Daily Everolimus in Combination With Trastuzumab and Vinorelbine in HER2/Neu Positive Women With Locally Advanced or Metastatic Breast Cancer: Complete Statistical Analysis

1. Trial at a Glance

This page covers the registry results for Daily Everolimus in Combination With Trastuzumab and Vinorelbine in HER2/Neu Positive Women With Locally Advanced or Metastatic Breast Cancer (NCT01007942), a randomized phase 3 study. The primary outcome is Progressive-free Survival (PFS) Per Investigator Assessment.

2. Clinical Question

The trial was randomized, double-blind, placebo-controlled, and parallel-group. The registry record describes the study as multinational and multicenter. Its primary focus was the time until progression or death; overall survival and other outcomes were listed as secondary outcomes. The primary result is therefore a comparison of the assigned treatment strategies in this defined population, not a comparison of everolimus alone with no treatment.

3. Trial Design

This completed phase III trial randomized participants between two parallel treatment groups. The registry record lists enrollment as 569. The reported progression-free survival analysis included 284 participants in the everolimus group and 285 in the placebo group. Randomization supports a comparative interpretation by assigning participants to treatment groups, while double masking and a matching placebo were intended to keep treatment assignment concealed from participants and relevant trial personnel.

Both groups received vinorelbine and trastuzumab. The experimental group also received daily oral everolimus; the comparator group received matching daily placebo. The recorded regimen specifies weekly intravenous vinorelbine and trastuzumab, including a trastuzumab loading dose on Day 1 of Cycle 1. These details identify the treatment contrast: the randomized comparison concerns adding everolimus to the shared regimen.

The primary outcome was investigator-assessed progression-free survival. The record defines it as the time from randomization to the first radiologically documented tumor progression or death from any cause, whichever occurred first. Tumor progression was defined using RECIST v1.0. The record says assessment was scheduled every 6 weeks until progression or death, up to about 41 months, and that the primary analysis was performed when 415 events were reached.

The Full Analysis Set included all randomized patients. The registry record does not report the original sample-size justification, the detailed randomization procedure, allocation concealment procedures, adherence, treatment discontinuation, or crossover information. Those details should not be inferred from the randomized and masked design labels alone.

4. Participant Flow and Accounting

The registry reports how participants moved through each stage of the study. All counts below are exactly as posted; missing counts are not inferred from other stages.

Before assignment: 284 patients randomized to the Everolimus + trastuzumab + vinorelbine arm, 280 took drug. 285 patients randomized to the placebo + trastuzumab + vinorelbine arm, 282 too drug. A total of 569 were comprised to randomized total and 562 to safety.

Overall Study

Reasons for not completing this period:

Analysis population for the primary outcome, as defined in the registry: The Full Analysis Set (FAS) consisted of all randomized patients.

5. Baseline Characteristics

The table reproduces the baseline characteristics posted in the registry, which describe the participants analysed at baseline and help show whether the randomized groups were comparable.

Population: The Full Analysis Set (FAS) consisted of all randomized patients.

6. Primary Endpoint

The registry lists the primary outcome as Progressive-free Survival (PFS) Per Investigator Assessment, measured over Every 6 weeks until disease progression or death which ever occurred first up to about 41 months.

Progression-free survival measures the time from randomization until the first qualifying event: radiologically documented tumor progression or death from any cause. The registry record specifies that whichever event occurred first counted. Progression was assessed by investigators using RECIST v1.0, with progression defined by an increase in the sum of the longest diameters of target lesions, measurable growth in a non-target lesion, or new lesions.

A time-to-event endpoint retains information about when an event occurs, rather than reducing follow-up to a simple count of participants who did or did not progress. This can be useful when participants are followed for different lengths of time or when some have not had an event by the analysis. Such participants may contribute information up to the time they are last observed, depending on the trial’s analysis procedures. The detailed handling of censoring is not reported in the registry record.

The registry reports median progression-free survival and a hazard ratio. The median describes the time by which half of the relevant survival distribution has experienced progression or death; it is a summary of the distribution, not a measure of how long every participant remained progression-free. The hazard ratio compares event rates over follow-up under the time-to-event model. Neither summary alone describes the full course of each participant’s disease or establishes what would happen outside the trial population.

This endpoint was chosen to capture disease progression and death together. It differs from overall survival, which concerns death, and from response outcomes, which describe tumor response rather than time to progression or death.

7. Results Posted in the Registry

• Everolimus + Vinorelbine + Trastuzumab vs Placebo + Vinorelbine + Trastuzumab: hazard ratio 0.78 (95% CI 0.65–0.95), reported p = 0.007 (as reported in the registry).

8. Secondary Efficacy Endpoints

The registry also posts results for secondary outcomes. For each outcome below, the endpoint type, the test applied to the posted summary statistics, the result and its interpretation are given in turn. Secondary endpoints are usually not adjusted for multiple testing, so they support rather than confirm the primary finding.

Overall Response Rate (ORR)

• Endpoint type: Binary: the percentage of participants with the event (counts rebuilt from the posted percentages and group sizes).
• Test: Unadjusted two-proportion z-test, with a Wald 95% confidence interval for the risk difference.
• Result:
  • Everolimus + Vinorelbine + Trastuzumab: 40.8% (116/284) versus 37.2% (106/285) on control; risk difference 3.7 percentage points (95% CI -4.4 to 11.7), risk ratio 1.10 (0.89–1.35); unadjusted two-proportion z-test p = 0.372.
• Interpretation:
  • The 95% confidence interval includes no difference, so these data do not show a statistically significant difference between Everolimus + Vinorelbine + Trastuzumab and the control. The interval gives the range of effects that remain compatible with the data.

Clinical Benefit Rate (CBR)

• Endpoint type: Binary: the percentage of participants with the event (counts rebuilt from the posted percentages and group sizes).
• Test: Unadjusted two-proportion z-test, with a Wald 95% confidence interval for the risk difference.
• Result:
  • Everolimus + Vinorelbine + Trastuzumab: 59.2% (168/284) versus 53.3% (152/285) on control; risk difference 5.8 percentage points (95% CI -2.3 to 14.0), risk ratio 1.11 (0.96–1.28); unadjusted two-proportion z-test p = 0.162.
• Interpretation:
  • The 95% confidence interval includes no difference, so these data do not show a statistically significant difference between Everolimus + Vinorelbine + Trastuzumab and the control. The interval gives the range of effects that remain compatible with the data.

The registry lists overall survival, overall response rate, clinical benefit rate, performance-status deterioration, quality-of-life deterioration, and blood-concentration outcomes among its secondary outcomes. Numerical results are posted for overall response rate and clinical benefit rate. Their group estimates are numerically higher in the everolimus group, but the available unadjusted comparisons do not provide clear inferential confirmation of a difference. This pattern is less decisive than the primary time-to-event result and should not be treated as independent proof that the primary finding is replicated.

Overall survival and the other listed secondary outcomes address different questions from progression-free survival. Overall survival concerns death, response and clinical benefit concern tumor outcomes, and patient-reported quality of life and performance status concern other aspects of follow-up. Blood concentrations are pharmacokinetic measures rather than direct measures of clinical benefit. Detailed results for those secondary outcomes are not reported in the registry record.

Secondary outcomes can help assess whether a primary finding is accompanied by changes in related clinical measures, but multiple comparisons create a greater chance of apparently notable results arising by chance. The registry record does not report a multiplicity adjustment for the secondary analyses. The unadjusted secondary comparisons should therefore carry less inferential weight than a prespecified primary analysis, particularly when several outcomes are considered together. They are best treated as supportive or exploratory evidence unless fuller analysis plans and results are available.

Secondary outcomes registered for this trial, for reference (only those above were recalculated):

• Overall Survival (OS)
• Overall Response Rate (ORR)
• Clinical Benefit Rate (CBR)
• Median Time to Deterioration of the ECOG Performance Status Score
• PRO: Time to Deterioration in Global Health Status/QoL Domain Score of the European Organization for the Research and Treatment of Cancer (EORTC)-Core Quality of Life Questionnaire (QLQ-C30) (by at Least 10%)
• Everolimus Blood Concentrations by Leading Dose and Time Point
• Vinorelbine Blood Concentrations by Leading Dose and Time Point
• Trastuzumab Blood Concentrations by Leading Dose and Time Point

9. Statistical Interpretation

10. Statistical Methods Explained

The primary analysis is a time-to-event comparison. Each participant’s progression-free survival time begins at randomization and ends at the first recorded progression or death. A participant without a recorded event during observed follow-up may contribute information up to the last time they are known to be event-free. This situation is commonly called censoring. The registry record does not describe its censoring rules or the details for handling missing assessments.

What does the median describe?

A median time-to-event estimate marks the point at which the estimated event-free proportion has reached half of its starting level. It is a distributional summary, not an average duration and not a guarantee about an individual. The reported medians are 7.0 months for everolimus plus vinorelbine and trastuzumab and 5.78 months for placebo plus vinorelbine and trastuzumab. Their confidence intervals describe uncertainty in each group’s median estimate. Comparing the two medians alone does not replace the trial’s reported comparative analysis.

What does the hazard ratio describe?

A hazard is the event rate at a given point in follow-up among participants who have not yet had the event. A hazard ratio compares these rates between randomized groups. The reported hazard ratio is 0.78, with a 95% confidence interval from 0.65 to 0.95. A value below 1 favors the everolimus group in the direction of fewer progression-or-death events over time. The hazard ratio is not a ratio of median survival times, nor does it mean that an identical relative change applies to each participant.

Interpreting a hazard ratio as a single summary across follow-up commonly relies on the proportional-hazards assumption: the relative hazards are treated as sufficiently stable over time. The registry identifies a Log Rank analysis and reports a hazard ratio but does not describe assumption checks or provide time-specific estimates. The appropriateness of that assumption therefore cannot be evaluated from the registry record alone.

What role does the Log Rank method play?

The Log Rank method compares the timing of events between groups over follow-up, taking account of participants who remain event-free for part of the observation period. It is a test of whether the observed event-time patterns differ under the null hypothesis. The reported p-value is 0.007. A p-value describes how compatible the observed data are with that null comparison under the test’s assumptions; it is not the probability that the null hypothesis is true, and it does not quantify the chance that the result will be reproduced.

The record says the primary analysis was performed when 415 events were reached. This identifies the event count at analysis, but does not describe whether interim analyses took place, how the analysis timing was selected, or whether any adjustment for repeated testing was planned. Those details are not reported in the registry record.

How should confidence intervals be read?

A confidence interval expresses the uncertainty around an estimate under the analysis procedure. For the hazard ratio, the interval from 0.65 to 0.95 gives a range of values compatible with the data and method at the stated confidence level. Its position below 1 is consistent with the reported test result. A confidence interval is not a range containing every participant’s outcome, and it does not capture every possible source of bias or uncertainty.

General statistical context: precision depends on the amount and pattern of information available, not only on the number enrolled. For time-to-event analyses, event timing and censoring affect how much information the comparison contains. The registry’s event-trigger statement provides relevant context, but it does not give enough detail to reconstruct the analysis or independently verify its assumptions.

What is the analysis population?

The Full Analysis Set consisted of all randomized patients. Using all randomized participants as the stated analysis population keeps the analysis linked to the randomized comparison. The registry does not describe exclusions after randomization, missing-data procedures, or whether every randomized participant contributed to every component of the primary analysis. Those points are not reported in the registry record.

The primary outcome was assessed by investigators. That is the assessment method explicitly reported; the registry does not provide details about blinding of outcome assessors beyond the overall double-blind design label, adjudication, or an independent review process. General statistical context: outcome assessment can affect the classification and timing of events, so transparent reporting of assessment procedures helps readers judge potential measurement uncertainty.

11. Confidence Intervals and Precision

The median estimates have 95% confidence intervals: 6.74 to 8.18 months for everolimus plus vinorelbine and trastuzumab, and 5.49 to 6.9 months for placebo plus vinorelbine and trastuzumab. These intervals show uncertainty around the estimated median in each group. They do not give a range for individual progression-free survival times.

The comparative estimate is a hazard ratio of 0.78, with a 95% confidence interval from 0.65 to 0.95. This interval is entirely below 1, the value corresponding to equal hazards under the ratio interpretation. The reported p-value is 0.007. The interval communicates both the direction of the estimated comparison and its uncertainty; it does not establish that every value inside the interval is equally plausible or that the interval accounts for all study limitations.

Confidence intervals for group-specific medians are not the same as a confidence interval for a difference between medians. Overlap or non-overlap between separate group intervals is not a substitute for the reported comparative analysis. The registry’s reported hazard ratio and Log Rank test address the randomized comparison, while the medians describe each group separately.

Precision also depends on analysis details that are not reported, including censoring and missing-assessment procedures and checks of the time-to-event assumptions. Accordingly, the intervals should be read as statistical uncertainty under the reported analysis, not as a complete measure of uncertainty about all possible sources of error or about results in other populations.

12. Planning a Similar Trial: Sample Size

This is a planning illustration, not a re-evaluation of the original trial. It asks: if a new study wanted to detect a difference like the one observed, or a smaller one, how large would it need to be? It is deliberately not “observed power” of the finished trial, which is not a meaningful quantity.

The planning figures are illustrations based on the observed effect, not the trial’s original design justification. The registry record does not report the original design assumptions or sample-size rationale. For the time-to-event outcome, the figures represent total events, not participants per arm. For the other outcomes, the figures represent participants per arm.

The stated illustration assumes a two-sided significance level of 0.05, equal allocation between the two arms, and a normal approximation. It considers effects set at 100%, 75%, and 50% of the observed effect. The time-to-event calculations use the Schoenfeld approach. These are planning assumptions for interpreting the illustration, not claims about the trial’s own design process.

The 80% power column gives the planned event count or per-arm participant count that would correspond to an 80% chance of detecting the stated effect under those assumptions, if that effect were the true effect. The 90% power column gives the corresponding planning count for a higher chance of detection. Higher planned power generally requires more information. Smaller effects are harder to distinguish from random variation and therefore require more events or participants than larger effects.

For example, the primary time-to-event illustration lists 509 total events for planning at 80% power and the full observed effect, and 681 total events for 90% power at that effect. The entries change as the assumed fraction of the effect changes. These are planning quantities; they are not the event count used to describe the trial result. This is not observed power.

Such calculations are sensitive to the assumed effect and other planning choices. The observed effect is uncertain, so a planning illustration based on it should not be read as a guarantee that a future study will achieve the listed power or reproduce the observed estimate.

13. How the Sample Size Is Derived

Power is the probability that a study of a given size rejects the null hypothesis when a specific true effect exists. It depends on four things: the effect to detect, the variability of the outcome, the significance level (here a two-sided α of 0.05, so z_1−α/2 = 1.960) and the desired power (z_1−β = 0.842 for 80% and 1.282 for 90%).

Formula

d is the total number of events needed (Schoenfeld's formula) with 1:1 allocation and proportional hazards.

Worked calculation for this trial

1. Hazard ratio HR = 0.780, so ln(HR) = -0.2485.
2. d = 4 × (1.96 + 0.842)² / [ln(0.780)]² = 508.6, rounded up to 509 events in total for 80% power.

The smaller-effect rows in the table above repeat the same calculation with the effect multiplied by 0.75 and 0.5 (for ratios, applied to the logarithm of the ratio).

R code

alpha <- 0.05
z_a   <- qnorm(1 - alpha / 2)          # two-sided
hr <- 0.78                              # observed hazard ratio
events_needed <- function(power, frac = 1) ceiling(round(4 * (z_a + qnorm(power))^2 / log(hr^frac)^2, 6))   # Schoenfeld, 1:1 allocation
for (f in c(1, 0.75, 0.5))
  cat(sprintf("%3.0f%% of observed effect: events = %s (80%% power), %s (90%% power)\n", 100 * f,
              format(events_needed(0.80, f), big.mark = ","), format(events_needed(0.90, f), big.mark = ",")))

# Simulation: given d total events under proportional hazards, events in the treatment arm are Binomial(d, hr / (1 + hr)).
# The score (log-rank type) statistic is (D1 - d / 2) / sqrt(d / 4).
set.seed(1007942)
d    <- events_needed(0.80)
reps <- 20000
D1 <- rbinom(reps, d, hr / (1 + hr))
z  <- (D1 - d / 2) / sqrt(d / 4)
cat(sprintf("Simulated power with %s events: %.3f (%d replicates)\n", format(d, big.mark = ","), mean(abs(z) > z_a), reps))

Output from running the code

100% of observed effect: events = 509 (80% power), 681 (90% power)
 75% of observed effect: events = 905 (80% power), 1,211 (90% power)
 50% of observed effect: events = 2,035 (80% power), 2,724 (90% power)
Simulated power with 509 events: 0.803 (20000 replicates)

The simulation uses the fact that, with d events in total and proportional hazards, the number of events in the treatment arm is binomial. It is an approximation that conditions on the total number of events, and it should land close to the target power.

14. Group-Sequential Design Analysis

Many time-to-event trials, especially in oncology, stop or report early when interim data are overwhelming, so a single fixed-sample number understates the real design problem. The calculation below is an illustrative group-sequential design built from this trial's posted control median and hazard ratio. The registry record does not state the planned number of looks or the spending function, so the design is a planning illustration, not a reconstruction of the protocol. Assumed: one-sided α = 0.025, 90% power, three equally spaced looks on information, Lan-DeMets O'Brien-Fleming spending, 24 months of accrual, 60 months in total and a 1% monthly dropout hazard.

Under these assumptions the design needs about 758 participants in total, with the boundaries below.

Fixed design versus group-sequential alternatives

Group-sequential designs cost a small increase in maximum sample size and events compared with the fixed design, in exchange for the chance to stop early for overwhelming efficacy. O'Brien-Fleming spending keeps early boundaries very conservative and the final boundary close to the fixed-design value, whereas Pocock spending spends more alpha early.

Sensitivity to a smaller true effect

Planning for exactly the observed effect is optimistic, because observed effects in published trials tend to be somewhat larger than the true effect. The table repeats the main design (three looks, O'Brien-Fleming spending, 90% power) for smaller true hazard ratios.

For context, this trial enrolled 569 participants.

Cross-check, upper boundaries: gsDesign 3.71, 2.511, 1.993 | rpact 3.71, 2.511, 1.993. Agreement between the two packages confirms the boundary calculation.

R code and output

suppressWarnings(suppressMessages({ library(gsDesign); library(rpact) }))
med_control <- 5.78          # control-arm median, months (converted from the posted unit)
hr          <- 0.78          # hazard ratio used for planning
lambdaC     <- log(2) / med_control
eta         <- 0.01               # dropout hazard per month (assumption)
acc_months  <- 24                 # accrual duration (assumption)
study_months<- 60                 # total study duration (assumption)

design <- function(k, sfu, beta, h = hr) gsSurv(k = k, test.type = 1, alpha = 0.025, beta = beta, sfu = sfu,
  lambdaC = lambdaC, hr = h, hr0 = 1, eta = eta, gamma = 1, R = acc_months, T = study_months, minfup = study_months - acc_months)

# Main design: 3 looks, Lan-DeMets O'Brien-Fleming spending, one-sided alpha 0.025, 90% power
x <- design(3, sfLDOF, 0.10)
cat(sprintf("N_TOTAL|%s\n", format(ceiling(sum(x$gamma * x$R)), big.mark = ",")))
for (i in 1:3) cat(sprintf("GS|%d|%.1f|%.0f|%.3f|%.5f|%.5f\n", i, x$T[i], x$n.I[i], x$upper$bound[i], pnorm(-x$upper$bound[i]), cumsum(x$upper$spend)[i]))
cat("\n"); print(gsBoundSummary(x))

# Comparison of designs
cmp <- list(list("Fixed design, one analysis", 1, sfLDOF), list("Two looks, O'Brien-Fleming", 2, sfLDOF),
            list("Three looks, O'Brien-Fleming", 3, sfLDOF), list("Three looks, Pocock", 3, sfLDPocock))
cat("\n")
for (b in c(0.20, 0.10)) for (d in cmp) {
  y <- design(d[[2]], d[[3]], b)
  cat(sprintf("DES|%s|%d|%.0f|%s|%.1f\n", d[[1]], round(100 * (1 - b)), tail(y$n.I, 1), format(ceiling(sum(y$gamma * y$R)), big.mark = ","), tail(y$T, 1)))
}

# Sensitivity: what if the true effect is smaller than the observed one? (3 looks, O'Brien-Fleming, 90% power)
cat("\n")
for (fr in c(1, 0.75, 0.5)) {
  y <- design(3, sfLDOF, 0.10, exp(log(hr) * fr))
  cat(sprintf("SENS|%.2f|%.3f|%.0f|%s\n", fr, exp(log(hr) * fr), tail(y$n.I, 1), format(ceiling(sum(y$gamma * y$R)), big.mark = ",")))
}

# Independent cross-check of the boundaries with rpact
r <- getDesignGroupSequential(kMax = 3, alpha = 0.025, beta = 0.10, sided = 1, typeOfDesign = "asOF", informationRates = x$timing)
cat(sprintf("\nCross-check, upper boundaries: gsDesign %s | rpact %s\n", paste(round(x$upper$bound, 3), collapse = ", "), paste(round(r$criticalValues, 3), collapse = ", ")))

Method: LachinFoulkes 
    Analysis               Value Efficacy
   IA 1: 33%                   Z   3.7103
      N: 476         p (1-sided)   0.0001
 Events: 230        ~HR at bound   0.6126
   Month: 15    P(Cross) if HR=1   0.0001
             P(Cross) if HR=0.78   0.0338
   IA 2: 67%                   Z   2.5114
      N: 758         p (1-sided)   0.0060
 Events: 459        ~HR at bound   0.7910
   Month: 24    P(Cross) if HR=1   0.0060
             P(Cross) if HR=0.78   0.5603
       Final                   Z   1.9930
      N: 758         p (1-sided)   0.0231
 Events: 689        ~HR at bound   0.8590
   Month: 60    P(Cross) if HR=1   0.0250
             P(Cross) if HR=0.78   0.9000



Cross-check, upper boundaries: gsDesign 3.71, 2.511, 1.993 | rpact 3.71, 2.511, 1.993

15. Analyses Reported by the Sponsor

The primary comparison reported in the registry is the sponsor’s analysis: a Log Rank hazard ratio of 0.78, with a 95% confidence interval from 0.65 to 0.95 and p = 0.007. The reported median progression-free survival estimates are 7.0 months and 5.78 months for the everolimus and placebo groups, respectively. The registry record does not flag a discrepancy between the reported analysis and the analysis used for the posted primary result.

For this time-to-event outcome, the reported hazard ratio is the relevant comparative estimate. No separate recalculated hazard ratio is presented as an alternative to the sponsor’s analysis. The group medians and their intervals provide additional descriptive context, but they do not independently replace the sponsor-reported comparison.

Any simple re-analysis based on summary statistics would not reproduce the participant-level time-to-event analysis, including event timing and censoring. The sponsor’s reported analysis should therefore be read as the primary inferential result in the registry record. The record does not include enough detail to independently reproduce that analysis or assess all its assumptions.

16. Multiplicity, Missing Data and Analysis Populations

The registry identifies progression-free survival as the primary outcome and lists multiple secondary outcomes. The primary analysis is reported with a Log Rank p-value. The record states that the primary analysis occurred when 415 events were reached, but does not describe whether interim efficacy analyses were planned or conducted, whether the timing was prespecified, or whether any adjustment was made for repeated looks at the data.

Multiplicity arises when several outcomes, comparisons, or analysis opportunities are examined. Without an adjustment or a clearly stated hierarchy, the chance of at least one apparently unusual result can increase. The registry record does not report a multiplicity strategy for the secondary outcomes. Their unadjusted results should therefore not be interpreted as if each were the sole confirmatory test.

The Full Analysis Set consisted of all randomized patients, which is the analysis population reported for the primary endpoint. The registry does not specify the detailed treatment of participants with missing assessments, early discontinuation, protocol deviations, or incomplete follow-up. It also does not report the censoring rules used for the time-to-event analysis. These details affect how the analysis population and available event information are translated into estimates.

The primary outcome was investigator-assessed. The record does not report whether there was independent central review, adjudication, or a separate blinded assessment procedure for progression. The overall design is described as double-blind, but that label alone does not document every outcome-assessment safeguard.

The registry record also does not report the original sample-size justification, detailed statistical analysis plan, model diagnostics, or missing-data sensitivity analyses. These omissions limit what can be concluded about prespecification and robustness beyond the methods and results explicitly recorded.

17. Safety Results

The registry reports serious adverse events as affected participants divided by participants at risk.

Other (non-serious) adverse events

Participants with at least one non-serious event at or above the registry's 5% reporting threshold.

Most frequent serious adverse event terms

Most frequent non-serious adverse event terms

Serious events with the highest arm-level rate: febrile neutropenia (10.7%), pyrexia (4.6%), neutropenia (4.3%), anaemia (3.6%), stomatitis (3.2%).

The serious adverse event record reports 122 affected participants among 280 at risk in the everolimus group, and 58 affected participants among 282 at risk in the placebo group. These are group-level serious adverse event counts; they do not describe the severity or course of every event, nor establish that the study treatment caused an event. Participants may have more than one event, so affected-participant counts and event-term counts need not represent the same quantity.

The listed serious adverse event terms include febrile neutropenia at 10.7%, pyrexia at 4.6%, neutropenia at 4.3%, anaemia at 3.6%, stomatitis at 3.2%, and pneumonia at 2.9%. These are the prominent terms recorded, not an exhaustive description of all safety outcomes. The record does not provide the term-specific group denominators in the information available for this summary, so those percentages should not be converted into counts.

Safety comparisons require attention to definitions, exposure time, event attribution, and follow-up. Detailed definitions, exposure-adjusted comparisons, and attribution procedures are not reported in the registry record. The figures describe reported events during the trial and do not by themselves determine the cause of an event or establish an individual risk.

18. What the Result Does, and Does Not, Mean

The primary result shows a statistically supported difference in investigator-assessed progression-free survival between the randomized treatment groups in this trial. The estimated hazard ratio was below 1, and its 95% confidence interval did not include 1. The group median estimates also favored the everolimus-containing regimen numerically. These observations describe the trial results; they are not a recommendation or a prediction for an individual.

The result concerns adding everolimus to vinorelbine and trastuzumab in pretreated women with HER2/Neu-overexpressing locally advanced or metastatic breast cancer. It does not isolate the effect of everolimus from the shared regimen in every possible setting, and it does not establish what would happen with different doses, treatment partners, patient populations, or care contexts.

Progression-free survival combines radiologically documented progression and death. It is not the same outcome as overall survival, quality of life, or response rate. Although the registry lists overall survival and other secondary outcomes, detailed results for most of those outcomes are not reported in the registry record. The primary finding should not be taken as proof of a change in outcomes that were not reported.

Statistical significance does not settle clinical importance. The hazard ratio and median estimates summarize group-level experience and have uncertainty. They do not describe every participant’s outcome, guarantee the same effect across subgroups, or show how an individual would fare. The registry record does not report the subgroup results or the additional clinical context needed to assess individual circumstances.

The result should also be read with the reported outcome assessment and analysis details in mind. Progression was investigator-assessed, and important details about missing data, censoring, analysis assumptions, and multiplicity are not reported. Those limitations do not erase the posted result, but they constrain how broadly and confidently it can be interpreted.

19. Why This Trial Matters Statistically

This trial offers a randomized comparison of adding everolimus to an existing vinorelbine and trastuzumab regimen. That design makes the treatment contrast clear: both groups received the shared drugs, while everolimus was compared with matching placebo. Randomization and double masking are important features for reducing some sources of bias in a comparative trial.

The endpoint illustrates why time-to-event methods are useful in clinical research. Progression-free survival incorporates both when events occur and whether an event was observed during follow-up. Reporting medians alongside a hazard ratio gives readers complementary summaries: a time point within each group’s distribution and a relative comparison over follow-up. The confidence interval makes uncertainty around the comparative estimate visible rather than presenting a point estimate alone.

The record also illustrates the distinction between a primary result and secondary evidence. The primary analysis has a reported comparative test, whereas several secondary outcomes are listed without detailed results. Even when secondary group estimates point in a similar direction, multiple comparisons and incomplete reporting affect how much weight they can carry.

Finally, the registry record shows why design details and analysis transparency matter. It identifies the population, regimen, primary endpoint, analysis population, and event-trigger statement, but leaves important questions about the original sample-size rationale, missing data, interim analyses, and multiplicity unanswered. Statistical interpretation depends not only on a p-value, but also on the endpoint definition, assumptions, precision, and scope of the evidence.

20. Limitations

• The posted summary statistics are the only data these results rest on; patient-level data, full protocols and statistical analysis plans are not available.
• Recalculated results are simple, unadjusted calculations from posted summaries. They do not replace the sponsor's prespecified analysis.

The primary outcome was investigator-assessed progression-free survival. Although the trial is described as double-blind, the registry record does not report whether progression was independently reviewed or adjudicated. The method used to assess progression can affect event classification and timing, and the available record does not allow that process to be evaluated in detail.

The registry specifies that the primary analysis was conducted when 415 events were reached, but does not report whether interim analyses were planned, how analysis timing was chosen, or whether repeated testing was addressed. The original sample-size justification and statistical analysis plan are not reported. The planning illustrations based on the observed effect do not fill those gaps.

Details of censoring, missing assessments, follow-up completeness, treatment adherence, treatment discontinuation, and crossover are not reported in the registry record. These omissions limit assessment of how the available observations contributed to the time-to-event analysis. The record also does not describe checks of the assumptions underlying the hazard-ratio summary.

The secondary outcome list is broader than the detailed results available in the record. Most listed secondary outcomes do not have results reported in the information summarized, and no multiplicity strategy is reported. This makes it difficult to judge consistency across outcomes or distinguish confirmatory from exploratory analyses.

Finally, the trial population is specific: pretreated women with HER2/Neu-overexpressing locally advanced or metastatic breast cancer, including resistance to trastuzumab and prior taxane treatment. Results should not be assumed to apply to other populations or regimens without additional evidence.

21. Related Tutorials

22. Related Statistical Calculators

23. Sources

• ClinicalTrials.gov: Daily Everolimus in Combination With Trastuzumab and Vinorelbine in HER2/Neu Positive Women With Locally Advanced or Metastatic Breast Cancer — NCT01007942.
• Registry record last updated 2017-04-05; status last verified 2017-02.
• Results first posted 2017-04-05.
• Data retrieved from ClinicalTrials.gov on 2026-11-09.
• Group sizes, means, event counts and sponsor-reported p-values are reproduced as posted. Effect estimates, confidence intervals and p-values not attributed to the sponsor are independently calculated from the posted summary statistics.

24. Record Summary

The registry record describes a completed randomized, double-blind, placebo-controlled phase III trial in pretreated women with HER2/Neu-overexpressing locally advanced or metastatic breast cancer. Everolimus plus vinorelbine and trastuzumab was compared with matching placebo plus vinorelbine and trastuzumab. Enrollment was 569.

The primary outcome was investigator-assessed progression-free survival, defined from randomization to radiologically documented tumor progression or death. The reported medians were 7.0 months and 5.78 months, respectively. The sponsor-reported hazard ratio was 0.78, with a 95% confidence interval from 0.65 to 0.95 and a Log Rank p-value of 0.007.

These results describe the randomized comparison for the stated population and endpoint. Important details about the original design assumptions, missing data, multiplicity, and some secondary outcomes are not reported in the registry record.