This page provides an independent statistical analysis and educational interpretation of publicly reported results. ClinicalTrials.gov provides the official trial registry record.
1. Trial at a Glance
iFR-SWEDEHEART was a randomized, parallel-design clinical trial evaluating iFR versus FFR in patients with stable angina or acute coronary syndrome. The registry lists a 12-month primary endpoint combining all-cause death, non-fatal myocardial infarction, and unplanned revascularization.
| Feature | iFR-SWEDEHEART |
|---|---|
| Trial acronym | iFR-SWEDEHEART |
| ClinicalTrials.gov identifier | NCT02166736 |
| Status | Completed |
| Phase | NA |
| Conditions | Angina Pectoris; Acute Myocardial Infarction |
| Enrollment | 2037 |
| Allocation | Randomized |
| Design model | Parallel |
| Masking | Single |
| Primary purpose | Treatment |
| Number of arms | 2 |
| Lead sponsor | Uppsala University |
| Sponsor type | OTHER |
2. Clinical Question
The clinical question is whether an invasive physiologic assessment strategy using iFR differs from an FFR strategy with respect to the registry's 12-month primary endpoint in patients with stable angina or acute coronary syndrome.
Population
Patients represented by the registry conditions of angina pectoris and acute myocardial infarction.
Intervention
iFR, listed in the registry as a device intervention.
Comparator
FFR, listed in the registry as a device intervention.
Primary question
How does assignment to iFR versus FFR relate to all-cause death, non-fatal myocardial infarction, and unplanned revascularization over 12 months?
3. Trial Design
iFR
- Intervention type: device
- Assigned within a randomized parallel design
FFR
- Intervention type: device
- Assigned within a randomized parallel design
The parallel design means that participants are assigned to one of the two intervention groups rather than moving through both interventions as part of the primary randomized comparison. Randomization is important statistically because it establishes the treatment assignment before outcome assessment and, in expectation, balances measured and unmeasured prognostic factors across groups.
4. Endpoints
| Endpoint | Registry definition | Time frame |
|---|---|---|
| Primary endpoint | All cause death, non-fatal Myocardial infarction (MI), Unplanned revascularization | 12 months |
The registry lists one primary endpoint composed of three clinical events: all-cause death, non-fatal myocardial infarction, and unplanned revascularization. The stated time frame is 12 months.
Why a composite endpoint matters
A composite endpoint combines multiple clinically distinct outcomes into one statistical endpoint. This can increase the number of observed events and therefore provide more statistical information than relying on a relatively uncommon individual component alone. The trade-off is interpretational: the components may differ substantially in clinical importance, frequency, and susceptibility to treatment effects.
All-cause death
Death from any cause is an objective clinical outcome and does not require adjudication according to a particular cause-of-death classification for the endpoint to occur.
Non-fatal MI
Myocardial infarction is an event component distinct from death. A participant who experiences a non-fatal MI remains eligible to experience later components during follow-up.
Unplanned revascularization
Unplanned revascularization is a procedure-based clinical event and differs conceptually from death or myocardial infarction.
Composite interpretation
The overall endpoint should be interpreted as a combined outcome rather than assuming that all three components have identical clinical meaning or frequency.
5. Planned Analysis
The registry identifies the primary endpoint as the occurrence of all-cause death, non-fatal myocardial infarction, or unplanned revascularization over 12 months. No formal statistical analyses are posted on ClinicalTrials.gov for this endpoint.
For a randomized two-arm trial with a fixed 12-month composite clinical endpoint, a conventional analysis would begin by defining whether the primary estimand is the proportion of participants experiencing at least one component by 12 months or a time-to-first-event measure during the 12-month follow-up. The distinction is important because these approaches use different statistical information.
| Analytic approach | What it answers | Typical effect measure |
|---|---|---|
| Fixed-time binary analysis | What proportion experienced at least one component by 12 months? | Risk difference, risk ratio, or odds ratio |
| Time-to-first-event analysis | How quickly did participants experience the first component during follow-up? | Hazard ratio or survival probability |
| Component analysis | Which individual components account for the composite? | Component-specific risks or time-to-event measures |
The appropriate primary method depends on the prespecified statistical analysis plan and endpoint construction. The registry information summarized here identifies the endpoint and its 12-month time frame but does not provide a posted statistical-analysis specification.
6. Statistical Methodology
Randomized comparison
Randomization is the central statistical feature of this study design. By assigning participants to iFR or FFR through a randomized process, the trial is structured to compare outcomes between treatment strategies while reducing systematic differences in baseline prognostic factors that could otherwise confound the comparison.
The exact statistical scale depends on the prespecified endpoint analysis. For a binary 12-month endpoint, this could be an absolute risk difference or a relative measure. For a time-to-event endpoint, it could be expressed through a hazard ratio or survival probability.
Composite endpoint analysis
The primary endpoint contains three event types. In a typical time-to-first-event analysis, each participant contributes the time until the first qualifying component, after which that participant is counted as having experienced the composite event. This prevents one participant from contributing multiple primary events to the same first-event analysis.
In a fixed-time binary analysis, the participant is classified according to whether at least one qualifying event occurred by the 12-month assessment. The resulting treatment comparison is then a comparison of event proportions rather than a comparison of event timing.
Time-to-event methods
If the prespecified analysis treats the composite as a time-to-first-event outcome, Kaplan-Meier estimation and a log-rank comparison are common methods for comparing event-free survival between randomized groups. A Cox proportional-hazards model can then provide a hazard ratio and confidence interval.
Here, di is the number of events at event time ti, while ni is the number of participants at risk immediately before that time.
Risk-based methods
If the endpoint is analyzed as a binary outcome at 12 months, the basic comparison is the event probability in the iFR group versus the event probability in the FFR group. An absolute risk difference directly describes the difference in observed event probability, while a risk ratio expresses the relative event probability.
An absolute risk difference is particularly useful for clinical interpretation because it preserves the scale of the event probability rather than expressing the treatment contrast only relatively.
Confidence intervals
Regardless of whether the primary effect is expressed as a risk difference, risk ratio, odds ratio, or hazard ratio, a confidence interval communicates statistical uncertainty around the estimated treatment effect. A point estimate alone does not show how precisely the trial estimates the underlying treatment contrast.
7. Statistical Methods Explained
What does a composite endpoint mean?
A composite endpoint treats several clinically defined events as one outcome for the primary comparison. Here, the components are all-cause death, non-fatal MI, and unplanned revascularization. The first qualifying event can determine whether a participant has experienced the composite in a time-to-first-event framework.
Why might a composite endpoint be useful?
Combining multiple event types can increase the total number of observed endpoint events, which can improve statistical efficiency when the components are sufficiently related and clinically coherent. However, a high-frequency component can contribute much more information than a rare component, so the overall composite may be driven disproportionately by one event type.
Why should the individual components also be examined?
The composite alone cannot show whether its treatment contrast comes from death, myocardial infarction, or revascularization. Component-specific analyses therefore provide important clinical context. They should be interpreted with awareness that the primary study may not have been powered for each component individually.
What is the difference between risk and hazard?
Risk is the probability that an event has occurred by a specified time. Hazard describes the instantaneous event rate among participants who remain event-free at a particular time. A hazard ratio therefore should not automatically be interpreted as a ratio of 12-month risks.
Why does censoring matter?
In a time-to-event analysis, participants may reach the end of observed follow-up without experiencing the endpoint. Their information is retained up to the time they are censored. Appropriate censoring assumptions are therefore part of the validity of Kaplan-Meier and Cox analyses.
Why does randomization matter for statistical interpretation?
Randomization establishes the treatment groups before outcomes occur. This supports a causal comparison of the assigned strategies under the trial's adherence, follow-up, and analysis framework. It does not guarantee that every baseline characteristic will be numerically identical between groups.
Why is the masking designation important?
Masking can reduce the potential for knowledge of treatment assignment to influence behavior, assessment, or reporting. The registry records iFR-SWEDEHEART as single-masked, but the summarized registry information does not identify which study participants or personnel were masked.
8. Missing Data and Follow-Up
Missing outcome information can affect either a fixed-time or time-to-event analysis, although the statistical consequences differ. In a binary 12-month analysis, missing endpoint status at the target time can directly affect classification of the participant. In a time-to-event analysis, loss to follow-up can lead to censoring at the last known event-free time.
Binary endpoint
The analysis must establish how participants without an observed 12-month outcome are handled and whether assumptions about missing outcomes are required.
Time-to-event endpoint
The analysis must establish when participants are censored and whether censoring can reasonably be treated as non-informative given the analysis framework.
The registry information summarized here does not report a missing-data or imputation strategy. Consequently, no specific imputation method can be attributed to the study from the registry record alone.
9. Multiplicity and the Composite Endpoint
Multiplicity becomes relevant when investigators move beyond the single primary comparison and examine multiple endpoints, multiple treatment contrasts, or numerous secondary hypotheses. A composite primary endpoint does not itself mean that its three components are three independent primary hypotheses.
| Question | Statistical issue |
|---|---|
| Primary composite | One combined endpoint contains three distinct clinical events. |
| Individual components | Separate component comparisons can create additional hypothesis-testing questions. |
| Secondary analyses | Additional endpoints or subgroup analyses require appropriate interpretation of multiplicity and precision. |
| Exploratory analyses | Nominal statistical significance does not automatically establish confirmatory evidence when many analyses are examined. |
The registry information available here does not report an alpha-allocation or multiplicity-adjustment strategy. Therefore, a specific multiplicity procedure should not be assumed.
10. Interim Analysis
An interim analysis examines accumulating trial data before the planned completion of follow-up. Such analyses can be useful for decisions about efficacy, futility, safety, or trial continuation, but repeated unadjusted significance testing can increase the overall type I error.
When interim efficacy analyses are prespecified, methods such as group-sequential boundaries or alpha-spending procedures can preserve a desired overall error rate while allowing the accumulating evidence to be examined at predefined information times.
11. Stratification
Stratification can be incorporated into randomization and statistical analysis when important prognostic factors are identified in advance. It can improve balance across treatment groups and, when used appropriately in the analysis, can account for the structure introduced by stratified randomization.
No stratification factors are reported in the registry information summarized here. Accordingly, no particular stratified analysis should be assumed.
12. Non-Inferiority, Superiority, and Equivalence
The registry information identifies a randomized comparison between iFR and FFR but does not report a non-inferiority margin, equivalence margin, superiority hypothesis, or formal hypothesis-testing framework.
This distinction matters because the statistical interpretation of a randomized comparison depends strongly on the prespecified estimand and hypothesis. In a non-inferiority trial, for example, the treatment effect is evaluated against a clinically justified margin rather than simply asking whether a conventional two-sided null hypothesis was rejected.
| Design question | What would be required for formal interpretation? |
|---|---|
| Superiority | A prespecified superiority null hypothesis and type I error framework. |
| Non-inferiority | A prespecified non-inferiority margin and an analysis appropriate to that margin. |
| Equivalence | Prespecified lower and upper equivalence margins. |
None of these specific margins or testing frameworks is reported in the registry information summarized here.
13. Safety
The registry information identifies the primary endpoint but does not provide serious adverse-event counts by arm or a posted safety-results analysis. A safety assessment is nevertheless an important part of any randomized treatment comparison because efficacy and safety represent different dimensions of the treatment effect.
For a two-arm randomized trial, serious adverse events would ordinarily be summarized by treatment assignment, with counts and percentages calculated according to a prespecified safety population and adverse-event definitions. Depending on the study and the event frequency, time-to-event methods may also be appropriate for selected safety outcomes.
14. Trial Timeline
Trial start
The registry lists May 2014 as the study start date.
Primary completion
The registry lists October 2015 as the primary completion date.
Current registry status
The study is recorded as completed.
15. What the Primary Endpoint Can and Cannot Tell Us
A properly conducted randomized comparison of the 12-month composite endpoint can estimate whether assignment to iFR is associated with a different rate or timing of the combined outcome than assignment to FFR, according to the prespecified statistical estimand.
A composite endpoint does not reveal how much of the overall treatment contrast is attributable to death, myocardial infarction, or unplanned revascularization. Those components require separate examination.
Relative measures such as risk ratios or hazard ratios can be useful for comparing treatment groups, but absolute event probabilities and risk differences are often more directly interpretable when considering the practical magnitude of a treatment effect.
Any estimated treatment effect should be accompanied by an appropriate confidence interval. The interval describes statistical uncertainty around the estimated population effect under the specified model; it is not a range containing the individual treatment effects experienced by patients.
16. Interpretation of a Hypothetical Hazard Ratio
If the primary endpoint were analyzed as time to first composite event and produced a hazard ratio below 1 for iFR versus FFR, the correct interpretation would be that the estimated instantaneous rate of experiencing the composite was lower under the iFR assignment during the modeled follow-up.
That interpretation would not mean that the same percentage of participants avoided the composite, nor would it mean that the 12-month absolute risk was reduced by the same percentage. Hazard ratios and cumulative risks are different statistical quantities.
The magnitude and clinical importance of such an estimate would still depend on the confidence interval, absolute event probabilities, component events, follow-up, and validity of the underlying model.
17. Limitations
- No posted statistical analyses: ClinicalTrials.gov does not provide formal statistical-analysis results for the primary endpoint in the registry information summarized here.
- Composite endpoint: the primary endpoint combines all-cause death, non-fatal MI, and unplanned revascularization, which are clinically distinct events.
- Component interpretation: a composite result can be influenced disproportionately by whichever component occurs most frequently.
- Endpoint construction: the registry identifies the endpoint and its 12-month time frame but does not specify in the summarized information whether the formal analysis uses a fixed-time binary estimand or a time-to-first-event estimand.
- Analysis specification: the registry information summarized here does not report a detailed statistical analysis plan, including a specific effect measure, confidence-interval construction, or multiplicity procedure.
- Missing data: the registry information summarized here does not report an imputation strategy or detailed missing-outcome handling.
- Stratification: no randomization or analysis stratification factors are reported in the summarized registry information.
- Interim analysis: no interim-analysis boundary or alpha-spending method is reported in the summarized registry information.
- Masking: the study is recorded as single-masked, but the summarized registry information does not specify who was masked.
- Safety: serious adverse-event results by arm are not included in the summarized registry information.
18. Why This Trial Matters Statistically
iFR-SWEDEHEART provides a useful teaching example for understanding how randomized clinical-trial design intersects with composite clinical outcomes. The study combines randomized allocation, a parallel two-arm structure, device-based interventions, and a clinically meaningful 12-month composite endpoint.
| Concept | How it appears in iFR-SWEDEHEART |
|---|---|
| Randomization | The registry identifies the allocation as randomized. |
| Parallel design | The registry identifies the design model as parallel. |
| Two-arm comparison | The study has two intervention arms: iFR and FFR. |
| Composite endpoint | Death, non-fatal MI, and unplanned revascularization form the primary endpoint. |
| Fixed follow-up window | The primary endpoint has a 12-month time frame. |
| Time-to-event analysis | If the endpoint is analyzed by event timing, Kaplan-Meier and Cox methods are common approaches. |
| Risk-based analysis | If the endpoint is analyzed as a 12-month binary outcome, risks and risk differences provide direct absolute comparisons. |
| Component analysis | Separate examination of death, MI, and revascularization is important for interpreting the composite. |
| Confidence intervals | Uncertainty should accompany any estimated treatment effect. |
| Missing data | The treatment of incomplete 12-month outcome information must be prespecified. |
19. Related Statistical Concepts
The iFR-SWEDEHEART design illustrates several core principles of clinical-trial statistics. The most important distinction is between the clinical endpoint being defined and the statistical estimand used to compare it. A composite outcome can be analyzed as a fixed-time binary event or as a time-to-first-event outcome, and those choices lead to different effect measures and assumptions.
Another important distinction is between the overall composite and its individual components. A statistically clear composite effect does not automatically imply that every component has the same direction or magnitude of effect. Component frequency and clinical importance therefore remain central to interpretation.
20. Statistical Questions for Reading the Trial
What is the estimand?
Is the treatment contrast defined as a 12-month risk difference, a risk ratio, a time-to-first-event hazard ratio, or another prespecified quantity?
What drives the composite?
Are the observed events predominantly deaths, non-fatal MIs, or unplanned revascularizations?
How much uncertainty is present?
What confidence interval surrounds the estimated treatment effect, and how does its width relate to the enrollment of 2037 participants?
Are the assumptions appropriate?
If a time-to-event model is used, do its assumptions reasonably describe the observed follow-up and censoring process?
21. Sources
- ClinicalTrials.gov: NCT02166736 — iFR-SWEDEHEART.
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22. Record Summary
iFR-SWEDEHEART is a completed randomized, parallel-design trial with 2037 participants and two device intervention arms, iFR and FFR. The registry identifies a single primary endpoint at 12 months: all-cause death, non-fatal myocardial infarction, and unplanned revascularization.
From a statistical perspective, the central issue is the analysis of that composite endpoint. A complete interpretation requires clarity about whether the prespecified estimand concerns the probability of experiencing at least one component by 12 months or the timing of the first qualifying event. The individual components also matter because death, myocardial infarction, and unplanned revascularization do not carry identical clinical meaning.
The randomized design supports a treatment comparison anchored to assignment, while confidence intervals, absolute event risks, component-specific outcomes, missing-data handling, and the assumptions of any time-to-event model determine how precisely and appropriately the treatment effect can be interpreted. The registry currently identifies the endpoint and trial structure but does not post formal statistical analyses for the primary outcome.