated Hospitalisation for Heart Failure (HHF). The registry definition in the ClinicalTrials.gov record also describes an incidence-rate calculation per 100 patient-years and states that patients without a specific endpoint event were censored. The registry-reported definition text ends before the complete censoring rule is reproduced, so no additional censoring rule is inferred here.
5. Analysis Populations and Statistical Structure
The statistical analyses use more than one analysis population. The distinction matters because the population used for a time-to-event efficacy analysis is not necessarily the same as the population used for an on-treatment longitudinal measurement.
| Analysis | Analysis population | Statistical role |
|---|---|---|
| Primary composite endpoint | Randomised Set (RS), including all randomised patients. | Primary randomized efficacy comparison. |
| HHF, first and recurrent | Randomised Set (RS), including all randomised patients. | Recurrent-event analysis accounting for dependence with cardiovascular death. |
| eGFR slope | Only patients included in the treated set (TS) and with available data for this endpoint. | Longitudinal analysis of renal function trajectory. |
| Renal composite endpoint | Randomised Set (RS), including all randomised patients. | Time-to-event efficacy analysis. |
| First HHF | Randomised Set (RS), including all randomised patients. | Time-to-first-event analysis. |
| CV death | Randomised Set (RS), including all randomised patients. | Time-to-event analysis. |
| All-cause mortality | Randomised Set (RS), including all randomised patients. | Time-to-event analysis. |
| Diabetes onset in pre-DM | Randomised Set (RS), including all randomised patients and with available data for this endpoint. | Time-to-event analysis in the specified population. |
| KCCQ Clinical Summary Score | Only patients included in the treated set (TS), including values obtained on treatment, with available data at the relevant timepoint. | Repeated-measures analysis. |
| All-cause hospitalization, first and recurrent | Randomised Set (RS), including all randomised patients. | Recurrent-event analysis accounting for dependence with all-cause mortality. |
6. Primary Result: Cardiovascular Death or Hospitalisation for Heart Failure
The primary analysis compared placebo with 10 mg empagliflozin in the Randomised Set. The registry reports a Cox proportional-hazards model with treatment and prespecified baseline and regional covariates, and identifies the hypothesis as superiority.
Hazard ratio for first adjudicated CV death or HHF
95.03% CI: 0.69–0.90 · P = 0.0003
Comparison: 10 mg Empagliflozin vs Placebo
| Feature | Primary analysis |
|---|---|
| Endpoint | Time to First Event of Adjudicated Cardiovascular (CV) Death or Adjudicated Hospitalisation for Heart Failure (HHF) |
| Analysis population | Randomised Set (RS), including all randomised patients. |
| Method | Cox proportional-hazards model |
| Effect measure | Hazard Ratio (HR) |
| Estimate | 0.79 |
| Confidence interval | 95.03% two-sided CI: 0.69–0.90 |
| P-value | 0.0003 |
| Hypothesis | Superiority |
| Alpha | 0.0497, resulting from interim analysis |
The estimated hazard ratio of 0.79 means that, under the fitted Cox model, the estimated instantaneous rate of experiencing the composite event was 0.79 times that of the comparison group. Expressed as a simple relative-hazard interpretation, this corresponds to an estimated 21% lower hazard for the empagliflozin group relative to placebo.
The HR does not mean that 21% of patients avoided the event, nor does it represent a 21-percentage-point absolute reduction in event probability. It is a relative, model-based measure of the event rate over time.
The 95.03% confidence interval from 0.69 to 0.90 describes statistical uncertainty around the estimated hazard ratio under the analysis framework. It does not describe the range of individual patient effects.
The P-value of 0.0003 addresses evidence against the null hypothesis under the prespecified testing framework. It does not measure the size or clinical importance of the treatment effect. Here, the registry specifically reports an interim-analysis-adjusted alpha of 0.0497, so the P-value should be understood in the context of that sequential design.
Because this is a Cox model, interpretation also depends on the usual proportional-hazards framework. The registry does not provide a time-varying hazard-ratio assessment in the ClinicalTrials.gov record, so the single HR should not be interpreted as proof that the relative hazard was identical at every point in follow-up.
7. Secondary Results: Recurrent Heart-Failure Hospitalisation
The registry separately analyzes the occurrence of adjudicated hospitalization for heart failure as a first and recurrent event. Rather than treating repeated hospitalizations as independent observations, the analysis uses a joint frailty model that accounts for dependence between recurrent HHF and cardiovascular death.
Hazard ratio for first and recurrent HHF
95.03% CI: 0.61–0.88 · P = 0.0009
Comparison: 10 mg Empagliflozin vs Placebo
An HR of 0.73 corresponds to an estimated 27% lower hazard under the reported joint frailty model. This is not equivalent to saying that the number of hospitalizations was reduced by exactly 27%, because recurrent-event models incorporate repeated events and their dependence with mortality.
The 95.03% CI of 0.61–0.88 quantifies uncertainty around the model-based treatment effect. Its width reflects the precision of the estimate; it is not a prediction interval for how much an individual patient's hospitalization risk will change.
The P-value of 0.0009 indicates the strength of evidence against the reported null hypothesis under the analysis framework. It is not a measure of the magnitude of the HR.
The joint frailty approach is important because a patient can experience recurrent hospitalization, while cardiovascular death is a competing terminal outcome that prevents further observed hospitalizations. Treating every hospitalization as an isolated independent observation would not represent that structure adequately.
8. Secondary Result: eGFR Slope
The eGFR analysis is fundamentally different from the time-to-event analyses. Measurements were collected at baseline and multiple scheduled weeks, and the registry reports a random-coefficient model with a random intercept and random slope per patient.
Treatment-by-time interaction
99.9% CI: 0.861–1.865 · P < 0.0001
Outcome unit: mL/min/1.73 meters squared/year
| Feature | eGFR slope analysis |
|---|---|
| Endpoint | eGFR (CKD-EPI) cr Slope of Change From Baseline |
| Time points | Baseline, week 4, 12, 32, 52, 76, 100, 124, 148, 172 and week 196 |
| Maximum stated follow-up | Up to 1043 days |
| Analysis population | Only patients included in the treated set (TS) and with available data for this endpoint. |
| Method | Random intercept random coefficient model |
| Effect measure | Treatment by time interaction |
| Estimate | 1.363 |
| Confidence interval | 99.9% two-sided CI: 0.861–1.865 |
| P-value | <0.0001 |
The reported 1.363 is a treatment-by-time interaction estimate rather than a hazard ratio. It describes a difference in the modeled longitudinal trajectory of eGFR between treatment groups, in the registry's stated unit of mL/min/1.73 meters squared/year.
It should therefore not be translated into a percentage reduction in an event hazard. The estimate is tied to the random-coefficient model, which allows each patient to have a patient-specific intercept and slope.
The 99.9% confidence interval of 0.861–1.865 reflects the confidence level reported by the registry and is intentionally not converted into a 95% interval. The P-value is <0.0001, while the analysis notes specify alpha = 0.001.
The analysis uses on-treatment data from treated patients. That population definition is important when comparing this result conceptually with the primary endpoint, which uses the Randomised Set.
9. Secondary Time-to-Event Results
The registry reports several additional time-to-event outcomes. Most use Cox proportional-hazards models with adjustment for age, baseline eGFR, region, baseline diabetes status, sex, baseline LVEF, and treatment.
| Endpoint | Method | Effect | 95% CI | P-value |
|---|---|---|---|---|
| Time to first event in composite renal endpoint | Cox proportional-hazards model | HR 0.95 | 0.73–1.24 | 0.7243 |
| Time to first adjudicated HHF | Cox proportional-hazards model | HR 0.71 | 0.60–0.83 | <0.0001 |
| Time to adjudicated CV death | Cox proportional-hazards model | HR 0.91 | 0.76–1.09 | 0.2951 |
| Time to all-cause mortality | Cox proportional-hazards model | HR 1.00 | 0.87–1.15 | 0.9893 |
| Time to onset of diabetes mellitus in patients with pre-DM | Cox proportional-hazards model | HR 0.84 | 0.65–1.07 | 0.1539 |
Renal composite endpoint
The reported HR for the time to the first event in the composite renal endpoint was 0.95, with a 95% two-sided CI of 0.73–1.24 and P = 0.7243. The composite consisted of chronic dialysis, renal transplant, or sustained reduction in eGFR (CKD-EPI)cr.
First adjudicated HHF
The HR for time to first adjudicated HHF was 0.71, with a 95% two-sided CI of 0.60–0.83 and P < 0.0001. This is a time-to-first-event analysis and is therefore conceptually different from the recurrent-HHF analysis above.
Cardiovascular death
The HR for time to adjudicated cardiovascular death was 0.91, with a 95% two-sided CI of 0.76–1.09 and P = 0.2951.
All-cause mortality
The HR for time to all-cause mortality was 1.00, with a 95% two-sided CI of 0.87–1.15 and P = 0.9893. An HR of exactly 1.00 is the point estimate of no relative difference in the modeled hazard between the two groups.
Diabetes onset among patients with pre-DM
For time to onset of diabetes mellitus among patients with pre-DM, the HR was 0.84, with a 95% two-sided CI of 0.65–1.07 and P = 0.1539. This endpoint used the Randomised Set with available data for the endpoint.
10. Secondary Result: KCCQ Clinical Summary Score
The registry reports a longitudinal analysis of change from baseline in the Kansas City Cardiomyopathy Questionnaire Clinical Summary Score at week 52. Unlike the time-to-event endpoints, this analysis uses an MMRM framework.
Adjusted mean difference at Week 52
95% CI: 0.45–2.19 · P = 0.0028
Comparison: 10 mg Empagliflozin vs Placebo
The adjusted mean difference of 1.32 represents the model-adjusted difference between treatment groups for change from baseline in the KCCQ Clinical Summary Score at week 52, using the registry's stated MMRM analysis.
The 95% CI of 0.45–2.19 describes uncertainty around that adjusted mean difference. It does not imply that every patient experienced an improvement within that range.
The P-value of 0.0028 quantifies evidence against the null hypothesis under the reported model. It does not tell us how important a 1.32-point difference is to an individual patient.
MMRM is appropriate for repeated measurements because it models the longitudinal structure rather than treating each patient-timepoint observation as an independent cross-sectional observation. The registry reports fixed effects for treatment-by-visit interaction and baseline KCCQ Clinical Summary Score-by-visit interaction, along with age, baseline eGFR, baseline LVEF, region, baseline diabetes status, sex, and week reachable.
11. Secondary Result: Recurrent All-cause Hospitalisation
Hazard ratio for recurrent all-cause hospitalisation
95% CI: 0.85–1.01 · P = 0.1012
Comparison: 10 mg Empagliflozin vs Placebo
The registry used a joint frailty model accounting for dependence between recurrent all-cause hospitalization and all-cause mortality. The model included age, baseline eGFR, baseline LVEF, treatment, region, baseline diabetes status and sex.
An HR of 0.93 corresponds to an estimated 7% lower hazard under the reported model. Because this is a recurrent-event analysis, that statement should not be interpreted as a simple 7% reduction in the total number of hospitalizations.
The 95% CI of 0.85–1.01 indicates uncertainty around the estimate. The interval crosses 1.00, while the P-value is 0.1012. The P-value is evidence information under the specified model; it is not an effect-size metric.
The use of a joint frailty model is statistically meaningful because all-cause mortality terminates a patient's ability to experience additional observed hospitalizations. The model therefore attempts to represent the dependence between the recurrent hospitalization process and the terminal mortality process.
12. Statistical Methodology
Cox proportional-hazards model
The registry identifies Cox proportional-hazards regression as the principal method for the primary endpoint and several secondary time-to-event endpoints. The primary model includes treatment, region, baseline diabetes status, age, sex, LVEF and baseline eGFR.
The hazard ratio associated with treatment is obtained from the treatment coefficient. An HR below 1 indicates a lower estimated instantaneous event rate in the empagliflozin group relative to placebo under the model.
Covariate adjustment
The primary Cox analysis adjusts for region, baseline diabetes status, age, sex, LVEF and baseline eGFR in addition to treatment. The same general covariate set is described for several secondary Cox analyses.
Adjustment does not replace randomization. Rather, it incorporates prespecified prognostic information into the statistical model and can improve the precision of the estimated treatment contrast when the covariates explain some outcome variation.
Random-coefficient model for eGFR
The eGFR analysis uses a random coefficient model allowing a random intercept and random slope for each patient. Fixed effects include age, baseline eGFR, baseline LVEF, region, baseline diabetes status, sex, baseline-by-time interaction, treatment-by-time interaction and treatment.
This structure is useful for repeated measurements because patients can begin at different modeled baseline levels and can have different individual trajectories over time.
MMRM for KCCQ
The KCCQ analysis uses a Mixed Model Repeated Measures approach. The registry reports treatment-by-visit interaction and baseline KCCQ Clinical Summary Score-by-visit interaction among the fixed effects, together with baseline covariates and week reachable.
The treatment effect at a particular visit is therefore estimated within a longitudinal model rather than by simply subtracting two raw group means at week 52.
Joint frailty models
Two recurrent-event outcomes use joint frailty models. The HHF analysis accounts for dependence between recurrent HHF and cardiovascular death. The all-cause hospitalization analysis accounts for dependence between recurrent all-cause hospitalization and all-cause mortality.
The frailty component provides a way to represent unobserved patient-level propensity that can link repeated events within a patient and the terminal event process. This is materially different from applying an ordinary Cox model to each hospitalization as though repeated observations were independent.
13. Interim Analysis and Alpha Spending
The primary analysis notes state that the interim analysis resulted in an adjusted alpha of 0.0497. The registry also identifies interim analysis / alpha spending as a concept associated with the primary and some secondary analyses.
Why interim analysis changes alpha
Looking at accumulating trial data creates multiple opportunities to declare an effect. A sequential design therefore needs an error-control mechanism so that the overall probability of a false-positive conclusion remains consistent with the prespecified design.
What alpha = 0.0497 tells us
The registry explicitly reports alpha = 0.0497 for the primary Cox analysis as resulting from the interim analysis. The reported P-value should therefore be interpreted against the stated sequential-testing framework rather than assuming an unadjusted 0.05 threshold.
The important statistical point is that an interim analysis changes the reference testing framework. The registry explicitly connects the primary analysis alpha to the interim analysis.
14. Why the Hazard Ratio Is the Natural Primary Effect Measure
The primary endpoint records time until the first occurrence of either adjudicated cardiovascular death or adjudicated hospitalization for heart failure. Time-to-event data contain more information than a simple event/no-event indicator because patients can have different lengths of follow-up.
A Cox model uses that timing information while accommodating right censoring. A patient who has not experienced the primary event by the end of observed follow-up does not simply disappear from the analysis; that patient's available event-free follow-up contributes information up to the censoring time.
A hazard ratio below 1 indicates a lower estimated instantaneous event rate in the empagliflozin group under the fitted model. It is not an absolute risk difference, probability, or number-needed-to-treat measure.
The primary estimate of 0.79 is therefore best understood together with its 95.03% CI of 0.69–0.90 and P = 0.0003. Each quantity answers a different question: effect magnitude, uncertainty, and evidence against the null hypothesis.
15. Statistical Methods Explained
Why was a Cox proportional-hazards model used for the primary endpoint?
The primary endpoint is explicitly a time-to-event endpoint: time from randomization until the first adjudicated cardiovascular death or adjudicated hospitalization for heart failure. Cox regression is designed for this setting because it estimates a relative hazard while allowing follow-up to differ between patients and incorporating censoring.
What does an HR of 0.79 mean?
It means that the fitted model estimates the hazard in the empagliflozin group at 0.79 times the hazard in the placebo group. A simple transformation, 1 − 0.79, gives a 21% lower estimated hazard. It does not mean a 21% absolute reduction in event probability.
Why is the primary confidence interval 95.03% rather than 95%?
The registry reports a two-sided 95.03% confidence interval for the primary analysis and states that alpha = 0.0497 resulted from the interim analysis. The confidence level therefore reflects the sequential testing framework reported in the registry rather than an assumed generic 95% level.
Why is the recurrent HHF analysis different from the first-HHF analysis?
First HHF counts only the first hospitalization event. The recurrent-HHF endpoint explicitly includes first and recurrent events. Repeated events from the same patient are correlated, and cardiovascular death terminates future observed HHF events. The joint frailty model accounts for this dependence rather than treating every hospitalization as an independent observation.
Why was an MMRM used for the KCCQ endpoint?
KCCQ Clinical Summary Score was measured repeatedly at baseline, week 12, week 32 and week 52. MMRM models those repeated measurements jointly and allows the treatment effect to be evaluated within the longitudinal structure of the data.
Why does the eGFR analysis use a random coefficient model?
The eGFR endpoint is a trajectory rather than a single event. The registry's random coefficient model allows each patient to have a random intercept and random slope, while treatment-by-time and baseline-by-time interactions capture differences in modeled trajectories over follow-up.
Why should the secondary P-values not automatically be treated as separate confirmatory conclusions?
The trial evaluates multiple secondary outcomes in addition to the primary endpoint. Multiplicity can increase the chance of false-positive findings when many hypotheses are tested. The ClinicalTrials.gov record does not provide a complete endpoint hierarchy or multiplicity procedure for every secondary analysis, so the individual P-values should be interpreted in the context of the prespecified trial testing framework.
16. Safety Results
The ClinicalTrials.gov record reports serious adverse events by treatment arm as affected patients over patients at risk.
| Safety measure | Placebo | 10 mg Empagliflozin |
|---|---|---|
| Serious adverse events | 1543 / 2989 | 1436 / 2996 |
These figures are presented exactly as reported in the registry: affected patients divided by patients at risk. They should not be confused with the efficacy analysis populations or with the denominators used for individual longitudinal endpoints.
17. Primary Endpoint Statistical Walkthrough
Establish the treatment comparison
The trial uses randomized allocation with two parallel treatment arms: placebo and 10 mg empagliflozin.
Observe time to the first composite event
The primary endpoint follows patients from randomization until completion of the planned treatment phase, up to 1403 days.
Fit a Cox proportional-hazards model
The registry specifies treatment, region, baseline diabetes status, age, sex, LVEF and baseline eGFR as model terms.
Express the treatment contrast as an HR
The reported hazard ratio is 0.79, with a 95.03% two-sided confidence interval of 0.69–0.90.
Interpret evidence under the interim framework
The reported P-value is 0.0003, with alpha = 0.0497 resulting from the interim analysis.
18. Comparing First-Event and Recurrent-Event Analysis
One of the most instructive features of EMPEROR-Preserved's statistical analysis is that hospitalization appears in both first-event and recurrent-event formulations.
| Question | Endpoint | Method | Estimate |
|---|---|---|---|
| What is the hazard of the first adjudicated HHF? | Time to First Adjudicated Hospitalisation for Heart Failure | Cox proportional-hazards model | HR 0.71 (95% CI 0.60–0.83), P < 0.0001 |
| What is the treatment effect when first and recurrent HHF events are considered together with CV death? | Occurrence of Adjudicated Hospitalisation for Heart Failure (HHF) (First and Recurrent) | Joint frailty model | HR 0.73 (95.03% CI 0.61–0.88), P = 0.0009 |
These are not redundant analyses. The first-HHF endpoint asks about the first hospitalization event, whereas the recurrent endpoint incorporates additional hospitalization events and explicitly models dependence with cardiovascular death. Consequently, their estimates should not be expected to be identical.
19. Comparing Time-to-Event and Longitudinal Outcomes
The trial also illustrates why statistical method should follow endpoint structure rather than being selected merely because one method is common in clinical trials.
Time-to-event
The primary composite, first HHF, cardiovascular death, all-cause mortality, diabetes onset and renal composite endpoint are analyzed with time-to-event methods. Cox regression is the principal reported approach.
Repeated continuous outcome
KCCQ Clinical Summary Score is measured repeatedly and analyzed using MMRM, with treatment-by-visit interaction and baseline score-by-visit interaction.
Trajectory outcome
eGFR slope is analyzed with a random-coefficient model that allows patient-specific intercepts and slopes.
Recurrent events
First and recurrent HHF and first and recurrent all-cause hospitalization use joint frailty models to account for dependence with mortality.
20. Confidence Intervals and P-values
The trial's results provide an opportunity to distinguish three related but different statistical quantities.
| Quantity | Example from EMPEROR-Preserved | What it communicates |
|---|---|---|
| Point estimate | Primary HR = 0.79 | The estimated treatment effect under the specified model. |
| Confidence interval | 95.03% CI 0.69–0.90 | Statistical uncertainty around the primary HR estimate under the stated confidence framework. |
| P-value | P = 0.0003 | Evidence against the reported null hypothesis under the specified testing framework. |
A P-value should not be read as the probability that the null hypothesis is true. Likewise, a confidence interval should not be interpreted as a range containing a particular patient's treatment effect. The point estimate, interval and P-value answer different statistical questions.
21. What the Primary HR Does — and Does Not — Mean
The primary HR of 0.79 indicates a lower estimated hazard for the empagliflozin group relative to placebo under the fitted Cox model. In simple relative terms, it corresponds to a 21% lower estimated hazard.
The HR does not tell us the absolute probability that a patient will experience the composite endpoint, nor does it give the percentage of patients who personally benefited. Absolute risk measures require event probabilities at a specified time point or another absolute-effect framework.
The 95.03% CI of 0.69–0.90 provides the uncertainty interval reported by the registry for the primary analysis. A confidence interval is more informative than the point estimate alone because it shows the statistical precision of the estimated treatment contrast.
The P-value of 0.0003 is evidence information under the prespecified hypothesis-testing framework. It is not a measure of the clinical magnitude of the treatment effect and should not be compared across endpoints as though smaller P-values represented larger effects.
22. Limitations
- Registry-level scope: this analysis is restricted to the ClinicalTrials.gov record. The registry fields provided here do not include a full baseline-characteristics table, Kaplan-Meier estimates, subgroup results, or detailed treatment-exposure information.
- Primary endpoint is composite: the primary outcome combines adjudicated cardiovascular death and adjudicated hospitalization for heart failure. A composite can be influenced differently by its component events.
- Cox model assumptions: hazard-ratio interpretation is model-based and relies on the proportional-hazards framework. The ClinicalTrials.gov record does not provide a formal assessment of proportional hazards.
- Different analysis populations: the primary analysis uses the Randomised Set, whereas the eGFR and KCCQ analyses use treated patients with available data. Results from these analyses therefore should not be treated as though they arise from identical populations.
- Multiple endpoints: the trial reports numerous secondary outcomes. The ClinicalTrials.gov record does not provide a complete multiplicity hierarchy for all of them.
- Recurrent events require specialized methods: the use of joint frailty models reflects dependence between recurrent hospitalization and mortality. The resulting HRs should not be interpreted like simple first-event HRs.
- Interim analysis: the primary analysis uses alpha = 0.0497 as a result of interim analysis. This sequential framework matters when interpreting the P-value.
- Safety detail: serious adverse events are reported as affected/at-risk counts, but the ClinicalTrials.gov record does not provide a detailed safety-event taxonomy or formal comparative safety analysis.
23. Why This Trial Matters Statistically
EMPEROR-Preserved is a useful teaching case because its registry analyses span several important branches of modern clinical-trial statistics. The primary endpoint uses randomized time-to-event analysis, while secondary endpoints demonstrate why recurrent events and repeated continuous measurements require different modeling strategies.
| Statistical concept | How it appears in EMPEROR-Preserved |
|---|---|
| Randomization | Randomized, parallel phase 3 design with two treatment arms. |
| Blinding | Double masking. |
| Time-to-event analysis | Primary composite and multiple secondary endpoints. |
| Cox regression | Primary endpoint and several secondary time-to-event outcomes. |
| Hazard ratio | Primary and multiple secondary time-to-event effect measures. |
| Covariate adjustment | Age, baseline eGFR, baseline LVEF, region, baseline diabetes status, sex and treatment are used in several models. |
| Interim analysis | Primary analysis alpha reported as 0.0497 resulting from interim analysis. |
| Recurrent events | Joint frailty models for recurrent HHF and recurrent all-cause hospitalization. |
| MMRM | KCCQ Clinical Summary Score measured longitudinally through week 52. |
| Random coefficients | eGFR trajectory modeled with random intercept and random slope per patient. |
| Different analysis populations | Randomised Set for major efficacy time-to-event analyses versus treated-set analyses for eGFR and KCCQ. |
24. Overall Statistical Interpretation
The primary result is a Cox-model hazard ratio of 0.79 for time to first adjudicated cardiovascular death or adjudicated hospitalization for heart failure, with a 95.03% two-sided confidence interval of 0.69–0.90 and P = 0.0003. The registry identifies the hypothesis as superiority and reports an alpha of 0.0497 resulting from interim analysis.
The secondary analyses show why a clinical trial cannot be reduced to one number. First HHF is analyzed with Cox regression, recurrent HHF with a joint frailty model, eGFR trajectory with a random-coefficient model, and KCCQ with MMRM. The renal composite, cardiovascular death, all-cause mortality, diabetes onset and recurrent all-cause hospitalization each have their own statistical estimand and uncertainty.
From a statistical perspective, the central lesson is that endpoint definition, follow-up structure, analysis population and model must be interpreted together. A hazard ratio for a first event is not interchangeable with a recurrent-event hazard ratio; a treatment-by-time interaction is not a hazard ratio; and a longitudinal adjusted mean difference should not be interpreted as an event-risk measure.
25. Related Tutorials
Learn more about the methods used in this trial:
26. Related Calculators
27. Sources
- ClinicalTrials.gov: EMPEROR-Preserved, NCT03057951.
- PubMed: PMID 41493412.
- PubMed: PMID 39895437.
- PubMed: PMID 38770818.
- PubMed: PMID 38558314.
- PubMed: PMID 38439582.
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28. Record Summary
EMPEROR-Preserved provides a compact example of how statistical analysis must adapt to the structure of clinical-trial endpoints. The primary randomized comparison uses a Cox proportional-hazards model for a time-to-first composite endpoint, with an interim-analysis-adjusted alpha. Secondary analyses extend the statistical framework to recurrent hospitalization through joint frailty models, renal-function trajectories through a random-coefficient model, and repeated patient-reported outcomes through MMRM.
The reported primary HR of 0.79, its 95.03% CI of 0.69–0.90, and P = 0.0003 summarize one prespecified treatment comparison, but understanding the trial statistically requires more than the headline result. The analysis populations, endpoint definitions, censoring structure, covariate adjustment, recurrent-event dependence, repeated measurements, and interim-testing framework all affect how the reported estimates should be interpreted.