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
NIRVANA-1 is a randomized phase 2 trial in ovarian cancer evaluating a treatment strategy involving niraparib and bevacizumab-Awwb after complete cytoreduction. The registry describes a parallel, unmasked design with 390 planned participants and two arms. Its registered primary endpoint is progression-free survival rate up to 24 months.
| Feature | NIRVANA-1 |
|---|---|
| Trial name | NIRVANA-1 |
| NCT identifier | NCT05183984 |
| Phase | Phase 2 |
| Status | Recruiting |
| Condition | Ovarian Cancer |
| Allocation | Randomized |
| Design model | Parallel |
| Masking | None |
| Primary purpose | Treatment |
| Enrollment | 390 |
| Number of arms | 2 |
| Lead sponsor | ARCAGY/ GINECO GROUP |
| Sponsor type | Other |
2. Clinical Question
The clinical question is whether a randomized treatment strategy involving niraparib with bevacizumab after complete cytoreduction can produce a favorable progression-free survival outcome in patients with ovarian cancer. The registered endpoint focuses specifically on progression-free survival through 24 months.
Population
Patients with ovarian cancer undergoing the clinical setting described by the trial title: treatment after complete cytoreduction.
Intervention components
The registry identifies niraparib and bevacizumab-Awwb among the study interventions.
Additional intervention
Chemotherapy is also identified as an intervention in the registry.
Primary question
What is the progression-free survival rate up to 24 months, with progression-free survival defined from randomization to objective tumor progression or death?
The registry information identifies the interventions and randomized two-arm structure but does not specify the treatment components of each randomized arm in the information summarized here. The statistical interpretation therefore focuses on the randomized design and the registered endpoint rather than assigning an unreported regimen to either arm.
3. Trial Design
The design has an important statistical consequence: because participants are randomized, the treatment-group comparison can be framed around the assigned groups rather than around a comparison of patients who independently chose to receive different treatments. Randomization is the mechanism that balances both measured and unmeasured prognostic factors in expectation, although chance imbalances can still occur in any finite sample.
4. Interventions and Randomized Structure
Niraparib / bevacizumab strategy
- Niraparib
- Bevacizumab-Awwb
- Trial context: after complete cytoreduction
Chemotherapy
- Chemotherapy is listed as a study intervention.
- The registry information summarized here does not specify the complete arm-by-arm regimen.
The important design distinction is between the intervention inventory and the randomized comparison. The registry identifies chemotherapy, bevacizumab-Awwb, and niraparib as interventions, but the information available here does not establish a complete treatment recipe for each arm. A statistical analysis should therefore avoid treating an incompletely specified regimen as if its exact dosing or scheduling were known.
5. Primary Endpoint
| Endpoint | Registry definition and time frame | Statistical role |
|---|---|---|
| Progression-Free survival (PFS) rate up to 24 months | Progression-Free Survival (PFS) is defined as time from randomization until objective tumor progression or death, whichever occurs first, assessed up to 24 months. | Registered primary endpoint |
Why the endpoint is a time-to-event endpoint
PFS is fundamentally a time-to-event outcome. Each participant enters the risk set at randomization and is followed until the first qualifying event: objective tumor progression or death. Participants who have not experienced either event by their last relevant assessment contribute follow-up information up to that point and may be censored.
The phrase “assessed up to 24 months” gives the endpoint a clinically defined time horizon. Conceptually, the quantity of interest can be represented as the probability of remaining progression-free through month 24:
Here, T represents the time from randomization to objective tumor progression or death, whichever occurs first.
This is different from simply calculating the percentage of participants who have not been documented to progress at 24 months. A proper time-to-event analysis accounts for the timing of events and the information contributed by participants who are censored before the 24-month landmark.
6. Statistical Methodology
Kaplan-Meier estimation
For a progression-free survival endpoint defined by time from randomization to progression or death, a standard approach is the Kaplan-Meier estimator. It estimates the survival function while accommodating right-censoring.
At each event time ti, di is the number of events and ni is the number of participants at risk immediately before that event time.
For NIRVANA-1, the Kaplan-Meier curve would provide a visual description of the progression-free survival experience over time. The 24-month primary endpoint can then be expressed as the estimated survival probability at the 24-month landmark.
Comparison between randomized groups
When the objective is to compare PFS between randomized groups, a log-rank test is commonly used for a time-to-event hypothesis test, while a Cox proportional-hazards model can summarize the relative event rate using a hazard ratio.
Neither approach changes the underlying definition of PFS. The event remains objective tumor progression or death, whichever occurs first. The choice of statistical method determines how the observed event and censoring times are summarized and compared.
24-month PFS as a landmark estimate
A 24-month PFS rate is an absolute probability at a prespecified time point. If the Kaplan-Meier estimate at 24 months were 0.60, for example, that would mean an estimated 60% probability of remaining progression-free through 24 months under the statistical assumptions of the analysis. That example is illustrative only and is not a result for NIRVANA-1.
7. Planned Analysis
The registry identifies Progression-Free survival (PFS) rate up to 24 months as the primary endpoint. PFS is defined as the time from randomization until objective tumor progression or death, whichever occurs first, with assessment through 24 months.
For an endpoint of this type, a typical analysis would estimate the PFS distribution using the Kaplan-Meier method and report the estimated PFS probability at 24 months. A treatment-group comparison would commonly use a time-to-event method such as the log-rank test, with a Cox proportional-hazards model providing a hazard-ratio summary when appropriate.
Interpretation should account for censoring because not every participant will necessarily experience progression or death before the 24-month assessment window. The timing of each event matters: two participants who both eventually progress contribute different information if one progresses substantially earlier than the other.
Primary endpoint
Time from randomization to objective tumor progression or death, whichever occurs first.
ClinicalTrials.gov has not posted formal statistical analyses or outcome estimates for this endpoint.
8. Statistical Methods Explained
What exactly is being measured by PFS?
PFS measures the time from randomization until the first of two qualifying events: objective tumor progression or death. It therefore combines disease progression and death into a single time-to-event endpoint. A participant who dies without a previously documented progression experiences a PFS event at death.
Why use time-to-event methods instead of a simple proportion?
A simple proportion at 24 months treats everyone as though they registry-reported the same amount of follow-up information. Time-to-event methods preserve the timing of events and allow participants who have not experienced an event by their last assessment to contribute information before censoring.
What does the 24-month PFS rate mean?
The 24-month PFS rate is an estimated probability of remaining free from progression or death through 24 months. It is an absolute measure. Unlike a hazard ratio, it does not directly express the relative event rate between two randomized groups.
Why is Kaplan-Meier useful here?
Kaplan-Meier estimation is designed for right-censored time-to-event data. It updates the estimated probability of remaining event-free each time an event occurs while retaining participants who were censored as part of the risk-set calculations up to their censoring time.
What would a hazard ratio add?
A hazard ratio would provide a relative summary of the instantaneous event rate between randomized groups under a Cox model. An HR below 1 would indicate a lower estimated event hazard in the treatment group relative to the reference group, while an HR above 1 would indicate a higher estimated event hazard. It would not directly equal the difference between the two 24-month PFS percentages.
Why does censoring matter?
Censoring is informative because it determines how much follow-up information is available for each participant. Standard Kaplan-Meier and Cox analyses rely on assumptions about the relationship between censoring and the event process. Heavy or differential censoring can reduce precision and can threaten interpretation if those assumptions are not reasonable.
Why should the 24-month estimate have a confidence interval?
A point estimate alone does not communicate sampling uncertainty. A confidence interval around the 24-month PFS estimate would indicate the statistical precision of the estimated landmark probability. A wide interval would indicate less precision than a narrow interval, all else equal.
9. Understanding the Primary Endpoint Statistically
Event definition
The first event is objective tumor progression or death, whichever occurs first. This preserves a single time origin and a single event definition for the primary PFS analysis.
Time origin
Time zero is randomization. This is important because randomized treatment comparisons should be anchored to the point at which treatment assignment occurs.
24-month landmark
The primary endpoint focuses attention on the estimated progression-free probability at a clinically specified 24-month time point.
Censoring
Participants without an observed PFS event by their available follow-up may contribute censored observations rather than being treated as if an event had occurred.
The distinction between an event time and a landmark probability is central. The underlying PFS data contain timing information for each participant, while the primary endpoint can summarize that distribution at 24 months. A participant progressing at 3 months and another progressing at 21 months both have PFS events, but they do not provide the same time-to-event information.
10. Hazard Ratios and the Proportional-Hazards Assumption
If a Cox model is used for the randomized PFS comparison, the hazard ratio would summarize the relative hazard of progression or death between treatment groups. A hazard is an instantaneous event rate conditional on having remained event-free up to a given time.
An HR below 1 represents a lower estimated instantaneous event rate in the treatment group relative to the reference group under the fitted model.
The Cox proportional-hazards model traditionally assumes that the hazard ratio is reasonably stable over time. If the treatment effect changes substantially during follow-up, a single HR can become an incomplete description of the treatment difference. In that situation, the 24-month PFS probability and the Kaplan-Meier curves can provide important complementary information.
For NIRVANA-1, no hazard ratio has been posted in the registry information summarized here. Therefore, the appropriate statistical interpretation at present is methodological rather than numerical.
11. Confidence Intervals and Precision
A confidence interval around the 24-month PFS estimate would quantify uncertainty associated with estimating the underlying survival probability from a finite randomized sample. It would not represent the range of PFS times for individual patients.
The 24-month PFS estimate would describe the estimated probability of remaining progression-free through 24 months.
The confidence interval would communicate the statistical precision of that estimate. Wider intervals indicate more uncertainty around the estimated landmark probability than narrower intervals.
The landmark estimate describes a population-level probability. It does not mean that the same percentage of every subgroup or individual patient will remain progression-free for 24 months.
12. P-Values and Effect Size
If a formal hypothesis test is performed for the randomized PFS comparison, its p-value would quantify the compatibility of the observed data with the specified null hypothesis under the statistical model. It would not measure the magnitude of the treatment effect.
A very small p-value can occur with a relatively modest effect in a large sample, while a clinically meaningful effect can fail to reach a conventional significance threshold when the sample is small or the estimate is imprecise. For that reason, a complete PFS analysis should be interpreted using the effect estimate, confidence interval, absolute PFS probabilities, event counts, and clinical context, rather than the p-value alone.
13. Analysis Population and Randomization
Randomization is central to the inferential framework of NIRVANA-1. Once participants are randomly assigned, the primary comparison can be based on randomized treatment assignment rather than on post-randomization treatment choices.
An intention-to-treat analysis is commonly used for the primary efficacy comparison in randomized trials because it preserves the treatment assignment created by randomization. The precise analysis population for NIRVANA-1's primary endpoint is not specified in the registry information summarized here, so no particular analysis set is assigned beyond the randomized trial structure.
This distinction matters because excluding participants after randomization based on events that occur after treatment assignment can introduce selection into the treatment comparison. A clean randomized analysis asks what happened among the groups created by the randomization process.
14. Censoring and Missing Follow-Up
Because PFS is a time-to-event endpoint, some participants may reach the end of their available observation period without experiencing objective tumor progression or death. Their observation is typically handled through censoring rather than by classifying them as progression-free for an arbitrarily long period.
| Observation | Statistical meaning |
|---|---|
| Objective tumor progression observed | PFS event |
| Death observed before progression | PFS event |
| No progression or death by last evaluable follow-up | Potentially censored observation, subject to the prespecified analysis rules |
| Follow-up through 24 months without an event | Contributes evidence supporting the estimated 24-month PFS probability |
The censoring mechanism is therefore part of the statistical design, not merely a data-cleaning issue. The validity of Kaplan-Meier and Cox analyses depends on assumptions concerning why and when participants become censored.
15. Multiplicity, Interim Analysis, and Other Design Features
The registry information identifies a single registered primary endpoint: PFS rate up to 24 months. It does not specify a multiplicity strategy, an interim efficacy analysis, an alpha-spending procedure, or a Bayesian analysis in the information summarized here.
| Design topic | Registry-supported interpretation |
|---|---|
| Primary endpoint | One registered primary endpoint: PFS rate up to 24 months. |
| Multiplicity | No multiplicity procedure is specified in the information summarized here. |
| Interim analysis | No interim statistical analysis is specified in the information summarized here. |
| Bayesian methods | No Bayesian method is specified in the information summarized here. |
| Crossover | No crossover design is specified; the registry describes a parallel design. |
| Factorial design | No factorial design is specified. |
This distinction is important because the absence of a documented procedure should not be silently replaced with a presumed method. Multiplicity and interim-monitoring decisions can materially affect the interpretation of confirmatory p-values and confidence intervals, so they should be tied to the prespecified statistical plan when available.
16. Safety and Adverse Events
The registry information summarized here identifies the study interventions but does not provide posted numerical serious adverse-event results or treatment-arm safety estimates.
For a randomized treatment trial, safety would normally be summarized separately from the efficacy endpoint. Typical safety summaries distinguish the number and proportion of participants experiencing adverse events, serious adverse events, treatment-related events, and clinically important event categories. Those measures should be presented by randomized or treatment-exposure group according to the prespecified safety analysis population.
17. What a Complete PFS Analysis Would Show
A statistically informative presentation of the NIRVANA-1 primary endpoint would ordinarily contain several complementary pieces of information rather than one number.
| Component | What it tells the reader |
|---|---|
| Kaplan-Meier curve | How progression-free survival evolves over time in each randomized group. |
| 24-month PFS rate | The estimated absolute probability of remaining progression-free through 24 months. |
| Confidence interval | The precision of the estimated 24-month probability. |
| Number of PFS events | How much event information accumulated during follow-up. |
| Number at risk | How much follow-up population remains available to inform the curve at successive times. |
| Median PFS, if estimable | The time at which the estimated PFS probability reaches 50%. |
| Hazard ratio, if prespecified | A model-based relative comparison of the event hazard between groups. |
| Statistical test, if prespecified | The formal evidence against the specified null hypothesis. |
These measures answer different questions. The 24-month rate provides an absolute landmark interpretation; the Kaplan-Meier curve shows the full observed time pattern; the hazard ratio summarizes relative event rates under a model; and the confidence interval communicates uncertainty. A strong statistical report uses these measures together rather than treating any one of them as a complete description of the trial.
18. Statistical Interpretation of the Trial Design
Randomization creates the comparison
The randomized allocation is the foundation for comparing outcomes between treatment groups without relying solely on observational adjustment.
Parallel design simplifies follow-up
Participants are assigned to parallel groups rather than being deliberately exposed to multiple treatment sequences.
Unmasked treatment
The registry classifies masking as none. For an objective tumor-progression endpoint, the method of progression assessment remains important when interpreting potential assessment-related bias.
Phase 2 setting
The phase designation places the study within a treatment-development setting in which randomized efficacy information can be generated before later-stage confirmatory development.
The statistical value of a randomized phase 2 trial is not determined solely by whether its endpoint is statistically significant. The design, event definition, follow-up completeness, censoring behavior, treatment adherence, and precision of the effect estimate all contribute to the strength and interpretability of the evidence.
19. Limitations
- No posted outcome estimates: The ClinicalTrials.gov record has no posted statistical analyses for the registered primary endpoint, so numerical treatment effects cannot be interpreted from the registry record at this time.
- Endpoint complexity: PFS combines objective tumor progression and death, meaning the endpoint can be affected by either component.
- Censoring: PFS estimation depends on the timing and mechanism of censoring, particularly when follow-up differs between randomized groups.
- 24-month landmark: A single time-point PFS estimate does not fully describe the shape of the time-to-event distribution.
- Unmasked design: The registry classifies masking as none, so the lack of masking should be considered when interpreting outcomes that involve assessment or treatment decisions.
- Unspecified arm details: The registry information summarized here does not specify the complete treatment composition of each randomized arm, limiting treatment-specific interpretation.
- Unspecified statistical plan: Multiplicity, interim monitoring, Bayesian methods, and detailed analysis-population rules are not specified in the registry information summarized here.
- Recruiting status: The study is listed as recruiting, with primary completion planned for 2027-02, so the eventual evidence base may differ from the information currently available in the registry.
20. Why This Trial Matters Statistically
NIRVANA-1 provides a useful teaching example because its primary endpoint illustrates a central principle of clinical-trial statistics: an endpoint can be expressed as a clinically intuitive landmark while still requiring a full time-to-event framework underneath it.
| Statistical concept | How it appears in NIRVANA-1 |
|---|---|
| Randomization | Participants are randomized between two arms. |
| Parallel design | The registry specifies a parallel trial model. |
| Time-to-event analysis | PFS is defined as time from randomization to progression or death. |
| Kaplan-Meier estimation | A standard method for estimating the PFS distribution with censoring. |
| Landmark estimation | The registered primary endpoint focuses on PFS through 24 months. |
| Censoring | Participants without an observed PFS event may contribute follow-up until censoring. |
| Hazard ratio | A potential model-based summary for comparing time-to-event hazards between randomized groups. |
| Confidence interval | Would quantify precision around an estimated PFS rate or treatment-effect estimate. |
| Analysis population | Randomization provides the basis for an intention-to-treat efficacy framework. |
| Unmasked design | The registry identifies no masking, which is relevant to interpretation of outcome assessment. |
21. Trial Timeline
Study begins
The registry lists 2022-02-01 as the NIRVANA-1 start date.
Enrollment remains active
The ClinicalTrials.gov record lists the trial status as Recruiting and identifies an enrollment target of 390 participants.
Planned primary completion
The registry lists 2027-02 as the primary completion date.
22. Statistical Interpretation vs Clinical Interpretation
Statistical interpretation
The primary endpoint is a time-to-event quantity with a prespecified 24-month landmark. Its analysis should account for event timing and censoring and should distinguish an absolute PFS probability from a relative hazard comparison.
Clinical interpretation
The clinical meaning of a future 24-month PFS estimate would depend on the magnitude and precision of the estimate, the observed treatment-group difference, the event definition, and the broader safety and treatment context.
Keeping these two levels separate is important. Statistical significance, if eventually demonstrated, would address evidence under a specified statistical model. Clinical importance would require consideration of the absolute size and durability of the treatment difference, uncertainty, safety, and the population in which the evidence was generated.
23. What to Look for When Results Become Available
When formal outcome results are posted, the most informative first questions will concern the 24-month PFS estimate in each randomized group, its confidence interval, the number and timing of PFS events, and the number of participants remaining at risk over follow-up.
| Question | Why it matters |
|---|---|
| What is the estimated PFS probability at 24 months? | This directly addresses the registered primary endpoint. |
| What is the confidence interval? | It indicates the precision of the landmark estimate. |
| How many PFS events occurred? | Event counts determine how much direct time-to-event information supports the analysis. |
| How much censoring occurred? | Heavy or differential censoring can affect precision and assumptions. |
| What is the randomized-group comparison? | The treatment effect should be interpreted from the prespecified comparison rather than from within-arm changes alone. |
| Was a hazard ratio reported? | If so, it provides a relative time-to-event summary that should be interpreted alongside absolute PFS rates. |
| Was the proportional-hazards assumption reasonable? | If not, a single Cox hazard ratio may incompletely describe the treatment difference. |
These questions help prevent a common statistical mistake: reducing a time-to-event trial to a single p-value. The shape of the survival curves, the absolute 24-month probability, the uncertainty interval, and the event/censoring structure all matter.
24. Sources
- ClinicalTrials.gov: NCT05183984 — NIRVANA-1.
- PubMed: PubMed record.
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25. Record Summary
NIRVANA-1 is a randomized, parallel phase 2 trial in ovarian cancer with two arms and a planned enrollment of 390 participants. The registry identifies niraparib, bevacizumab-Awwb, and chemotherapy as study interventions and specifies a primary endpoint of Progression-Free survival (PFS) rate up to 24 months, with PFS defined as time from randomization until objective tumor progression or death, whichever occurs first.
Statistically, the key feature of NIRVANA-1 is the connection between a clinically interpretable 24-month landmark and the underlying time-to-event data. A rigorous analysis would use the full timing of progression and death events, account for censoring, estimate the PFS distribution, and report the 24-month probability with appropriate uncertainty. If a comparative hazard ratio is prespecified, it should be interpreted as a relative time-to-event measure rather than as an absolute 24-month treatment difference.