This page provides an independent statistical analysis and educational interpretation of publicly reported results. ClinicalTrials.gov provides the official trial registry record. Numerical trial results on this page are limited to the information reported in the ClinicalTrials.gov record for INAVO120.
1. Trial at a Glance
INAVO120 is a randomized, double-blind, parallel-group phase 2/3 oncology trial comparing inavolisib plus palbociclib and fulvestrant with placebo plus palbociclib and fulvestrant. The registered primary endpoint is progression-free survival, a time-to-event endpoint assessed for up to 3.7 years.
| Feature | INAVO120 |
|---|---|
| Trial name | INAVO120 |
| NCT identifier | NCT04191499 |
| Phase | Phase 2/3 |
| Condition | Breast Cancer |
| Allocation | Randomized |
| Design model | Parallel |
| Masking | Double |
| Primary purpose | Treatment |
| Enrollment | 325 |
| Arms | 2 |
| Primary endpoint type | Time-to-event |
| Registered primary endpoints | 1 |
| Results posted | Yes |
| Outcome measures posted | 14 |
| Statistical analyses posted | 1 |
| Lead sponsor | Hoffmann-La Roche |
| Sponsor type | Industry |
2. Clinical Question
The primary clinical-statistical question is whether the addition of inavolisib to palbociclib and fulvestrant changes progression-free survival compared with placebo plus palbociclib and fulvestrant in participants with PIK3CA-mutant, hormone receptor-positive, HER2-negative, locally advanced or metastatic breast cancer.
Population
Participants with PIK3CA-mutant, hormone receptor-positive, HER2-negative, locally advanced or metastatic breast cancer.
Intervention
Inavolisib plus palbociclib and fulvestrant.
Comparator
Placebo plus palbociclib and fulvestrant.
Primary question
Does inavolisib plus palbociclib and fulvestrant improve progression-free survival relative to placebo plus palbociclib and fulvestrant?
3. Trial Design
Inavolisib combination
- Inavolisib
- Palbociclib
- Fulvestrant
Placebo combination
- Placebo
- Palbociclib
- Fulvestrant
The randomized parallel-group structure provides the fundamental framework for comparing the two treatment strategies. Because participants are randomized before the outcome is observed, the treatment comparison is anchored to assignment rather than to the treatment actually received or to post-randomization characteristics.
4. Endpoints
| Endpoint | Registry definition / time frame | Endpoint type |
|---|---|---|
| Progression-Free Survival (PFS) | PFS was defined as the time from randomization to the first occurrence of disease progression, as determined by the investigator according to Response Evaluation Criteria in Solid Tumors (RECIST), Version 1.1, or death from any cause, whichever occurs first. Time frame: Up to 3.7 years. | Time-to-event |
The registry definition identifies two ways for the PFS endpoint to occur: documented disease progression or death from any cause, with whichever event occurs first determining the endpoint time. Participants who have not experienced either event at the relevant follow-up point contribute censored information to a time-to-event analysis.
The endpoint therefore combines disease-control information and survival information into a single time-to-event measure. A patient can reach the endpoint through progression without dying, or through death before a documented progression.
5. Statistical Methodology
Primary analysis population
The posted primary analysis used the full analysis set (FAS), defined as including all participants who were randomized to receive the treatment they were assigned. This preserves the randomized treatment comparison for the primary efficacy analysis.
Stratified log-rank test
The primary PFS comparison used a stratified log-rank test. The registry analysis notes specify stratification by Visceral Disease, Endocrine Resistance, and Region.
The registered statistical analysis identifies the hypothesis type as superiority. The reported p-value therefore addresses evidence against the null comparison under the prespecified analysis framework; it is not itself a measure of how large the treatment effect is.
Cox regression for the hazard ratio
The analysis notes state that hazard ratios were estimated by Cox regression. The hazard ratio and log-rank p-value used stratified methods based on Visceral Disease, Endocrine Resistance, and Region.
An HR below 1 indicates a lower estimated event hazard for the inavolisib combination relative to the comparator under the fitted survival model. It is a relative time-to-event measure, not an absolute difference in the probability of progression or death.
Why stratification matters
Stratification allows the primary comparison to account for prespecified factors that can be related to prognosis or treatment assignment. Here, the reported analysis stratified by Visceral Disease, Endocrine Resistance, and Region. Rather than treating all participants as though these factors were irrelevant, the stratified approach evaluates the treatment comparison within the specified strata and combines the resulting evidence.
This distinction is especially important when interpreting a hazard ratio. A stratified hazard ratio is not simply the result of dividing two crude event rates. It is produced from a survival model that incorporates the stratification structure specified for the analysis.
6. Results
Primary Endpoint: Progression-Free Survival
The registry reports a formal statistical analysis for the primary endpoint of progression-free survival. The analysis compared the full analysis set receiving the assigned treatment between the inavolisib plus palbociclib and fulvestrant group and the placebo plus palbociclib and fulvestrant group.
Hazard ratio for progression or death
95% CI: 0.32–0.59 · P < 0.0001
Analysis: stratified log-rank test; hazard ratios estimated by Cox regression.
| Primary endpoint | Inavolisib + Palbociclib + Fulvestrant | Placebo + Palbociclib + Fulvestrant | Effect estimate |
|---|---|---|---|
| Progression-Free Survival | Randomized participants in the FAS | Randomized participants in the FAS | HR 0.43 (95% CI 0.32–0.59); P < 0.0001 |
An HR of 0.43 means that, under the reported Cox regression framework, the estimated instantaneous hazard of progression or death in the inavolisib combination group was approximately 43% of the corresponding hazard in the placebo combination group. Equivalently, 1 − 0.43 = 0.57, so the estimated hazard was approximately 57% lower under the model.
This does not mean that 57% of participants avoided progression, that 57% of participants were cured, or that each individual participant experienced exactly a 57% reduction in risk. A hazard ratio is a relative model-based measure describing the comparison of event hazards over follow-up.
The 95% confidence interval of 0.32–0.59 describes uncertainty around the estimated hazard ratio under the analysis model and sampling framework. It does not describe the range of outcomes that individual patients can experience, and it is not a probability statement about where the true effect lies.
The reported P < 0.0001 addresses the statistical evidence against the null hypothesis under the stratified log-rank analysis. A p-value does not measure effect size. The magnitude of the estimated treatment effect is conveyed by the hazard ratio, while its statistical precision is conveyed in part by the confidence interval.
Because this is a time-to-event analysis, interpretation also depends on censoring and on the assumptions underlying the Cox model. In particular, a single hazard ratio is most straightforward to interpret when the proportional-hazards framework is a reasonable description of the treatment comparison over time. The ClinicalTrials.gov record does not provide enough information to independently evaluate that assumption.
How the three primary statistics fit together
HR = 0.43
Describes the estimated relative event hazard between the two randomized groups under the Cox model.
95% CI = 0.32–0.59
Shows the statistical uncertainty around the estimated hazard ratio under the specified analysis framework.
P < 0.0001
Quantifies evidence against the null hypothesis in the reported stratified log-rank test; it does not quantify the size of the treatment effect.
Superiority
Identifies the hypothesis type as superiority rather than non-inferiority or equivalence.
7. Stratification and the Primary Comparison
The reported primary analysis used stratified methods based on three factors: Visceral Disease, Endocrine Resistance, and Region. These factors were incorporated into both the hazard-ratio analysis and the log-rank p-value according to the registry analysis notes.
| Stratification factor | Role in the reported analysis |
|---|---|
| Visceral Disease | Stratification factor for hazard-ratio and log-rank analyses |
| Endocrine Resistance | Stratification factor for hazard-ratio and log-rank analyses |
| Region | Stratification factor for hazard-ratio and log-rank analyses |
Stratification should not be confused with a subgroup analysis. The purpose of stratification in the primary analysis is to account for specified factors while estimating the overall treatment comparison. A subgroup analysis instead asks whether the estimated treatment effect differs across categories of a characteristic.
That distinction matters statistically. A stratified hazard ratio of 0.43 does not mean that the hazard ratio was independently estimated as 0.43 in every stratum. It is an overall treatment-effect estimate obtained using the stratified analysis framework.
8. Statistical Methods Explained
Why was a log-rank test used?
PFS is a time-to-event endpoint, so participants can experience progression or death at different times and some participants may remain event-free at the end of observed follow-up. A log-rank test is designed to compare survival-type event-time distributions while accounting for the timing of events rather than reducing the data to a single proportion at one arbitrary time point.
Why was the log-rank test stratified?
The registry specifies stratification by Visceral Disease, Endocrine Resistance, and Region. Stratification allows the treatment comparison to respect these specified factors rather than treating the entire study population as a single homogeneous risk set. The resulting test is therefore aligned with the trial's stated analysis structure.
What does an HR of 0.43 mean?
An HR of 0.43 indicates a lower estimated instantaneous hazard of progression or death in the inavolisib combination group relative to the placebo combination group under the fitted Cox model. The simple complementary interpretation is that 0.43 corresponds to an estimated hazard approximately 57% lower than the comparator hazard. This is not the same as saying that 57% of participants benefited or that time to progression increased by a particular percentage.
Why is the confidence interval important?
The 95% CI of 0.32–0.59 provides information about the precision of the hazard-ratio estimate. It is substantially more informative than reporting the HR alone because two studies can have the same point estimate but very different uncertainty. The interval also describes the range of parameter values compatible with the specified statistical framework at the stated confidence level; it is not a range of individual patient outcomes.
Why doesn't the p-value measure treatment effect size?
The p-value answers a hypothesis-testing question: how incompatible are the observed data with the null hypothesis under the specified test? It depends on both the magnitude of the observed difference and the amount of information in the data. The hazard ratio is the statistic that directly describes the relative treatment effect, while the confidence interval communicates its uncertainty.
Why is the analysis population important?
The reported analysis population is the full analysis set, including all participants randomized to receive the treatment they were assigned. An analysis based on randomized assignment helps preserve the comparison created by randomization. Restricting an efficacy analysis after randomization to only participants who remained on treatment could introduce post-randomization selection into the comparison.
What does PFS actually measure?
PFS measures time from randomization until the first qualifying progression or death, whichever occurs first. It therefore incorporates both radiographic disease progression and death. A participant who has not experienced either event by the relevant follow-up point is not treated as having a progression event merely because follow-up ended; that participant contributes censored time to the analysis.
9. Understanding Kaplan-Meier Estimation
The registry classifies PFS as a time-to-event endpoint, and Kaplan-Meier estimation is a standard descriptive method for this type of endpoint. Kaplan-Meier estimation represents the estimated probability of remaining event-free over time while accounting for participants whose event time is censored.
Here, di represents events at an observed event time and ni represents participants at risk immediately before that time.
The registry-reported INAVO120 data do not provide the event-by-event risk sets required to reconstruct a Kaplan-Meier curve. Consequently, this page does not fabricate a survival curve or derive median PFS from the reported hazard ratio.
10. Safety
The ClinicalTrials.gov record reports serious adverse events by treatment arm. The reported figures are the number affected divided by the number at risk in each arm.
| Safety measure | Inavolisib + Palbociclib + Fulvestrant | Placebo + Palbociclib + Fulvestrant |
|---|---|---|
| Serious adverse events | 39/162 | 17/162 |
These figures should be read as a safety comparison rather than as an efficacy endpoint. The serious-adverse-event data do not establish a causal mechanism for any particular event and do not by themselves summarize the full safety profile of the trial.
11. Trial Timeline
Trial start
INAVO120 began on 2020-01-29.
Primary completion
The registered primary completion date was 2023-09-29.
Active, not recruiting
The trial status in the ClinicalTrials.gov record is ACTIVE_NOT_RECRUITING.
12. What the PFS Hazard Ratio Does — and Does Not — Mean
The reported HR of 0.43 is a relative measure of the hazard of progression or death. Within the Cox model, the estimated hazard in the inavolisib combination group is 0.43 times the corresponding comparator hazard.
The complementary calculation, 1 − 0.43 = 0.57, means the estimated hazard is approximately 57% lower under that model. This is an interpretation of the hazard ratio, not a statement that 57% of patients experienced a benefit.
The HR does not tell us the median PFS, the percentage of participants who progressed, the percentage alive without progression at a specific time, or how much longer an individual participant will remain progression-free.
It also should not be interpreted as a relative risk ratio or as a direct absolute probability difference.
The 95% CI of 0.32–0.59 communicates uncertainty around the estimated HR. A narrower interval generally indicates greater statistical precision than a wider interval, although precision should always be interpreted in the context of the model and information available.
The reported P < 0.0001 indicates strong statistical evidence against the null hypothesis under the reported stratified log-rank analysis. It does not mean that there is a probability of less than 0.0001 that the null hypothesis is true, nor does it measure clinical importance.
13. Limitations
- Limited registry result detail: the ClinicalTrials.gov record contains one formal statistical analysis for the primary endpoint but do not provide median PFS, event counts, Kaplan-Meier time-point estimates, or a detailed event/censoring table.
- No subgroup results reported: although stratification factors are reported, the ClinicalTrials.gov record does not contain subgroup-specific treatment-effect estimates. Stratification should therefore not be interpreted as evidence of treatment-effect heterogeneity.
- Hazard-ratio interpretation: the Cox model produces a relative hazard measure. A single HR can be less descriptive if the proportional-hazards assumption is not an adequate approximation over follow-up. The ClinicalTrials.gov record does not permit an independent assessment of that assumption.
- Censoring: time-to-event analyses depend on how censored observations contribute information. The ClinicalTrials.gov record does not provide enough detail to independently evaluate the censoring mechanism or the complete censoring pattern.
- Multiplicity: the ClinicalTrials.gov record does not describe an alpha-allocation strategy, endpoint hierarchy, multiplicity adjustment, or interim-analysis procedure. No such methods are inferred here.
- Missing data and imputation: the ClinicalTrials.gov record does not describe an imputation strategy for missing efficacy assessments. No imputation method is therefore attributed to the trial.
- Safety denominator: serious adverse events are reported as 39/162 and 17/162. The ClinicalTrials.gov record does not specify the broader safety-analysis conventions needed to generalize these figures to all safety outcomes.
- Registry scope: this analysis is limited to the ClinicalTrials.gov record. External numerical results have not been added to fill gaps in the registry record.
14. Why This Trial Matters Statistically
INAVO120 provides a useful teaching example of how a modern randomized oncology trial can combine a clinically meaningful time-to-event endpoint with stratified hypothesis testing and model-based effect estimation.
| Statistical concept | How it appears in INAVO120 |
|---|---|
| Randomization | The trial uses randomized allocation to two parallel treatment groups. |
| Double masking | The registered design is double-blind. |
| Time-to-event endpoint | PFS measures time from randomization to progression or death. |
| Stratified analysis | The primary analysis stratifies by Visceral Disease, Endocrine Resistance, and Region. |
| Log-rank test | The primary PFS comparison uses a stratified log-rank test. |
| Cox regression | Hazard ratios are estimated by Cox regression. |
| Hazard ratio | The reported PFS effect estimate is HR 0.43. |
| Confidence interval | The PFS HR has a two-sided 95% CI of 0.32–0.59. |
| Superiority testing | The registered hypothesis type is superiority. |
| Analysis population | The formal analysis uses the full analysis set of randomized participants receiving their assigned treatment. |
| Safety analysis | Serious adverse events are reported by treatment arm. |
The particularly important statistical lesson is that these components answer different questions. Randomization addresses how treatment groups are formed. The log-rank test addresses evidence for a difference in event-time distributions. The hazard ratio quantifies a relative treatment effect under a survival model. The confidence interval describes uncertainty around that estimate. None of these statistics should be substituted for the others.
15. Primary Analysis: A Step-by-Step Statistical Reading
Step 1 · Define time zero
The PFS clock begins at randomization.
Step 2 · Define the event
The first event is investigator-determined progression according to RECIST Version 1.1 or death from any cause.
Step 3 · Preserve assignment
The formal analysis uses the full analysis set of randomized participants receiving their assigned treatment.
Step 4 · Compare event times
The groups are compared using a stratified log-rank test.
Step 5 · Quantify the effect
Cox regression supplies the hazard ratio and its confidence interval.
Step 6 · Interpret uncertainty
The 95% CI and p-value provide complementary information about precision and evidence against the null hypothesis.
This sequence illustrates why clinical-trial statistics should be read as an integrated analysis rather than as isolated numbers. The endpoint definition determines what counts as an event. The analysis population determines which randomized participants enter the comparison. The stratification variables determine the structure of the primary comparison. The log-rank test supplies the hypothesis-test result, while Cox regression supplies the reported relative effect measure.
16. Stratified Log-Rank Test vs Hazard Ratio
The primary analysis contains two closely related but distinct statistical components. The stratified log-rank test evaluates evidence for a difference between the treatment groups in their time-to-event experience. The Cox regression hazard ratio quantifies the direction and relative magnitude of that difference.
| Component | Primary role | INAVO120 result |
|---|---|---|
| Stratified log-rank test | Hypothesis test for the time-to-event comparison | P < 0.0001 |
| Cox regression | Estimation of the hazard ratio | HR 0.43 |
| Confidence interval | Quantification of uncertainty around the HR | 95% CI 0.32–0.59 |
| Hypothesis type | Direction of the prespecified statistical question | Superiority |
The distinction is useful because a p-value without an effect estimate tells the reader little about the magnitude of the observed difference. Conversely, an effect estimate without uncertainty leaves the precision of that estimate unclear. Reporting the HR, CI, and p-value together gives a more complete statistical description.
17. What Is Not Reported in the Supplied Trial Data
The ClinicalTrials.gov record contains a formal primary PFS analysis, but several commonly reported clinical-trial quantities are not included. Following the data rules for this record, these are intentionally not reconstructed or imported from external sources.
| Potential result | Status in the ClinicalTrials.gov record |
|---|---|
| Median PFS | Not reported in the ClinicalTrials.gov record. |
| Kaplan-Meier PFS time-point estimates | Not reported in the ClinicalTrials.gov record. |
| PFS event counts by arm | Not reported in the ClinicalTrials.gov record. |
| Baseline characteristics | Not reported in the ClinicalTrials.gov record. |
| Subgroup hazard ratios | Not reported in the ClinicalTrials.gov record. |
| Overall survival results | Not included in the statistical analyses posted on ClinicalTrials.gov. |
| Objective response results | Not included in the statistical analyses posted on ClinicalTrials.gov. |
| Multiplicity strategy | Not reported in the ClinicalTrials.gov record. |
| Interim-analysis procedure | Not reported in the ClinicalTrials.gov record. |
| Missing-data or imputation strategy | Not reported in the ClinicalTrials.gov record. |
| Bayesian methods | Not reported in the ClinicalTrials.gov record. |
| Non-inferiority margin | Not applicable to the reported superiority analysis. |
| Crossover analysis | Not reported in the ClinicalTrials.gov record. |
| Factorial design | Not reported; the registered design model is parallel. |
This distinction is important for reproducibility. A statistical analysis page should distinguish between what the registry actually reports and what might ordinarily appear in a complete clinical-study report. Adding familiar quantities from another source would make the page look more complete but would no longer preserve the requested source boundary.
18. Clinical Interpretation vs Statistical Interpretation
Statistical interpretation
The reported primary PFS analysis produced a hazard ratio of 0.43 with a two-sided 95% CI of 0.32–0.59 and a stratified log-rank P < 0.0001. The registered hypothesis type was superiority.
Clinical interpretation
The reported result indicates a lower estimated hazard of progression or death for the inavolisib combination relative to the placebo combination under the trial's statistical model. The ClinicalTrials.gov record does not provide median PFS or absolute PFS estimates, so the magnitude of benefit cannot be translated here into those measures.
Keeping these interpretations separate prevents a common statistical error: treating statistical significance as synonymous with a particular clinical magnitude. The p-value provides evidence against the null hypothesis, whereas the hazard ratio describes the relative effect. The confidence interval then indicates how precisely that relative effect has been estimated.
19. Serious Adverse Events by Arm
The safety information posted on ClinicalTrials.gov for INAVO120 includes the number of participants affected by serious adverse events and the corresponding number at risk in each treatment group.
Reported serious adverse events
Inavolisib + palbociclib + fulvestrant vs placebo + palbociclib + fulvestrant
The numerator and denominator structure matters. The reported figures are not hazard ratios, risk differences, or odds ratios. They are counts of affected participants over participants at risk. Without additional information about timing, exposure, event definitions, recurrent events, and the full safety analysis population, these figures should not be transformed into a more elaborate safety model.
20. Why This Trial Is a Useful Time-to-Event Example
INAVO120 demonstrates several recurring principles in clinical-trial survival analysis. First, the endpoint is defined in terms of time, not merely whether an event eventually occurred. Second, censoring allows participants without an observed event during follow-up to contribute partial information. Third, treatment groups can be compared using a log-rank framework while simultaneously estimating a hazard ratio with Cox regression.
The stratification structure adds another important lesson. The treatment effect is not analyzed as though Visceral Disease, Endocrine Resistance, and Region were irrelevant. Instead, the primary statistical comparison incorporates those factors through the stratified analysis.
Finally, the reported HR of 0.43 illustrates why relative measures need context. An HR can summarize a complex sequence of event and censoring times in a single number, but that number cannot substitute for the underlying survival curve, absolute event probabilities, median event time, or clinically relevant time-point estimates when those quantities are available.
21. Summary of the Primary Statistical Evidence
| Element | Reported INAVO120 result | How to interpret it |
|---|---|---|
| Endpoint | Progression-Free Survival | Time from randomization to progression or death, whichever occurs first |
| Time frame | Up to 3.7 years | Registered endpoint follow-up window |
| Analysis population | Full analysis set | All randomized participants receiving the treatment assigned |
| Primary test | Stratified log-rank | Tests the time-to-event treatment comparison |
| Stratification | Visceral Disease; Endocrine Resistance; Region | Factors incorporated into the reported primary analysis |
| Effect measure | Hazard ratio | Relative event-hazard measure from Cox regression |
| Estimate | 0.43 | Lower estimated hazard in the inavolisib combination group |
| 95% CI | 0.32–0.59 | Uncertainty around the estimated HR |
| P-value | <0.0001 | Evidence against the null under the reported test |
| Hypothesis | Superiority | Trial was testing for a superior treatment effect rather than non-inferiority |
22. Related Tutorials
Learn more about the methods used in this trial:
23. Related Statistical Calculators
Apply the core methods behind the INAVO120 analysis with these statistical tools:
24. Sources
- ClinicalTrials.gov: NCT04191499 — INAVO120.
- Linked publication: PubMed record: PMID 40454641.
- Linked publication: PubMed record: PMID 39476340.
Continue through the Clinical Biostats statistical library
Explore the statistical methods behind randomized clinical trials, survival analysis, confidence intervals, hypothesis testing, and related analytical workflows.
25. Record Summary
INAVO120 is a randomized, double-blind, parallel-group phase 2/3 trial with 325 participants evaluating inavolisib plus palbociclib and fulvestrant versus placebo plus palbociclib and fulvestrant. Its registered primary endpoint is progression-free survival, defined as the time from randomization to the first occurrence of investigator-determined disease progression according to RECIST Version 1.1 or death from any cause, whichever occurs first, with a time frame of up to 3.7 years.
The formal primary analysis used the full analysis set and a stratified log-rank test, with stratification by Visceral Disease, Endocrine Resistance, and Region. Hazard ratios were estimated using Cox regression. The reported PFS hazard ratio was 0.43, with a two-sided 95% confidence interval of 0.32–0.59 and a stratified log-rank P < 0.0001. The registered hypothesis type was superiority.
The central statistical lesson is the distinction among the endpoint, test, effect estimate, and uncertainty measure. PFS defines the event-time question; the stratified log-rank test evaluates evidence for a treatment difference; the Cox hazard ratio describes the relative event hazard; and the confidence interval describes uncertainty around that estimate. The reported serious-adverse-event figures, 39/162 and 17/162, provide a separate safety perspective and should not be combined mathematically with the efficacy result into a single treatment-effect statistic.