A Study of MabThera/Rituxan (Rituximab) Alone and in Combination With Roferon-A in Patients With Follicular or Other CD20+ Low-Grade (Indolent) Lymphoma: Complete Statistical Analysis

1. Trial at a Glance

This page covers the registry results for A Study of MabThera/Rituxan (Rituximab) Alone and in Combination With Roferon-A in Patients With Follicular or Other CD20+ Low-Grade (Indolent) Lymphoma (NCT01609010), a randomized phase 3 study. The primary outcome is Treatment Failure - Percentage of Participants With an Event.

2. Clinical Question

3. Trial Design

This was a Phase III, randomized, open-label trial with a parallel-group design. The registry record lists 313 participants enrolled: 156 in the rituximab monotherapy group and 157 in the rituximab-plus-interferon group. Masking was listed as none, so the study was not described as blinded. The sponsor was Hoffmann-La Roche. The study is listed as completed.

The comparison was between rituximab alone and rituximab combined with interferon alfa-2a. In the monotherapy group, participants received rituximab at 375 mg/m2 intravenously weekly for 4 weeks. Those achieving minor response, partial response, or complete response received a second treatment cycle. The combination regimen included rituximab and subcutaneous interferon alfa-2a, with the registry description specifying different interferon doses across treatment weeks and no interferon on days when rituximab was administered. The brief summary describes an additional cycle for participants who had a response.

The primary outcome was the percentage of participants with treatment failure, evaluated at baseline, Week 10 and 16, and Months 6, 12, 18, 24, 30, and 42 of follow-up. The analysis population is identified as ITT. The record also lists secondary outcomes covering response, duration of response, disease progression, and overall survival. The registry record does not report the trial’s original sample-size justification, the allocation procedure in further detail, or the analysis model used for the primary comparison. Further details about treatment duration and follow-up beyond the listed endpoint time points are not reported in the registry record.

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

Reasons for not completing this period:

Analysis population for the primary outcome, as defined in the registry: ITT population

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: Intent to treat (ITT) population included all randomized participants.

6. Primary Endpoint

The registry lists the primary outcome as Treatment Failure - Percentage of Participants With an Event, measured over Baseline (BL), Week 10 and 16, and Months 6, 12, 18, 24, 30, and 42 of the follow-up period.

The primary endpoint was the percentage of participants with a treatment-failure event in the ITT population. The registry definition combines several clinically distinct events: progressive disease during study treatment, death due to any cause, or initiation of another type of treatment because of stable disease, progressive disease, relapse, or intolerance to study treatment. The endpoint therefore captures whether a participant experienced at least one of the specified events, rather than describing only tumor response.

Combining these events can provide a broad summary of treatment failure across the study period. It also means that the percentage alone does not show which component drove the result. The posted primary outcome is measured at baseline, Week 10 and 16, and Months 6, 12, 18, 24, 30, and 42 of follow-up. The registry record does not report separate primary-endpoint results for each component of the composite definition, nor does it describe how events occurring between assessment times were handled.

For interpretation, this is a binary event outcome: participants are counted according to whether an event occurred, and the displayed percentage summarizes the proportion with an event in each randomized group. It does not by itself provide the timing of events or describe the severity of the different event types. Those distinctions require component-level or time-to-event results, which are not part of the posted primary result.

7. Results Posted in the Registry

• Rituximab + IFN: 66.9% (105/157) versus 67.9% (106/156) on control; risk difference -1.1 percentage points (95% CI -11.5 to 9.3), risk ratio 0.98 (0.84–1.15); unadjusted two-proportion z-test p = 0.840.

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.

Disease Progression - Percentage of Participants With an Event

• 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:
  • Rituximab + IFN: 61.8% (97/157) versus 60.3% (94/156) on control; risk difference 1.5 percentage points (95% CI -9.3 to 12.3), risk ratio 1.03 (0.86–1.22); unadjusted two-proportion z-test p = 0.782.
• Interpretation:
  • The 95% confidence interval includes no difference, so these data do not show a statistically significant difference between Rituximab + IFN and the control. The interval gives the range of effects that remain compatible with the data.

Overall Survival (OS) - Percentage of Participants With an Event

• 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:
  • Rituximab + IFN: 10.8% (17/157) versus 12.8% (20/156) on control; risk difference -2.0 percentage points (95% CI -9.1 to 5.2), risk ratio 0.84 (0.46–1.55); unadjusted two-proportion z-test p = 0.585.
• Interpretation:
  • The 95% confidence interval includes no difference, so these data do not show a statistically significant difference between Rituximab + IFN and the control. The interval gives the range of effects that remain compatible with the data.

The secondary outcomes cover response categories, duration of response, disease progression, and overall survival. Across the posted secondary event-percentage comparisons, the broad pattern is similar to the primary result: the displayed group estimates are relatively close, and the unadjusted comparisons do not provide a clear separation. This general consistency offers context for the primary endpoint but does not establish that all outcomes behave alike, since endpoints differ in definition and timing.

The registry lists eight secondary outcomes, but detailed numerical comparisons are posted for disease progression and overall survival. Results for the other listed secondary outcomes are not reported in the registry record in the detail needed to assess their group contrasts. The posted secondary event percentages summarize whether participants had an event; they do not, on their own, convey when an event occurred. Time-to-event outcomes are separately listed, but the registry record does not include their detailed estimates in the results.

Unadjusted secondary results carry less inferential weight when several outcomes are examined. In general, testing multiple outcomes creates more opportunities for apparently notable results to arise by chance, even if there is no true effect on any outcome. Adjustment methods or a prespecified outcome hierarchy can address that concern, but the registry record does not report whether such procedures were used. The secondary comparisons should therefore be treated as supportive descriptive context, not as a set of independent confirmatory tests.

The secondary pattern is compatible with the primary finding of close event percentages and an uncertain difference. It cannot replace the primary analysis, explain the components of treatment failure, or settle whether a clinically meaningful difference is absent. Those conclusions would require the relevant prespecified analysis details and complete outcome reporting, which are not reported in the registry record.

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

• Percentage of Participants Achieving Complete Response (CR), Unconfirmed CR (CRu), or Partial Response (PR)
• Percentage of Participants Achieving CR or CRu
• Duration of Response - Percentage of Participants With an Event
• Duration of Response
• Disease Progression - Percentage of Participants With an Event
• Time to Disease Progression
• Overall Survival (OS) - Percentage of Participants With an Event
• Overall Survival

9. Statistical Interpretation

10. Statistical Methods Explained

The posted primary and selected secondary outcomes are binary event measures, expressed as percentages of participants. For each arm, the reported percentage describes the share of participants who experienced the outcome. The comparison is expressed as a risk difference, with the monotherapy group as the reference and the combination group compared against it. A risk difference describes the absolute contrast between the two group proportions. A value below zero points toward a lower event proportion in the combination group; a value above zero points toward a higher proportion. The primary estimate is close to zero, and its interval crosses zero.

What does an interval express?

In general, a confidence interval represents uncertainty around an estimated effect under the assumptions of the analysis. It gives a range of values compatible with the observed data and the method used to construct the interval. It is not a guarantee that the true effect lies within that particular range. When an interval for a risk difference includes zero, the data do not rule out no difference under that interval procedure. It also remains possible that the true difference is in either direction, and the width of the interval indicates how much uncertainty remains. The registry record does not identify the confidence level or the procedure used to calculate the posted intervals.

How should the p-value be read?

In general, a p-value measures how unusual data at least as incompatible with a specified null hypothesis would be if that null hypothesis and the statistical model were correct. It is not the probability that the null hypothesis is true, nor does it measure the size or importance of an effect. The unadjusted p-value for the primary comparison is 0.84. Read alongside the estimated group percentages and interval, it does not provide strong evidence against a no-difference null hypothesis. The registry record does not report whether the p-value came from a particular prespecified test, whether it was adjusted, or what other covariates may have been considered.

Why distinguish relative and absolute comparisons?

General statistical interpretation benefits from distinguishing an absolute contrast from a relative one. The risk difference compares event proportions by subtraction; a risk ratio compares them by division; an odds ratio compares event odds. These effect measures answer related but different questions, and their numerical values are not interchangeable. The posted comparisons include relative measures as well as risk differences, but the registry record does not describe which measure was designated as the primary inferential target. The main interpretation above therefore follows the posted risk-difference comparison and the arm-specific event proportions.

Why can a composite outcome be difficult to interpret?

In general, a composite endpoint increases the range of events counted toward a single outcome. This can make a broad treatment-failure measure useful for summarizing whether a participant experienced one of several defined events, but the combined result can conceal differences among components. For example, the percentage with any treatment-failure event does not show how many events arose from progression, death, or a change in treatment. The registry record defines the components but does not report the primary result separately for each component. It also does not describe adjudication procedures or how multiple qualifying events in the same participant were handled.

What does the ITT label contribute?

The registry identifies the primary population as ITT. In general, an intention-to-treat analysis aims to retain participants in the groups to which they were randomized, regardless of treatment received after randomization. This preserves the randomized comparison as assigned and can reduce bias that might arise if participants are excluded according to events occurring after assignment. The registry record does not explain how the ITT population was operationalized, whether every randomized participant contributed to the primary outcome, or how missing outcome data were handled. The arm denominators reported for this outcome are 156 and 157.

These explanations describe general statistical concepts. The registry record does not report enough detail to verify the assumptions or implementation of the statistical procedures used for the posted estimates. The comparisons should therefore be interpreted as the displayed registry results, not as a reconstruction of an undocumented analysis plan.

11. Confidence Intervals and Precision

The primary comparison is imprecise enough that its posted interval includes the null value of zero for the risk difference. In practical terms, the uncertainty permits a range of possible group contrasts, including a difference favoring either group and a value close to no difference. The interval therefore does not support a confident claim that one group has a distinct treatment-failure proportion. At the same time, crossing zero does not show that the groups are equivalent or that any potentially important difference has been excluded.

The observed percentages, 68.0% for monotherapy and 67.0% for the combination, are close. The comparison’s unadjusted p-value is 0.84. Neither the closeness of the percentages nor the p-value alone describes precision: the interval is needed to convey the range of estimates compatible with the data under the method used. The registry record does not state the confidence level or the interval calculation method, so those details cannot be independently assessed from the posted record.

The primary endpoint includes multiple event types, and the posted percentage does not show which type accounts for the uncertainty or group pattern. The sample sizes are 156 and 157, but the registry record does not report a precision target or a prespecified clinically important difference against which to judge the interval. Accordingly, the most defensible reading is descriptive: the posted estimates are similar, while the interval leaves uncertainty about the size and direction of the underlying contrast.

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 express participants per arm for 80% power and participants per arm for 90% power. The calculations are planning illustrations based on the observed effect, not the original trial design. The stated assumptions are a two-sided significance level of 0.05, equal allocation between the two arms, and a normal approximation. The effect to detect is set to the stated fraction of the observed effect. The registry record does not report the original sample-size justification or its planning assumptions.

In general, power is the probability that a specified analysis will detect an effect of a chosen size if that effect is truly present and the assumptions of the calculation hold. The 80% column describes an illustrative per-arm sample size associated with 80% power for the stated effect and assumptions; the 90% column does the same for 90% power. Higher target power requires more participants under otherwise identical assumptions. The rows use 100%, 75%, and 50% of the observed effect. As the effect to detect becomes smaller, more participants are required because smaller differences are harder to distinguish from random variation.

These values are not observed power. They are prospective illustrations that depend on the selected effect size and calculation assumptions. The observed trial enrollment was 313, with 156 participants in the monotherapy group and 157 in the combination group. Comparing that enrollment with a planning illustration does not establish what the original trial was designed to detect, because its original design assumptions are not reported in the registry record. The sample-size calculations are for participants per arm; they are not totals of events.

In general, planning estimates based on an observed effect can be unstable, especially when the observed effect is small or uncertain. The displayed fractions make the consequence of choosing a smaller target effect explicit, but they do not resolve uncertainty in the observed effect or account for unreported design features. The planning figures should be read as conditional illustrations rather than as evidence about the trial’s achieved ability to detect a difference.

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 = 0.679; treatment proportion p₁ = 0.669; average p̄ = 0.674.
2. First term: z_1−α/2 × √[2p̄(1−p̄)] = 1.96 × 0.6628 = 1.2991.
3. Second term: z_1−β × √[p₀(1−p₀) + p₁(1−p₁)] = 0.842 × 0.6628 = 0.5578.
4. n = (1.2991 + 0.5578)² / (-0.0107)² = 30,133.4, rounded up to 30,134 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 <- 0.67948717948718                          # control proportion
p1 <- 0.668789808917197                          # 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(1609010)
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 = 30,134 (80% power), 40,340 (90% power)
 75% of observed effect: n per arm = 53,458 (80% power), 71,564 (90% power)
 50% of observed effect: n per arm = 120,023 (80% power), 160,676 (90% power)
power.prop.test: 30133.4 
Simulated power at n = 30,134 per arm: 0.799 (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. Multiplicity, Missing Data and Analysis Populations

The registry record lists multiple secondary outcomes in addition to the primary treatment-failure endpoint. It does not report a multiplicity adjustment, a testing hierarchy, or a strategy for controlling the overall chance of false-positive findings across outcomes. In general, when many hypotheses are examined, interpreting each unadjusted p-value as if it were the only test can overstate the evidence. This is why the secondary results are best viewed as exploratory or descriptive unless an analysis plan establishes otherwise.

The record identifies the primary analysis population as ITT, but it does not describe the precise population rules or the treatment of participants without an observed outcome. Missing-data methods, censoring rules for the binary event summaries, and sensitivity analyses are not reported in the registry record. These omissions matter because the amount and pattern of missing information can affect observed proportions, particularly when outcome availability differs between groups or is related to participants’ outcomes.

The registry record also does not report the statistical analysis plan, adjustment for baseline characteristics, the method used to construct confidence intervals, or whether interim analyses were conducted. No assumptions about these procedures can be made from the posted results. The primary p-value is labeled unadjusted, and the secondary comparisons are described as unadjusted in the posted results; whether other analyses were performed is not reported in the registry record.

These gaps limit independent assessment of whether the reported analyses followed prespecified methods and how robust the results are to alternative assumptions. They do not invalidate the reported summaries, but they narrow the claims that can be made from them. Interpretation should remain anchored to the posted arm percentages and unadjusted comparisons, with missing-data, multiplicity, and analysis-plan uncertainty acknowledged.

15. 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 0% reporting threshold.

Most frequent serious adverse event terms

Most frequent non-serious adverse event terms

Serious events with the highest arm-level rate: general disorders and administration site conditions (asc) (5.7%), gastrointestinal disorders (3.8%), blood and lymphatic system disorders (3.8%), infections and infestations (3.2%), nervous system disorders (2.5%).

The registry reports 13 participants affected by serious adverse events among 156 at risk in the rituximab monotherapy group, and 34 affected among 157 at risk in the rituximab-plus-interferon group. For other adverse events, it reports 138 affected among 156 at risk in the monotherapy group and 155 among 157 at risk in the combination group. These are the recorded counts; the registry record does not provide further information about event severity gradations, duration, or whether individual participants experienced multiple events.

The serious-event categories listed most frequently include general disorders and administration site conditions (ASC), gastrointestinal disorders, blood and lymphatic system disorders, infections and infestations, nervous system disorders, and injury, poisoning and procedural complications (PC). The recorded category percentages are 5.7%, 3.8%, 3.8%, 3.2%, 2.5%, and 1.9%, respectively, as listed in the registry. The record does not specify in this summary whether these categories are presented by treatment arm or as an overall listing, so they should not be assigned to an individual group.

Deaths are not reported in the registry record in the safety summary. Adverse-event counts are descriptive and do not by themselves establish that treatment caused an event or explain differences between groups.

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

The result shows similar reported treatment-failure percentages in the two randomized groups: 68.0% with rituximab monotherapy and 67.0% with rituximab plus interferon. The unadjusted p-value is 0.84, and the posted interval for the risk difference crosses zero. Taken together, these results do not show a clear difference in the primary event percentage. They also do not demonstrate that the regimens are equivalent or exclude differences that could matter in practice.

The endpoint combines progression during study treatment, death from any cause, and starting another treatment for specified reasons. A participant counted as having treatment failure could therefore have experienced different types of events. The overall percentages do not identify the contribution of each component, indicate the timing of events, or describe the severity of the event. The registry record does not provide component-specific primary results that would resolve those questions.

The trial was randomized and open-label, and the primary outcome is identified for the ITT population. Randomization supports comparison of the assigned groups, while lack of masking may matter for outcomes or decisions involving judgment. The registry record does not report how treatment decisions were adjudicated or whether outcome assessment was blinded. The available result is thus an account of the posted comparison, not a complete evaluation of every possible source of bias or uncertainty.

The secondary outcome pattern is broadly in line with the primary result, but secondary comparisons are unadjusted and should be interpreted cautiously. The registry record does not report the original sample-size justification, missing-data procedures, or a multiplicity strategy. The findings describe this trial’s posted outcomes; they do not establish a universal result for all patients with lymphoma or answer questions not measured by the reported endpoints.

17. Why This Trial Matters Statistically

This trial illustrates why an event percentage, an effect estimate, and its uncertainty should be read together. The arm-specific treatment-failure percentages are close, while the risk-difference interval crosses zero and the unadjusted p-value is 0.84. Looking only at the percentages could make the comparison appear definitive; the interval makes clear that uncertainty remains. Conversely, a non-significant comparison is not proof of equivalence.

The composite primary endpoint also highlights a common interpretive challenge. A single summary can combine progression, death, and treatment changes, making it efficient as a broad measure but less informative about which event types account for the result. The distinction between an overall composite outcome and its separate components matters when translating a statistical comparison into a clinical interpretation.

The registry record identifies the population and outcome timing, but leaves important analysis details unreported, including the original sample-size justification, missing-data methods, and multiplicity strategy. This demonstrates why statistical interpretation depends not only on numerical results but also on the prespecified analysis and the completeness of reporting. The planning illustrations further show how sample-size requirements depend on the effect size selected, without establishing what the original trial was designed to detect.

For biostatisticians and clinical researchers, the central lesson is to avoid turning an uncertain comparison into a claim of sameness or a claim of benefit. Results should be described in the scale actually reported, with the event definition, population, uncertainty, and analysis limitations kept visible.

18. 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.

The registry record leaves several trial-specific limitations unresolved. Although the study is randomized and the primary population is identified as ITT, the method of randomization and details of allocation concealment are not reported. The trial is open-label, and the record does not describe blinded outcome assessment or independent adjudication of treatment failure. These omissions make it difficult to assess how treatment knowledge may have influenced event classification or decisions to initiate another treatment.

The primary outcome combines multiple events, but the posted primary result does not separate progression, death, and treatment initiation. The registry record does not describe the handling of multiple events, the timing of first events, or the adjudication process. It also does not report missing-outcome procedures, sensitivity analyses, or the amount of missing data. These details would help establish how closely the reported proportions reflect the full randomized groups.

The original sample-size justification and statistical analysis plan are not reported in the registry record. Nor are methods for multiplicity control, adjustment for baseline factors, or the confidence-interval procedure. The primary result is presented as an unadjusted comparison; the record does not state whether additional prespecified analyses were conducted. Secondary outcomes are listed, but detailed estimates are not available for all of them.

Finally, the safety summary does not report deaths, explain the category-level serious-event percentages by arm, or provide detailed event descriptions. These reporting gaps constrain interpretation but do not justify assumptions about what may have occurred. Conclusions should be limited to the posted randomized comparison and its stated endpoint definitions.

19. Related Tutorials

20. Related Statistical Calculators

21. Sources

• ClinicalTrials.gov: A Study of MabThera/Rituxan (Rituximab) Alone and in Combination With Roferon-A in Patients With Follicular or Other CD20+ Low-Grade (Indolent) Lymphoma — NCT01609010.
• Registry record last updated 2014-09-08; status last verified 2014-08.
• Results first posted 2014-06-25.
• 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.

22. Record Summary

This completed, randomized, open-label Phase III trial compared rituximab monotherapy with rituximab plus interferon alfa-2a in 313 participants with follicular or other CD20+ low-grade lymphoma. The primary endpoint was treatment failure in the ITT population, defined by progression during study treatment, death from any cause, or initiation of another treatment for specified reasons.

Treatment failure was reported for 68.0% of the monotherapy group and 67.0% of the combination group. The unadjusted p-value was 0.84, and the posted risk-difference interval crossed zero. The registry record lists additional response, progression, and survival outcomes, but detailed comparisons are not reported for every listed secondary endpoint. Important analysis details, including the original sample-size justification and missing-data methods, are not reported in the registry record.