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
WASID was a randomized, double-blind, phase 3 clinical trial with a primary purpose of prevention. The registry identifies aspirin and warfarin as the interventions and lists stroke, cerebral infarction, atherosclerosis, and constriction, pathologic among the conditions studied.
| Feature | WASID |
|---|---|
| Study acronym | WASID |
| Official title | Aspirin Or Warfarin To Prevent Stroke |
| Phase | Phase 3 |
| Status | Terminated |
| Allocation | Randomized |
| Masking | Double |
| Primary purpose | Prevention |
| Interventions | Warfarin; Aspirin |
| Conditions | Stroke; Cerebral Infarction; Atherosclerosis; Constriction, Pathologic |
| Enrollment | Not reported in the registry record |
| Lead sponsor | National Institute of Neurological Disorders and Stroke (NINDS) |
| Sponsor type | NIH |
| ClinicalTrials.gov | NCT00004728 |
2. Clinical Question
The study title identifies the central clinical objective as prevention of stroke through a comparison of aspirin and warfarin. The registry classifies the trial's primary purpose as prevention and lists stroke, cerebral infarction, atherosclerosis, and pathologic constriction among its conditions.
Population
The registry identifies the study conditions as stroke, cerebral infarction, atherosclerosis, and constriction, pathologic. It does not report an enrollment number in the available record.
Interventions
Warfarin and aspirin are the two interventions listed in the registry.
Comparator
The randomized design establishes a comparison between the two listed interventions.
Primary question
The title frames the clinical question as whether aspirin or warfarin should be used in the context of preventing stroke.
3. Trial Design
Warfarin
- Warfarin is listed as a drug intervention.
- The study uses randomized allocation.
- The trial is classified as double-masked.
- The primary purpose is prevention.
Aspirin
- Aspirin is listed as a drug intervention.
- The study uses randomized allocation.
- The trial is classified as double-masked.
- The primary purpose is prevention.
4. Randomization and Masking
The registry classifies allocation as RANDOMIZED and masking as DOUBLE. These two design features are central to the statistical interpretation of a controlled clinical trial.
| Design feature | Registry classification | Statistical role |
|---|---|---|
| Allocation | Randomized | Creates a treatment comparison in which allocation is determined by the randomization process rather than routine clinical selection. |
| Masking | Double | Reduces the opportunity for knowledge of treatment assignment to influence behavior, assessment, or other trial processes. |
| Primary purpose | Prevention | Frames the trial around preventing a clinical event rather than treating an already established event. |
Randomization does not guarantee that every baseline characteristic will be numerically identical between treatment groups. Its statistical value is that, in expectation, treatment assignment is independent of both measured and unmeasured prognostic factors at the time of randomization.
5. Endpoints
The registry record available for this analysis contains no registered primary endpoints. Consequently, there is no endpoint definition or prespecified time frame in the record that can be reproduced as a trial-specific primary endpoint.
| Endpoint element | Registry record |
|---|---|
| Registered primary endpoints | None reported |
| Endpoint definitions | Not reported |
| Primary endpoint time frames | Not reported |
| Posted statistical analyses | None reported |
6. Statistical Methodology
The trial's randomized, double-masked structure determines the basic statistical framework: outcomes should be compared between the randomized warfarin and aspirin groups according to prespecified endpoint definitions and follow-up rules.
Randomized treatment comparison
For a randomized comparison, the treatment assignment is the principal explanatory variable. The appropriate analysis depends on the type of endpoint defined in the statistical analysis plan. A binary event endpoint would generally be summarized with event proportions and an appropriate comparative effect measure. A continuous endpoint would generally require a comparison of distributions or means, while a time-to-event endpoint would typically be analyzed with methods that account for censoring and unequal follow-up.
Intention-to-treat principle
For randomized efficacy comparisons, an intention-to-treat analysis generally retains participants in the groups to which they were randomized. This preserves the treatment contrast generated by randomization and avoids changing the comparison simply because participants later discontinue, switch, or otherwise deviate from assigned treatment.
Blinded assessment
Double masking is particularly relevant when endpoint assessment or participant behavior could be influenced by knowledge of treatment assignment. Masking does not replace randomization, but it can reduce additional sources of systematic bias after allocation.
Endpoint-dependent analysis
Because no registered primary endpoint is reported in the ClinicalTrials.gov record, there is no trial-specific endpoint model to identify from the registry. The statistical method must therefore be described conditionally rather than assigning a particular model to WASID without an endpoint definition.
The exact effect measure depends on the endpoint. For example, binary outcomes can use a risk difference, risk ratio, or odds ratio; time-to-event outcomes can use a hazard ratio; and continuous outcomes can use a mean or adjusted mean difference.
7. Planned Analysis
The registry identifies WASID as a randomized, double-masked phase 3 prevention trial comparing warfarin and aspirin. It does not, in the record represented here, provide registered primary endpoint definitions or posted statistical analyses.
How a prevention endpoint would typically be analysed
If the prespecified endpoint were a binary occurrence of a clinical event over a defined follow-up period, the primary comparison would typically summarize the number and proportion of participants experiencing the event in each randomized group and report an effect measure with a confidence interval. The choice among a risk difference, risk ratio, odds ratio, or another measure would depend on the endpoint definition and analysis plan.
If time to stroke were the endpoint
A time-to-event endpoint would typically be analysed using methods such as Kaplan-Meier estimation and a log-rank comparison, with a Cox proportional-hazards model commonly used to estimate a hazard ratio and confidence interval. Such methods account for participants who are censored before experiencing the event.
Kaplan-Meier estimation describes the event-free survival function over follow-up. A hazard ratio, when appropriate, describes the relative instantaneous event rate between randomized groups under the fitted model.
Results status
No formal statistical analyses are posted to ClinicalTrials.gov for the record represented here. Results have not been posted on ClinicalTrials.gov.
8. Statistical Methods Explained
Why randomize aspirin and warfarin?
Randomization provides the statistical basis for a causal comparison between the two interventions. If treatment assignment is properly randomized, systematic differences in prognosis should not determine which intervention a participant receives at baseline. The resulting treatment-group comparison can therefore be interpreted within the randomized trial framework rather than as a simple observational comparison.
Why use double masking?
Masking can reduce bias that arises when participants, investigators, or outcome assessors know treatment assignment. For outcomes involving clinical judgment or participant behavior, this protection can be particularly important. Double masking is therefore complementary to randomization rather than a substitute for it.
What does the prevention purpose imply statistically?
A prevention trial asks whether an intervention changes the occurrence of a future clinical event or other prespecified outcome. The analysis therefore needs a clearly defined event, an analysis population, and a follow-up framework. Without those elements, the statistical endpoint cannot be reconstructed from the registry record alone.
Why does the endpoint definition matter so much?
An endpoint is not simply the name of a disease. A statistical endpoint specifies what constitutes an event, how events are assessed, when follow-up begins, and how competing or censored observations are handled. Different definitions can lead to different estimands and different statistical analyses even when they use the same general clinical concept.
When would a hazard ratio be appropriate?
A hazard ratio is appropriate when the primary question concerns the relative rate at which an event occurs over time and the analysis uses a suitable time-to-event model. It is not automatically appropriate merely because a study concerns prevention. The endpoint and analysis plan determine whether a hazard-based analysis is justified.
Why report confidence intervals rather than only p-values?
A p-value addresses the compatibility of the observed data with a specified null hypothesis under the statistical model. It does not measure the size or clinical importance of an effect. A confidence interval adds information about the precision and range of effect estimates supported by the data and model.
9. Interpreting Treatment Effects
A randomized comparison estimates a contrast between the outcomes associated with assignment to warfarin and assignment to aspirin. The meaning of that contrast depends on the prespecified endpoint and effect measure.
A treatment effect does not automatically mean that every participant experiences the same individual effect. Trial-level estimates describe differences between treatment groups, not deterministic outcomes for individual patients.
For an effect estimate, the confidence interval quantifies statistical uncertainty under the selected model and sampling framework. A narrow interval indicates greater statistical precision than a wide interval, all else being equal.
A p-value is not a measure of the magnitude of a treatment difference. The effect estimate, its confidence interval, the endpoint definition, and the clinical context are needed to understand the size and precision of an observed treatment contrast.
10. Analysis Population and Censoring
For a randomized efficacy analysis, the intention-to-treat principle generally preserves the original randomized comparison. If participants discontinue treatment or otherwise deviate from their assigned intervention, removing them from the primary analysis can change the treatment comparison that randomization created.
Time-to-event analyses introduce an additional issue: censoring. A participant who has not experienced the event by the end of observed follow-up may contribute information up to that point without being classified as an event. Valid analysis requires assumptions about the relationship between censoring and the underlying event process.
Randomization
Defines the treatment groups used for the principal causal comparison.
Masking
Helps limit treatment-assignment knowledge from influencing trial conduct or assessment.
Censoring
Matters when participants have different observed follow-up times before an event occurs.
Estimand
Defines precisely what treatment effect the analysis is intended to quantify.
11. Multiplicity and Multiple Endpoints
No registered primary endpoints are reported in the ClinicalTrials.gov record represented here, so a trial-specific multiplicity hierarchy cannot be established from the registry.
Multiplicity becomes important when a trial evaluates several hypotheses, several endpoints, multiple treatment contrasts, or repeated interim looks at accumulating data. If each hypothesis is tested independently at the same nominal significance level, the probability of at least one false-positive conclusion can exceed the nominal level.
| Situation | Statistical issue |
|---|---|
| One prespecified primary endpoint | A single primary hypothesis can define the principal confirmatory comparison. |
| Several primary endpoints | The analysis plan should specify how success across endpoints is defined and how type I error is controlled. |
| Several secondary endpoints | Results require distinction between confirmatory and supportive or exploratory interpretation. |
| Repeated interim analyses | Repeated testing can require group-sequential or alpha-spending methods to maintain type I error control. |
12. Interim Analysis and Sequential Monitoring
The available registry information does not report an interim-analysis schedule, alpha-spending strategy, stopping boundary, or information-time plan.
In a phase 3 randomized prevention trial, an interim analysis may be used for efficacy, futility, safety, or other prespecified decisions. If such looks at the data are part of the formal statistical design, the analysis plan must account for the repeated opportunities to make a decision. Otherwise, repeatedly testing accumulating data at an unadjusted significance level can alter the overall type I error rate.
Efficacy monitoring
Early evidence of a treatment difference can be evaluated against a prespecified stopping boundary.
Futility monitoring
A trial may be stopped when continuing is unlikely to establish the prespecified objective, depending on the design.
13. Missing Data and Imputation
The registry record does not report a missing-data strategy or an imputation method.
For a prevention endpoint, missing outcome information can affect the estimated treatment effect if loss to follow-up is related to future event risk. The appropriate approach depends on the estimand and endpoint. For time-to-event outcomes, censoring rules are usually part of the primary analysis rather than replacing missing observations with a single imputed value. For other endpoint types, prespecified imputation or sensitivity analyses may be appropriate.
14. Stratification and Covariate Adjustment
The registry information provided for WASID does not report randomization strata or prespecified covariates for the primary analysis.
Randomization itself does not require covariate adjustment for an unbiased treatment comparison. Covariate adjustment can nevertheless improve precision when important baseline prognostic variables are prespecified and measured reliably. If stratified randomization has been used, the primary analysis commonly accounts for the same stratification factors.
Adjustment should be distinguished from selecting variables after seeing treatment-group differences. A baseline characteristic does not become a required covariate merely because the observed groups differ numerically.
15. Bayesian Methods
The registry record does not identify a Bayesian analysis, prior distribution, posterior probability, Bayesian decision threshold, or other Bayesian statistical method.
A Bayesian analysis would combine prior information with the observed trial data to produce a posterior distribution for the treatment effect or other parameter of interest. The choice of prior can matter, particularly when the trial contains relatively little information. A Bayesian analysis therefore requires explicit specification of the prior and the decision rule rather than simply replacing a frequentist p-value with a posterior probability.
16. Safety Analysis
The registry information represented here does not provide serious adverse-event counts by randomized arm or other numerical safety results.
In a randomized clinical trial, safety is usually summarized separately from the primary efficacy endpoint. Common safety summaries include the number and proportion of participants experiencing at least one adverse event, serious adverse events, treatment discontinuations, and predefined events of special interest.
| Safety element | Registry record |
|---|---|
| Serious adverse events by arm | Not reported |
| Adverse-event counts by arm | Not reported |
| Treatment discontinuation counts | Not reported |
| Numerical safety estimates | Not reported |
Safety comparisons also require attention to exposure time. A simple proportion can be misleading when treatment duration differs substantially between groups. Depending on the event and design, exposure-adjusted incidence rates or time-to-first-event methods may be more informative.
17. Clinical Interpretation vs Statistical Interpretation
Statistical interpretation
WASID uses randomized allocation and double masking, creating a strong framework for comparing the two listed interventions. The registry does not report registered primary endpoints or posted statistical analyses.
Clinical interpretation
The study title identifies stroke prevention as the central clinical objective, while the registry identifies stroke, cerebral infarction, atherosclerosis, and pathologic constriction among the conditions studied.
These two perspectives should remain distinct. Statistical design describes how an effect would be estimated and tested; clinical interpretation asks what a prespecified effect would mean in the context of the condition, intervention, comparator, and patient population.
18. Important Limitations and Interpretation Issues
- No registered primary endpoints: the ClinicalTrials.gov record represented here does not report registered primary endpoint definitions or time frames.
- No posted statistical analyses: the registry does not provide formal numerical statistical analyses for the trial.
- No enrollment number: the enrollment field is not reported in the record represented here, limiting quantitative assessment of the planned or analyzed sample size.
- No numerical safety results: serious adverse events by randomized arm and other safety estimates are not reported in the available registry information.
- No endpoint-specific model can be assigned from the registry: different endpoint types require different statistical methods, so a particular model should not be assumed without the endpoint definition.
- Masking is not a guarantee against every form of bias: double masking can reduce certain biases, but trial conduct, outcome ascertainment, follow-up, missing data, and analysis decisions can still affect inference.
- Randomization does not eliminate random imbalance: treatment groups can differ by chance in measured baseline characteristics even when allocation is properly randomized.
19. Why This Trial Matters Statistically
WASID provides a useful framework for understanding how a randomized prevention trial should be interpreted even when the registry record does not contain a complete statistical results package. Its design combines randomized allocation, double masking, a phase 3 setting, and a direct comparison between two drug interventions.
| Concept | How it appears in WASID |
|---|---|
| Randomization | The registry classifies allocation as randomized. |
| Double masking | The registry classifies masking as double. |
| Phase 3 development | The trial is classified as phase 3. |
| Prevention | The primary purpose is prevention. |
| Active intervention comparison | Warfarin and aspirin are the listed interventions. |
| Endpoint definition | No registered primary endpoints are reported in the registry record represented here. |
| Effect estimation | The appropriate effect measure depends on the prespecified endpoint. |
| Time-to-event analysis | If the prespecified endpoint were time to a clinical event, survival-analysis methods could account for censoring and unequal follow-up. |
| Multiplicity | A trial-specific multiplicity strategy cannot be identified from the registry record because no registered primary endpoint hierarchy is reported. |
| Missing data | The registry does not report a missing-data or imputation strategy. |
20. Sources
- ClinicalTrials.gov: NCT00004728 — WASID.
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21. Record Summary
WASID is registered as a phase 3, randomized, double-masked prevention trial comparing warfarin and aspirin. The registry identifies stroke, cerebral infarction, atherosclerosis, and pathologic constriction as conditions and lists the National Institute of Neurological Disorders and Stroke (NINDS) as the lead sponsor. The record represented here does not report an enrollment number, registered primary endpoints, endpoint-specific statistical analyses, or numerical safety results.
The most important statistical lesson is therefore methodological. A randomized comparison supplies the framework for causal inference, while the endpoint definition determines the appropriate estimand, effect measure, analysis population, handling of censoring or missingness, and inferential procedure. Without a registered primary endpoint and posted statistical analysis, the trial's design can be described precisely, but an endpoint-specific numerical treatment effect cannot be assigned from the ClinicalTrials.gov record.