This page provides an independent statistical analysis and educational interpretation of publicly reported results. ClinicalTrials.gov provides the official trial registry record. The registry indicates that results have been posted and 12 outcome measures are posted, but the ClinicalTrials.gov record contains no formal statistical analyses.
1. Trial at a Glance
PATINA is a phase 3, randomized, open-label, parallel-group clinical study of targeted therapy including palbociclib in patients with HER-2 positive and estrogen receptor positive breast cancer. The registry lists progression-free survival as the single primary endpoint, assessed by the investigator.
| Feature | PATINA |
|---|---|
| Trial name | PATINA |
| NCT identifier | NCT02947685 |
| Phase | Phase 3 |
| Conditions | HER-2 Positive Breast Cancer; Estrogen Receptor Positive Breast Cancer |
| Allocation | Randomized |
| Design model | Parallel |
| Masking | None; open-label |
| Primary purpose | Treatment |
| Enrollment | 518 |
| Arms | 2 |
| Status | Active, not recruiting |
| Lead sponsor | Alliance Foundation Trials, LLC. |
| Sponsor type | Other |
2. Clinical Question
The trial addresses a treatment question in patients with HER-2 positive and estrogen receptor positive breast cancer: how does a randomized treatment strategy involving palbociclib compare with the alternative randomized strategy in the trial, with the primary comparison defined through investigator-assessed progression-free survival?
Population
Patients with HER-2 positive breast cancer and estrogen receptor positive breast cancer enrolled in the phase 3 PATINA study.
Interventions
The registered interventions include palbociclib, trastuzumab, pertuzumab, letrozole, anastrozole, exemestane, and fulvestrant.
Comparator
The ClinicalTrials.gov record identifies two randomized arms but does not provide arm-specific treatment regimens or a formal statistical comparison.
Primary question
For the registered primary endpoint, what is the time from registration to disease progression or death under the randomized treatment strategies?
3. Trial Design
Treatment strategy
- The ClinicalTrials.gov record identifies palbociclib and other registered interventions for the study.
- The exact arm-specific regimen is not included in the ClinicalTrials.gov record.
Comparator strategy
- A second parallel randomized arm is registered.
- The ClinicalTrials.gov record does not specify its complete regimen.
4. Trial Timeline and Status
Study start
The ClinicalTrials.gov record gives 2017-06-21 as the trial start date.
Primary completion
The ClinicalTrials.gov record gives 2024-10-15 as the primary completion date.
Active, not recruiting
The study is listed as ACTIVE_NOT_RECRUITING in the ClinicalTrials.gov record.
The distinction between study completion and availability of a formal statistical analysis is important. The ClinicalTrials.gov record says results have been posted, but the statistical-analysis field in the ClinicalTrials.gov record is empty. A posted outcome measure is therefore not the same thing as a posted treatment-effect analysis.
5. Primary Endpoint
| Endpoint | Registry definition | Time frame | Endpoint type |
|---|---|---|---|
| Progression-free Survival (PFS) as Assessed by Investigator | The time from registration to disease progression or death. | 7 years 3 months | Time-to-event |
This is the only registered primary endpoint reported in the ClinicalTrials.gov record. The registry reports one primary endpoint, and its endpoint type is time-to-event.
The endpoint combines two possible events. A participant who experiences disease progression reaches the endpoint at progression; a participant who dies before documented progression also reaches the endpoint through death.
6. Results Availability
This distinction is central to an independent statistical analysis. A registry can contain an outcome measure and associated posted information without supplying a formal comparative analysis object. A statistical review should not convert an endpoint definition into an assumed treatment effect.
7. Planned Analysis
Because the ClinicalTrials.gov record contains no formal statistical analysis for the primary endpoint, the appropriate approach is to distinguish what the registry actually specifies from the statistical framework normally used for a randomized time-to-event comparison.
Kaplan-Meier estimation
For a randomized trial with progression-free survival as the primary time-to-event endpoint, Kaplan-Meier methods would ordinarily be used to estimate the PFS distribution in each randomized arm. The method accommodates right-censored observations, allowing participants who have not experienced progression or death by their last evaluable follow-up to contribute information up to that point.
Here, di represents the number of events at event time ti, while ni represents the number at risk immediately before that event time.
Hazard ratio framework
A randomized comparison of PFS would commonly be summarized with a hazard ratio from a Cox proportional-hazards model. A hazard ratio below 1 would indicate a lower estimated instantaneous rate of the event in one randomized group relative to the other, subject to the model and its assumptions.
The hazard ratio is a relative time-to-event measure. It is not itself a median PFS, an absolute risk difference, or the proportion of participants who benefit.
Log-rank comparison
A conventional randomized time-to-event analysis would typically compare the survival experience between treatment groups using a log-rank test. The test evaluates evidence against equality of the event-time distributions; it does not measure the magnitude of the treatment effect.
Confidence intervals
A formal treatment-effect estimate should be accompanied by a confidence interval. The interval communicates statistical precision around the estimated effect and should be interpreted alongside the point estimate rather than replaced by a p-value alone.
Analysis population
For a randomized efficacy endpoint, an intention-to-treat framework is generally important because participants remain associated with their randomized assignment. The ClinicalTrials.gov record does not provide a formal analysis-population definition, so this page does not assert that a particular ITT, per-protocol, or modified population was used for the posted registry result.
8. How to Interpret Investigator-Assessed PFS
Investigator-assessed PFS measures the elapsed time from registration until disease progression or death. It is therefore a time-to-event endpoint rather than a simple proportion of patients with progression.
PFS does not directly measure overall survival. A participant can experience progression and subsequently remain alive, while another participant can die without a documented prior progression. The registered endpoint combines those two event pathways.
Participants who have not experienced progression or death by the relevant observation point may be censored rather than treated as if they had remained event-free indefinitely. Correct handling of censoring is therefore part of the validity of a Kaplan-Meier or Cox analysis.
A hazard ratio summarizes relative event rates under a model. It should not be translated directly into an absolute increase in months of PFS, and it should not be interpreted as though every participant experienced the same proportional change in risk.
9. Statistical Methods Explained
Why is randomization important for PFS?
Randomization creates the basis for comparing outcomes between treatment groups while reducing systematic differences in measured and unmeasured baseline characteristics in expectation. The PATINA registry identifies the allocation as randomized and the design as parallel. That makes the randomized treatment comparison the central inferential framework for the primary endpoint.
Why is PFS analyzed as a time-to-event endpoint?
Participants can experience progression or death at different times, and not every participant necessarily has an event during the observation period. Time-to-event methods preserve information about when an event occurred and appropriately handle participants whose event status is censored at the end of available follow-up.
What would a hazard ratio mean for PATINA?
If a formal Cox analysis produced an HR below 1 for the palbociclib-containing randomized strategy relative to its comparator, the estimate would represent a lower modeled instantaneous event rate in that group. The numerical HR itself would still need a confidence interval and information about the analysis population and model assumptions for proper interpretation.
Why does the p-value not measure effect size?
A p-value describes the compatibility of the observed data with a specified null hypothesis under the statistical testing framework. It does not state how large the treatment effect is. Effect magnitude is conveyed by quantities such as a hazard ratio, median PFS difference, or absolute survival probability, while confidence intervals describe uncertainty around estimates.
Why does investigator assessment matter?
The registered endpoint is specifically defined as PFS as assessed by investigator. The source of endpoint assessment therefore forms part of the endpoint definition. An investigator-assessed endpoint should not automatically be described as an independently centrally reviewed endpoint when the ClinicalTrials.gov record does not say that such review occurred.
What does the 7 years 3 months time frame mean?
The registry gives 7 years 3 months as the time frame for the primary PFS endpoint. This is the registered time frame associated with the endpoint. It should not be converted into a median PFS or interpreted as an observed survival time for participants.
10. Safety Results
The ClinicalTrials.gov record includes serious adverse events by randomized arm. They provide the number affected and the number at risk, but no formal statistical comparison of serious adverse events is reported.
| Randomized arm | Serious adverse events affected | At risk |
|---|---|---|
| Arm A | 75 | 261 |
| Arm B | 54 | 257 |
The arm-specific counts show that serious adverse events were reported in both randomized arms. Because the ClinicalTrials.gov record does not provide a formal safety analysis, these counts should be treated as descriptive rather than as evidence of a statistically significant difference.
11. Statistical Interpretation of the Available Safety Data
Descriptive evidence
Arm A has 75 affected participants among 261 at risk, while Arm B has 54 affected participants among 257 at risk.
No formal comparison reported
The registry-reported statistical-analysis field is empty, so there is no reported risk ratio, odds ratio, confidence interval, or p-value for serious adverse events.
Different question from PFS
Serious adverse events and progression-free survival answer different clinical questions and should not be combined into a single efficacy statistic.
Denominators are explicit
The affected/at-risk format makes clear that the two arms have different numbers at risk, which should be retained when interpreting the descriptive safety data.
12. What Can and Cannot Be Concluded From the Posted Data
| Question | Supported by the ClinicalTrials.gov record? | Reason |
|---|---|---|
| Was PATINA randomized? | Yes | The allocation is listed as randomized. |
| Was the study open-label? | Yes | Masking is listed as none. |
| Was the design parallel? | Yes | The design model is listed as parallel. |
| What was the enrollment? | Yes | Enrollment is 518. |
| What was the primary endpoint? | Yes | Investigator-assessed PFS. |
| What was the PFS hazard ratio? | No | No statistical analysis is reported. |
| What was median PFS? | No | No median PFS estimate is reported. |
| What was the PFS p-value? | No | No p-value is reported. |
| What was the PFS confidence interval? | No | No confidence interval is reported. |
| Were serious adverse events reported? | Yes | Affected/at-risk counts are posted on ClinicalTrials.gov for both arms. |
| Was a formal serious-adverse-event comparison reported? | No | The registry-reported statistical-analysis field is empty. |
13. Design Features That Affect Statistical Interpretation
Randomization
Randomization is one of the most important statistical features of PATINA. With 518 enrolled participants allocated across 2 parallel arms, the design establishes a framework for comparing outcomes according to assigned treatment strategy.
Open-label treatment
The study is listed as having no masking. This means treatment assignment was not blinded according to the ClinicalTrials.gov record. For an investigator-assessed endpoint, the open-label design is an important interpretive feature because assessment occurs in the context of known treatment assignment.
Parallel-group design
The parallel structure means participants are assigned to one of the randomized treatment arms rather than sequentially receiving multiple randomized treatment strategies. The primary comparison therefore concerns outcomes accumulated after assignment to the respective arms.
Time-to-event endpoint
The primary endpoint is not a binary response rate. It incorporates follow-up time and event timing. That makes censoring, event definitions, follow-up completeness, and the statistical model important parts of the analysis.
Absence of registry-reported formal analysis
The most important limitation of the available statistical information is that the ClinicalTrials.gov record contains no formal statistical analyses. That prevents a responsible reconstruction of the numerical treatment effect even though the registry says results have been posted.
14. Why This Trial Matters Statistically
PATINA is useful as a teaching example because its primary endpoint illustrates a central problem in clinical-trial statistics: the difference between having an endpoint and having a formal statistical estimate of treatment effect.
| Concept | How it appears in PATINA |
|---|---|
| Randomization | The study uses randomized allocation. |
| Parallel design | Participants are allocated to 2 parallel arms. |
| Open-label design | Masking is listed as none. |
| Time-to-event analysis | The primary endpoint is investigator-assessed PFS. |
| Kaplan-Meier estimation | A standard framework for estimating PFS distributions in randomized groups. |
| Cox proportional-hazards modeling | A standard framework for estimating a relative PFS hazard when appropriate. |
| Log-rank testing | A standard method for comparing time-to-event distributions between randomized groups. |
| Censoring | Relevant whenever participants do not experience progression or death during observed follow-up. |
| Open-label assessment | Important because the primary endpoint is investigator-assessed. |
| Safety denominators | Serious adverse events are reported as affected/at-risk counts by arm. |
| Registry interpretation | Posted outcome measures do not necessarily provide a formal treatment-effect analysis. |
15. Important Limitations and Interpretation Issues
- No formal statistical analyses reported: no formal statistical analyses were posted, so numerical efficacy comparisons cannot be responsibly reproduced.
- No effect estimates: no PFS hazard ratio, confidence interval, p-value, median PFS, or treatment-group PFS estimate is provided in the ClinicalTrials.gov record.
- Open-label design: masking is listed as none, while the primary endpoint is investigator-assessed. This is relevant when considering potential assessment and management influences.
- Endpoint-specific uncertainty: the primary endpoint is time-to-event, so censoring and follow-up patterns would be important to any formal analysis.
- No formal multiplicity information: the ClinicalTrials.gov record does not describe multiplicity procedures, alpha allocation, or an endpoint hierarchy beyond identifying one registered primary endpoint.
- No interim-analysis information: the ClinicalTrials.gov record does not specify an interim analysis, stopping boundary, or alpha-spending procedure.
- No missing-data or imputation specification: the ClinicalTrials.gov record does not describe rules for missing assessments or censoring.
- No stratification variables reported: the registry data identify randomization but do not provide stratification factors.
- No Bayesian methodology reported: the ClinicalTrials.gov record does not identify a Bayesian analysis.
- Safety interpretation is descriptive: serious adverse-event counts are available by arm, but no formal statistical comparison is reported.
16. Clinical Interpretation vs Statistical Interpretation
Statistical interpretation
The study is a randomized, parallel-group phase 3 trial with a single registered time-to-event primary endpoint. The ClinicalTrials.gov record does not provide the numerical comparative analysis required to quantify the treatment effect.
Clinical interpretation
The registry identifies a targeted-therapy study in HER-2 positive and estrogen receptor positive breast cancer and identifies investigator-assessed PFS as the primary outcome. No treatment recommendation follows from the registry-reported numerical information alone.
17. What a Complete PFS Analysis Would Normally Report
For educational purposes, a complete statistical presentation of the registered primary endpoint would ordinarily bring several complementary quantities together rather than relying on one number.
| Component | Purpose |
|---|---|
| Number randomized | Defines the population entering the randomized comparison. |
| Number of PFS events | Describes the amount of event information available for the time-to-event analysis. |
| Kaplan-Meier estimates | Describe the estimated PFS experience over time. |
| Median PFS | Provides a time-scale summary when the estimated survival curve reaches 0.50. |
| Hazard ratio | Summarizes the relative instantaneous event rate under a Cox model. |
| 95% confidence interval | Describes statistical uncertainty around the estimated hazard ratio. |
| Log-rank p-value | Tests the prespecified null comparison under the applicable testing framework. |
| Censoring information | Shows how much follow-up information comes from participants without an observed event. |
None of the numerical PFS results in this list should be inferred from the fact that results are posted. The ClinicalTrials.gov record provides the endpoint definition and time frame, but not these formal treatment-effect quantities.
18. Statistical Concepts in This Trial
Learn more about the methods relevant to this trial:
19. Related Statistical Calculators
20. Sources
- ClinicalTrials.gov: PATINA, NCT02947685.
- Related PubMed record: PubMed record, PMID 25524798.
- Related PubMed record: PubMed record, PMID 12954578.
- Related PubMed record: PubMed record, PMID 41604639.
- Related PubMed record: PubMed record, PMID 33369581.
Continue through Clinical Biostats
Explore the statistical concepts that underpin randomized clinical trials and their time-to-event endpoints.
21. Record Summary
PATINA is a phase 3, randomized, open-label, parallel-group study with 518 enrolled participants and 2 treatment arms. Its single registered primary endpoint is Progression-free Survival (PFS) as Assessed by Investigator, defined as the time from registration to disease progression or death over a registered time frame of 7 years 3 months.
The statistical lesson is equally important: the ClinicalTrials.gov record states that results have been posted, but they do not contain formal statistical analyses for the primary endpoint. Consequently, a responsible independent analysis can explain the appropriate time-to-event framework—Kaplan-Meier estimation, hazard-ratio modeling, confidence intervals, log-rank testing, and censoring—without fabricating a numerical treatment effect.