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
VALUE was a randomized, parallel, quadruple-masked phase 3 trial in essential hypertension. The registry lists 15,313 enrolled participants and two treatment arms comparing valsartan with amlodipine, with a primary purpose of prevention.
| Feature | VALUE |
|---|---|
| Trial acronym | VALUE |
| ClinicalTrials.gov identifier | NCT06395194 |
| Phase | Phase 3 |
| Status | Completed |
| Condition | Essential Hypertension |
| Allocation | Randomized |
| Design model | Parallel |
| Masking | Quadruple |
| Primary purpose | Prevention |
| Enrollment | 15,313 |
| Number of arms | 2 |
| Lead sponsor | Oslo University Hospital |
| Sponsor type | Other |
| Start date | September 27, 1997 |
| Primary completion date | September 5, 2003 |
2. Clinical Question
The registered study title is Effects of Valsartan vs Amlodipine and Low BP on Kidney Outcomes in Essential Hypertension. The registered primary endpoint, however, is a composite cardiac event evaluated over up to 6 years. The primary statistical question represented by that endpoint is therefore a comparison of the occurrence of major composite cardiac events between the randomized valsartan and amlodipine treatment groups.
Population
Participants with essential hypertension enrolled in a phase 3 prevention trial.
Intervention
Cardiac outcomes on treatment with valsartan.
Comparator
Cardiac outcomes on treatment with amlodipine.
Primary question
How does treatment assignment to valsartan compare with treatment assignment to amlodipine with respect to the registered composite cardiac event?
3. Trial Design
The registry describes VALUE as randomized, parallel, and quadruple-masked, with a prevention objective. A parallel design means that participants are assigned to one treatment arm and remain part of that randomized comparison rather than being assigned sequentially to both treatments.
Random allocation is the defining design feature used to support a comparison between the two treatment groups.
The registry identifies two parallel treatment arms rather than a crossover design.
The registry records quadruple masking for the trial.
The registry classifies the primary purpose of the trial as prevention.
Valsartan
- Cardiac outcomes on treatment with valsartan
- Randomized treatment arm
Amlodipine
- Cardiac outcomes on treatment with amlodipine
- Randomized treatment arm
4. Endpoints
The registry identifies one registered primary endpoint. It is a composite cardiac endpoint assessed for up to 6 years.
| Endpoint | Time frame | Registry definition |
|---|---|---|
| Composite cardiac event | Up to 6 years | Composite cardiac events, which included a combination of fatal or non-fatal myocardial infarction, sudden cardiac death, death from revascularization procedures or heart failure, hospitalization for heart failure, and emergency procedures to prevent myocardial infarction. |
Why a composite endpoint changes the statistical question
A composite endpoint combines multiple clinically distinct events into one outcome. The analysis therefore asks whether the time to the first qualifying component of the composite differs between randomized treatment groups, assuming a conventional time-to-first-event analysis is used.
That distinction matters because the components do not necessarily have identical clinical importance, frequency, or treatment responsiveness. A statistically significant composite result would describe the composite endpoint as defined; it would not automatically establish the same effect for every individual component.
5. Statistical Methodology
The ClinicalTrials.gov record identifies the primary endpoint and its follow-up window but does not report a posted statistical analysis for the endpoint. For a composite endpoint observed over time, the conventional analytical framework would be a time-to-event analysis, because participants can have different lengths of follow-up and some may remain event-free when observation ends.
Time-to-first-event analysis
A natural analysis for this type of endpoint is to measure the time from the relevant trial starting point to the first qualifying component of the composite. Participants who have not experienced a qualifying event by the end of their observation period are treated as censored observations under the assumptions of the selected survival-analysis method.
Here, T represents the time until the first qualifying composite cardiac event. The survival function describes the probability of remaining free of the event beyond time t.
Kaplan-Meier estimation
Kaplan-Meier estimation is commonly used to display the event-free experience of randomized groups over time. It accommodates right-censored observations and produces an estimated survival curve for each treatment group.
Log-rank comparison
A log-rank test is commonly used to compare time-to-event distributions between randomized groups. In a simple two-group setting, it evaluates whether the observed pattern of events over follow-up differs systematically between the groups under the null hypothesis of no difference in the event-time distributions.
Cox proportional-hazards model
A Cox proportional-hazards model is another standard approach for a time-to-event endpoint. It estimates a hazard ratio comparing the instantaneous event rates between treatment groups, subject to the proportional-hazards assumption when that model is used.
The hazard ratio is a relative time-to-event measure. It is not the same as an absolute risk difference, a risk ratio at a particular time point, or the percentage of participants who experience an event.
Why randomization is central
Randomization establishes the treatment comparison before outcomes are observed. If the randomized groups are analyzed according to their assigned treatments, the comparison is protected against many forms of confounding that can arise when treatment selection is determined by patient or clinician characteristics.
6. Planned Analysis
The registry states that the primary endpoint is a composite cardiac event assessed for up to 6 years. The composite includes fatal or non-fatal myocardial infarction, sudden cardiac death, death from revascularization procedures or heart failure, hospitalization for heart failure, and emergency procedures to prevent myocardial infarction.
For an endpoint of this type, a typical primary analysis would use a time-to-first-event framework. Kaplan-Meier methods can estimate the probability of remaining free of the composite event over time, while a log-rank test can compare the event-time distributions between randomized groups. A Cox proportional-hazards model can provide a hazard ratio and confidence interval describing the relative event rate between treatment groups.
| Statistical element | Typical role for this endpoint |
|---|---|
| Analysis unit | Time from trial entry to the first qualifying component of the composite event |
| Primary event | First occurrence of a qualifying composite cardiac event |
| Censoring | Participants without a qualifying event at the end of observed follow-up would ordinarily contribute information until their censoring time |
| Descriptive estimate | Kaplan-Meier event-free estimates over time |
| Group comparison | Typically a log-rank comparison for time-to-event distributions |
| Relative effect | Typically a Cox-model hazard ratio with a confidence interval |
| Follow-up window | Up to 6 years |
7. Statistical Methods Explained
Why would a time-to-event method be appropriate?
The primary endpoint is defined over a follow-up period of up to 6 years rather than as a simple yes/no measurement at one fixed time. Participants can experience the composite event at different times, while others may remain event-free when observation ends. Time-to-event methods use the timing of events as well as the censoring information.
What does the composite cardiac event actually measure?
It measures the occurrence of any one of the qualifying components in the registry definition. The components include myocardial infarction, sudden cardiac death, specified deaths from revascularization procedures or heart failure, hospitalization for heart failure, and emergency procedures to prevent myocardial infarction. The endpoint is therefore broader than any single cardiovascular event.
Why use the first event in a composite analysis?
A conventional time-to-first-event framework assigns each participant an event time based on the first qualifying component encountered during follow-up. This creates a single analyzable time-to-event outcome, but it also means that later events are not represented in the primary event time once a participant has experienced the first qualifying component.
What would a hazard ratio mean?
If a Cox model were used, a hazard ratio below 1 would indicate a lower estimated instantaneous rate of the composite event in the valsartan group relative to the amlodipine group, under the model. It would not mean that the same percentage of participants avoided the event or that the relative difference is constant in every individual.
Why does censoring matter?
Censoring allows participants who have not experienced the endpoint during their observed follow-up to contribute partial information. Valid interpretation depends on the censoring process and on the assumptions underlying the selected survival-analysis method.
Why should the composite components also be examined?
A composite can increase the number of observed events, which can improve statistical efficiency, but it combines events that may differ in severity and frequency. Interpretation is stronger when the overall composite is understood alongside the individual components, provided those component analyses are prespecified and appropriately interpreted.
8. Interpreting a Future Hazard Ratio
No hazard ratio is posted for the registered primary endpoint in the ClinicalTrials.gov record. If a future analysis reports one, its interpretation should remain tied to the composite endpoint rather than being treated as a direct measure of every individual component.
A hazard ratio is a relative time-to-event measure. For example, a hazard ratio of 0.70 would conventionally be interpreted as a 30% lower estimated instantaneous event rate in the treatment group relative to the comparator under the fitted proportional-hazards model. It would not mean that 30% fewer participants necessarily experienced the endpoint by the end of follow-up.
A 95% confidence interval would describe the statistical uncertainty surrounding the estimated hazard ratio under the specified model and sampling framework. A narrow interval indicates greater precision than a wide interval; it does not describe the range of effects experienced by individual participants.
A p-value addresses evidence against a specified null hypothesis under the statistical model. It does not measure the magnitude of the treatment effect, the clinical importance of the result, or the probability that one treatment is superior.
If a Cox proportional-hazards model is used, the hazard ratio is most naturally interpreted under an assumption that the relative hazard is reasonably stable over time. If hazards change substantially over follow-up, a single hazard ratio may not fully describe the treatment difference and complementary measures such as time-specific event-free probabilities can be important.
9. Composite Endpoint Interpretation
The VALUE primary endpoint is statistically interesting because it combines several types of cardiac events. A composite can provide more events than any individual component and can therefore make a time-to-event comparison more informative. At the same time, the meaning of the composite depends on the frequency and clinical relevance of its components.
Statistical advantage
Combining qualifying events can increase the number of endpoint events available for a treatment comparison and can reduce the time required to accumulate a sufficient number of events.
Interpretive caution
A composite result is not automatically equivalent to the treatment effect on myocardial infarction, heart failure hospitalization, sudden cardiac death, or any other individual component.
Timing matters
Time-to-event methods retain information about when the first qualifying event occurs rather than treating all events as if they happened simultaneously.
Component balance matters
If one component occurs much more frequently than the others, the overall composite comparison can be influenced heavily by that component.
10. Limitations
- No posted endpoint analysis: the ClinicalTrials.gov record does not post a formal statistical analysis for the registered primary endpoint.
- No numerical efficacy estimate: the registry does not provide a primary-endpoint hazard ratio, confidence interval, p-value, event count, or time-specific event-free estimate.
- Composite endpoint: the primary outcome combines several distinct cardiac events, so a composite result should not automatically be interpreted as an identical effect on each component.
- Time-to-event assumptions: if a Cox proportional-hazards model is used, the proportional-hazards assumption should be evaluated rather than treated as automatic.
- Censoring: interpretation of survival-analysis estimates depends on the pattern and assumptions surrounding censoring.
- Unreported analysis details: the ClinicalTrials.gov record does not report a statistical analysis plan, stratification factors, multiplicity strategy, interim-analysis framework, missing-data procedure, or analysis population definitions.
- Endpoint scope: the registered title references kidney outcomes and low blood pressure, while the registered primary endpoint shown here is a composite cardiac event. Those concepts should not be conflated when interpreting the primary statistical endpoint.
11. Why This Trial Matters Statistically
VALUE is a useful teaching example because it illustrates how the statistical question in a randomized prevention trial can be substantially more specific than the broad clinical title. The registry title refers to valsartan, amlodipine, low blood pressure, and kidney outcomes, while the registered primary endpoint is a time-bounded composite cardiac event.
| Concept | How it appears in VALUE |
|---|---|
| Randomization | Participants were randomized to one of two treatment arms. |
| Parallel design | The registry identifies a parallel design model. |
| Masking | The registry records quadruple masking. |
| Prevention | The registry identifies prevention as the primary purpose. |
| Composite endpoint | The primary endpoint combines several cardiac event types. |
| Time-to-event analysis | The endpoint is defined over up to 6 years, making event timing relevant to analysis. |
| Kaplan-Meier estimation | A standard method for estimating event-free survival over time for an endpoint of this type. |
| Hazard ratio | A standard relative measure for comparing event rates over time when a Cox model is used. |
| Censoring | Participants who remain event-free through their observed follow-up can contribute censored information in a survival analysis. |
| Component interpretation | The composite must be distinguished from its individual myocardial infarction, sudden-death, heart-failure, revascularization, and emergency-procedure components. |
12. Trial Timeline
Trial start
The registry records September 27, 1997 as the study start date.
Primary endpoint window
The registered composite cardiac event is assessed over a time frame of up to 6 years.
Primary completion
The registry records September 5, 2003 as the primary completion date.
Study status
The ClinicalTrials.gov record identifies VALUE as completed.
13. Statistical Questions a Complete Results Analysis Would Address
A complete numerical analysis of the primary endpoint would normally allow readers to examine several distinct questions rather than relying on a single p-value.
| Question | Statistical quantity | Interpretive purpose |
|---|---|---|
| How often did the composite event occur? | Number and proportion of participants with a qualifying event | Describes the absolute event burden |
| When did events occur? | Kaplan-Meier estimates and event-time summaries | Describes the timing of events and event-free follow-up |
| How do treatment groups compare? | Hazard ratio or another prespecified effect measure | Quantifies the relative treatment difference |
| How precise is the estimate? | Confidence interval | Describes statistical uncertainty |
| Is there evidence against the null hypothesis? | Formal hypothesis test and p-value | Addresses statistical evidence under the specified testing framework |
| Which component drives the composite? | Component-specific event analyses | Clarifies the clinical composition of the overall endpoint |
14. Related Clinical Trial Results
15. Sources
- ClinicalTrials.gov: VALUE — NCT06395194.
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16. Record Summary
VALUE is a randomized, parallel, quadruple-masked phase 3 prevention trial enrolling 15,313 participants with essential hypertension. The registry identifies two treatment arms, valsartan and amlodipine, and defines a primary composite cardiac event measured over up to 6 years. The composite includes fatal or non-fatal myocardial infarction, sudden cardiac death, death from revascularization procedures or heart failure, hospitalization for heart failure, and emergency procedures to prevent myocardial infarction.
From a statistical perspective, the endpoint naturally leads to a time-to-event framework in which the timing of the first qualifying composite event and the censoring of participants who remain event-free are incorporated into the analysis. Kaplan-Meier estimation, log-rank comparison, and Cox proportional-hazards modeling are standard methods for this type of endpoint, although the registry record does not post a formal statistical analysis for VALUE's primary endpoint.
The key lesson is that the interpretation of a composite cardiovascular endpoint depends on more than whether a treatment comparison reaches statistical significance. The effect estimate, confidence interval, absolute event experience, timing of events, censoring, model assumptions, and contribution of individual components all matter when evaluating the statistical evidence.