This page separates reported trial results from statistical interpretation. Numerical results are restricted to the information reported in the ClinicalTrials.gov record. ClinicalTrials.gov provides the official trial registry record.
1. Trial at a Glance
TULIP was a randomized, parallel-group, open-label phase 3 trial evaluating (vic-)trastuzumab duocarmazine versus physician's choice in participants with HER2-positive locally advanced or metastatic breast cancer. The primary endpoint was progression-free survival, analyzed using a stratified log-rank test with a stratified Cox regression model for the hazard ratio.
| Feature | TULIP |
|---|---|
| Phase | Phase 3 |
| Condition | Metastatic breast cancer |
| Population | Participants with HER2-positive locally advanced or metastatic breast cancer |
| Design | Randomized, parallel-group, open-label |
| Allocation | Randomized |
| Primary purpose | Treatment |
| Primary endpoint | Progression-free survival |
| Primary hypothesis | Superiority |
| Enrollment | 437 |
| Lead sponsor | Byondis B.V. |
| ClinicalTrials.gov | NCT03262935 |
2. Clinical Question
The central statistical question was whether (vic-)trastuzumab duocarmazine improved progression-free survival compared with physician's choice in participants with HER2-positive locally advanced or metastatic breast cancer.
Population
Participants with HER2-positive locally advanced or metastatic breast cancer.
Intervention
(Vic-)trastuzumab duocarmazine.
Comparator
Physician's choice.
Primary question
Does (vic-)trastuzumab duocarmazine improve progression-free survival relative to physician's choice under a superiority framework?
3. Trial Design
(Vic-)trastuzumab duocarmazine
- Intervention: (vic-)trastuzumab duocarmazine.
- Serious adverse events were reported for 53 of 288 participants at risk.
Physician's choice
- Comparator: physician's choice.
- Serious adverse events were reported for 12 of 137 participants at risk.
4. Randomization and Stratified Analysis
The primary progression-free survival analysis used the full-analysis set (FAS), comprising all randomized patients analyzed according to the treatment group and strata to which they were assigned. This is an important distinction: the efficacy comparison remains anchored to randomized treatment assignment rather than being restricted to patients who completed treatment.
The primary analysis incorporated the stratification factors assigned at randomization:
| Stratification factor | Categories used in the analysis |
|---|---|
| World region | Europe, Singapore, and North America |
| Number of prior treatment lines for locally advanced or metastatic breast cancer | 1 to 2; >2, excluding hormone therapy |
| Prior treatment with pertuzumab | Yes; no |
Stratification is useful when important prognostic factors are known before randomization. Instead of treating all participants as if they came from one homogeneous risk population, the analysis compares treatment groups while accounting for the prespecified strata.
5. Endpoints
| Endpoint | Registry definition / time frame | Analysis method |
|---|---|---|
| Progression Free Survival | Time from the date of randomization to the date of first documented disease progression by central assessment according to Response Evaluation Criteria In Solid Tumors (RECIST) v1.1 or death due to any cause, whichever occurred earlier. Time frame: baseline until primary analysis data cut-off date of 31March2021. | Stratified log-rank test; stratified Cox regression for HR |
| Overall Survival | Time frame: baseline until final Overall Survival analysis data cut-off date of 30June2022. | Stratified log-rank test; stratified Cox regression for HR |
| Investigator Assessed Progression Free Survival | Time frame: baseline until primary analysis data cut-off date of 31March2021. | Stratified log-rank test; stratified Cox regression for HR |
| Objective Response Rate | Percentage of patients. Time frame: baseline until primary analysis data cut-off date of 31March2021. | Cochran-Mantel-Haenszel test |
| Patient Reported Outcomes for Health Related Quality of Life | Scores on a scale. Time frame: baseline until primary analysis data cut-off date of 31March2021. | MMRM |
The registry therefore contains one primary endpoint—progression-free survival—and four posted secondary outcome measures. The statistical methods correspond to the measurement structure of those endpoints: time-to-event methods for survival outcomes, a stratified categorical-data method for objective response rate, and a repeated-measures model for longitudinal quality-of-life scores.
6. Statistical Methodology
Primary progression-free survival analysis
The primary endpoint was analyzed in the full-analysis set, comprising all randomized patients. The treatment groups were compared using a two-sided stratified log-rank test. A stratified Cox regression analysis estimated the hazard ratio and its 95% confidence interval.
The log-rank test addresses evidence of a difference between time-to-event distributions, while the Cox model provides an estimated relative hazard together with a confidence interval.
Secondary overall survival analysis
Overall survival was also analyzed using a stratified log-rank test and stratified Cox regression. The final overall survival analysis used a data cutoff of 30June2022. The same three randomization stratification factors were incorporated into the reported Cox analysis.
Investigator-assessed progression-free survival
Investigator-assessed progression-free survival was analyzed as a secondary time-to-event endpoint. The registry reports a two-sided stratified log-rank comparison and a stratified Cox regression estimate of the hazard ratio.
Objective response rate
Objective response rate is a percentage-of-patients endpoint rather than a time-to-event endpoint. The registry reports use of the Cochran-Mantel-Haenszel test, with strata based on the baseline stratification factors, to compare the treatment groups at a two-sided 5% significance level.
Patient-reported quality of life
The change from baseline in the global health status/quality-of-life scale transformed score was analyzed using a mixed model for repeated measures (MMRM). This is a longitudinal method designed to use repeated observations rather than reducing each participant's follow-up to a single post-baseline value.
7. Primary Result: Progression-Free Survival
The registry reports a formal primary analysis of progression-free survival using the full-analysis set. The treatment groups were compared with a stratified log-rank test, and the hazard ratio was estimated using a stratified Cox regression model.
Hazard ratio for progression or death
95% CI: 0.4885–0.8389 · P = 0.002
Superiority hypothesis; two-sided 95% confidence interval.
| Primary endpoint | Comparison | Estimate | 95% CI | P-value |
|---|---|---|---|---|
| Progression Free Survival | (Vic-)Trastuzumab Duocarmazine vs Physician's Choice | HR 0.6401 | 0.4885–0.8389 | =0.002 |
The estimated hazard ratio of 0.6401 means that, under the stratified Cox model, the estimated instantaneous hazard of the PFS event was about 64% of the corresponding hazard in the physician's-choice group. Expressed as a relative complement, this corresponds to an estimated 35.99% lower hazard under the fitted model.
The hazard ratio does not mean that 35.99% of participants avoided progression, that 35.99% of participants were cured, or that every participant experienced the same proportional reduction in risk. It is a model-based relative measure of the event hazard over follow-up.
The 95% confidence interval of 0.4885 to 0.8389 describes statistical uncertainty around the estimated hazard ratio. It does not describe the range of treatment effects experienced by individual participants.
The P = 0.002 value addresses evidence against the null hypothesis under the specified testing framework. It is not a measure of the magnitude of the treatment effect and does not tell us how clinically important an effect is.
Because the analysis is based on a Cox proportional-hazards model, interpretation of a single hazard ratio is most straightforward when the proportional-hazards assumption is reasonably appropriate. The ClinicalTrials.gov record does not provide a diagnostic assessment of that assumption, so the HR should not be interpreted as a literal constant risk ratio at every time point.
What the primary PFS result establishes statistically
The reported two-sided P-value of 0.002 provides evidence of a difference between the randomized treatment groups under the registered superiority analysis. The effect estimate is below 1, and the reported 95% confidence interval lies below 1. Together, these quantities provide both an estimate of relative treatment effect and a measure of its statistical precision.
Importantly, the PFS definition is clinically specific: the event was the first documented disease progression by central assessment according to RECIST v1.1 or death from any cause, whichever occurred earlier. Thus, the endpoint combines radiographic disease progression and death into one time-to-event outcome.
8. Secondary Result: Overall Survival
Overall survival was a secondary time-to-event endpoint with a final analysis data cutoff of 30June2022. The registry reports a full-analysis-set analysis using a stratified log-rank test and stratified Cox regression.
Hazard ratio for overall survival
95% CI: 0.676–1.1145 · P = 0.236
Superiority hypothesis; two-sided 95% confidence interval.
| Secondary endpoint | Comparison | Estimate | 95% CI | P-value |
|---|---|---|---|---|
| Overall Survival | (Vic-)Trastuzumab Duocarmazine vs Physician's Choice | HR 0.868 | 0.676–1.1145 | =0.236 |
The estimated OS hazard ratio of 0.868 corresponds to an estimated instantaneous hazard of death approximately 86.8% of that in the physician's-choice group under the stratified Cox model. The corresponding relative complement is approximately a 13.2% lower estimated hazard.
This does not mean that mortality was reduced by exactly 13.2% for every patient, nor does it provide an absolute difference in survival probability. The HR is a relative time-to-event measure.
The 95% confidence interval, 0.676 to 1.1145, spans 1.0. Thus the interval is compatible with a range of relative hazard differences that includes no difference under the model.
The P = 0.236 value is not evidence about the size or clinical importance of the estimated HR. It describes the statistical evidence against the null hypothesis under the reported superiority testing framework. A nonsignificant P-value should not be translated into proof that the treatments are equivalent.
The OS analysis also has a different data cutoff from the primary PFS analysis. These results should therefore not be treated as though they arose from one common time point.
9. Secondary Result: Investigator-Assessed Progression-Free Survival
Investigator-assessed progression-free survival was analyzed through the same primary-analysis data cutoff of 31March2021. The registry reports a two-sided stratified log-rank comparison and a stratified Cox regression estimate.
Hazard ratio for investigator-assessed PFS
95% CI: 0.4666–0.7703 · P < 0.001
Superiority hypothesis; two-sided comparison.
| Secondary endpoint | Estimate | 95% CI | P-value |
|---|---|---|---|
| Investigator Assessed Progression Free Survival | HR 0.5995 | 0.4666–0.7703 | <0.001 |
An HR of 0.5995 means that the estimated instantaneous hazard of the investigator-assessed PFS event was approximately 60% of the corresponding hazard in the physician's-choice group under the stratified Cox model. The relative complement is approximately a 40.05% lower estimated hazard.
The estimate should not be interpreted as a 40.05% absolute improvement in progression-free survival, and it does not mean that 40.05% of patients necessarily benefited.
The 95% CI of 0.4666 to 0.7703 provides a range of values expressing uncertainty around the estimated relative hazard. Its location below 1 is consistent with the direction of the reported treatment effect.
The P < 0.001 result indicates strong statistical evidence against the null hypothesis under the reported test. It does not quantify the clinical magnitude of the effect.
Because this endpoint is investigator assessed rather than the centrally assessed primary PFS endpoint, it is useful as a separate outcome rather than being silently substituted for the primary endpoint.
10. Secondary Result: Objective Response Rate
Objective response rate was analyzed as a percentage-of-patients endpoint. The registry reports use of a Cochran-Mantel-Haenszel test, with strata based on the baseline stratification factors, to compare the treatment groups at a two-sided 5% significance level.
Objective response rate comparison
Cochran-Mantel-Haenszel test; two-sided 5% significance level.
| Secondary endpoint | Method | P-value | Analysis framework |
|---|---|---|---|
| Objective Response Rate | Cochran-Mantel-Haenszel test | =0.732 | Strata based on baseline stratification factors |
The registry's reported P = 0.732 does not provide evidence of a statistically detectable difference in objective response rate under the specified Cochran-Mantel-Haenszel comparison.
The ClinicalTrials.gov record does not report the response percentages, response counts, or a confidence interval for the response-rate comparison. Therefore, the P-value should not be converted into an estimated treatment effect or interpreted as evidence that the response rates are identical.
The choice of a stratified Cochran-Mantel-Haenszel test is appropriate to the endpoint structure: response is categorical, while the baseline stratification factors can be incorporated into the treatment comparison rather than ignored.
11. Secondary Result: Patient-Reported Health-Related Quality of Life
The patient-reported outcome analysis evaluated the change from baseline in the global health status/quality-of-life scale transformed score using an MMRM approach.
Quality-of-life analysis
MMRM analysis of change from baseline in the global health status/QoL scale transformed score.
The reported P = 0.473 does not provide statistical evidence of a treatment-group difference under the reported MMRM analysis.
The ClinicalTrials.gov record does not provide an estimated between-group change, confidence interval, or detailed repeated-measures parameter estimates. The P-value therefore should not be used to infer the size of any difference or to claim that the treatment groups had identical quality-of-life trajectories.
MMRM is particularly useful for longitudinal outcomes because repeated observations from the same participant are correlated. The model is designed to account for this within-participant structure rather than treating every observation as independent.
12. Safety Results
The ClinicalTrials.gov record reports serious adverse events by treatment arm. These are presented as affected participants over the corresponding number at risk.
| Safety measure | (Vic-)Trastuzumab Duocarmazine | Physician's Choice |
|---|---|---|
| Serious adverse events | 53/288 | 12/137 |
The ClinicalTrials.gov record does not provide a complete adverse-event table, severity breakdown, treatment-relatedness classification, or discontinuation analysis. Those quantities are therefore not inferred from the serious-adverse-event counts.
13. Statistical Methods Explained
Why was a stratified log-rank test used for progression-free survival?
Progression-free survival is a time-to-event endpoint, so participants may have different follow-up times and may be censored before experiencing progression or death. The log-rank test compares the event-time distributions between randomized treatment groups while accounting for the timing of events. In this trial, the comparison was stratified according to the factors used at randomization.
What does an HR of 0.6401 mean?
An HR of 0.6401 means that the estimated instantaneous event hazard under the fitted Cox model was 0.6401 times that in the comparison group. The complementary percentage, approximately 35.99%, describes the relative reduction implied by the point estimate. It is not an absolute reduction in the probability of progression or death.
Why was a Cox regression model used?
The Cox model provides a way to estimate a relative hazard while allowing the baseline hazard to remain unspecified. In TULIP, the registry reports a stratified Cox regression analysis, allowing the primary PFS hazard ratio to account for the prespecified randomization strata.
What does the 95% confidence interval tell us?
A 95% confidence interval quantifies statistical uncertainty around an estimated parameter under the specified inferential framework. For the primary PFS HR, the interval was 0.4885–0.8389. The interval should not be interpreted as saying that 95% of individual patients experience an HR somewhere inside those limits.
Why was the Cochran-Mantel-Haenszel test used for objective response rate?
Objective response rate is a categorical outcome. The Cochran-Mantel-Haenszel approach allows treatment groups to be compared while accounting for prespecified strata. This can be preferable to an unstratified comparison when the trial's randomization and analysis plan explicitly incorporate baseline stratification factors.
Why was MMRM used for quality-of-life measurements?
Quality-of-life scores can be measured repeatedly over time. Observations from the same participant are correlated, so ordinary methods that assume complete independence of all observations are not appropriate. MMRM provides a framework for modeling repeated measurements and estimating treatment-group differences in longitudinal change.
Why should the P-value not be treated as the effect size?
A P-value describes the strength of statistical evidence against a specified null hypothesis under a particular model and sampling framework. It does not tell us how large the treatment effect is. The estimated HR and its confidence interval provide effect-size and precision information, while the P-value provides a different piece of statistical evidence.
14. Confidence Intervals and Precision
The TULIP results illustrate why an effect estimate should be read together with its confidence interval rather than in isolation.
| Endpoint | Point estimate | 95% confidence interval | What the interval adds |
|---|---|---|---|
| Primary PFS | HR 0.6401 | 0.4885–0.8389 | Shows the statistical uncertainty around the estimated relative hazard. |
| Overall Survival | HR 0.868 | 0.676–1.1145 | Shows greater uncertainty around the direction and magnitude of the secondary OS estimate. |
| Investigator-assessed PFS | HR 0.5995 | 0.4666–0.7703 | Shows uncertainty around the investigator-assessed relative hazard. |
Comparing the width and location of these intervals is more informative than simply sorting P-values. The primary PFS estimate has a confidence interval entirely below 1, whereas the OS interval extends above 1. These are descriptive properties of the reported estimates and uncertainty intervals, not substitute measures of clinical importance.
15. Time-to-Event Endpoints and Censoring
Both progression-free survival and overall survival are time-to-event outcomes. The key statistical feature is that not every participant necessarily experiences the event during the observation period. A participant can therefore contribute follow-up information without having an observed event.
The survival function describes the probability that the event time exceeds a specified time t. Kaplan-Meier estimation is commonly used to estimate this function when observations can be right censored.
For the registered primary endpoint, the event definition is itself composite: either the first documented disease progression by central RECIST v1.1 assessment or death from any cause, whichever occurs earlier. This means that death can produce a PFS event even when radiographic progression has not previously been documented.
16. Stratification and the Cochran-Mantel-Haenszel Framework
Three baseline factors were incorporated into the primary stratified analysis: world region, number of prior treatment lines, and prior pertuzumab treatment. These factors were assigned at randomization and then used in the stratified Cox analysis.
The same baseline stratification factors were also used for the Cochran-Mantel-Haenszel comparison of objective response rate. This creates a coherent statistical structure across the trial: the factors used to organize randomization are also represented in the corresponding stratified analyses.
Why stratify?
Stratification can account for important baseline factors that may influence outcome and can improve alignment between the randomization scheme and the inferential analysis.
What stratification does not do
It does not turn the study into an observational matched comparison. The fundamental treatment contrast remains based on randomized assignment.
17. Primary vs Secondary Evidence
The trial's statistical evidence should be organized according to endpoint role rather than treating every reported P-value as equally confirmatory.
| Endpoint | Role | Method | Reported result |
|---|---|---|---|
| Progression Free Survival | Primary | Stratified log-rank; stratified Cox regression | HR 0.6401; 95% CI 0.4885–0.8389; P = 0.002 |
| Overall Survival | Secondary | Stratified log-rank; stratified Cox regression | HR 0.868; 95% CI 0.676–1.1145; P = 0.236 |
| Investigator Assessed Progression Free Survival | Secondary | Stratified log-rank; stratified Cox regression | HR 0.5995; 95% CI 0.4666–0.7703; P < 0.001 |
| Objective Response Rate | Secondary | Cochran-Mantel-Haenszel test | P = 0.732 |
| Patient Reported Outcomes for Health Related Quality of Life | Secondary | MMRM | P = 0.473 |
This hierarchy matters. The primary PFS analysis is the central confirmatory result identified in the ClinicalTrials.gov record. Secondary endpoints provide additional evidence about survival, investigator-assessed disease control, response, and patient-reported outcomes, but they should retain their registered endpoint role when interpreting the overall evidence.
18. How the Results Fit Together
The reported results are not all pointing to the same statistical quantity. PFS and investigator-assessed PFS are time-to-event endpoints summarized with hazard ratios. Overall survival is another time-to-event endpoint but uses a later data cutoff. Objective response rate is a categorical percentage outcome, while health-related quality of life is longitudinal and analyzed with MMRM.
PFS
Primary endpoint with HR 0.6401, 95% CI 0.4885–0.8389, and P = 0.002.
Overall survival
Secondary endpoint with HR 0.868, 95% CI 0.676–1.1145, and P = 0.236 at the final OS cutoff.
Investigator-assessed PFS
Secondary time-to-event endpoint with HR 0.5995, 95% CI 0.4666–0.7703, and P < 0.001.
Response and quality of life
ORR was analyzed with the Cochran-Mantel-Haenszel test, while quality-of-life change was analyzed with MMRM.
A statistically coherent reading therefore avoids collapsing these outcomes into a single number. The primary endpoint supplies the main superiority test; the secondary analyses describe additional dimensions of treatment effect and patient experience.
19. Limitations and Interpretation Issues
- Different data cutoffs: the primary PFS, investigator-assessed PFS, objective response rate, and quality-of-life analyses use a cutoff of 31March2021, whereas the final overall survival analysis uses 30June2022. Results from these analyses should not be treated as if they were measured at one common time point.
- Endpoint-specific methods: PFS and OS are time-to-event outcomes, ORR is categorical, and quality of life is longitudinal. Their P-values and effect measures answer different statistical questions.
- Hazard-ratio interpretation: a Cox hazard ratio is a model-based relative measure and should not be interpreted as an absolute risk reduction or as the proportion of patients who benefit.
- Proportional-hazards assumption: interpretation of a single Cox HR is most straightforward when the proportional-hazards assumption is reasonably appropriate. The ClinicalTrials.gov record does not report a diagnostic assessment of that assumption.
- Confidence intervals: the ClinicalTrials.gov record provides confidence intervals for the three hazard-ratio analyses but not for objective response rate or the MMRM quality-of-life comparison. The missing intervals should not be reconstructed from the reported P-values.
- Secondary endpoints: secondary results should be interpreted according to their registered role and should not automatically be treated as equivalent to the primary endpoint.
- Safety detail: the ClinicalTrials.gov record contains serious adverse-event counts by arm but do not provide a complete safety profile. Broader toxicity conclusions are therefore not inferred.
- Analysis population: the primary efficacy analysis used the full-analysis set of randomized participants. This preserves the randomized treatment comparison and differs conceptually from restricting analysis to participants who completed treatment.
20. Why This Trial Matters Statistically
TULIP is a useful teaching case because the registry data bring several core clinical-trial methods together in one randomized phase 3 analysis: a time-to-event primary endpoint, stratified randomization factors, log-rank testing, Cox regression, a categorical response analysis, and a repeated-measures quality-of-life analysis.
| Concept | How it appears in TULIP |
|---|---|
| Randomization | Participants were randomized to two parallel treatment arms. |
| Full-analysis set | The primary PFS analysis included all randomized patients according to assigned treatment group and strata. |
| Time-to-event endpoint | PFS was defined from randomization to first documented progression or death, whichever occurred earlier. |
| Stratified log-rank test | Used for the primary PFS comparison and the reported secondary survival analyses. |
| Hazard ratio | Used to quantify relative treatment effects for PFS, OS, and investigator-assessed PFS. |
| Confidence interval | 95% two-sided CIs were reported for the hazard-ratio analyses. |
| Cox regression | Stratified Cox regression estimated the hazard ratios for time-to-event endpoints. |
| Cochran-Mantel-Haenszel test | Used for the stratified objective response rate comparison. |
| MMRM | Used to analyze change from baseline in the global health status/QoL scale transformed score. |
| Multiple endpoint types | The trial combines survival, categorical response, and longitudinal patient-reported outcomes, each requiring a method matched to its data structure. |
21. Statistical Methods Explained: A Deeper Walkthrough
Why does the primary PFS definition include both progression and death?
The registered PFS definition treats either first documented disease progression or death as the event, whichever occurs earlier. This creates a composite time-to-event endpoint that captures both radiographic disease worsening and death without requiring a participant to have a documented progression before death.
Why use a full-analysis set?
Using the full-analysis set keeps the primary efficacy comparison tied to randomized assignment. This reduces the risk that post-randomization treatment adherence or discontinuation changes the population being compared and thereby weakens the protection provided by randomization.
Why is the PFS HR different from the investigator-assessed PFS HR?
The registry identifies them as distinct endpoints: the primary PFS endpoint uses progression by central assessment according to RECIST v1.1, whereas the secondary endpoint is investigator assessed. Different assessment mechanisms can produce different event classifications and therefore different time-to-event estimates.
Why is an HR not the same as a median survival difference?
A hazard ratio compares event hazards under a statistical model over follow-up. A median is a time point at which an estimated survival function reaches 0.5. These are different summaries and cannot be converted into one another without additional information about the survival distributions.
Why can a confidence interval be more informative than a P-value alone?
The P-value addresses statistical evidence against a null hypothesis, while the confidence interval provides information about the range of effect estimates compatible with the statistical model and data. For example, the primary PFS HR of 0.6401 is accompanied by a 95% CI of 0.4885–0.8389, allowing the reader to assess both direction and precision.
What does the OS result mean when its confidence interval crosses 1?
The OS estimate was HR 0.868 with a 95% CI of 0.676–1.1145 and P = 0.236. The interval includes 1.0, so the reported interval includes a no-difference value under the hazard-ratio scale. This should not be converted into a claim that the treatments are equivalent; it indicates that the registry-reported analysis did not establish a statistically detectable difference under its reported superiority framework.
22. Related Tutorials
Learn more about the methods used in this trial:
23. Related Calculators
24. Sources
- ClinicalTrials.gov: NCT03262935 — TULIP.
- Linked publication: PubMed record for PMID 39442070.
Continue with the statistical methods behind TULIP
Explore tutorials on survival analysis, stratified testing, confidence intervals, repeated-measures models, and related clinical-trial methods.
25. Record Summary
TULIP provides a compact example of how a randomized phase 3 trial can use different statistical methods for different clinical questions. The primary endpoint, progression-free survival, was analyzed in the full-analysis set using a stratified log-rank test and stratified Cox regression, producing an HR of 0.6401 with a two-sided 95% CI of 0.4885–0.8389 and P = 0.002. Secondary analyses included overall survival, investigator-assessed progression-free survival, objective response rate, and patient-reported health-related quality of life, with methods matched to their respective data structures.
The overall survival analysis reported an HR of 0.868 with a 95% CI of 0.676–1.1145 and P = 0.236. Investigator-assessed PFS reported an HR of 0.5995 with a 95% CI of 0.4666–0.7703 and P < 0.001. Objective response rate was compared with a Cochran-Mantel-Haenszel test with P = 0.732, while the MMRM analysis of change from baseline in the global health status/QoL scale transformed score reported P = 0.473.