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
E1199 was a randomized phase 3 treatment trial conducted in women with stage II or stage IIIA breast cancer that had spread to the lymph nodes. The ClinicalTrials.gov record identifies breast cancer as the condition and lists combination chemotherapy-related interventions including cyclophosphamide, docetaxel, doxorubicin hydrochloride, paclitaxel, tamoxifen citrate, and radiation therapy.
| Feature | E1199 |
|---|---|
| Acronym | E1199 |
| ClinicalTrials.gov identifier | NCT00004125 |
| Title | Combination Chemotherapy in Treating Women With Stage II or Stage IIIA Breast Cancer That Has Spread to the Lymph Nodes |
| Phase | Phase 3 |
| Status | Completed |
| Allocation | Randomized |
| Primary purpose | Treatment |
| Condition | Breast Cancer |
| Lead sponsor | Eastern Cooperative Oncology Group |
| Sponsor type | Network |
2. Clinical Question
The clinical question described by the registry is whether combination chemotherapy can be evaluated as a treatment strategy for women with stage II or stage IIIA breast cancer that has spread to the lymph nodes. Because the registry record does not identify the treatment assignment structure beyond stating that allocation was randomized, the exact pairwise treatment comparison cannot be specified from the record alone.
Population
Women with stage II or stage IIIA breast cancer that has spread to the lymph nodes.
Interventions
The registry lists cyclophosphamide, docetaxel, doxorubicin hydrochloride, paclitaxel, tamoxifen citrate, and radiation therapy.
Design question
Randomized allocation indicates that treatment groups were assigned through a randomized design rather than solely by investigator or patient choice.
Primary purpose
The registry classifies the primary purpose as treatment.
3. Trial Design
The registry classifies E1199 as a phase 3, randomized treatment trial. Those two design features are statistically important. A phase 3 trial is generally intended to provide a rigorous comparative assessment of a treatment strategy, while randomization creates the framework for comparing outcomes between treatment assignments while reducing systematic differences attributable to measured and unmeasured baseline factors.
What randomization contributes statistically
Randomization is valuable because treatment assignment is determined independently of patients' underlying prognosis, subject to the operation of the randomization procedure. In a sufficiently large randomized trial, this tends to balance prognostic characteristics between groups in expectation. The resulting comparison can therefore be interpreted as a comparison generated by the assigned treatment strategy rather than a simple comparison of patients who happened to receive different therapies.
Randomization does not guarantee that every baseline characteristic will be numerically identical between groups. It also does not eliminate missing outcomes, treatment discontinuation, protocol deviations, or other complications that arise after assignment. Those issues are handled through the prespecified statistical analysis plan.
4. Endpoints
The ClinicalTrials.gov record does not report any registered primary endpoints for E1199. Consequently, no endpoint-specific definition, assessment time frame, event count, treatment effect estimate, confidence interval, or p-value can be reported from the registry record.
| Endpoint information | Registry record |
|---|---|
| Registered primary endpoints | None reported |
| Endpoint definitions | Not reported in the record |
| Endpoint time frames | Not reported in the record |
| Posted statistical analyses | None |
| Numerical efficacy results | Not reported on ClinicalTrials.gov |
5. Treatment Components Listed in the Registry
The ClinicalTrials.gov record lists six interventions: cyclophosphamide, docetaxel, doxorubicin hydrochloride, paclitaxel, tamoxifen citrate, and radiation therapy. The record identifies these as drug or radiation interventions but does not, in the information reported here, specify the randomized arm structure or state which interventions were combined within each assignment.
| Intervention | Registry classification |
|---|---|
| Cyclophosphamide | Drug |
| Docetaxel | Drug |
| Doxorubicin hydrochloride | Drug |
| Paclitaxel | Drug |
| Tamoxifen citrate | Drug |
| Radiation therapy | Radiation |
Why the exact arm structure matters
For statistical interpretation, an intervention list is not equivalent to a treatment-arm description. If several interventions are used within one regimen, the estimand is the effect of the complete assigned strategy. If interventions are distributed across multiple randomized groups, the comparison may instead involve several pairwise or factorial contrasts. The statistical model, multiplicity adjustment, and interpretation depend on that structure.
For E1199, the registry's randomized designation establishes the comparative framework, but the information reported here does not establish the exact randomized contrasts. The treatment components therefore should not be rearranged into assumed treatment arms.
6. Statistical Methodology
Because the registry does not identify a registered primary endpoint or post a statistical analysis, a trial-specific primary analysis cannot be reconstructed from the ClinicalTrials.gov record. The appropriate statistical method would depend first on the endpoint definition and then on the prespecified estimand.
The exact form of the treatment effect depends on the endpoint. It may be expressed as a difference in means, risk ratio, odds ratio, hazard ratio, difference in survival probability, or another prespecified measure.
Time-to-event endpoints
If an endpoint were defined as time from randomization to an event such as death or recurrence, a survival-analysis framework would ordinarily be appropriate. Kaplan-Meier estimation can describe the event-time distribution while accounting for patients whose event has not occurred by their last observation. A log-rank test can compare survival distributions, and a Cox proportional-hazards model can estimate a hazard ratio.
Here, di is the number of events at time ti and ni is the number of individuals at risk immediately before that time.
Binary endpoints
If a primary endpoint were binary, such as whether a patient experienced a defined event by a prespecified time, an analysis could compare event proportions between randomized groups. Possible effect measures include an absolute risk difference, risk ratio, or odds ratio. The choice should be specified before examining the results because each measure answers a different question.
Continuous endpoints
If the primary outcome were continuous, the analysis could compare treatment-group means or another prespecified summary. Regression adjustment can sometimes improve precision by accounting for important baseline covariates, but the covariates and model should be prespecified rather than selected solely because they produce a preferred treatment estimate.
Intention-to-treat principle
For a randomized treatment comparison, the intention-to-treat principle generally analyzes participants according to their randomized assignment. This preserves the connection between randomization and causal comparison. The exact analysis population and handling of protocol deviations would normally be defined in the statistical analysis plan.
7. Planned Analysis
No formal statistical analyses were posted to ClinicalTrials.gov. The registry also does not report registered primary endpoints for E1199. The following describes the statistical approach that would ordinarily be considered for a randomized phase 3 treatment trial once the endpoint and estimand are explicitly defined.
First: define the estimand
The analysis begins by defining exactly what treatment effect is being estimated, in which population, over what period, and with what handling of intercurrent events.
Second: preserve randomization
For the primary efficacy comparison, participants would ordinarily remain associated with their randomized assignment rather than being reclassified according to treatment received.
Third: match method to endpoint
Time-to-event, binary, continuous, and count outcomes require different statistical models and effect measures.
Fourth: quantify uncertainty
A treatment estimate should ordinarily be accompanied by a confidence interval or another prespecified measure of statistical uncertainty.
If the endpoint is time-to-event
A typical confirmatory analysis would use Kaplan-Meier methods for descriptive estimation, a prespecified comparison such as the log-rank test, and a Cox model or another suitable survival model for relative treatment-effect estimation. The interpretation would depend on the censoring rules, event definition, follow-up period, and proportional-hazards assumption if a Cox hazard ratio were used.
If the endpoint is binary
A prespecified binary endpoint could be analyzed through a comparison of event probabilities, with an absolute and/or relative effect measure and a confidence interval. Logistic regression may be used when appropriate, particularly if baseline covariate adjustment is part of the prespecified strategy.
If multiple primary endpoints exist
When a trial has more than one primary endpoint, the statistical analysis must specify how type I error is controlled. Possible approaches include a hierarchical testing strategy, alpha allocation, or another multiplicity-control procedure. Without the registered endpoint hierarchy, no particular multiplicity structure can be assigned to E1199.
8. Statistical Methods Explained
Why does randomization matter?
Randomization establishes the treatment comparison before outcomes are observed. In expectation, randomized groups have comparable distributions of prognostic characteristics. This is fundamentally different from an observational comparison, where treatment selection can be associated with prognosis and create confounding.
Why can the endpoint not be separated from its statistical method?
A statistical method is chosen because of the structure of the outcome. A survival endpoint contains both event and follow-up information and may be censored. A binary endpoint records whether an event occurred. A continuous endpoint records a quantitative measurement. Treating these outcomes as interchangeable would change the scientific question being estimated.
What does a hazard ratio mean if survival is the endpoint?
A hazard ratio compares the estimated instantaneous event rates between two groups under a survival model. A value below 1 indicates a lower estimated event rate in the numerator group, whereas a value above 1 indicates a higher estimated event rate. It is not the same as a ratio of median survival times or a difference in survival probabilities at a particular time.
Why is the confidence interval important?
A point estimate alone gives only one estimate of the treatment effect. A confidence interval describes the statistical uncertainty associated with that estimate under the specified model and sampling framework. Wider intervals indicate less precision; narrower intervals indicate greater precision. The interval does not describe the range of effects experienced by individual patients.
Why does a p-value not measure effect size?
A p-value addresses compatibility of the observed data with a specified null hypothesis under the assumed statistical model. It is affected by both the magnitude of the observed effect and the amount of information in the study. A small p-value therefore does not, by itself, establish that an effect is large or clinically important.
Why is intention-to-treat different from an as-treated analysis?
Intention-to-treat maintains the randomized assignment and therefore preserves the principal basis for causal comparison created by randomization. An as-treated analysis instead classifies participants according to treatment actually received. That classification can introduce post-randomization selection because treatment received may be affected by factors related to prognosis.
9. Missing Data and Censoring
The ClinicalTrials.gov record does not report missing-data procedures, censoring rules, imputation methods, or sensitivity analyses for E1199. These details can materially affect statistical inference and would ordinarily be prespecified according to the endpoint.
| Issue | Statistical importance |
|---|---|
| Missing outcome measurements | Can reduce information and potentially bias estimates when missingness is related to prognosis or treatment. |
| Loss to follow-up | May produce censoring in time-to-event analyses and requires prespecified rules for defining the censoring time. |
| Protocol deviations | Can affect analysis populations and may motivate sensitivity analyses alongside the primary randomized comparison. |
| Intercurrent events | May change the interpretation of the treatment effect depending on whether treatment discontinuation, subsequent therapy, or another event is part of the estimand. |
Why imputation cannot simply be assumed
Different endpoints call for different approaches to missing information. Multiple imputation, likelihood-based methods, inverse-probability weighting, censoring rules, and sensitivity analyses each rely on different assumptions. The choice should follow the endpoint and estimand rather than being applied automatically.
10. Multiplicity and Statistical Error Control
The registry does not report a primary endpoint hierarchy, multiplicity adjustment, alpha allocation, or formal testing sequence for E1199. These elements are important because a trial can evaluate multiple outcomes, treatment contrasts, time points, or interim looks.
Type I error
Testing many hypotheses without an appropriate adjustment can increase the probability of at least one false-positive finding.
Endpoint hierarchy
A prespecified hierarchy can determine which hypotheses receive confirmatory interpretation and which are secondary or exploratory.
For a phase 3 randomized trial, the distinction between confirmatory and exploratory analyses is especially important. A result from an unplanned secondary comparison should not automatically be treated as having the same inferential status as a prespecified primary endpoint.
11. Stratification and Covariate Adjustment
The registry information reported here does not identify randomization strata or prespecified covariates for E1199. Stratification variables can be incorporated into the analysis when they were part of the randomization scheme, particularly for survival analyses. Covariate adjustment can also improve precision, but the variables and model should be prespecified.
Adjustment is intended to improve precision or respect the randomization design; it should not be used retrospectively to search for a favorable result.
12. Subgroup Analysis
The ClinicalTrials.gov record does not report subgroup results or prespecified subgroup variables for E1199. Subgroup analyses can be informative when they address clinically important questions defined before outcome analysis, but they require substantially more caution than the primary randomized comparison.
A treatment effect appearing statistically significant in one subgroup and nonsignificant in another does not by itself demonstrate that the treatment effect differs between subgroups. A formal interaction test or another prespecified heterogeneity analysis is needed to evaluate effect modification.
13. Interim Analysis
The registry information does not report whether E1199 used an interim efficacy analysis, interim safety monitoring, group-sequential boundaries, alpha spending, or a data-monitoring stopping rule.
If interim efficacy analyses are part of a randomized phase 3 design, the repeated looks at accumulating data must be incorporated into the statistical error-control strategy. An unadjusted final p-value after repeated unplanned looks can have a different type I error interpretation from a p-value produced under a prespecified group-sequential design.
Why interim looks matter
Repeated hypothesis testing can increase the chance of a false-positive conclusion if the overall error rate is not controlled.
Why stopping rules matter
Early stopping can change the amount of information available and can affect the interpretation of treatment-effect estimates and confidence intervals.
14. Bayesian Methods
The ClinicalTrials.gov record does not report Bayesian methods for E1199. A Bayesian analysis would require explicit specification of prior distributions, the likelihood, the estimand, and the posterior decision criterion.
This distinction matters because Bayesian probability statements and frequentist confidence intervals answer related but different inferential questions. A Bayesian posterior probability depends on the prior as well as the observed data, whereas a frequentist confidence interval is defined through a repeated-sampling procedure.
15. Results
The absence of posted statistical analyses means that the registry record provides design information without a corresponding numerical statistical-results section. The completed status of the trial indicates that the study itself is recorded as completed; it does not supply an outcome estimate or establish the result of a particular treatment comparison.
16. Safety
The ClinicalTrials.gov record does not report serious adverse events by treatment arm, adverse-event rates, grade-specific toxicity results, or treatment-related discontinuations. Accordingly, no arm-specific safety comparison can be described from the registry record.
| Safety information | Registry record |
|---|---|
| Serious adverse events by arm | Not reported |
| Adverse-event rates | Not reported |
| Grade-specific toxicity | Not reported |
| Treatment discontinuations for toxicity | Not reported |
| Safety analysis population | Not reported |
Why efficacy and safety need separate estimands
Efficacy and safety are not naturally summarized by one statistical measure. An efficacy endpoint may describe survival or disease control, while safety outcomes may include multiple adverse events with different frequencies, severities, timings, and clinical importance. A statistically rigorous trial therefore treats safety as a distinct family of outcomes with its own definitions and analysis population.
17. Trial Timeline
Trial starts
The registry records 1999-11-16 as the study start date.
Randomized treatment evaluation
The study is classified as a phase 3 randomized treatment trial in women with stage II or stage IIIA breast cancer that has spread to the lymph nodes.
Primary completion
The registry records 2007-05 as the primary completion date.
Registry status
The current registry record identifies the study status as completed.
18. What Can and Cannot Be Learned From the Registry Record
| Question | What the registry establishes |
|---|---|
| Was the study randomized? | Yes. Allocation is listed as randomized. |
| What phase was the study? | Phase 3. |
| What was the primary purpose? | Treatment. |
| What condition was studied? | Breast cancer. |
| Who was the described population? | Women with stage II or stage IIIA breast cancer that has spread to the lymph nodes. |
| What interventions are listed? | Cyclophosphamide, docetaxel, doxorubicin hydrochloride, paclitaxel, tamoxifen citrate, and radiation therapy. |
| How many participants were enrolled? | The registry record does not report an enrollment value. |
| What were the primary endpoints? | No registered primary endpoints are reported. |
| What was the treatment effect? | No posted statistical analysis or numerical treatment-effect estimate is reported. |
| What were the confidence intervals and p-values? | Not reported in the registry record. |
19. Statistical Interpretation of a Randomized Phase 3 Design
The randomized allocation is the central statistical feature of E1199. It creates a design in which treatment assignment is not determined solely by observed patient characteristics or clinical judgment. That provides the foundation for estimating a causal treatment contrast, assuming the trial is conducted and analyzed according to its prespecified framework.
The phase 3 designation places the study within a confirmatory stage of clinical development. Statistically, this makes prespecification particularly important: the endpoint definitions, treatment contrasts, analysis populations, multiplicity strategy, and handling of missing information should be established before the results are examined.
A randomized design alone does not determine the statistical method. The endpoint determines what constitutes an event, how follow-up contributes information, which observations are censored or missing, and what effect measure best represents the treatment contrast.
Any treatment-effect estimate from a randomized trial should be considered together with its uncertainty. Confidence intervals are especially useful because they show the precision of the estimated effect rather than reducing the statistical conclusion to a binary significant-versus-nonsignificant label.
20. Limitations
- No registered primary endpoints: the ClinicalTrials.gov record does not identify a primary endpoint definition or time frame.
- No posted statistical analyses: the registry does not report numerical treatment-effect estimates, confidence intervals, p-values, or event counts.
- Enrollment not reported: the registry record does not provide an enrollment total, limiting assessment of the information available for statistical precision.
- Exact treatment contrasts are not specified: although multiple interventions are listed, the reported information does not establish the exact randomized treatment-arm structure.
- No analysis populations reported: the registry does not specify the efficacy or safety analysis populations used for statistical comparisons.
- No missing-data strategy reported: the record does not identify imputation, censoring, or sensitivity-analysis procedures.
- No multiplicity strategy reported: the record does not identify an endpoint hierarchy, alpha allocation, or other type I error-control strategy.
- No subgroup methodology reported: the registry does not provide prespecified subgroup variables or interaction analyses.
- No interim-analysis plan reported: the record does not state whether interim efficacy analyses or formal stopping boundaries were used.
21. Why This Trial Matters Statistically
E1199 is a useful statistical teaching case because the registry establishes several fundamental elements of a clinical trial design while leaving the endpoint and results structure unspecified. That makes the distinction between study design, estimand, analysis method, and observed result particularly clear.
| Statistical concept | How it relates to E1199 |
|---|---|
| Randomization | The registry classifies allocation as randomized. |
| Comparative treatment design | The study is classified as a phase 3 treatment trial. |
| Population definition | The title identifies women with stage II or stage IIIA breast cancer that has spread to the lymph nodes. |
| Intervention definition | Six interventions are listed in the registry. |
| Endpoint specification | No registered primary endpoints are reported. |
| Estimand | Must be tied to a precisely defined endpoint and treatment contrast. |
| Uncertainty | Confidence intervals would ordinarily accompany treatment-effect estimates. |
| Multiplicity | Requires prespecification if multiple confirmatory hypotheses are tested. |
| Missing data | Requires endpoint-specific rules and sensitivity analyses when applicable. |
| Safety analysis | Requires separate definitions and an appropriate analysis population. |
22. A Practical Statistical Reading of E1199
When reading a randomized phase 3 trial such as E1199, the first statistical question is not whether a particular p-value is below a threshold. The first question is what comparison was randomized and what outcome was designated as the primary estimand. Only after those elements are established can the treatment effect be interpreted correctly.
The next step is to identify the effect measure. For survival outcomes, a hazard ratio may be appropriate; for binary outcomes, a risk difference or risk ratio may be more directly interpretable; for continuous outcomes, a mean difference or another prespecified contrast may be appropriate. The confidence interval then provides information about precision.
Finally, the analysis should be read in the context of the trial design. Randomization supports causal interpretation, but post-randomization events, missing observations, treatment changes, competing events, multiple endpoints, and subgroup analyses can all influence how a particular estimate should be understood.
23. Sources
- ClinicalTrials.gov: NCT00004125 — E1199.
- PubMed: PubMed record.
- PubMed: PubMed record.
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24. Record Summary
E1199 is recorded as a completed randomized phase 3 treatment trial conducted in women with stage II or stage IIIA breast cancer that has spread to the lymph nodes. The ClinicalTrials.gov record identifies Eastern Cooperative Oncology Group as the lead sponsor and lists cyclophosphamide, docetaxel, doxorubicin hydrochloride, paclitaxel, tamoxifen citrate, and radiation therapy among the interventions. The study began on 1999-11-16 and has a primary completion date of 2007-05.
From a statistical perspective, the most important limitation of the registry record is that it does not report registered primary endpoints or posted statistical analyses. Consequently, the record establishes the randomized phase 3 design but does not provide the endpoint-specific information required to interpret a treatment effect. A rigorous analysis therefore begins by identifying the exact randomized comparison and prespecified endpoint before selecting the corresponding estimand, statistical model, uncertainty measure, and multiplicity strategy.