Everolimus Versus Placebo in Head and Neck Cancer: Complete Statistical Analysis

1. Trial at a Glance

This page covers the registry results for Everolimus Versus Placebo in Head and Neck Cancer (NCT01111058), a randomized phase 2 study. The primary outcome is 2 Year Progression Free Survival Rate.

2. Clinical Question

The clinical question is about an adjuvant treatment comparison following definitive local therapy, not a comparison of treatments for every stage or setting of head and neck cancer. The registry describes the study as a randomized phase II trial and identifies the primary outcome as the 2-year progression-free survival rate. The posted group estimates are 56.1% for everolimus and 55.9% for placebo, with wide 95% confidence intervals around each estimate. These results describe the groups enrolled in this trial; they do not by themselves establish the effect that would be expected in other populations or settings.

The registry record does not report further detail about eligibility criteria, the definition of definitive local therapy, or the analysis population used for the primary outcome. Those details would help clarify which patients the comparison represents and how participants were handled if follow-up was incomplete.

3. Trial Design

The registry describes a randomized, parallel-group phase II trial with triple masking. The groups received everolimus (RAD001) or placebo; the everolimus arm description specifies 10 mg daily, while the placebo arm description specifies double-blind placebo. The study title identifies the treatment as adjuvant therapy for patients with locally advanced squamous cell cancer of the head and neck.

Enrollment is reported as 52. The posted primary outcome gives group denominators of 28 for everolimus and 24 for placebo. The registry status is terminated. The study start is recorded as 2010-04 and primary completion as 2018-04. The record does not explain the reason for termination, so the status alone does not establish why the trial ended or whether termination affected the reported outcome.

The primary endpoint was the 2-year progression-free survival rate, defined as time to disease progression or death from any cause, with a 2-year timeframe. Other posted outcomes include toxicity, progression sites, second primary tumor, and Akt/mTOR pathway and PTEN-related measures. The registry record does not report the original sample-size justification, the planned treatment duration, details of follow-up procedures, or the full statistical analysis plan. It also does not report whether randomization was stratified or how allocation concealment was handled. These absences limit assessment of design details beyond the randomized, triple-masked, parallel-group description.

The posted results include a comparison of Kaplan-Meier estimates and a log-rank analysis for the primary endpoint. The record does not supply enough detail to reconstruct all aspects of those analyses, including censoring rules or the handling of missing outcome information. The findings should therefore be read as the registered results for this trial, with those unreported methods kept distinct from the methods that are explicitly named.

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.

Overall Study

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.

6. Primary Endpoint

The registry lists the primary outcome as 2 Year Progression Free Survival Rate, measured over 2 years.

The primary endpoint is the 2-year progression-free survival rate. The registry defines progression-free survival as time to disease progression or death from any cause, and specifies a 2-year timeframe. This endpoint combines two clinically important events into a time-to-event outcome: progression of disease and death. Participants who have neither event through the specified time contribute to the estimated progression-free survival at that time, while the analysis can also account for different observed follow-up times through survival methods.

A fixed-time survival rate offers a way to describe the proportion of participants estimated to remain free of progression or death at a defined point. It is useful when the timing of events matters and when participants may have differing lengths of observation. The registry reports the primary result as a percentage and gives a 95% confidence interval for each group. Kaplan-Meier estimation is the named method for comparing survival proportions, and the log-rank test is also reported.

The endpoint is not simply a count of participants with progression. Death from any cause is included, and the outcome is framed as time to the first qualifying event. The registry record does not report the detailed rules for censoring, event adjudication, or missing follow-up. Those details can affect how a time-to-event estimate is formed, so interpretation should remain tied to the definition and methods that are actually recorded.

7. Results Posted in the Registry

• Everolimus (RAD001): 57.1% (16/28) versus 54.2% (13/24) on control; risk difference 3.0 percentage points (95% CI -24.1 to 30.1), risk ratio 1.05 (0.65–1.72); unadjusted two-proportion z-test p = 0.829.

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.

Number of Participants With Toxicity

• Endpoint type: Binary: the number of participants with the event, compared as proportions.
• Test: Unadjusted two-proportion z-test, with a Wald 95% confidence interval for the risk difference.
• Result:
  • Everolimus (RAD001): 89.3% (25/28) versus 66.7% (16/24) on control; risk difference 22.6 percentage points (95% CI 0.6 to 44.7), risk ratio 1.34 (0.98–1.83); unadjusted two-proportion z-test p = 0.046.
• Interpretation:
  • The 95% confidence interval excludes no difference, so the proportion with the event is statistically higher for Everolimus (RAD001) than for the control. This is an unadjusted, secondary comparison, so it is suggestive rather than confirmatory.

Site of Progression: Local-regional

• Endpoint type: Binary: the number of participants with the event, compared as proportions.
• Test: Unadjusted two-proportion z-test, with a Wald 95% confidence interval for the risk difference.
• Result:
  • Everolimus (RAD001): 7.1% (2/28) versus 20.8% (5/24) on control; risk difference -13.7 percentage points (95% CI -32.5 to 5.2), risk ratio 0.34 (0.07–1.61); unadjusted two-proportion z-test p = 0.149.
• Interpretation:
  • The 95% confidence interval includes no difference, so these data do not show a statistically significant difference between Everolimus (RAD001) and the control. The interval gives the range of effects that remain compatible with the data.

Site of Progression: Distant

• Endpoint type: Binary: the number of participants with the event, compared as proportions.
• Test: Unadjusted two-proportion z-test, with a Wald 95% confidence interval for the risk difference.
• Result:
  • Everolimus (RAD001): 7.1% (2/28) versus 8.3% (2/24) on control; risk difference -1.2 percentage points (95% CI -15.8 to 13.4), risk ratio 0.86 (0.13–5.63); unadjusted two-proportion z-test p = 0.872.
• Interpretation:
  • The 95% confidence interval includes no difference, so these data do not show a statistically significant difference between Everolimus (RAD001) and the control. The interval gives the range of effects that remain compatible with the data.

The secondary outcomes cover several different domains: toxicity, the site of progression, second primary tumors, and biological pathway or biomarker measures. These outcomes do not all address the same question as the primary progression-free survival endpoint. Some describe events or clinical features, while others concern pathway activation or possible relationships between biomarkers and progression-free survival.

Across the posted secondary comparisons, estimates vary in direction and precision. The toxicity comparison has a reported unadjusted p-value below 0.05 in the re-calculation, while the registry's Fisher Exact analysis reports p = 0.086. Other posted comparisons also show considerable uncertainty. These results should not be treated as a unified pattern of benefit or harm, and they do not overturn the primary endpoint findings. Each secondary outcome is a separate comparison with its own definition, event frequency, and limitations.

Multiplicity is important because evaluating several outcomes increases the opportunity for chance findings. Unless an analysis plan specifies how multiple tests are handled, an isolated unadjusted result is less persuasive than a result that was prespecified and interpreted within an appropriate multiplicity strategy. The registry record does not report a multiplicity adjustment or enough detail to determine which secondary analyses were prespecified. Consequently, secondary results are best treated as exploratory or descriptive rather than as confirmatory evidence.

The biological outcomes may be relevant to future hypotheses, but the registry record does not report enough detail to assess their measurement, analysis, or predictive value. In particular, a biomarker association is not automatically evidence that the biomarker identifies a subgroup with a different treatment effect. The overall secondary pattern provides context, but the primary endpoint remains the main basis for interpreting the trial's principal question.

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

• Number of Participants With Toxicity
• Site of Progression: Local-regional
• Site of Progression: Distant
• Site of Progression: Unknown
• Second Primary Tumor
• Akt/mTOR Pathway Activation
• Correlation of Akt/mTOR Status With Progression-free Survival
• Determine if PTEN Status is a Predictive Biomarker

9. Statistical Interpretation

10. Statistical Methods Explained

Several statistical ideas help interpret the reported results. The distinctions below are general explanations, followed by how they relate to the methods named in the registry record.

What does a confidence interval describe?

In general, a confidence interval expresses the uncertainty around an estimate under the assumptions of the method used to calculate it. A narrower interval indicates greater precision; a wider interval indicates less precision. It is not a guarantee that the true value lies within a particular interval, and it does not describe the range of outcomes for individual participants. The registry reports 95% confidence intervals for each group's 2-year progression-free survival estimate and for the difference in survival proportions.

The group-specific intervals are 31.1% to 81.2% for everolimus and 34.1% to 77.8% for placebo. Their breadth signals substantial uncertainty around the estimated survival rates. For a difference, an interval spanning zero means that the data are compatible with no difference as well as differences in either direction, within the interval and under the method's assumptions. Both the registry's interval for the Kaplan-Meier difference and the unadjusted re-calculation interval span zero.

What does the Kaplan-Meier method do?

In general, Kaplan-Meier estimation is a way to estimate survival over time when participants have different follow-up durations. It uses the timing of events and accounts for participants whose observation ends before an event is recorded, under assumptions about censoring. The registry names a comparison of Kaplan-Meier estimates and reports a difference in survival proportions for the primary outcome. The record does not report the detailed censoring rules or the number and timing of censored observations, so those elements cannot be evaluated from the recorded summary alone.

How does the log-rank test differ?

In general, the log-rank test compares the event experience between groups over the observed follow-up period. It uses the ordering and timing of events, rather than comparing only a single fixed-time percentage. The registry reports a log-rank p-value of 0.54. A p-value describes how compatible the observed data are with a specified null model, given the test assumptions; it is not the probability that the null hypothesis is true. The registry record does not provide further details about the test specification or whether any other analyses were prespecified.

What is the unadjusted re-calculation?

The posted re-calculation is a simple unadjusted comparison based on the summary statistics in the record. For the primary endpoint, it reports a risk difference estimate of 0.030, a 95% confidence interval from -0.241 to 0.301, and p = 0.829. These are not the registry's Kaplan-Meier or log-rank results. A summary-statistic re-calculation does not incorporate participant-level event timing and should not be treated as a substitute for the registered time-to-event analysis. It is useful as a supplementary check on the posted group summaries, but it answers a simpler question.

Why can small trials produce broad intervals?

In general, an estimate based on limited information is more sensitive to the outcomes of individual participants, and its uncertainty can be considerable. This trial's registry record reports enrollment of 52 and primary group denominators of 28 and 24. Those figures provide context for the precision of the estimates; they do not establish the trial's original target or whether the trial met a planned information goal. The original sample-size justification is not reported in the registry record.

In general, over-dispersion matters when observed variation is greater than a statistical model expects. If it is ignored, uncertainty can be understated. The registry record does not report an over-dispersion model or indicate that over-dispersion was assessed for the primary endpoint. This general issue should not be taken as evidence that over-dispersion occurred in this trial.

How should p-values be read?

In general, a p-value measures how unusual data at least as incompatible with a null hypothesis would be if that null hypothesis and the test assumptions were correct. It does not measure the size or clinical importance of an effect, and a larger p-value does not prove the treatments are the same. The primary reported p-values are 0.90 for the Kaplan-Meier comparison and 0.54 for the log-rank test; the re-calculated unadjusted p-value is 0.829. These results should be interpreted alongside the effect estimates and confidence intervals, not in isolation.

The registry record does not report the complete statistical analysis plan, the original sample-size assumptions, interim analyses, handling of missing data, or analysis-population definitions. Those details should not be inferred from the named methods or the posted results.

11. Confidence Intervals and Precision

The primary group estimates are 56.1% for everolimus and 55.9% for placebo. Their 95% confidence intervals, 31.1% to 81.2% and 34.1% to 77.8%, are broad. Each interval spans a sizable range of possible 2-year rates, so the point estimates should not be treated as highly precise values. The intervals also overlap, consistent with the close estimates, though overlap alone is not a formal test of the difference.

For the registry's Kaplan-Meier comparison, the reported difference in survival proportions is 0.002, with a 95% confidence interval from -0.33 to 0.33. That interval includes zero and permits uncertainty in either direction. The log-rank p-value is 0.54. The unadjusted re-calculation reports a risk difference of 0.030, with a 95% confidence interval from -0.241 to 0.301 and p = 0.829. Its interval also includes zero. These estimates arise from different approaches and should not be conflated.

Confidence intervals quantify sampling uncertainty under their methods; they do not address every possible source of uncertainty. The registry record does not report enough information to assess the impact of missing follow-up, censoring details, or choices in the analysis population. The confidence intervals therefore support a cautious reading: the recorded estimates are close, but the uncertainty is substantial, and the results do not establish equivalence or rule out meaningful differences.

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 assumptions. For these binary outcomes, the displayed figures are participants per arm, not total participants or total events. The planning method specified in the registry-derived information uses a two-sided significance level of 0.05, equal allocation between the two arms, and a normal approximation. It considers detecting the stated fraction of the observed effect. The original trial's own sample-size assumptions are not reported in the registry record.

The 80% power column indicates the number of participants per arm in a hypothetical future study that would give an 80% chance of detecting the stated effect under the planning assumptions, if that effect and those assumptions held. The 90% power column uses a higher chance of detecting the effect and therefore calls for more participants per arm. These are planning scenarios, not guarantees about a future study's results.

Rows representing 100%, 75%, and 50% of the observed effect show how the planning requirements change when the effect to detect is made smaller. Smaller effects require more participants because they are harder to distinguish from random variation. The displayed requirements are therefore highly dependent on the observed effect used as a starting point. With small trial estimates, that starting point can be uncertain, so the figures should be treated as illustrations rather than definitive design recommendations. This is not observed power.

For time-to-event planning, the stated convention is total number of events; for other outcomes, it is participants per arm. The primary outcome and the secondary comparisons are presented as binary outcomes, so their planning values use participants per arm. The registry record does not report that these planning illustrations were the assumptions used to design the original trial.

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

n is the number of participants in each arm, p₀ and p₁ are the control and treatment proportions, and p̄ is their average.

Worked calculation for this trial

1. Control proportion p₀ = 0.542; treatment proportion p₁ = 0.571; average p̄ = 0.557.
2. First term: z_1−α/2 × √[2p̄(1−p̄)] = 1.96 × 0.7026 = 1.3770.
3. Second term: z_1−β × √[p₀(1−p₀) + p₁(1−p₁)] = 0.842 × 0.7023 = 0.5910.
4. n = (1.3770 + 0.5910)² / (0.0298)² = 4,372.7, rounded up to 4,373 participants per arm 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
p0 <- 0.541666666666667                          # control proportion
p1 <- 0.571428571428571                          # treatment proportion
n_per_arm <- function(power, frac = 1) {
  q1 <- p0 + (p1 - p0) * frac               # smaller effect = fraction of the observed difference
  pbar <- (p0 + q1) / 2
  z_b <- qnorm(power)
  ceiling(round((z_a * sqrt(2 * pbar * (1 - pbar)) + z_b * sqrt(p0 * (1 - p0) + q1 * (1 - q1)))^2 / (q1 - p0)^2, 6))
}
for (f in c(1, 0.75, 0.5))
  cat(sprintf("%3.0f%% of observed effect: n per arm = %s (80%% power), %s (90%% power)\n", 100 * f,
              format(n_per_arm(0.80, f), big.mark = ","), format(n_per_arm(0.90, f), big.mark = ",")))

# Check against R's built-in function (unrounded n per arm)
cat("power.prop.test:", round(power.prop.test(p1 = p0, p2 = p1, power = 0.80)$n, 1), "\n")

# Simulation: how often does the z-test reject at the 80%-power sample size?
set.seed(1111058)
n    <- n_per_arm(0.80)
reps <- 20000
x0 <- rbinom(reps, n, p0); x1 <- rbinom(reps, n, p1)
ph <- (x0 + x1) / (2 * n)
z  <- ((x1 - x0) / n) / sqrt(ph * (1 - ph) * 2 / n)
cat(sprintf("Simulated power at n = %s per arm: %.3f (%d replicates)\n", format(n, big.mark = ","), mean(abs(z) > z_a, na.rm = TRUE), reps))

Output from running the code

100% of observed effect: n per arm = 4,373 (80% power), 5,854 (90% power)
 75% of observed effect: n per arm = 7,788 (80% power), 10,425 (90% power)
 50% of observed effect: n per arm = 17,551 (80% power), 23,494 (90% power)
power.prop.test: 4372.7 
Simulated power at n = 4,373 per arm: 0.803 (20000 replicates)

The simulation draws many pairs of event counts at the planned sample size and counts how often the two-proportion z-test rejects the null. It should land close to the target power; at very small required sample sizes it can fall below it, because the normal approximation is rough there.

14. Analyses Reported by the Sponsor

The registry reports two sponsor analyses for the primary endpoint: a comparison of Kaplan-Meier estimates and a log-rank test. The Kaplan-Meier comparison gives a difference in survival proportions of 0.002, a 95% confidence interval from -0.33 to 0.33, and p = 0.90. The log-rank result is p = 0.54. These are the trial's reported analyses and are the most direct account of the registered time-to-event comparison.

The separate simple unadjusted re-calculation gives a risk difference estimate of 0.030, a 95% confidence interval from -0.241 to 0.301, and p = 0.829. It uses posted summary statistics and does not reproduce participant-level event timing or the Kaplan-Meier procedure. The re-calculated result is therefore supplementary and should not be read as a replacement for the sponsor's time-to-event analyses.

The discrepancy flag is false, so no discrepancy between the sponsor analysis and the re-calculation is identified in the record. The estimates come from different methods, and the registry does not explain method-specific differences in detail. Both sets of results indicate substantial uncertainty; interpretation should emphasize the sponsor's reported analyses for the time-to-event endpoint and use the simpler re-calculation only as a secondary descriptive check.

15. Multiplicity, Missing Data and Analysis Populations

The registry lists multiple secondary outcomes, including toxicity, several progression-site categories, second primary tumor, Akt/mTOR pathway activation, a correlation between Akt/mTOR status and progression-free survival, and PTEN status as a potential predictive biomarker. The record reports some secondary analyses, but it does not report a multiplicity adjustment, a hierarchy of testing, or a complete plan for interpreting multiple outcomes. Without such information, the chance of at least one apparently notable result across a collection of tests is difficult to judge. Unadjusted secondary p-values should therefore be read cautiously and should not be treated as confirmatory on their own.

The registry record does not report how missing outcome data were handled, how participants lost to follow-up were treated, or the censoring rules for the primary time-to-event analysis. It also does not report the analysis-population definitions, such as whether the primary analysis followed an intention-to-treat principle or used another specified population. These omissions matter because exclusions, missing observations, and censoring choices can affect estimates and their uncertainty.

The registry names randomized allocation and triple masking but does not provide enough detail to evaluate concealment, adherence, treatment changes, or the effect of unmasking, if any. The record does not report interim analyses or stopping rules. The terminated status is recorded, but the reason for termination and its relationship to planned analyses are not reported in the registry record. These missing details limit assessment of potential bias and of how closely the posted analyses follow the original plan.

16. Safety Results

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

Deaths

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: dysphagia (4.2%), hypertension (4.2%), leukemia secondary to oncology chemotherapy (4.2%), neoplasms benign, malignant and unspecified (incl cysts and polyps) - other, specify (4.2%), suicidal ideation (4.2%).

The serious adverse-event summary reports 3 participants affected in the everolimus group and 5 in the placebo group, with group denominators of 28 and 24, respectively. Deaths are reported for 4 participants in each group. The summary also lists other adverse events for 25 participants in the everolimus group and 14 in the placebo group. These counts describe the registry's recorded safety categories; they do not by themselves establish that treatment caused an event or explain its severity, duration, or relationship to treatment.

The serious-event terms with the largest listed percentages include dysphagia, hypertension, leukemia secondary to oncology chemotherapy, neoplasms benign, malignant and unspecified (including cysts and polyps), suicidal ideation, and pharyngitis. The record does not specify in this summary how these terms were distributed by treatment group, so they should not be attributed to one arm. The listed percentages are 4.2% for each of the first five terms and 3.6% for pharyngitis.

Safety counts should be read alongside the trial's size and the registry's limited detail. The record does not report event-level timing, attribution, exposure duration, or a complete safety-analysis plan. These summary counts are descriptive and do not support a causal conclusion by themselves.

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

The primary result shows similar posted 2-year progression-free survival point estimates for the two groups: 56.1% with everolimus and 55.9% with placebo. The sponsor's Kaplan-Meier comparison reports a difference estimate of 0.002 with a 95% confidence interval from -0.33 to 0.33 and p = 0.90; the log-rank test reports p = 0.54. The intervals are broad, so the results do not establish that the groups are equivalent or that any potentially important difference is absent.

The findings apply to the randomized phase II trial described in the registry: adjuvant everolimus versus placebo for patients with locally advanced squamous cell cancer of the head and neck after definitive local therapy. They do not establish what would happen in other patient groups, stages, treatment settings, or regimens. The registry record does not report enough detail about eligibility criteria, missing data, or analysis populations to refine those boundaries further.

The results also do not prove that everolimus improves outcomes or that it has no effect. A p-value is not the probability that a treatment works, and the observed point estimates are not definitive estimates of what would occur in a larger or different study. The confidence intervals indicate meaningful uncertainty around the rates and their difference.

Secondary outcomes and safety summaries offer additional descriptive information, but multiple outcomes, unadjusted analyses, and incomplete methodological details limit their confirmatory value. The terminated status is part of the registry record, but the reason for termination is not reported. Overall, the record supports a cautious description of the observed comparison, not a broad clinical recommendation or a claim of equivalence.

18. Why This Trial Matters Statistically

This trial illustrates why an estimated effect and its precision need to be considered together. The primary group estimates are close, but the 95% confidence intervals are wide. A numerical similarity between point estimates does not establish equivalence, particularly when the interval around the difference remains broad. Conversely, a non-significant test does not show that no effect exists.

The record also demonstrates that different statistical methods can address related but not identical questions. The registry reports a Kaplan-Meier comparison and a log-rank test for the time-to-event endpoint. A separate simple unadjusted re-calculation based on summary statistics is available as a check, but it does not use participant-level timing. Keeping those methods distinct prevents a simpler calculation from being mistaken for the registered survival analysis.

Secondary outcomes bring another core issue into view: multiplicity. A trial may report outcomes spanning clinical events, safety, and biomarkers, but a notable unadjusted result among multiple comparisons carries less weight when the testing strategy is not reported. The record does not report a multiplicity adjustment or full analysis plan, so secondary findings should be interpreted as limited evidence rather than independent confirmation.

Finally, the planning illustrations show how assumptions about effect size influence the number of participants needed for future studies. They are not the original design justification and are not observed power. Together, these features make the record useful for statistical learning: it highlights uncertainty, method-specific estimates, multiple testing, and the need to distinguish planning calculations from the design and results of the trial itself.

19. 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.
• Participant counts were reconstructed from posted percentages and arm sizes, so small rounding differences are possible.

Several limitations are specific to the available record. The study is listed as terminated, but the reason for termination is not reported in the registry record. The record gives enrollment and outcome denominators, but does not report whether the primary analysis included all randomized participants or how deviations from assigned treatment were handled. The analysis-population definitions are not reported.

The primary endpoint depends on time-to-event methods, yet the record does not report detailed censoring rules, the number and timing of censored observations, or the handling of missing follow-up. These details are important for assessing the assumptions underlying the Kaplan-Meier and log-rank results. The trial's original sample-size justification and the statistical assumptions used in its design are also not reported; planning illustrations based on observed effects do not fill that gap.

The registry lists multiple secondary outcomes but does not report a complete statistical analysis plan, a multiplicity strategy, or whether analyses were prespecified. For the biological outcomes, measurement and analysis details are not reported, limiting assessment of the biomarker questions. The serious adverse-event summary does not provide event-level timing or attribution, and the listed serious terms are not allocated by treatment group in the summary.

These limitations do not invalidate the recorded estimates, but they constrain how fully they can be evaluated. They support a careful, descriptive interpretation centered on the posted primary estimates and their uncertainty, rather than strong conclusions about treatment effects, subgroup effects, or causation.

20. Related Tutorials

21. Related Statistical Calculators

22. Sources

• ClinicalTrials.gov: Everolimus Versus Placebo in Head and Neck Cancer — NCT01111058.
• Registry record last updated 2020-04-07; status last verified 2020-03.
• Results first posted 2020-04-07.
• 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.

23. Record Summary

The registry describes a randomized, triple-masked, parallel-group phase II trial comparing everolimus (RAD001) with placebo as adjuvant therapy for locally advanced squamous cell cancer of the head and neck. Enrollment is 52, and the record lists the study status as terminated.

The primary endpoint is the 2-year progression-free survival rate, defined as time to progression or death from any cause. The posted estimates are 56.1% for everolimus and 55.9% for placebo, with wide 95% confidence intervals. The reported Kaplan-Meier and log-rank analyses do not show a clear separation between groups, while the uncertainty remains substantial.

Secondary outcomes and safety counts are also reported, but multiplicity handling, missing-data methods, analysis populations, and the reason for termination are not reported in the registry record. The results support a cautious account of this trial's comparison and do not establish equivalence or a definitive treatment effect.