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
TRINOVA-2 was a randomized, double-blind, parallel-design phase 3 trial evaluating AMG 386 plus PLD against placebo plus PLD in participants with fallopian tube cancer, ovarian cancer, or primary peritoneal cancer.
| Feature | TRINOVA-2 |
|---|---|
| Phase | Phase 3 |
| Status | Terminated |
| Conditions | Fallopian Tube Cancer; Ovarian Cancer; Primary Peritoneal Cancer |
| Design | Randomized, double-blind, parallel |
| Primary purpose | Treatment |
| Enrollment | 223 |
| Number of arms | 2 |
| Lead sponsor | Amgen |
| Sponsor type | Industry |
| ClinicalTrials.gov | NCT01281254 |
2. Clinical Question
The central question was whether AMG 386 plus PLD was superior to placebo plus PLD with respect to progression-free survival in participants with ovarian cancer and related gynecologic cancers represented in the registry.
Population
Participants with fallopian tube cancer, ovarian cancer, or primary peritoneal cancer enrolled in the phase 3 TRINOVA-2 study.
Intervention
AMG 386 plus PLD.
Comparator
Placebo plus PLD.
Primary question
Is AMG 386 plus PLD superior to placebo plus PLD as measured by progression-free survival?
3. Trial Design
AMG 386 plus PLD
- AMG 386
- Pegylated liposomal doxorubicin (PLD)
Placebo plus PLD
- Placebo
- Pegylated liposomal doxorubicin (PLD)
The parallel design makes the randomized treatment assignment the principal comparison between the two groups. Because both arms contain PLD, the contrast is specifically the addition of AMG 386 rather than a comparison of completely different treatment backbones.
4. Primary Endpoint
| Endpoint | Registry definition | Assessment schedule |
|---|---|---|
| Progression-free survival | To determine if AMG 386 plus PLD is superior to placebo plus PLD as measured by progression-free survival, defined as the time from randomization to the earliest of the dates of first radiologic disease progression per RECIST 1.1 with modifications | Radiological imaging will be performed 8 weeks ± 1 week, starting from date of randomization for the first 64 weeks, then every 16 weeks ± 1 week for the next 32 weeks, and then every 24 weeks ± 4 weeks thereafter. |
The endpoint is a time-to-event outcome. Its clock begins at randomization, and the event is defined through radiologic disease progression using RECIST 1.1 with modifications. The registry therefore defines PFS around the occurrence of a specified disease-progression event rather than around a simple binary assessment at a single follow-up visit.
5. Planned Analysis
The ClinicalTrials.gov record identifies progression-free survival as the primary endpoint and defines it as the time from randomization to the earliest date of first radiologic disease progression per RECIST 1.1 with modifications. The registry does not post a formal statistical analysis for this endpoint, and it does not provide an estimated treatment effect, confidence interval, or p-value.
For a randomized trial with a time-to-event primary endpoint such as this one, a conventional analysis would estimate the distribution of progression-free survival in each randomized group using the Kaplan-Meier method. A treatment comparison would commonly be performed using a log-rank test, with a Cox proportional-hazards model commonly used to estimate a hazard ratio and its confidence interval when the proportional-hazards framework is appropriate.
The resulting analysis would distinguish three related but different quantities: the estimated progression-free survival function over time, a relative comparison such as a hazard ratio, and an absolute time-specific difference between treatment groups. These measures describe different aspects of the same time-to-event process and should not be treated as interchangeable.
Here, S(t) represents the probability of remaining free of the defined event beyond time t. In a clinical trial, patients who have not experienced the event by their last evaluable observation may contribute censored follow-up rather than being treated as having experienced progression at that time.
6. Statistical Methodology
Kaplan-Meier estimation
Progression-free survival is naturally analyzed with methods that account for differing follow-up times and right censoring. The Kaplan-Meier estimator provides an estimate of the probability of remaining event-free over time without requiring every participant to experience the event.
At each observed event time, di represents the number of events and ni represents the number of participants at risk immediately beforehand.
For TRINOVA-2, the radiological assessment schedule means that progression is observed through periodic imaging rather than continuously. The exact timing of a radiologic progression event therefore depends on the protocol's assessment process and the registry's endpoint definition.
Log-rank comparison
A log-rank test provides a way to compare two time-to-event distributions across follow-up. Conceptually, it compares the observed number of events in each treatment group with the number expected under the hypothesis that the groups have the same underlying event experience.
The log-rank framework is particularly useful when the question concerns whether the treatment groups differ across the follow-up period rather than at one prespecified time point.
Cox proportional-hazards model
A Cox model can summarize the relative event rate between treatment groups using a hazard ratio. In a simple two-group formulation, the treatment coefficient is exponentiated to obtain the estimated hazard ratio.
A hazard ratio below 1 would correspond to a lower estimated instantaneous event rate in the AMG 386 plus PLD group relative to the placebo plus PLD group, while a value above 1 would correspond to a higher estimated event rate. The hazard ratio is not itself a probability of progression-free survival.
Censoring
Time-to-event analysis must distinguish an observed progression event from a participant whose progression status is not observed after a particular point in follow-up. Such participants can be censored according to prespecified rules. Correct handling of censoring is important because treating every censored participant as if an event occurred at the last observation would distort the estimated survival distribution.
Radiologic endpoint assessment
The registry defines the primary endpoint using radiologic disease progression per RECIST 1.1 with modifications. This means that the endpoint is tied to a structured tumor-assessment framework rather than an investigator's unrestricted clinical impression alone.
7. Statistical Methods Explained
Why is progression-free survival a time-to-event endpoint?
Progression-free survival incorporates both whether progression occurs and when it occurs. Two participants may both eventually experience progression, but the participant who progresses later has a different PFS outcome from the participant who progresses earlier. Time-to-event methods preserve that temporal information.
Why use Kaplan-Meier rather than simply compare the proportion progressed?
A simple proportion requires a fixed observation time and can discard information from participants followed for different lengths of time. Kaplan-Meier estimation instead uses the available follow-up while accounting for censoring, allowing the estimated event-free probability to be described across time.
What would a hazard ratio represent?
A hazard ratio summarizes the relative instantaneous event rate between the treatment groups under a fitted survival model. For example, an HR of 0.70 would be interpreted as a 30% lower estimated hazard under the model. It would not mean that 30% fewer participants necessarily experienced progression, nor would it imply that every participant had the same proportional reduction in risk.
Why does the confidence interval matter?
A point estimate alone does not communicate how precisely the treatment effect has been estimated. A confidence interval describes uncertainty around the estimated parameter under the statistical model and sampling framework. A wide interval indicates greater uncertainty than a narrow interval, even when the point estimates are identical.
Why is the imaging schedule statistically important?
Progression is detected through radiological assessment, and the registry specifies when those assessments occur. If progression is discovered at an imaging visit, the recorded event timing is connected to that assessment process. The prespecified imaging schedule therefore forms part of the measurement system for the PFS endpoint.
What is the difference between PFS and a response rate?
PFS measures time until the defined progression event or other event incorporated into the endpoint definition. A response rate is generally a proportion of participants achieving a specified tumor-response category. PFS therefore contains a temporal component that a single response proportion does not.
8. Reading the Primary Endpoint Correctly
Time origin
The registry defines PFS from the date of randomization. Randomization therefore establishes the starting point for the primary time-to-event measurement.
Event definition
The event is based on the earliest date of first radiologic disease progression according to RECIST 1.1 with modifications.
Repeated assessment
Radiological imaging is scheduled repeatedly over follow-up rather than being performed only once.
Treatment comparison
The registry frames the primary question as superiority of AMG 386 plus PLD over placebo plus PLD.
This structure is important because a time-to-progression analysis is not equivalent to comparing the number of participants who have progressed by the end of the study. The statistical analysis uses the timing of events and the available follow-up information to characterize the treatment groups.
9. Trial Timeline
Study start
The ClinicalTrials.gov record lists April 18, 2011 as the trial start date.
Randomized treatment comparison
The study used randomized allocation, a parallel design, double masking, and two treatment arms.
Primary completion
The ClinicalTrials.gov record lists August 29, 2014 as the primary completion date.
Terminated
The ClinicalTrials.gov record lists the study status as terminated.
10. What the Registry Does and Does Not Establish Statistically
The registry establishes the design framework and the definition of the primary endpoint, including the randomization structure, masking, treatment arms, enrollment, and radiological assessment schedule. These details are sufficient to identify the principal statistical problem: comparison of two randomized groups on a radiologically defined time-to-event endpoint.
The registry does not post statistical analyses for TRINOVA-2. Consequently, the registry record does not provide a numerical estimate of the treatment effect on PFS or its associated statistical uncertainty.
| Statistical quantity | Registry status |
|---|---|
| Primary endpoint | Progression-free survival |
| Endpoint time origin | Randomization |
| Progression definition | First radiologic disease progression per RECIST 1.1 with modifications |
| PFS hazard ratio | Not reported in posted statistical analyses |
| PFS confidence interval | Not reported in posted statistical analyses |
| PFS p-value | Not reported in posted statistical analyses |
| Median PFS | Not reported in posted statistical analyses |
This distinction is central to reading a registry record. A clearly specified endpoint can tell the reader what the investigators intended to measure, but the endpoint definition itself is not evidence that one treatment performed better than another.
11. Limitations
- No posted statistical analysis: The ClinicalTrials.gov record does not provide a formal statistical analysis for the primary endpoint, so the registry does not supply an estimated PFS treatment effect or its uncertainty.
- Limited endpoint reporting: The registry information identifies progression-free survival as the primary endpoint but does not provide numerical PFS results in the posted statistical analyses.
- Radiologic measurement: Progression is determined through scheduled imaging and a RECIST 1.1-based definition with modifications. The timing and completeness of imaging assessments are therefore relevant to interpretation.
- Censoring: Time-to-event methods require explicit rules for handling participants who have not experienced progression at their last evaluable assessment.
- Proportional-hazards assumption: If a Cox proportional-hazards model is used, its hazard-ratio interpretation depends on the appropriateness of the proportional-hazards framework. A single hazard ratio can be less informative when relative hazards vary materially over time.
- Registry versus full statistical analysis: The registry identifies the clinical question and endpoint but does not provide the complete statistical-analysis framework needed to reproduce an efficacy analysis.
12. Why This Trial Matters Statistically
TRINOVA-2 is a useful teaching example because its primary endpoint illustrates a central problem in clinical-trial biostatistics: how to compare treatments when the outcome is defined not merely by whether an event occurs, but by when that event occurs.
| Concept | How it appears in TRINOVA-2 |
|---|---|
| Randomization | The trial uses randomized allocation to two treatment arms. |
| Double masking | The registry identifies the study as double-blind. |
| Parallel design | The two interventions are evaluated in parallel treatment groups. |
| Time-to-event analysis | The primary endpoint is progression-free survival measured from randomization. |
| Kaplan-Meier estimation | A standard approach for describing progression-free survival over time. |
| Log-rank testing | A standard framework for comparing time-to-event distributions between randomized groups. |
| Cox regression | A standard model for estimating a hazard ratio when its assumptions are appropriate. |
| Censoring | Participants may require censoring rules when progression is not observed during available follow-up. |
| Radiologic assessment | Progression is defined using radiologic disease progression per RECIST 1.1 with modifications. |
| Repeated assessments | The registry specifies imaging intervals that change over the course of follow-up. |
13. Clinical Interpretation vs Statistical Interpretation
Statistical interpretation
The registry defines a randomized comparison of progression-free survival between AMG 386 plus PLD and placebo plus PLD. A time-to-event framework is appropriate because the endpoint incorporates the time from randomization to radiologic progression.
Clinical interpretation
The clinical question is whether adding AMG 386 to PLD improves the duration of time before the registry-defined radiologic progression event compared with PLD plus placebo.
The two perspectives should remain connected but distinct. Statistical analysis determines how the observed event-time data are summarized and compared. Clinical interpretation asks what those estimates mean in the context of disease progression and treatment. Neither perspective should be substituted for the other.
14. A Practical Framework for Analysing TRINOVA-2
If a complete patient-level or formal results analysis were available, a rigorous statistical workflow for the primary endpoint would proceed through several linked steps.
Step 1: Define the analysis time origin
The registry specifies randomization as the starting point for progression-free survival. Every participant's event-time calculation therefore begins from the date of randomization.
Step 2: Classify progression
Radiologic progression is identified using RECIST 1.1 with modifications. A statistical dataset would need to preserve the assessment history sufficiently to determine the first qualifying progression date according to the protocol's rules.
Step 3: Apply censoring rules
Participants without an observed qualifying event require protocol-defined censoring rules. These rules are important because censoring determines how much follow-up information contributes to the estimated survival distribution.
Step 4: Estimate the survival functions
Kaplan-Meier methods can summarize the estimated probability of remaining progression-free across follow-up for each randomized group. Graphical presentation can reveal differences that a single summary statistic may obscure.
Step 5: Quantify the treatment comparison
A hazard ratio from a Cox model can summarize the relative event rate, while a confidence interval communicates statistical uncertainty. A log-rank test can provide a formal comparison of the time-to-event distributions.
15. Sources
- ClinicalTrials.gov: NCT01281254 — TRINOVA-2.
- PubMed: PubMed record.
- PubMed: PubMed record.
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16. Record Summary
TRINOVA-2 was a randomized, double-blind, parallel-design phase 3 trial with 223 participants and two treatment arms: AMG 386 plus PLD and placebo plus PLD. The registry identifies progression-free survival as the primary endpoint and defines it as the time from randomization to the earliest of the dates of first radiologic disease progression per RECIST 1.1 with modifications.
The statistical structure is therefore fundamentally a time-to-event comparison. Kaplan-Meier estimation can describe progression-free survival over time, log-rank methods can compare the treatment groups, and a Cox model can provide a hazard-ratio summary when its assumptions are appropriate. The registry's specified radiological assessment schedule is an important part of the endpoint measurement process.
The ClinicalTrials.gov record lists the study as terminated and does not post statistical analyses for the primary endpoint. As a result, the registry establishes the design and planned endpoint framework but does not provide a numerical PFS treatment effect, confidence interval, or p-value from which an efficacy conclusion can be drawn.