This page separates reported trial results from statistical interpretation. Numerical results are restricted to the ClinicalTrials.gov record. ClinicalTrials.gov provides the official trial registry record.
1. Trial at a Glance
LEAP-002 was a randomized, double-masked, parallel phase 3 trial evaluating lenvatinib plus pembrolizumab versus lenvatinib plus saline placebo as first-line therapy in participants with advanced hepatocellular carcinoma. The trial enrolled 794 participants and had two primary time-to-event endpoints: progression-free survival and overall survival.
| Feature | LEAP-002 |
|---|---|
| Trial name | LEAP-002 |
| ClinicalTrials.gov identifier | NCT03713593 |
| Phase | Phase 3 |
| Status | Completed |
| Condition | Carcinoma, Hepatocellular |
| Enrollment | 794 |
| Allocation | Randomized |
| Design model | Parallel |
| Masking | Double |
| Primary purpose | Treatment |
| Primary endpoints | Progression-free survival and overall survival |
| Primary endpoint type | Time-to-event |
| Results posted | Yes |
| Statistical analyses posted | 7 |
2. Clinical Question
The clinical question was whether adding pembrolizumab to lenvatinib, compared with lenvatinib plus placebo, produced a difference in the primary time-to-event endpoints of progression-free survival and overall survival when used as first-line therapy in participants with advanced hepatocellular carcinoma.
Population
Participants with advanced hepatocellular carcinoma receiving first-line therapy.
Intervention
Lenvatinib in combination with pembrolizumab.
Comparator
Lenvatinib in combination with saline placebo.
Primary question
How does lenvatinib plus pembrolizumab compare with lenvatinib plus placebo for PFS and OS?
3. Trial Design
Lenvatinib + Pembrolizumab
- Lenvatinib
- Pembrolizumab
Lenvatinib + Placebo
- Lenvatinib
- Saline placebo
The ClinicalTrials.gov record identifies the trial as randomized, parallel, double-masked, and treatment-focused. The enrollment was 794 participants. The ClinicalTrials.gov record does not provide an allocation ratio, dosing schedule, treatment-cycle schedule, or crossover information, so those details are not inferred here.
4. Randomization, Stratification, and Analysis Population
The primary efficacy analyses used all randomized participants. Participants were analyzed in the treatment group to which they were randomized. This is an intention-to-treat-style analysis principle and is important because it preserves the treatment comparison created by randomization rather than reassigning participants according to treatment actually received.
| Analysis feature | Registry-supported description |
|---|---|
| Primary efficacy population | All randomized participants |
| Analysis assignment | Participants analyzed in the treatment group to which they were randomized |
| PFS model | Cox proportional-hazards model |
| OS comparison | Log-rank test |
| Stratification / covariates | Geographic region, MPVI or extrahepatic spread or both, AFP and ECOG PS |
The PFS and OS analysis notes both identify stratification by geographic region, portal-vein invasion/extrahepatic spread status, AFP status, and ECOG performance status. The registry-reported PFS analysis describes these factors as covariates or stratification variables in the Cox model. This adjustment attempts to account for important prognostic structure while preserving the randomized treatment comparison.
5. Primary Endpoints
| Endpoint | Registered definition | Time frame | Primary analysis |
|---|---|---|---|
| Progression-free Survival (PFS) Per RECIST 1.1 | PFS was defined as the time from the date of the first documentation of disease progression, as determined by blinded independent central review (BICR) per RECIST 1.1, or death due to any cause (whichever occurred first). | Up to approximately 41 months | Cox proportional-hazards model |
| Overall Survival (OS) | OS was defined as the time from randomization until death from any cause. | Up to approximately 41 months | Log-rank test; Cox model used in the analysis description |
6. Statistical Methodology
Time-to-event analysis
Both primary endpoints are time-to-event outcomes. The analysis is therefore concerned not only with whether an event occurred, but also with the time until that event. Participants who have not experienced the event during available follow-up contribute information up to their censoring time.
Cox proportional-hazards model
The primary PFS analysis used a Cox proportional-hazards model. The analysis note specifies Efron's method for handling tied event times and treatment as a covariate, with stratification by geographic region, MPVI or extrahepatic spread or both, AFP and ECOG PS.
An HR below 1 indicates a lower estimated instantaneous event rate in the pembrolizumab-containing group under the fitted model. It is a relative time-to-event measure, not an absolute probability of experiencing an event.
Log-rank test
The OS analysis used a log-rank test. The registry analysis note describes a one-sided p-value based on a log-rank test stratified by geographic region, portal vein invasion/extrahepatic spread/both, AFP status, and ECOG status. The same analysis description also identifies a Cox model with Efron's method for tied event times and treatment as a covariate.
Score-based confidence intervals for proportions
The secondary ORR analyses used a score-based confidence-interval approach described in the normalized registry methods as the Miettinen-Nurminen, Newcombe, Wilson family. The analysis notes specifically identify the Miettinen-Nurminen method stratified by geographic region, MPVI or extrahepatic spread or both, AFP and ECOG PS.
A positive risk difference means the observed response proportion was higher in the pembrolizumab-containing group. The confidence interval describes statistical uncertainty around that between-group difference.
Covariate adjustment and stratification
Stratification is important because the treatment comparison is not interpreted as though all participants had identical baseline prognostic characteristics. The reported analyses account for geographic region and disease or clinical factors including portal-vein invasion or extrahepatic spread, AFP and ECOG performance status.
7. Primary Results: Progression-Free Survival
The primary PFS comparison used all randomized participants, analyzed according to randomized treatment assignment. The registry reports a hazard ratio comparing lenvatinib plus pembrolizumab with lenvatinib plus placebo of 0.834, with a two-sided 95% confidence interval of 0.712 to 0.978.
Progression-free survival
95% CI: 0.712–0.978 · Two-sided 95% CI
Cox proportional-hazards model with Efron's method of tie handling and stratification/covariate adjustment.
An HR of 0.834 means that, under the fitted Cox model, the estimated instantaneous rate of progression or death was lower in the lenvatinib-plus-pembrolizumab group than in the lenvatinib-plus-placebo group. Expressed as a simple relative interpretation, 0.834 corresponds to an estimated hazard that is about 16.6% lower in the pembrolizumab-containing group.
The HR does not mean that 16.6% of participants avoided progression, that individual patients experienced exactly a 16.6% reduction in risk, or that median PFS differed by 16.6%. It is a model-based relative comparison of event hazards over time.
The 95% CI of 0.712–0.978 describes uncertainty around the estimated hazard ratio under the statistical model. Because the interval is relatively close to 1 at its upper boundary, the estimate should not be treated as a precise statement of a large effect.
The ClinicalTrials.gov record does not report a PFS p-value. Therefore, no p-value is inferred from the confidence interval. A confidence interval and a p-value answer related but distinct questions, and the p-value is not a measure of effect size.
Interpretation of a Cox HR also depends on the proportional-hazards framework. The registry extract does not provide a diagnostic assessment of the proportional-hazards assumption, so this page does not claim that the assumption was formally verified.
8. Primary Results: Overall Survival
The primary OS comparison used the randomized population and compared lenvatinib plus pembrolizumab with lenvatinib plus placebo. The registry reports an OS hazard ratio of 0.840, with a two-sided 95% confidence interval of 0.708 to 0.997 and a reported p-value of 0.0227.
Overall survival
95% CI: 0.708–0.997 · P = 0.0227
Log-rank comparison with the reported stratification; Cox model with Efron's method of tie handling in the analysis description.
An HR of 0.840 corresponds to an estimated instantaneous hazard of death about 16.0% lower in the lenvatinib-plus-pembrolizumab group under the fitted time-to-event model.
The HR does not mean that 16.0% of participants survived because of pembrolizumab, nor does it provide an absolute survival probability. It is a relative measure of the death hazard between the randomized groups.
The 95% CI of 0.708–0.997 quantifies uncertainty around the HR. The upper confidence limit is close to 1, so the interval indicates a comparatively narrow separation from the null value in the direction of the observed effect.
The reported P = 0.0227 is evidence from the specified statistical test against its null hypothesis. It does not measure the size or clinical importance of the effect. The registry describes this as a one-sided p-value based on a stratified log-rank test.
Because the analysis involved a time-to-event endpoint, censoring and the proportional-hazards model are important considerations. The ClinicalTrials.gov record does not provide a formal proportional-hazards diagnostic, so no conclusion about that assumption is made here.
| Primary endpoint | Comparison | Estimate | 95% CI | P-value | Method |
|---|---|---|---|---|---|
| PFS | Lenvatinib + pembrolizumab vs lenvatinib + placebo | HR 0.834 | 0.712–0.978 | Not reported in the ClinicalTrials.gov record | Cox proportional-hazards model |
| OS | Lenvatinib + pembrolizumab vs lenvatinib + placebo | HR 0.840 | 0.708–0.997 | 0.0227 | Log-rank test; Cox model in analysis description |
9. Secondary Results: Objective Response Rate
The registry reports ORR as a secondary binary endpoint, analyzed using a score-based confidence-interval approach and expressed as a difference in percentage between the randomized groups.
ORR per RECIST 1.1
95% CI: 2.8–14.2 percentage points
Score-based CI using the Miettinen-Nurminen method, stratified by geographic region, MPVI or extrahepatic spread or both, AFP and ECOG PS.
The positive risk difference indicates a higher reported objective response proportion in the lenvatinib-plus-pembrolizumab group. The confidence interval quantifies uncertainty around the between-group difference. The registry-reported analysis does not report the separate ORR percentages, so they are not reconstructed from the risk difference.
ORR per modified RECIST
Objective response rate per mRECIST
95% CI: 0.0–13.4 percentage points
Score-based CI using the Miettinen-Nurminen method, with the same reported stratification factors.
The lower confidence limit of 0.0 means that the registry-reported interval reaches the no-difference value. This illustrates why the confidence interval should be presented alongside the point estimate rather than relying only on the estimated difference.
10. Secondary Results: Time to Disease Progression
Time to disease progression was analyzed separately under both RECIST 1.1 and modified RECIST definitions. These endpoints are time-to-event outcomes and were analyzed with Cox proportional-hazards models using the reported covariate and stratification framework.
| Secondary endpoint | Effect estimate | 95% CI | Method |
|---|---|---|---|
| Time to Disease Progression, RECIST 1.1 | HR 0.79 | 0.66–0.93 | Cox proportional-hazards model |
| Time to Disease Progression, mRECIST | HR 0.73 | 0.61–0.88 | Cox proportional-hazards model |
The TTP HR of 0.79 under RECIST 1.1 corresponds to an estimated instantaneous rate of documented disease progression about 21% lower in the pembrolizumab-containing group under the fitted model. The mRECIST HR of 0.73 corresponds to an estimated rate about 27% lower.
These are relative hazard interpretations, not absolute reductions in the probability of progression. They also should not be interpreted as interchangeable with PFS: PFS includes death as an event, whereas the registry labels these secondary endpoints specifically as time to disease progression.
The confidence intervals provide the relevant measure of statistical precision: 0.66–0.93 for RECIST 1.1 TTP and 0.61–0.88 for mRECIST TTP.
11. Secondary Results: Progression-Free Survival by mRECIST
PFS per modified RECIST
95% CI: 0.68–0.94
Cox proportional-hazards model with treatment as a covariate and reported stratification by geographic region, MPVI or extrahepatic spread or both, AFP and ECOG PS.
An HR of 0.80 corresponds to an estimated instantaneous hazard about 20% lower in the lenvatinib-plus-pembrolizumab group under the fitted model.
The 95% CI of 0.68–0.94 expresses uncertainty around that relative estimate. As with the primary PFS result, this does not establish an absolute 20% reduction in the proportion of participants who progress or die.
The ClinicalTrials.gov record does not report a p-value for this secondary endpoint, so no formal significance claim is added.
12. Statistical Results at a Glance
| Endpoint | Role | Effect | 95% CI | Interpretive scale |
|---|---|---|---|---|
| PFS per RECIST 1.1 | Primary | HR 0.834 | 0.712–0.978 | Lower hazard of progression or death |
| OS | Primary | HR 0.840 | 0.708–0.997 | Lower hazard of death |
| ORR per RECIST 1.1 | Secondary | Risk difference 8.5 | 2.8–14.2 | Percentage-point difference |
| TTP per RECIST 1.1 | Secondary | HR 0.79 | 0.66–0.93 | Lower hazard of disease progression |
| PFS per mRECIST | Secondary | HR 0.80 | 0.68–0.94 | Lower hazard of progression or death |
| ORR per mRECIST | Secondary | Risk difference 6.7 | 0.0–13.4 | Percentage-point difference |
| TTP per mRECIST | Secondary | HR 0.73 | 0.61–0.88 | Lower hazard of disease progression |
13. Serious Adverse Events
The ClinicalTrials.gov record reports serious adverse events by randomized treatment arm using affected participants over participants at risk. The reported counts were 185/395 for lenvatinib plus pembrolizumab and 159/395 for lenvatinib plus placebo.
| Safety measure | Lenvatinib + Pembrolizumab | Lenvatinib + Placebo |
|---|---|---|
| Serious adverse events | 185/395 | 159/395 |
The ClinicalTrials.gov record does not provide a formal statistical comparison, confidence interval, or p-value for these serious-adverse-event counts. Accordingly, the counts are presented descriptively rather than converted into a comparative inference.
14. Statistical Methods Explained
Why was a Cox proportional-hazards model used for PFS?
PFS is a time-to-event endpoint. A Cox model uses information about the timing of events while allowing participants without an observed event during follow-up to contribute censored information. It produces a hazard ratio that summarizes the relative event hazard between treatment groups while accommodating the reported stratification and covariate structure.
What does an HR of 0.834 mean?
An HR of 0.834 means that the fitted model estimates the instantaneous event hazard in the lenvatinib-plus-pembrolizumab group at 0.834 times that of the lenvatinib-plus-placebo group. The corresponding relative interpretation is approximately a 16.6% lower estimated hazard. It does not mean that 16.6% fewer participants necessarily experienced the event.
Why is the confidence interval important?
The point estimate is only one estimate from the observed trial data. The 95% CI of 0.712–0.978 for PFS describes the uncertainty around the estimated HR under the specified statistical framework. The width and location of the interval help communicate precision and how close the estimated effect remains to the null value of 1.
Why is the OS p-value not an effect size?
The reported OS p-value of 0.0227 addresses evidence against the null hypothesis under the specified test. It does not tell us that the treatment effect is "2.27%" or that the treatment reduces mortality by a particular percentage. The HR and its confidence interval provide the effect-size information.
Why were stratification factors used?
The reported analyses account for geographic region, MPVI or extrahepatic spread or both, AFP and ECOG performance status. Stratification allows the time-to-event comparison to be made while respecting clinically relevant structure in the randomized population. It can also improve the efficiency and interpretability of the treatment comparison when the stratification variables are prognostically relevant.
What does a risk difference of 8.5 mean for ORR?
A risk difference of 8.5 means that the objective response proportion in the lenvatinib-plus-pembrolizumab group was estimated to be 8.5 percentage points higher than in the lenvatinib-plus-placebo group. It is different from a hazard ratio because ORR is a binary endpoint rather than a time-to-event endpoint.
Why use the Miettinen-Nurminen method for ORR?
The registry identifies a score-based confidence-interval approach, specifically the Miettinen-Nurminen method, for the ORR comparisons. This provides an interval estimate for the difference between proportions while incorporating the structure of the two-group comparison. In LEAP-002, the analysis was additionally stratified by the reported geographic and clinical factors.
15. One-Sided Versus Two-Sided Inference
The ClinicalTrials.gov record contains an important distinction between confidence intervals and the reported OS hypothesis test. The OS result is accompanied by a two-sided 95% confidence interval, while the analysis note describes the p-value as one-sided.
| Quantity | Reported specification | How to interpret it |
|---|---|---|
| OS confidence interval | 95%, two-sided | Interval estimate around the hazard ratio |
| OS p-value | 0.0227, one-sided in analysis note | Hypothesis-test result under the reported one-sided log-rank framework |
| PFS confidence interval | 95%, two-sided | Interval estimate around the hazard ratio |
| ORR confidence intervals | 95%, two-sided | Interval estimates around risk differences |
This distinction matters because a p-value and a confidence interval are not interchangeable. The confidence interval communicates uncertainty about the effect estimate, whereas the p-value addresses a specified null hypothesis under a specified testing framework.
16. Multiplicity and Multiple Endpoints
LEAP-002 has two registered primary endpoints, PFS and OS, and the ClinicalTrials.gov record also contain five secondary statistical analyses. The trial therefore illustrates an important principle in confirmatory clinical-trial statistics: the number and hierarchy of endpoints affect how individual statistical results should be interpreted.
| Endpoint group | Registry role | Statistical method |
|---|---|---|
| PFS per RECIST 1.1 | Primary | Cox proportional-hazards model |
| OS | Primary | Log-rank test; Cox model in analysis description |
| ORR per RECIST 1.1 | Secondary | Miettinen-Nurminen score-based CI |
| TTP per RECIST 1.1 | Secondary | Cox proportional-hazards model |
| PFS per mRECIST | Secondary | Cox proportional-hazards model |
| ORR per mRECIST | Secondary | Miettinen-Nurminen score-based CI |
| TTP per mRECIST | Secondary | Cox proportional-hazards model |
17. Non-Inferiority, Bayesian Methods, and Crossover
The ClinicalTrials.gov record does not identify a non-inferiority margin, a Bayesian analysis, or a crossover analysis. The primary PFS analysis is labeled a descriptive assessment in the registry data rather than a non-inferiority analysis, and the OS analysis is explicitly associated with a superiority hypothesis.
No non-inferiority margin reported
The ClinicalTrials.gov record does not provide a non-inferiority margin, so the results should not be interpreted through a non-inferiority framework.
No Bayesian method reported
The normalized methods identify Cox, log-rank, and score-based proportion methods; no Bayesian method is listed.
No crossover analysis reported
The ClinicalTrials.gov record does not identify a crossover design or a crossover-adjusted analysis.
Superiority hypothesis for OS
The OS statistical analysis is explicitly classified as a superiority analysis in the ClinicalTrials.gov record.
18. Missing Data and Imputation
The registry analysis records do not specify a missing-data or imputation method for the primary or secondary analyses. For time-to-event endpoints, censoring is intrinsic to the analysis because participants may reach the end of available follow-up without experiencing the event. However, the registry extract reported here does not provide detailed censoring rules or sensitivity analyses.
19. Limitations of the Reported Statistical Evidence
- Limited numerical detail: the ClinicalTrials.gov record provides hazard ratios and confidence intervals for the primary time-to-event endpoints but do not provide median PFS or median OS, event counts, or time-specific survival estimates.
- No subgroup estimates: although stratification variables are identified, the ClinicalTrials.gov record does not provide treatment-effect estimates for subgroups. Therefore, subgroup conclusions cannot be constructed from the registry extract.
- No reconstructed survival curves: without underlying event and censoring data, Kaplan-Meier curves should not be fabricated from summary HRs.
- No formal PFS p-value reported: the PFS analysis includes a confidence interval but no p-value in the ClinicalTrials.gov record.
- Secondary-endpoint multiplicity: the ClinicalTrials.gov record does not state how multiplicity across the secondary endpoints was controlled.
- Proportional-hazards assumption: the registry identifies Cox modeling but does not supply a diagnostic assessment of proportional hazards.
- Safety inference: serious-adverse-event counts are available by arm, but the ClinicalTrials.gov record does not report a formal comparative test or confidence interval for those counts.
- Registry scope: a ClinicalTrials.gov results record is not equivalent to a complete statistical analysis plan. Important operational details may require the underlying protocol or SAP, which are not part of the ClinicalTrials.gov record.
20. Why This Trial Matters Statistically
LEAP-002 is a useful teaching case because its registry results bring together randomized treatment comparison, two primary time-to-event endpoints, blinded assessment, stratified Cox modeling, log-rank testing, and score-based confidence intervals for binary response endpoints.
| Concept | How it appears in LEAP-002 |
|---|---|
| Randomization | Randomized phase 3 parallel-group design with 794 enrolled participants. |
| Blinding | Double-masked trial design. |
| Time-to-event endpoints | PFS and OS are the two registered primary endpoints. |
| Hazard ratio | Used for PFS, OS, TTP, and mRECIST PFS analyses. |
| Cox proportional-hazards model | Primary PFS method and also reported in the OS and secondary time-to-event analysis descriptions. |
| Log-rank test | Reported method for the primary OS comparison. |
| Stratified analysis | Geographic region, MPVI or extrahepatic spread or both, AFP and ECOG PS are included in the reported analysis framework. |
| Risk difference | Used for ORR comparisons under RECIST 1.1 and mRECIST. |
| Score-based CI | Miettinen-Nurminen method used for ORR difference estimates. |
| Analysis population | All randomized participants analyzed according to randomized treatment group for the primary efficacy analyses. |
| Multiplicity | Two primary endpoints plus multiple secondary statistical analyses make endpoint hierarchy important. |
21. Statistical Methods Explained: A Deeper Walkthrough
Why does PFS use the time of progression or death?
The registered PFS endpoint combines two possible events: documented disease progression or death from any cause, whichever occurs first. This means a participant who dies before a documented progression still contributes a PFS event. The endpoint therefore captures both disease-control failure and death within one time-to-event measure.
Why is the OS endpoint simpler to define?
The registered OS definition is the time from randomization until death from any cause. Unlike PFS, it does not require radiologic determination of progression. The event definition is therefore directly tied to survival status.
Why does blinded independent central review matter for PFS?
Progression can require interpretation of imaging against prespecified criteria. The registry specifically states that progression for PFS was determined by blinded independent central review according to RECIST 1.1. Blinding helps reduce the opportunity for knowledge of randomized treatment to influence the classification of progression.
Why is the analysis population important?
The primary analyses use all randomized participants and analyze them according to randomized treatment assignment. This means the treatment comparison remains anchored to the randomization process. If participants discontinue treatment or receive other therapy, simply removing them from the primary efficacy population could compromise that randomized comparison.
Why are ORR and PFS not interchangeable?
ORR is a binary endpoint: a participant either meets the response definition or does not. PFS is a time-to-event endpoint: the timing of progression or death is part of the outcome. Consequently, ORR is naturally summarized with proportions and risk differences, while PFS is analyzed using survival methods such as Cox models.
Why can two hazard ratios not be directly treated as percentage differences in response?
A hazard ratio compares event hazards over time. A risk difference compares probabilities or proportions. Although an HR of 0.80 can be described as an estimated 20% lower hazard, it cannot be translated into a 20-percentage-point difference in event-free patients without additional information.
22. What the Primary Hazard Ratios Do — and Do Not — Mean
The PFS HR of 0.834 indicates an estimated lower instantaneous hazard of progression or death for lenvatinib plus pembrolizumab relative to lenvatinib plus placebo under the reported Cox model. The corresponding relative interpretation is approximately a 16.6% lower estimated hazard.
It does not mean that 16.6% of patients were protected from progression, nor does it provide an absolute probability of remaining progression-free at any particular time.
The OS HR of 0.840 indicates an estimated lower instantaneous hazard of death for lenvatinib plus pembrolizumab relative to lenvatinib plus placebo under the reported time-to-event analysis. The corresponding relative interpretation is approximately a 16.0% lower estimated hazard.
It does not mean that 16.0% more patients survived, nor does it imply a particular difference in median survival or survival probability at a particular time point.
The 95% CI for PFS is 0.712–0.978, while the 95% CI for OS is 0.708–0.997. These intervals describe uncertainty around the corresponding hazard-ratio estimates under the reported statistical framework. They do not describe the range of individual patient outcomes.
23. Clinical Interpretation vs Statistical Interpretation
Statistical interpretation
The registry-reported analyses produced hazard ratios below 1 for both primary time-to-event endpoints. The OS analysis reports P = 0.0227, while the registry-reported PFS record reports a 95% confidence interval without a p-value.
Clinical interpretation
The ClinicalTrials.gov record describes differences between randomized groups in PFS, OS, disease progression, and objective response. The ClinicalTrials.gov record does not include median survival or time-specific survival estimates, so those outcomes are not inferred.
24. Overall Statistical Reading of the Trial
The statistical story of LEAP-002 is internally consistent across several endpoint types in the ClinicalTrials.gov record. The primary PFS HR is 0.834, and the primary OS HR is 0.840. The secondary time-to-progression analyses also have HRs below 1: 0.79 under RECIST 1.1 and 0.73 under mRECIST. Secondary PFS under mRECIST has an HR of 0.80.
The response endpoints use a different effect measure. The reported ORR difference is 8.5 percentage points under RECIST 1.1 and 6.7 percentage points under mRECIST. Their confidence intervals are 2.8–14.2 and 0.0–13.4, respectively.
These results should not be collapsed into one statistic. Time-to-event endpoints and binary response endpoints answer different questions, and the choice of effect measure follows from the structure of the endpoint. The primary OS result additionally includes a reported one-sided p-value of 0.0227, while the PFS result is reported with a two-sided 95% confidence interval of 0.712–0.978 but no p-value in the ClinicalTrials.gov record.
25. Related Tutorials
Learn more about the methods used in this trial:
26. Related Statistical Calculators
27. Sources
- ClinicalTrials.gov: LEAP-002, NCT03713593.
- Linked PubMed publication: PMID 38039993.
Continue through Clinical Biostats
Connect this trial's endpoints and statistical methods to deeper tutorials and practical statistical calculators.
28. Record Summary
LEAP-002 provides a useful example of how a randomized phase 3 oncology trial can combine multiple statistical frameworks within a single evidence package. The two primary endpoints are time-to-event outcomes, with PFS analyzed using a stratified Cox proportional-hazards model and OS evaluated using a stratified log-rank framework with a Cox-model description. The secondary endpoints add both additional time-to-event analyses and binary response analyses using score-based confidence intervals.
The most important statistical distinction is between effect size, precision, and hypothesis testing. The hazard ratios describe relative event hazards; the risk differences describe percentage-point differences in response; the confidence intervals describe uncertainty around those estimates; and the reported OS p-value addresses the specified superiority test. None of these quantities alone provides a complete description of treatment effect.