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
IDEAL was a randomized, parallel-group phase 4 trial comparing atorvastatin with simvastatin in patients with known coronary heart disease. The registered primary endpoint was time to a major coronary event, defined as non-fatal acute myocardial infarction, coronary death, or resuscitated cardiac arrest.
| Feature | IDEAL |
|---|---|
| Official title | Atorvastatin Versus Simvastatin In The Prevention Of Coronary Heart Disease (CHD) In Patients With Known CHD |
| Acronym | IDEAL |
| Phase | Phase 4 |
| Status | Completed |
| Condition | Cardiovascular Diseases |
| Allocation | Randomized |
| Design model | Parallel |
| Masking | None |
| Primary purpose | Treatment |
| Enrollment | 8,600 |
| Interventions | Atorvastatin; simvastatin |
| Lead sponsor | Pfizer |
| Sponsor type | Industry |
| Start date | 1999-02 |
| ClinicalTrials.gov | NCT00159835 |
2. Clinical Question
The registered clinical question concerns prevention of coronary heart disease events in patients who already have known coronary heart disease. The comparison is between two statin interventions, atorvastatin and simvastatin, with the primary endpoint defined as time to a major coronary event.
Population
Patients with known coronary heart disease, within the registry condition category of cardiovascular diseases.
Intervention
Atorvastatin.
Comparator
Simvastatin.
Primary question
How does time to a major coronary event compare between atorvastatin and simvastatin in patients with known coronary heart disease?
3. Trial Design
Atorvastatin
- Drug intervention
- Randomized treatment assignment
- Evaluated in a parallel-group design
Simvastatin
- Drug intervention
- Randomized treatment assignment
- Evaluated in a parallel-group design
The absence of masking is an important design feature to recognize when interpreting a clinical trial. Randomization addresses allocation of treatment, whereas masking addresses knowledge of treatment assignment after randomization. These are distinct design properties.
4. Primary Endpoint
| Endpoint | Registry definition | Statistical structure |
|---|---|---|
| Primary endpoint | Time to of a major coronary event: 1) non-fatal acute myocardial infarction 2) coronary death 3) resuscitated cardiac arrest | Time-to-event composite endpoint |
The endpoint combines three clinically defined event types into a single outcome. The statistical object is not simply whether a patient ever experienced one of these events. It is the time to a major coronary event, making both event occurrence and follow-up time relevant to the analysis.
Understanding the composite endpoint
A composite endpoint allows several related clinical events to contribute to a single primary analysis. For this trial, the qualifying components are non-fatal acute myocardial infarction, coronary death, and resuscitated cardiac arrest.
What counts as an event?
An occurrence of any of the three registry-defined components qualifies as a major coronary event.
What is the time scale?
The endpoint is explicitly defined as time to the major coronary event, so the timing of the first qualifying event is central to the analysis.
Why combine events?
A composite can increase the number of observed primary events and create a single prespecified outcome for the randomized comparison.
Why component interpretation matters
A treatment effect on a composite does not automatically imply that every individual component changes by the same amount or in the same direction.
5. Planned Analysis
No statistical analyses or numerical efficacy results are posted in the ClinicalTrials.gov record. The primary endpoint is nevertheless clearly structured as a time-to-event outcome, allowing its statistical analysis to be described at the methodological level.
Time-to-event analysis
For an endpoint defined as time to the first occurrence of a major coronary event, a typical analysis would estimate the event-free distribution over time and compare the randomized treatment groups while accounting for participants who have not experienced the event by the end of their observed follow-up.
Here, T represents the time from the relevant trial origin to the first qualifying major coronary event. S(t) is the probability of remaining free of that event beyond time t.
A Kaplan-Meier estimator is commonly used to estimate this event-free survival function when follow-up times differ across participants. Participants who have not yet experienced a qualifying event when observation ends are treated as censored rather than as if they had experienced the event.
Comparing randomized groups
A standard comparison for two randomized time-to-event groups is the log-rank test. A Cox proportional-hazards model is also commonly used to estimate a hazard ratio comparing the instantaneous event rates between treatment groups.
A hazard ratio below 1 would indicate a lower estimated instantaneous rate of the composite event in the atorvastatin group under the fitted model; a hazard ratio above 1 would indicate a higher estimated instantaneous rate.
6. Statistical Methodology
Kaplan-Meier estimation
Kaplan-Meier analysis is designed for situations in which participants can be followed for different lengths of time and some participants have not experienced the event when observation ends. Instead of requiring every participant to have the same follow-up duration, the method uses the information available up to each participant's event or censoring time.
At each observed event time tᵢ, dᵢ is the number of events and nᵢ is the number at risk immediately before that time.
For IDEAL, the corresponding curve would represent the estimated probability of remaining free of a major coronary event over time. Because the registry does not report the underlying event and censoring data, a Kaplan-Meier curve cannot be derived from the registry record alone.
Log-rank comparison
The log-rank test provides a way to compare two survival distributions over the observed follow-up. At each event time, the method compares the number of events observed in each treatment group with the number expected under the hypothesis that the groups have the same underlying event-time distribution.
The test is fundamentally a comparison of time-to-event experience. It is therefore different from a simple comparison of the final proportion of participants who experienced an event.
Cox proportional-hazards model
A Cox model provides a relative measure of the event rate through a hazard ratio. The model does not require specification of the baseline hazard function, which makes it widely used for randomized time-to-event endpoints.
The treatment coefficient β is transformed as exp(β) to obtain the hazard ratio. The model compares instantaneous event rates rather than directly comparing cumulative incidence at one fixed time.
Randomization and causal comparison
Randomization is the central design mechanism for comparing the treatment groups. When successfully implemented, it balances measured and unmeasured prognostic factors in expectation and supports a causal interpretation of the randomized treatment comparison.
That benefit applies to the treatment assignment itself. It does not remove the need to define the endpoint precisely, account appropriately for censoring, or distinguish the composite endpoint from its individual components.
Composite-event analysis
For a composite endpoint, the primary time-to-event analysis ordinarily treats the first qualifying component as the event defining the endpoint. This means that the analysis answers a question about time to the first major coronary event as defined by the protocol or registry, rather than separately estimating a treatment effect for each component.
7. Statistical Methods Explained
What does "time to a major coronary event" mean statistically?
It means that the outcome contains both an event indicator and a time component. A participant who experiences a qualifying event contributes the time at which it occurred. A participant who remains event-free through the end of observed follow-up contributes information up to that point and is censored rather than classified as having experienced the event.
Why is this different from comparing event percentages?
A simple percentage comparison ignores when events occurred and can be distorted when participants have different amounts of follow-up. Time-to-event methods use the timing information and can incorporate censored observations.
Why would Kaplan-Meier estimation be useful?
Kaplan-Meier estimation describes the probability of remaining free of the primary event over time. It is especially useful when follow-up is not identical for every participant and when some participants leave observation without experiencing the event.
What would a hazard ratio tell us?
A hazard ratio summarizes the relative instantaneous event rate between the randomized groups under a Cox model. A value below 1 would correspond to a lower modeled event rate for atorvastatin relative to simvastatin; it would not mean that the same percentage of participants avoid an event.
Why does the composite endpoint need separate interpretation?
The composite combines non-fatal acute myocardial infarction, coronary death, and resuscitated cardiac arrest. Even if the composite shows a treatment difference, the magnitude and clinical importance of effects on the individual components can differ. The composite should therefore be interpreted as its prespecified combined endpoint rather than as a statement that every component behaves identically.
What does censoring mean?
Censoring occurs when a participant's exact event time is not observed during the available follow-up. For example, someone who remains free of the primary event through the last observation contributes information through that time. Censoring is not equivalent to having experienced the event.
What assumption is important for a Cox hazard ratio?
The conventional Cox proportional-hazards interpretation assumes that the relative hazard is reasonably stable over time. If the proportional-hazards assumption is substantially violated, a single hazard ratio can summarize the treatment contrast less completely than time-specific survival estimates or other approaches.
8. Understanding the Primary Estimand
The registered endpoint can be viewed as an estimand centered on time to the first major coronary event under randomized assignment to atorvastatin versus simvastatin.
| Element | IDEAL interpretation |
|---|---|
| Population | Patients with known coronary heart disease within the cardiovascular-disease trial population. |
| Treatment comparison | Atorvastatin versus simvastatin. |
| Outcome | Major coronary event consisting of non-fatal acute myocardial infarction, coronary death, or resuscitated cardiac arrest. |
| Time component | Time to the major coronary event. |
| Randomization | Randomized treatment allocation. |
| Design | Parallel-group, unmasked trial. |
This framing is important because the primary question is not simply whether the two interventions have different percentages of patients with events. It is a longitudinal question about how quickly the first qualifying major coronary event occurs.
9. Composite Endpoints: Statistical Interpretation
Composite endpoints are attractive in event-driven cardiovascular trials because several clinically relevant outcomes can be incorporated into one primary endpoint. The approach can increase the number of events available for analysis compared with relying on a single component alone.
Statistical advantage
Multiple qualifying events contribute to one prespecified time-to-event outcome, potentially providing more information for the primary comparison.
Interpretive challenge
The components may differ in frequency, clinical severity, and treatment responsiveness, so the composite should not automatically be treated as homogeneous.
First-event structure
A conventional time-to-first-event analysis focuses on the first qualifying component rather than counting every subsequent event experienced by the same participant.
Clinical meaning
The endpoint has to be interpreted using its exact registry definition rather than replacing the composite with a broader term such as "cardiovascular events."
10. Non-Masking and Statistical Interpretation
The ClinicalTrials.gov record identifies the study as having no masking. This does not invalidate randomization, but it is relevant when considering potential sources of bias outside the randomization mechanism.
For a primary endpoint containing events such as non-fatal acute myocardial infarction and coronary death, event ascertainment and classification are important parts of the endpoint definition. Knowledge of treatment assignment can potentially matter for some aspects of clinical assessment, even though randomization remains the fundamental method for creating comparable treatment groups.
The statistical analysis should therefore be considered together with the endpoint adjudication and follow-up procedures used by the trial. The registry information identifies the trial as unmasked but does not provide further details in the record summarized here about event-adjudication procedures.
11. Missing Data, Censoring, and Follow-Up
Time-to-event analysis has a different relationship with missing information than a simple cross-sectional endpoint. A participant who has not experienced the primary event by the last observed time does not necessarily represent missing outcome data. Instead, that participant may contribute censored follow-up information.
There is an important distinction between censoring and imputation. Standard Kaplan-Meier and Cox methods are specifically designed to use censored observations under assumptions about the censoring mechanism; they do not simply replace missing event times with guessed values.
12. Multiplicity and Secondary Analyses
The registry information identifies one registered primary endpoint: time to a major coronary event. No posted statistical analyses are listed for that endpoint, and no additional numerical statistical analyses are reported in the registry record summarized here.
When a trial evaluates multiple outcomes, subgroup comparisons, repeated analyses, or multiple component endpoints, the interpretation of nominal p-values can become more complicated because each additional statistical test can increase the opportunity for a chance finding. A prespecified hierarchy or multiplicity-control strategy can address that issue when such analyses are part of the formal trial design.
13. Limitations and Interpretation Issues
- No posted statistical results: The ClinicalTrials.gov record does not post numerical efficacy estimates, confidence intervals, p-values, event counts by treatment group, or formal statistical analyses for the registered primary endpoint.
- Limited endpoint detail: The registry defines the primary endpoint but does not provide the underlying event-time dataset, censoring information, or detailed statistical analysis plan in the record summarized here.
- Composite endpoint: The primary outcome combines non-fatal acute myocardial infarction, coronary death, and resuscitated cardiac arrest. These components can differ in frequency and clinical characteristics.
- Unmasked design: The registry identifies the study as unmasked, which is a relevant design feature when considering potential sources of bias in clinical assessment and outcome ascertainment.
- No numerical treatment effect: Without a posted hazard ratio, confidence interval, survival estimate, event count, or p-value, the registry record cannot establish the magnitude or statistical uncertainty of the treatment comparison.
- Proportional-hazards assumption: If a Cox model is used, interpretation of a single hazard ratio depends on the appropriateness of its modeling assumptions, including proportional hazards.
- Censoring assumptions: Time-to-event methods require assumptions about the relationship between censoring and the underlying event process. The registry record does not provide sufficient detail to evaluate those assumptions.
14. Why This Trial Matters Statistically
IDEAL is a useful teaching example because its primary outcome is a classic cardiovascular time-to-event composite embedded within a randomized parallel-group design. The statistical challenge is not simply to compare two percentages. It is to compare the timing of clinically defined events while appropriately handling participants whose follow-up ends before an event occurs.
| Concept | How it appears in IDEAL |
|---|---|
| Randomization | The trial uses randomized allocation to atorvastatin or simvastatin. |
| Parallel-group design | The two interventions are evaluated in a parallel trial structure. |
| Time-to-event analysis | The registered primary endpoint is explicitly defined as time to a major coronary event. |
| Composite endpoint | The primary endpoint combines non-fatal acute myocardial infarction, coronary death, and resuscitated cardiac arrest. |
| Kaplan-Meier estimation | A standard method for describing event-free probability over time. |
| Log-rank testing | A standard method for comparing survival distributions between two randomized groups. |
| Cox regression | A standard model for estimating a relative hazard between treatment groups. |
| Censoring | Participants without an observed qualifying event contribute information through their available follow-up. |
| Unmasked design | The registry identifies masking as none, which is relevant to interpretation of potential ascertainment and treatment-management biases. |
| Event definition | The exact composition of the primary endpoint determines what counts as a primary event. |
15. What a Complete Statistical Result Would Contain
For a randomized time-to-event endpoint such as the IDEAL primary outcome, a complete statistical report would ordinarily present several complementary quantities rather than relying on one statistic.
Event counts
The number of participants experiencing a qualifying major coronary event in each randomized group provides a direct description of observed events.
Kaplan-Meier estimates
Time-specific event-free probabilities show how the cumulative experience of the two groups evolves over follow-up.
Hazard ratio
A Cox-model hazard ratio summarizes the relative instantaneous event rate between the randomized groups.
Confidence interval
A confidence interval communicates statistical uncertainty around the estimated treatment effect.
These measures answer different questions. Event counts describe what occurred, Kaplan-Meier estimates describe when events occurred over time, the hazard ratio summarizes a relative treatment contrast, and the confidence interval describes uncertainty around that estimate.
16. Interpreting a Hypothetical Hazard Ratio Without Confusing It With Risk
If a Cox analysis produced a hazard ratio of 0.70 for one statin regimen versus the other, the appropriate interpretation would be that the estimated instantaneous rate of the major coronary event was approximately 30% lower in that group under the fitted model.
That would not mean that 30% of participants avoided an event, that each participant had exactly 30% less risk, or that the absolute event probability was reduced by 30 percentage points.
A 95% confidence interval around a hazard ratio would describe uncertainty about the estimated relative treatment effect under the statistical model and sampling framework. It would not describe the range of effects experienced by individual participants.
Time-specific event-free probabilities and absolute event differences can provide a clinical perspective that a hazard ratio alone cannot provide. A complete interpretation should therefore distinguish relative treatment effects from absolute outcome probabilities.
17. Statistical Interpretation vs Clinical Interpretation
Statistical interpretation
The registered primary endpoint is a randomized time-to-event composite. Its natural analysis involves methods that account for both event occurrence and follow-up time, such as Kaplan-Meier estimation, log-rank comparison, and Cox regression.
Clinical interpretation
The clinical outcome is defined specifically as non-fatal acute myocardial infarction, coronary death, or resuscitated cardiac arrest. Interpretation therefore depends on the exact composition of the endpoint rather than treating it as a generic measure of cardiovascular health.
18. Registry Record and Trial Status
Trial start
The registry lists February 1999 as the start date.
Treatment study
The registry classifies IDEAL as a phase 4 study with treatment as its primary purpose.
Trial status
The registry lists the study status as completed.
The ClinicalTrials.gov record identifies Pfizer as the lead sponsor and classifies the sponsor type as industry. The study enrolled 8,600 participants and compared atorvastatin with simvastatin in a randomized parallel design.
19. A Practical Statistical Reading of the IDEAL Endpoint
When reading a result for this trial, the first question should be: what exactly is the event? Here, the event is not an unspecified cardiovascular outcome. It is the registry-defined major coronary event consisting of three specified components.
The second question is: what is being compared? The randomized comparison is atorvastatin versus simvastatin. Because the endpoint is time-to-event, the analysis should preserve the longitudinal structure rather than collapsing all follow-up into a single binary outcome.
The third question is: how much uncertainty surrounds the estimate? A hazard ratio without a confidence interval gives an incomplete description of statistical precision. Conversely, a confidence interval without an understanding of the endpoint and analysis population can also be misleading.
Finally, the primary endpoint should be kept distinct from any component-specific or secondary analysis. A treatment effect for the composite does not automatically establish the same treatment effect for non-fatal acute myocardial infarction, coronary death, and resuscitated cardiac arrest individually.
20. Summary of the Statistical Framework
| Question | Statistical answer |
|---|---|
| What is being randomized? | Assignment to atorvastatin or simvastatin. |
| What is the primary outcome? | Time to a major coronary event. |
| What makes the outcome composite? | It includes non-fatal acute myocardial infarction, coronary death, and resuscitated cardiac arrest. |
| Why use survival analysis? | Because the endpoint includes the time until the first qualifying event and participants can have different observed follow-up times. |
| What does Kaplan-Meier provide? | An estimate of the probability of remaining free of the event over time. |
| What does the log-rank test compare? | The time-to-event distributions between the randomized groups. |
| What does a Cox model provide? | A model-based hazard ratio comparing instantaneous event rates. |
| What remains important beyond the hazard ratio? | Absolute event probabilities, event counts, confidence intervals, censoring, and the behavior of individual composite components. |
21. Sources
- ClinicalTrials.gov: NCT00159835.
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22. Record Summary
IDEAL is a randomized phase 4, parallel-group trial comparing atorvastatin with simvastatin in patients with known coronary heart disease. Its registered primary endpoint is time to a major coronary event, defined as non-fatal acute myocardial infarction, coronary death, or resuscitated cardiac arrest. That endpoint naturally calls for time-to-event statistical reasoning: the analysis must account for both whether a qualifying event occurs and when it occurs, while appropriately handling participants whose follow-up ends without an observed event.
The most important statistical concepts in this record are therefore randomization, time-to-event analysis, composite endpoints, Kaplan-Meier estimation, log-rank comparison, Cox proportional-hazards modeling, and censoring. The ClinicalTrials.gov record does not post formal statistical analyses or numerical results for the primary endpoint, so the statistical interpretation remains focused on the structure and appropriate analysis of the registered outcome rather than an observed treatment-effect estimate.