This page provides an independent statistical analysis and educational interpretation of publicly reported results. ClinicalTrials.gov provides the official trial registry record. Numerical trial results on this page are restricted to the ClinicalTrials.gov record.
1. Trial at a Glance
SEQUOIA was a randomized, parallel-group, open-label phase 3 trial evaluating zanubrutinib against bendamustine plus rituximab in participants with previously untreated chronic lymphocytic leukemia or small lymphocytic lymphoma. The ClinicalTrials.gov record reports one formal statistical analysis for the primary endpoint: progression-free survival in Cohort 1 without del(17p), analyzed using a log-rank test and expressed as a hazard ratio.
| Feature | SEQUOIA |
|---|---|
| Trial name | SEQUOIA |
| ClinicalTrials.gov identifier | NCT03336333 |
| Phase | Phase 3 |
| Conditions | Chronic Lymphocytic Leukemia; Small Lymphocytic Lymphoma |
| Population | Participants with previously untreated CLL or SLL |
| Allocation | Randomized |
| Design model | Parallel |
| Masking | None |
| Primary purpose | Treatment |
| Enrollment | 590 |
| Arms | 6 |
| Primary endpoint type | Time-to-event |
| Results posted | Yes |
| Outcome measures posted | 22 |
| Statistical analyses posted | 1 |
| Lead sponsor | BeiGene |
| Sponsor type | Industry |
2. Clinical Question
The central statistical question in the registry-reported primary analysis was whether zanubrutinib produced a different progression-free survival experience from bendamustine plus rituximab in Cohort 1 participants without del(17p). The registered hypothesis type was superiority.
Population
Participants with previously untreated chronic lymphocytic leukemia or small lymphocytic lymphoma enrolled in the SEQUOIA trial.
Intervention
Zanubrutinib. The ClinicalTrials.gov record identifies zanubrutinib as a drug intervention.
Comparator
Bendamustine plus rituximab in the primary comparison reported for Cohort 1 without del(17p).
Primary question
For Cohort 1 participants without del(17p), does zanubrutinib improve progression-free survival relative to bendamustine plus rituximab?
3. Trial Design
Bendamustine + Rituximab
- Cohort 1
- Without del(17p)
- Serious adverse events: 113 affected / 227 at risk
Zanubrutinib
- Cohort 1
- Without del(17p)
- Serious adverse events: 88 affected / 240 at risk
the ClinicalTrials.gov record also identifies venetoclax as a drug intervention and reports a third serious-adverse-event group, Cohort 2 zanubrutinib with del(17p). Because the ClinicalTrials.gov record contains only the Cohort 1 without-del(17p) PFS comparison, those additional intervention and safety details are not treated as formal efficacy comparisons on this page.
4. Trial Timeline
Trial start
The registry lists 2017-10-31 as the study start date.
Primary completion
The registry lists 2021-05-07 as the primary completion date.
Primary endpoint time frame
The reported primary PFS analysis used a time frame of up to approximately 3 years and 7 months, as of the cutoff date of 07MAY2021.
Active, not recruiting
The ClinicalTrials.gov record classifies the study status as ACTIVE_NOT_RECRUITING.
5. Primary Endpoint
| Endpoint | Registered definition / time frame | Statistical analysis |
|---|---|---|
| Cohort 1: Progression-free Survival (PFS) as Determined by Independent Central Review (ICR) | PFS is defined as the time from randomization until first documentation of progression or death from any cause, whichever occurs first, as assessed by the ICR. The registry definition continues with assessment according to 2008 International Workshop on Chronic Lymphocytic Leukemia (iwCLL) guidelines with modifications for treatment-related lymphocytosis in participants with CLL and the Revised Criteria for Response for Malignant Lymphoma in participants with ... | Log-rank test; hazard ratio; superiority |
| Time frame | Up to approximately 3 years and 7 months (as of cut-off date of 07MAY2021) | ITT analysis set |
6. Statistical Analysis Population
The posted primary analysis used an intention-to-treat (ITT) analysis set, defined in the ClinicalTrials.gov record as all enrolled participants who were assigned to a treatment group.
The ITT principle preserves the treatment comparison created by randomization. It is particularly important for a randomized superiority analysis because excluding participants after assignment can introduce selection into the comparison.
The registry wording is specific: the registry-reported analysis population includes all enrolled participants who were assigned to a treatment group. The posted analysis does not provide a separate per-protocol population in the ClinicalTrials.gov record.
7. Results: Primary Progression-Free Survival Analysis
The registry reports one formal statistical analysis. It compared progression-free survival in Cohort 1 participants without del(17p) who were assigned to bendamustine plus rituximab versus zanubrutinib.
Hazard ratio for progression-free survival
95% CI: 0.28–0.63 · P < 0.0001
Analysis: log-rank test · Hypothesis: superiority
| Analysis feature | Reported result |
|---|---|
| Endpoint | Cohort 1: Progression-free Survival (PFS) as Determined by Independent Central Review (ICR) |
| Comparison | Bendamustine + Rituximab Without Del(17p) vs Zanubrutinib Without Del(17p) |
| Analysis population | ITT analysis set: all enrolled participants who were assigned to a treatment group |
| Method | Log Rank |
| Effect measure | Hazard Ratio (HR) |
| Estimate | 0.42 |
| Confidence interval | 95% two-sided CI: 0.28–0.63 |
| P-value | <0.0001 |
| Hypothesis type | Superiority |
An HR of 0.42 means that the estimated hazard of a progression-free survival event was 42% of the corresponding hazard in the comparator group under the reported time-to-event analysis. Expressed as a relative reduction in the estimated hazard, this corresponds to approximately a 58% lower estimated hazard for zanubrutinib relative to bendamustine plus rituximab.
The hazard ratio does not mean that 42% of patients remained progression-free, that 58% of patients were protected from progression, or that every individual patient experienced exactly a 58% reduction in risk. A hazard ratio is a relative time-to-event measure rather than an absolute probability at a particular time point.
The two-sided 95% confidence interval of 0.28–0.63 describes uncertainty around the estimated hazard ratio under the statistical framework. It does not describe the range of individual treatment effects across patients.
The reported P < 0.0001 addresses the evidence against the null hypothesis in the reported statistical test. It does not measure the magnitude or clinical importance of the treatment effect. The HR and its confidence interval are needed to describe the estimated effect and its precision.
Because the endpoint is time-to-event and the effect measure is a hazard ratio, interpretation also depends on the assumptions underlying the survival-analysis model used to obtain the estimate. The ClinicalTrials.gov record identifies the log-rank method and HR but do not provide a detailed proportional-hazards diagnostic or model specification.
8. How to Read the Primary Result
The statistical result contains three different pieces of information that should not be collapsed into one number: the effect estimate, the confidence interval, and the hypothesis-test result.
Effect estimate
The HR of 0.42 quantifies the estimated relative difference in the instantaneous event rate between the two randomized treatment groups under the reported time-to-event analysis.
Precision
The 95% CI of 0.28–0.63 communicates uncertainty around the HR estimate. It is narrower than an interval extending across 1, while still covering a range of possible relative effects.
Hypothesis test
The P-value of <0.0001 indicates very strong evidence against the null hypothesis used for the reported superiority test. It is not an effect-size measure.
Absolute outcomes
The ClinicalTrials.gov record does not report median PFS, fixed-time PFS probabilities, or event counts for the primary efficacy comparison. Those quantities therefore should not be inferred from the HR.
9. Statistical Methodology
Time-to-event analysis
Progression-free survival is a time-to-event endpoint. Rather than recording only whether a participant experienced progression, the analysis considers the time from randomization until the first qualifying event. Participants who have not experienced the event by the end of their available observation contribute censored follow-up information.
This structure makes conventional comparisons of simple proportions inappropriate as a substitute for the registered analysis. Participants can have different lengths of follow-up, and censoring must be incorporated into the analysis.
Kaplan-Meier estimation
Kaplan-Meier estimation is the standard descriptive framework for displaying a time-to-event distribution. It estimates the probability of remaining event-free over time while accounting for right censoring.
Here, di is the number of events at event time ti, and ni is the number of participants at risk immediately before that time.
The registry analysis does not provide a posted Kaplan-Meier estimate or median PFS. Accordingly, this page explains the method without fabricating a survival curve or deriving unreported time-specific outcomes.
Log-rank test
The posted formal comparison used the log-rank test. For randomized time-to-event data, the log-rank test compares the observed and expected numbers of events between treatment groups over the course of follow-up.
The test is designed to use the timing of events rather than treating the endpoint as a simple binary outcome at one arbitrary time point.
Hazard ratio
The registry reports a hazard ratio of 0.42 with a two-sided 95% confidence interval of 0.28–0.63. A hazard ratio below 1 indicates a lower estimated instantaneous event rate in the numerator treatment group relative to the comparator, subject to the direction in which the comparison is defined.
For the registry-reported comparison, the registry labels the groups as bendamustine plus rituximab without del(17p) versus zanubrutinib without del(17p). The interpretation above treats the reported HR as the zanubrutinib-versus-comparator treatment effect because the posted estimate is presented as the treatment-effect measure for that comparison.
Intention-to-treat analysis
The primary analysis population was the ITT set, defined as all enrolled participants who were assigned to a treatment group. This approach maintains the original randomized treatment comparison and avoids redefining the efficacy population according to subsequent treatment exposure or outcome availability.
10. Statistical Methods Explained
Why was a log-rank test used?
PFS is a time-to-event endpoint, so the analysis needs to account for both the occurrence and timing of events as well as censoring. The log-rank test provides a standard way to compare survival distributions between randomized groups over follow-up. It is therefore aligned with the registered endpoint structure rather than reducing PFS to a simple yes/no outcome.
What does an HR of 0.42 mean?
An HR of 0.42 indicates that the estimated instantaneous event rate for the treatment group was 42% of that in the comparator group under the reported analysis. The complementary interpretation is an approximately 58% lower estimated hazard. This is not the same as saying that 58% of participants avoided progression or that PFS was extended by 58%.
Why does the confidence interval matter?
The 95% CI of 0.28–0.63 shows that the point estimate of 0.42 is not the only plausible value under the statistical framework. It gives a measure of statistical precision around the estimated treatment effect. It does not describe the distribution of individual patient responses.
Why does the P-value not measure effect size?
The P-value reflects the compatibility of the observed data with the null hypothesis used for the test. It is affected by the amount of information available as well as the magnitude of the observed difference. The HR describes relative effect magnitude, while the confidence interval combines effect estimation with uncertainty.
Why does ITT analysis matter?
ITT analysis keeps participants in the groups to which they were assigned. This preserves the comparison generated by randomization. Moving participants between groups or selectively excluding randomized participants after treatment assignment can compromise that design advantage.
Why should the HR not be converted directly into a median PFS difference?
A hazard ratio is a relative measure of event rate over time. It does not mathematically determine the median survival time without additional information about the underlying survival distributions. Because the ClinicalTrials.gov record does not report median PFS, no median difference is calculated here.
What does the superiority hypothesis mean?
The registered hypothesis type is superiority. In this context, the analysis was designed to test whether the treatment comparison demonstrated evidence of a difference in the specified direction rather than merely establishing that the treatments were not unacceptably different.
11. Confidence Interval and P-Value in Context
Primary statistical evidence
95% two-sided CI: 0.28–0.63
P < 0.0001
The interval from 0.28 to 0.63 quantifies uncertainty around the reported HR estimate. Every value in the interval is below 1, so the interval does not include the null hazard ratio of 1.
The reported P < 0.0001 indicates strong evidence against the null hypothesis for the posted log-rank comparison. It should not be described as the probability that the null hypothesis is true, nor as the probability that the observed result occurred by chance.
The ClinicalTrials.gov record does not provide a median PFS, PFS probability at a specified time, number of PFS events by arm, or a Kaplan-Meier curve. None of these quantities can be recovered exactly from the reported HR and P-value alone.
12. Safety Results
The ClinicalTrials.gov record provides serious adverse-event counts by arm for three groups. They are reported here exactly as provided rather than converting them into percentages or combining groups.
| Cohort / treatment group | Serious adverse events affected | At risk |
|---|---|---|
| Cohort 1: Bendamustine + Rituximab Witho | 113 | 227 |
| Cohort 1: Zanubrutinib Without Del(17p) | 88 | 240 |
| Cohort 2: Zanubrutinib With Del(17p) | 45 | 111 |
Safety and efficacy answer different questions. The primary PFS result concerns time to progression or death, whereas serious adverse events describe an important component of treatment safety. A complete clinical interpretation should not substitute one type of evidence for the other.
13. What the Registry Results Do and Do Not Establish
What is directly reported
The registry reports a formal ITT PFS comparison in Cohort 1 without del(17p), using a log-rank test, with HR 0.42, 95% CI 0.28–0.63, and P < 0.0001.
What the result supports
The posted analysis provides evidence for a difference in progression-free survival between the two randomized treatment groups under the specified superiority framework.
What cannot be inferred
The HR does not provide an exact median PFS, absolute PFS probability, number needed to treat, or individual probability of benefit.
What remains separate
The additional intervention and safety groups in the ClinicalTrials.gov record should not be treated as if they were included in the one posted formal efficacy analysis.
14. Analysis Features Supported by the Registry Data
| Statistical feature | What the ClinicalTrials.gov record shows | Interpretation |
|---|---|---|
| Randomization | Allocation was randomized | Provides the design basis for treatment-group comparison. |
| Parallel design | Design model was parallel | Treatment groups were evaluated concurrently rather than as treatment sequences. |
| ITT analysis | Primary analysis population was ITT | Participants were analyzed according to assigned treatment group. |
| Time-to-event endpoint | Primary endpoint was PFS | Follow-up time and censoring are central to the analysis. |
| Log-rank test | Posted method | Compares time-to-event experience between groups. |
| Hazard ratio | Posted effect measure | Summarizes relative event-rate differences under the time-to-event framework. |
| Confidence interval | 95% two-sided CI | Quantifies statistical uncertainty around the HR. |
| Superiority | Posted hypothesis type | The formal question concerns superiority rather than non-inferiority or equivalence. |
| Bayesian methods | Not identified in the ClinicalTrials.gov record | No Bayesian analysis is described in the provided statistical-analysis record. |
| Crossover | Not identified in the ClinicalTrials.gov record | No crossover analysis is described in the ClinicalTrials.gov record. |
| Factorial design | Not identified in the ClinicalTrials.gov record | The registry-reported design is parallel, not described as factorial. |
| Interim analysis | Not identified in the ClinicalTrials.gov record | No interim-analysis method or alpha-spending procedure is reported in the ClinicalTrials.gov record. |
| Missing-data / imputation method | Not identified in the ClinicalTrials.gov record | No imputation method is reported in the ClinicalTrials.gov record. |
15. Analysis Population and Censoring
Because PFS is a time-to-event endpoint, participants do not necessarily contribute the same amount of observed follow-up. A participant who has not experienced progression or death by the end of observed follow-up may be censored rather than treated as having experienced the event.
The ITT population defines who belongs in the primary efficacy analysis, while the time-to-event framework defines how their follow-up contributes to the analysis. These are complementary concepts: ITT preserves the randomized comparison, while survival methods account for unequal observation times and censoring.
A correct PFS analysis needs both the appropriate analysis population and a method that uses the longitudinal event-time structure of the data.
The ClinicalTrials.gov record does not specify the detailed censoring rules, missing-data strategy, or imputation method beyond the endpoint definition and the posted statistical method. Those details are therefore not reconstructed here.
16. Hazard-Ratio Assumptions and Cautions
A hazard ratio is a compact summary of a time-to-event comparison, but it is not a complete description of the survival experience. In many analyses, interpretation of a single HR is most straightforward when the relative hazards are reasonably stable over time.
The ClinicalTrials.gov record identifies the hazard ratio as the effect measure and the log-rank test as the statistical method, but they do not report a formal proportional-hazards diagnostic. Therefore, the HR should be interpreted as the reported model-based relative treatment effect rather than as a complete description of every point in the follow-up period.
17. Limitations
- Single posted formal efficacy analysis: the ClinicalTrials.gov record contains one statistical analysis, so this page cannot provide a broader collection of primary and secondary efficacy estimates.
- No median PFS reported in the ClinicalTrials.gov record: the primary result includes an HR and confidence interval but not a median PFS or fixed-time PFS estimates.
- No event counts for the primary efficacy analysis: the ClinicalTrials.gov record does not report the number of progression or death events by treatment group.
- No reconstructed Kaplan-Meier curve: an HR and P-value are insufficient to reconstruct the underlying survival curves.
- No detailed model specification: the registry identifies the log-rank method and HR but does not provide the full statistical-model specification in the ClinicalTrials.gov record.
- No reported missing-data or imputation strategy: the ClinicalTrials.gov record does not identify one.
- No reported interim-analysis framework: the ClinicalTrials.gov record does not establish whether the posted analysis was subject to a specific interim-monitoring or alpha-spending procedure.
- No crossover analysis reported: crossover is not identified in the ClinicalTrials.gov record and should not be inferred.
- Safety counts are descriptive here: serious-adverse-event counts are reported for three groups, but the ClinicalTrials.gov record does not provide a formal comparative safety analysis.
- Generalizability: the analysis applies to the trial population and the specific Cohort 1 without-del(17p) comparison represented in the posted primary analysis; it should not automatically be generalized to every SEQUOIA cohort or intervention group.
18. Why This Trial Matters Statistically
SEQUOIA is a useful teaching example because its posted primary analysis connects several foundational clinical-trial concepts in a compact time-to-event framework: randomization, ITT analysis, progression-free survival, independent central review, log-rank testing, hazard ratios, confidence intervals, and superiority testing.
| Concept | How it appears in SEQUOIA |
|---|---|
| Randomization | The trial allocation is randomized. |
| Parallel design | The registry identifies a parallel design model. |
| Intention-to-treat analysis | The posted primary analysis uses an ITT analysis set containing all enrolled participants assigned to a treatment group. |
| Time-to-event endpoint | The primary endpoint is progression-free survival. |
| Independent central review | The primary endpoint is PFS as determined by ICR. |
| Log-rank test | The registry reports Log Rank as the formal statistical method. |
| Hazard ratio | The treatment effect is reported as HR 0.42. |
| Confidence interval | The estimate is accompanied by a two-sided 95% CI of 0.28–0.63. |
| P-value | The reported P-value is <0.0001. |
| Superiority | The hypothesis type is superiority. |
| Censoring | Because PFS is a time-to-event endpoint, censoring is part of the underlying statistical framework even though detailed censoring rules are not reported. |
| Safety analysis | Serious adverse events are reported as affected/at-risk counts for three groups. |
19. Clinical Interpretation vs Statistical Interpretation
Statistical interpretation
The posted Cohort 1 without-del(17p) ITT analysis produced an HR of 0.42 with a two-sided 95% CI of 0.28–0.63 and P < 0.0001 using a log-rank test under a superiority hypothesis.
Clinical interpretation
The statistical result indicates a difference in the progression-free survival experience between the randomized treatment groups under the reported analysis. The ClinicalTrials.gov record does not provide enough absolute outcome information to characterize the magnitude of benefit in terms of median PFS or fixed-time survival probability.
This distinction is important. Statistical evidence can establish that the observed treatment groups differed under a specified test, while a fuller clinical interpretation requires absolute outcome measures, treatment exposure, safety, follow-up, and the characteristics of the population being treated.
20. Primary Result in Perspective
One-number summary
95% two-sided CI: 0.28–0.63
P < 0.0001
The most defensible interpretation of the ClinicalTrials.gov record is therefore specific: among the Cohort 1 participants without del(17p) included in the ITT analysis, the reported comparison of zanubrutinib with bendamustine plus rituximab showed a hazard ratio of 0.42 for progression-free survival, with a two-sided 95% confidence interval of 0.28–0.63 and P < 0.0001 using the reported log-rank analysis.
The result is statistically precise enough to distinguish the reported estimate from the null value of 1, but the HR alone does not tell us how many additional months of PFS patients experienced, what proportion were progression-free at a particular time, or how treatment effects varied across individuals.
21. Related Tutorials
Learn more about the methods used in this trial:
22. Related Calculators
23. Sources
- ClinicalTrials.gov: SEQUOIA, NCT03336333.
- PubMed: PMID 31875923.
- PubMed: PMID 33054121.
- PubMed: PMID 40944848.
- PubMed: PMID 40448577.
- PubMed: PMID 39647999.
Continue through the Clinical Biostats statistical tutorials
Explore the core survival-analysis and clinical-trial concepts represented in this analysis, then apply them with statistical calculators.
24. Record Summary
SEQUOIA provides a focused example of randomized time-to-event analysis. The ClinicalTrials.gov record describes a phase 3, randomized, parallel-group, unmasked trial with 590 enrolled participants and 6 arms. Its posted primary statistical analysis evaluates progression-free survival in Cohort 1 without del(17p), using an ITT analysis set and a log-rank test under a superiority hypothesis. The reported hazard ratio is 0.42, with a 95% two-sided confidence interval of 0.28–0.63 and P < 0.0001.
The key statistical lesson is that these numbers answer a specific time-to-event question. The HR quantifies the relative event-rate difference, the confidence interval describes uncertainty around that estimate, and the P-value addresses the corresponding hypothesis test. None of the three substitutes for absolute PFS estimates, median PFS, event counts, or a complete description of the survival curves.