This page separates reported trial results from statistical interpretation. All numerical trial results presented here are taken from the ClinicalTrials.gov record data. The registry provides one posted formal statistical analysis for the primary endpoint.
1. Trial at a Glance
DESTINY-Breast06 is a randomized, parallel-group phase 3 trial evaluating trastuzumab deruxtecan versus investigator's choice chemotherapy in hormone receptor-positive, HER2-low advanced or metastatic breast cancer. The registry reports one primary time-to-event endpoint and one formal statistical analysis for that endpoint.
| Feature | DESTINY-Breast06 |
|---|---|
| Trial | DESTINY-Breast06 |
| ClinicalTrials.gov identifier | NCT04494425 |
| Phase | Phase 3 |
| Status | Active, not recruiting |
| Population | Advanced or metastatic breast cancer; primary analysis in the hormone receptor-positive, HER2-low population |
| Allocation | Randomized |
| Design model | Parallel |
| Masking | None |
| Primary purpose | Treatment |
| Enrollment | 866 |
| Arms | 2 |
| Lead sponsor | AstraZeneca |
| Sponsor type | Industry |
| Primary endpoint | Progression-Free Survival (PFS) Assessed by Blinded Independent Central Review (BICR) in the Hormone Receptor-Positive (HR+), Human Epidermal Growth Factor Receptor 2 (HER2)-Low Population |
2. Clinical Question
The primary statistical question is whether the time from randomization to progression or death differs between participants assigned to trastuzumab deruxtecan and those assigned to chemotherapy in the hormone receptor-positive, HER2-low population.
Population
Participants with advanced or metastatic breast cancer, with the posted primary analysis focused on the hormone receptor-positive, HER2-low population.
Intervention
Trastuzumab deruxtecan (T-DXd).
Comparator
Investigator's choice chemotherapy, represented in the registry by capecitabine, paclitaxel, and nab-paclitaxel.
Primary question
Does T-DXd improve progression-free survival relative to investigator's choice chemotherapy?
3. Trial Design
Trastuzumab deruxtecan
- Trastuzumab deruxtecan (T-DXd)
Investigator's choice chemotherapy
- Capecitabine
- Paclitaxel
- Nab-paclitaxel
Statistically, randomization is important because it establishes the treatment assignment mechanism before outcome information is observed. In a randomized comparison, the primary efficacy analysis can preserve the treatment groups created by that randomization rather than attempting to reconstruct treatment groups retrospectively from observed outcomes.
4. Endpoints
| Endpoint | Registry definition / assessment | Analysis |
|---|---|---|
| Primary PFS endpoint | Progression-Free Survival (PFS) Assessed by Blinded Independent Central Review (BICR) in the Hormone Receptor-Positive (HR+), Human Epidermal Growth Factor Receptor 2 (HER2)-Low Population. PFS per RECIST 1.1 assessed by BICR was defined as the time from the date of randomization until the date of PD, as defined or death (by any cause in the absence of progression), regardless of whether the participant withdrew from randomized therapy or received another anticancer therapy prior to progression. PD was defined as at least a 20% increase in the sum of diameters of target lesions (TLs), taking as reference the smallest previous sum of diameters (nadir), this included the baseline sum if that was the smallest on study. In addition to the relative increase of 20%, the sum must demonstrate an absolute increase of at least 5 millimeter (mm) from nadir. Median PFS was calculated using Kaplan-Meier method and its confidence interval (CI) using Brookmeyer-Crowley method. | Stratified log-rank test; stratified Cox proportional-hazards model for HR and CI |
The registry's registry-reported time frame is: “Response evaluations performed at screening, every 6 weeks (q6w) ± 1 week from randomization for 48 weeks, and then every 9 weeks (q9w) ± 1 week, starting at Week 48 until PD, up to PCD of 18 March 2024 (maximum of approximately 43.85 months).” The primary endpoint is therefore a time-to-event outcome rather than a simple proportion measured at one fixed visit.
5. Analysis Population and Stratification
The posted primary analysis was conducted in the HER2-low population, described in the registry as the subset of participants in the ITT population with HER2 immunohistochemistry (IHC) 2+/in situ hybridization (ISH)- and IHC 1+ as determined per the interactive registry description.
| Analysis component | Registry-supported description |
|---|---|
| Underlying efficacy framework | Intention-to-treat population |
| Primary analysis population | HER2-low subset of the ITT population |
| HER2 categories specified | IHC 2+/ISH- and IHC 1+ |
| Groups compared | T-DXd vs Chemotherapy |
| Stratified analysis | Yes |
| Covariates used in Cox model | Prior CDK4/6 inhibitor use and HER2 IHC expression |
The registry specifically states that the Cox model adjusted for prior cyclin-dependent kinase (CDK)4/6 inhibitor use (yes versus no) and HER2 IHC expression (IHC 1+ versus IHC 2+/ISH-). This is important because the reported hazard ratio is not simply an unadjusted ratio of event rates; it comes from a stratified Cox proportional-hazards model incorporating these factors.
6. Primary Result: Progression-Free Survival
The registry contains one posted formal statistical analysis for the primary endpoint. The comparison was performed using a stratified log-rank test, with the hazard ratio and confidence interval calculated from a stratified Cox proportional-hazards model.
Progression-Free Survival
95% CI: 0.52–0.75 · P < 0.0001
Comparison: T-DXd vs Chemotherapy
| Primary endpoint | T-DXd | Chemotherapy | Effect estimate |
|---|---|---|---|
| PFS by BICR in HR+, HER2-low population | — | — | HR 0.62 (95% CI 0.52–0.75); P < 0.0001 |
The registry does not provide a median PFS or time-specific PFS percentages in the ClinicalTrials.gov record. Those quantities are therefore not added here. The formal result that is available is the hazard ratio, its two-sided 95% confidence interval, and the reported P-value.
An HR of 0.62 means that, under the fitted stratified Cox model, the estimated instantaneous hazard of progression or death was about 38% lower with T-DXd than with chemotherapy over the analyzed follow-up. The calculation is the direct interpretation of 1 − 0.62 = 0.38.
The HR does not mean that 38% of participants avoided progression, that every participant had a 38% reduction in their individual probability of progression, or that median PFS was reduced or increased by a particular percentage. A hazard ratio is a relative time-to-event measure, not an absolute risk difference.
The 95% CI of 0.52–0.75 describes uncertainty around the estimated hazard ratio under the model and sampling framework. It does not describe the range of individual treatment effects among participants. The interval also provides information that a single point estimate cannot: it shows the statistical precision of the estimated relative effect.
The P-value < 0.0001 addresses the strength of evidence against the relevant null hypothesis under the prespecified statistical framework. It is not a measure of the size of the treatment effect. A very small P-value does not mean that the effect is proportionally large, just as a larger P-value would not by itself quantify clinical importance.
The HR and CI were produced using a stratified Cox proportional-hazards model. Consequently, interpretation of a single HR should be made with awareness of the proportional-hazards framework. If the relative hazards vary materially over time, one summary HR can compress a more complicated time-varying treatment effect into a single number.
The primary comparison used a stratified log-rank test, while the HR and CI used the corresponding stratified Cox model. The analysis therefore combines a hypothesis test of the time-to-event distributions with a model-based estimate of relative treatment effect.
7. How to Read the Primary Result
Because the HR is below 1, the estimated event hazard is lower in the T-DXd group under the fitted model. The complementary interpretation, 1 − 0.62 = 0.38, corresponds to an approximately 38% lower estimated hazard.
Relative measure
The hazard ratio describes a relative difference in the event hazard. It does not directly give an absolute difference in months of PFS or the absolute probability of remaining progression-free.
Precision
The 95% CI of 0.52–0.75 gives the statistical uncertainty reported around the estimated HR of 0.62.
Hypothesis test
The reported P-value is evidence from the specified statistical test; it is not an effect-size metric and should not be interpreted as the probability that the treatment works.
Time-to-event context
The analysis accounts for event timing and censoring rather than reducing the endpoint to whether an event occurred by one arbitrary calendar date.
8. Statistical Methodology
Stratified log-rank test
The registry reports a stratified log-rank test as the formal comparison method. The log-rank test is designed for comparing time-to-event distributions between treatment groups while using information across the observed follow-up period.
In contrast with a simple comparison of event proportions, the log-rank framework uses the ordering and timing of events. Participants who are censored can still contribute information to the comparison before their censoring time.
The log-rank statistic accumulates treatment-group differences across event times. Stratification allows those comparisons to be made within defined strata and then combined according to the stratified test framework.
Stratified Cox proportional-hazards model
The hazard ratio and its confidence interval were calculated using a stratified Cox proportional-hazards model. The registry states that the model adjusted for prior CDK4/6 inhibitor use and HER2 IHC expression, with ties handled using the Efron approach.
The treatment coefficient is transformed to obtain a hazard ratio. In a stratified Cox analysis, the baseline hazard can differ across strata while the treatment effect is estimated within the common modeling framework.
Covariate adjustment
Adjustment is particularly relevant when the analysis specifies prognostic or design-related factors that may influence the timing of progression or death. Here, the registry identifies prior CDK4/6 inhibitor use and HER2 IHC expression as adjustment factors.
The purpose of covariate adjustment is not to turn a randomized comparison into an observational analysis. Rather, prespecified adjustment can account for relevant variation while estimating the treatment effect. The key distinction is that treatment assignment remains anchored to the randomized comparison, while the Cox model incorporates the specified covariates when estimating the hazard ratio.
Intention-to-treat framework
The posted analysis population is described as a HER2-low subset of the ITT population. The ITT principle maintains treatment assignment as the basis for efficacy comparison, which is important because post-randomization treatment changes, discontinuation, and other events should not simply redefine the treatment groups after randomization.
Efron handling of tied event times
The registry specifies that ties were handled using the Efron approach. Ties occur when multiple participants have events recorded at the same observed time. Cox-model implementations need a rule for handling those tied event times; the Efron method is one such approach.
9. Statistical Methods Explained
Why was a log-rank test used?
PFS is a time-to-event endpoint, so the analysis needs to use both whether an event occurred and when it occurred. The log-rank test is designed for this setting and compares the treatment groups across the observed event-time experience rather than using only a single end-of-study proportion.
What does an HR of 0.62 mean?
An HR of 0.62 means that the estimated hazard of progression or death under the fitted model was 0.62 times the corresponding hazard in the chemotherapy group. Equivalently, 1 − 0.62 = 0.38, so the estimated hazard was approximately 38% lower with T-DXd. This is not the same as saying that the probability of progression or death was 38% lower for every individual participant.
Why is the confidence interval important?
The point estimate of 0.62 is only one estimate from the observed data. The 95% CI of 0.52–0.75 communicates the statistical uncertainty around that estimate. A confidence interval is therefore more informative than a P-value alone when assessing the precision of a treatment-effect estimate.
Why doesn't the P-value measure treatment benefit?
The reported P-value of <0.0001 is evidence against the null hypothesis under the specified test. It does not tell us that the treatment effect is “99.99% certain,” nor does it quantify the magnitude of benefit. Effect size is communicated by the hazard ratio, while uncertainty is communicated by the confidence interval.
Why was the Cox model stratified?
The registry reports a stratified Cox model and identifies prior CDK4/6 inhibitor use and HER2 IHC expression as adjustment factors. Stratification and covariate adjustment can account for important differences in baseline risk or design variables while estimating the treatment effect.
What does the BICR designation add?
The primary endpoint was assessed by blinded independent central review. For a radiologically determined endpoint such as PFS, independent central review provides a prespecified assessment framework intended to reduce the influence of knowledge of treatment assignment on progression determinations.
10. Censoring and the Time-to-Event Framework
PFS differs fundamentally from a binary endpoint such as “progressed by the end of the study.” Participants enter the risk set at randomization and are followed until progression, death, or the applicable end of observed event information. Participants without a qualifying event at the end of their observed follow-up contribute censored information.
Event timing
An event occurring early and an event occurring later are not treated as equivalent observations in a time-to-event analysis.
Censoring
A censored participant contributes information up to the censoring point rather than being treated as though the event never occurred.
Death
The registry definition includes death from any cause in the absence of progression as a PFS event.
Post-randomization therapy
The registry-reported endpoint definition specifies that PFS is assessed regardless of whether a participant withdrew from randomized therapy or received another anticancer therapy prior to progression.
This structure is one reason the hazard ratio cannot be translated directly into a statement such as “62% of patients remained progression-free.” The HR summarizes relative event hazard; it is not a survival probability.
11. Safety Results
The ClinicalTrials.gov record reports serious adverse events by treatment arm as affected participants over participants at risk:
| Safety measure | T-DXd | Chemotherapy |
|---|---|---|
| Serious adverse events, affected / at risk | 88 / 434 | 67 / 417 |
The denominators in the registry-reported serious-adverse-event field are 434 for T-DXd and 417 for chemotherapy. The registry extract in the ClinicalTrials.gov record does not provide a broader adverse-event table, grade-specific event frequencies, or individual adverse-event categories, so those details are not added.
12. Trial Timeline
Trial start
The registry lists 2020-07-24 as the study start date.
Primary completion
The registry lists 2024-03-18 as the primary completion date.
Active, not recruiting
the ClinicalTrials.gov record lists the study status as ACTIVE_NOT_RECRUITING.
13. What the Primary PFS Result Does — and Does Not — Mean
The estimated hazard ratio of 0.62 indicates a lower estimated hazard of progression or death in the T-DXd group relative to chemotherapy under the fitted stratified Cox model.
It does not mean that 62% of participants were progression-free, that 38% of participants were prevented from progressing, or that every participant experienced the same proportional reduction in risk.
The 0.52–0.75 interval indicates the statistical uncertainty around the HR estimate of 0.62. It is not an interval containing the PFS times of individual patients.
The reported P < 0.0001 quantifies evidence against the null hypothesis under the specified statistical test. It should not be used as a substitute for the hazard ratio or confidence interval when describing the magnitude and precision of the treatment effect.
14. Stratification and Covariate Adjustment: Why They Matter
The registry reports two related but conceptually distinct elements: a stratified log-rank test and a stratified Cox proportional-hazards model that adjusts for prior CDK4/6 inhibitor use and HER2 IHC expression.
| Element | Role in the analysis |
|---|---|
| Stratified log-rank test | Formal comparison of time-to-event experience between T-DXd and chemotherapy. |
| Stratified Cox model | Estimation of the hazard ratio and its confidence interval. |
| Prior CDK4/6 inhibitor use | Adjustment factor: yes versus no. |
| HER2 IHC expression | Adjustment factor: IHC 1+ versus IHC 2+/ISH-. |
| Efron approach | Method used to handle tied event times in the Cox model. |
For statistical interpretation, it is useful not to collapse all of these components into the phrase “the trial used a Cox model.” The hypothesis test and the effect-estimation model have different purposes. The log-rank test provides the formal comparison, while the Cox model supplies the reported relative effect estimate and its confidence interval.
15. Why a Single Hazard Ratio Is Not the Whole PFS Story
A hazard ratio is an efficient summary of a time-to-event comparison, but it does not contain every clinically or statistically relevant feature of the underlying survival distributions.
For example, a hazard ratio does not directly provide a median PFS, a PFS probability at a specified time point, or the absolute difference between treatment groups at a particular time. None of those quantities is contained in the ClinicalTrials.gov record, so they are not reconstructed here.
Hazard ratio
Summarizes the relative event hazard under the Cox model.
Confidence interval
Describes statistical uncertainty around the estimated hazard ratio.
Kaplan-Meier curve
Would describe estimated event-free probability over time if the underlying event and censoring data were available.
Absolute PFS measures
Would describe the event-free probability or time scale directly, but are not reported in the registry-reported primary-analysis data.
16. Multiplicity, Interim Analysis, and Other Design Features
The ClinicalTrials.gov record supports a primary superiority analysis, but they do not provide details about an alpha-spending plan, interim-analysis schedule, multiplicity hierarchy, or formal power calculation. Those elements are therefore not inferred from the P-value or reconstructed from the observed hazard ratio.
| Design topic | What the ClinicalTrials.gov record supports |
|---|---|
| Hypothesis type | Superiority. |
| Primary endpoint | One registered time-to-event endpoint: PFS by BICR in the HR+, HER2-low population. |
| Formal statistical analysis posted | One. |
| Interim analysis | No interim-analysis details are provided in the ClinicalTrials.gov record. |
| Multiplicity adjustment | No multiplicity procedure is provided in the ClinicalTrials.gov record. |
| Non-inferiority margin | Not applicable to the reported superiority hypothesis; no non-inferiority margin is provided. |
| Crossover | No crossover information is provided in the ClinicalTrials.gov record. |
| Factorial design | No factorial design is reported; the design model is parallel. |
| Bayesian methods | No Bayesian method is reported. |
| Missing-data / imputation method | No specific imputation method is provided in the ClinicalTrials.gov record. |
This distinction is important. Absence of a reported design feature in the ClinicalTrials.gov record is not evidence that a particular procedure was or was not used in an underlying protocol or statistical analysis plan. For this page, unsupported methods are simply not attributed to the trial.
17. Limitations
- Single formal analysis in the ClinicalTrials.gov record: the posted statistical-analysis record contains one primary analysis, so secondary efficacy estimates are not presented as though they were available.
- Limited numerical endpoint reporting: the ClinicalTrials.gov record provides the PFS hazard ratio, confidence interval, and P-value, but not median PFS or time-specific PFS estimates.
- Hazard-ratio interpretation: the HR is a model-based summary and should not be confused with an absolute risk difference or an individual-level probability.
- Proportional-hazards framework: the Cox model provides a single HR summary, so interpretation depends on the appropriateness of that model for the observed event-time patterns.
- Analysis population: the reported primary analysis is specifically for the HER2-low subset of the ITT population rather than automatically representing every participant enrolled in the broader trial.
- Safety scope: the ClinicalTrials.gov record contains serious adverse-event counts by arm but do not provide a complete safety profile.
- Unreported design details: interim monitoring, multiplicity procedures, missing-data methods, crossover, and Bayesian methods are not described in the ClinicalTrials.gov record and therefore are not reconstructed.
18. Why This Trial Matters Statistically
DESTINY-Breast06 provides a compact teaching example of how a modern randomized oncology trial connects clinical design to survival-analysis methodology. The trial uses randomization, a time-to-event primary endpoint, independent central review, a stratified log-rank test, a stratified Cox model, covariate adjustment, confidence intervals, and a prespecified superiority hypothesis.
| Concept | How it appears in DESTINY-Breast06 |
|---|---|
| Randomization | The allocation is randomized. |
| Parallel design | The registry specifies a parallel design with 2 arms. |
| Intention-to-treat | The primary analysis population is described as a HER2-low subset of the ITT population. |
| Time-to-event endpoint | PFS is the registered primary endpoint. |
| Blinded independent central review | The primary PFS endpoint is assessed by BICR. |
| Log-rank test | A stratified log-rank test is the reported formal comparison. |
| Hazard ratio | The primary PFS effect measure is HR 0.62. |
| Confidence interval | The 95% CI is 0.52–0.75. |
| Covariate adjustment | The Cox model adjusts for prior CDK4/6 inhibitor use and HER2 IHC expression. |
| Stratified Cox model | Used to calculate the HR and CI. |
| Efron method | Used for tied event times in the Cox model. |
| Superiority testing | The registry identifies the hypothesis type as superiority. |
The particularly useful statistical lesson is the separation between the endpoint, the hypothesis test, and the effect estimate. PFS defines what is being measured. The stratified log-rank test addresses the treatment-group comparison. The stratified Cox model provides the HR and CI used to describe the relative effect.
19. Primary Result in Statistical Context
The reported statistical signal
HR for PFS: 0.62 · 95% CI 0.52–0.75 · P < 0.0001
Three pieces of information should be read together. First, the hazard ratio provides the estimated relative effect. Second, the confidence interval provides information about precision. Third, the P-value describes evidence against the null hypothesis under the specified statistical test.
None of these quantities, individually or collectively, supplies the complete clinical description of PFS. In particular, the ClinicalTrials.gov record does not contain a median PFS or absolute PFS rates, so those measures cannot be substituted into the interpretation.
20. Clinical Interpretation vs Statistical Interpretation
Statistical interpretation
The primary PFS comparison produced an HR of 0.62 with a two-sided 95% CI of 0.52–0.75 and a reported P-value of <0.0001 using the specified stratified analysis framework.
Clinical interpretation
The statistical result indicates a difference in the time-to-progression-or-death experience between the randomized treatment groups. The magnitude of that difference should be interpreted alongside absolute PFS measures when those measures are available.
Keeping these interpretations separate is important. Statistical evidence addresses the compatibility of the observed data with the null hypothesis and quantifies the estimated relative effect. Clinical interpretation additionally requires context about absolute benefit, treatment burden, adverse events, patient characteristics, and the duration and pattern of benefit. The ClinicalTrials.gov record does not provide all of those quantities.
21. Serious Adverse Events and Statistical Interpretation
The serious-adverse-event data illustrate another important distinction in clinical-trial statistics: efficacy and safety are different outcome domains with different denominators and different interpretation frameworks.
| Arm | Affected | At risk | Reported format |
|---|---|---|---|
| T-DXd | 88 | 434 | 88/434 |
| Chemotherapy | 67 | 417 | 67/417 |
The safety figures should not be interpreted as a formal superiority or non-inferiority comparison because the ClinicalTrials.gov record does not provide a statistical analysis of serious adverse events. They are descriptive arm-level counts as reported in the ClinicalTrials.gov record.
Similarly, the serious-adverse-event counts should not be combined mathematically with the PFS HR to create a composite benefit-risk score. Doing so would impose a weighting and statistical structure that is not contained in the registry data.
22. Data Interpretation Checklist
| Question | Answer for this trial |
|---|---|
| What was randomized? | Participants were randomized in a 2-arm parallel treatment trial. |
| What was the primary endpoint? | PFS assessed by BICR in the HR+, HER2-low population. |
| What kind of endpoint is PFS? | Time-to-event. |
| What was the formal test? | Stratified log-rank test. |
| What was the effect measure? | Hazard ratio. |
| What was the estimate? | 0.62. |
| What was the 95% CI? | 0.52–0.75. |
| What was the P-value? | <0.0001. |
| What was the hypothesis type? | Superiority. |
| What model produced the HR? | Stratified Cox proportional-hazards model. |
| Which covariates were specified? | Prior CDK4/6 inhibitor use and HER2 IHC expression. |
| How were ties handled? | Efron approach. |
23. Related Tutorials
Learn more about the methods used in this trial:
24. Related Calculators
25. Sources
- ClinicalTrials.gov: DESTINY-Breast06, NCT04494425.
- PubMed: PubMed record for PMID 39282896.
- PubMed: PubMed record for PMID 37499870.
Continue through Clinical Biostats
Explore the statistical concepts behind randomized trials, survival analysis, confidence intervals, and clinical-trial effect measures.
26. Record Summary
DESTINY-Breast06 provides a clear example of a randomized phase 3 time-to-event analysis. The ClinicalTrials.gov record describes an 866-participant, 2-arm, parallel randomized trial comparing T-DXd with investigator's choice chemotherapy. Its registered primary endpoint is PFS assessed by BICR in the HR+, HER2-low population, with response evaluations performed at screening, every 6 weeks ± 1 week from randomization for 48 weeks, and then every 9 weeks (q9w) ± 1 week, starting at Week 48 until PD, up to PCD of 18 March 2024 (maximum of approximately 43.85 months).
The formal analysis uses a stratified log-rank test, while the hazard ratio and two-sided 95% confidence interval are estimated from a stratified Cox proportional-hazards model. The model adjusts for prior CDK4/6 inhibitor use and HER2 IHC expression, with ties handled by the Efron approach. The reported HR of 0.62, 95% CI 0.52–0.75, and P-value <0.0001 describe the primary statistical comparison.
The most important statistical lesson is that these numbers answer different questions. The HR describes relative event hazard, the confidence interval describes uncertainty around that estimate, and the P-value describes evidence against the null hypothesis under the specified testing framework. None of them, by itself, is a median PFS, an absolute PFS probability, or a measure of benefit for an individual patient.