This page provides an independent statistical analysis and educational interpretation of publicly reported results. ClinicalTrials.gov provides the official trial registry record. Numerical results on this page are restricted to the ClinicalTrials.gov record.
1. Trial at a Glance
KEYNOTE-045 was a randomized, parallel-group, phase 3 trial in advanced urothelial cancer. The trial compared pembrolizumab with a control consisting of paclitaxel, docetaxel, or vinflunine, with overall survival and progression-free survival assessed as primary endpoints in all participants and in PD-L1-defined populations.
| Feature | KEYNOTE-045 |
|---|---|
| Trial name | KEYNOTE-045 |
| ClinicalTrials.gov identifier | NCT02256436 |
| Phase | Phase 3 |
| Condition | Urothelial Cancer |
| Allocation | Randomized |
| Design model | Parallel |
| Masking | None |
| Primary purpose | Treatment |
| Enrollment | 542 |
| Arms | 2 |
| Primary endpoint types | Binary; Time-to-event |
| Results posted | Yes |
| Outcome measures posted | 20 |
| Statistical analyses posted | 15 |
| Primary endpoint analyses | 6 |
| Primary analyses with estimate + CI | 6 |
2. Clinical Question
The primary statistical question was whether pembrolizumab differed from the control treatment with respect to progression-free survival and overall survival in participants with advanced urothelial cancer. The registered primary endpoints included all participants as well as participants with PD-L1-positive and strongly PD-L1-positive tumors.
Population
Participants with advanced urothelial cancer enrolled in the phase 3 KEYNOTE-045 trial. The ClinicalTrials.gov record reports 542 enrolled participants.
Intervention
Pembrolizumab, classified in the registry data as a biological intervention.
Comparator
The control intervention consisted of paclitaxel, docetaxel, or vinflunine.
Primary question
How does pembrolizumab compare with control for PFS and OS, including the prespecified PD-L1-defined populations?
3. Trial Design
Pembrolizumab
- Pembrolizumab was the intervention assigned in one randomized arm.
- The ClinicalTrials.gov record classifies pembrolizumab as a biological intervention.
Paclitaxel, docetaxel, or vinflunine
- The control intervention consisted of paclitaxel, docetaxel, or vinflunine.
- The ClinicalTrials.gov record identifies the comparison as Control vs Pembrolizumab.
The trial began on 22-Oct-2014 and reached primary completion on 07-Sep-2016. The primary time-to-event analyses used a database cutoff of 07-Sep-2016, corresponding to up to approximately 20 months. Several secondary analyses used a later final-analysis database cutoff of 26-Oct-2017, corresponding to up to approximately 34 months.
4. Endpoints
The registry lists six primary endpoints. Four concern all participants or participants with PD-L1-positive tumors, and two concern participants with strongly PD-L1-positive tumors. The primary endpoint family therefore combines time-to-event outcomes with biomarker-defined analysis populations.
| Primary endpoint | Time frame | Endpoint type |
|---|---|---|
| Progression-Free Survival (PFS) Per RECIST 1.1 - All Participants | Through primary analysis database cut-off date of 07-Sep-2016 (Up to approximately 20 months) | Time-to-event |
| Overall Survival (OS) - All Participants | Through primary analysis database cut-off date of 07-Sep-2016 (Up to approximately 20 months) | Time-to-event |
| PFS Per RECIST 1.1 - Participants With PD-L1 Positive Tumors | Through primary analysis database cut-off date of 07-Sep-2016 (Up to approximately 20 months) | Time-to-event |
| OS - Participants With PD-L1 Positive Tumors | Through primary analysis database cut-off date of 07-Sep-2016 (Up to approximately 20 months) | Time-to-event |
| PFS Per RECIST 1.1 - Participants With Strongly PD-L1 Positive Tumors | Through primary analysis database cut-off date of 07-Sep-2016 (Up to approximately 20 months) | Time-to-event |
| OS - Participants With Strongly PD-L1 Positive Tumors | Through primary analysis database cut-off date of 07-Sep-2016 (Up to approximately 20 months) | Time-to-event |
Progression-Free Survival
PFS was defined as the time from randomization to the first documented disease progression, or death due to any cause, whichever occurred first. Per RECIST 1.1, progressive disease was defined as at least a 20% increase in the sum of diameters of target lesions. In addition to the relative increase of 20%, the sum must also demonstrate an absolute increase of at least 5 mm.
Overall Survival
OS was defined as the time from randomization to death due to any cause. The all-participant OS endpoint was assessed through the primary analysis database cutoff date of 07-Sep-2016.
PD-L1-defined populations
For the purposes of the study, participants with PD-L1 CPS ≥1% were considered to have PD-L1-positive tumor status. Participants with PD-L1 CPS ≥10% were considered to have strongly PD-L1-positive tumor status. These definitions apply to the corresponding primary OS and PFS analyses.
5. Statistical Methodology
Analysis population
The primary analysis population consisted of all randomized participants, regardless of whether or not they received study treatment. Participants were included in the treatment group to which they were randomized. Corresponding PD-L1 analyses used all randomized participants within the applicable PD-L1-defined population.
Stratified Cox proportional-hazards model
The registry reports Cox regression as the statistical method for all six primary endpoint analyses. The statistical method is a Cox proportional-hazards model. The analysis notes specify treatment as a covariate, stratified by ECOG Performance Status, presence or absence of liver metastases, hemoglobin, and time from completion of the most recent chemotherapy.
The exponentiated treatment coefficient, exp(β), is interpreted as a hazard ratio under the Cox model. The model compares the estimated instantaneous event rate between treatment groups while incorporating the prespecified stratification structure.
Risk difference for response
The secondary objective response analyses used the Miettinen & Nurminen method. This is a score-based confidence-interval approach for differences in proportions, related to the Newcombe and Wilson methods. The effect measure was a difference in percentages, that is, a risk difference.
A positive risk difference means the observed response proportion was higher in the pembrolizumab group. Unlike a hazard ratio, a risk difference is expressed directly in percentage points.
Covariate adjustment and stratification
The primary and secondary analyses identify covariate adjustment and stratified analysis as additional concepts. The Cox analyses explicitly state that treatment was used as a covariate with stratification by ECOG Performance Status, liver metastasis status, hemoglobin, and time from completion of the most recent chemotherapy.
6. Primary Results: All Participants
Progression-Free Survival
PFS hazard ratio
95% CI: 0.81–1.19 · P = 0.41648
Primary analysis cutoff: 07-Sep-2016; up to approximately 20 months
| Endpoint | Effect estimate | 95% CI | P-value |
|---|---|---|---|
| PFS — all participants | HR 0.98 | 0.81–1.19 | 0.41648 |
The estimated hazard ratio of 0.98 is very close to 1. Under the fitted Cox model, this corresponds to an estimated instantaneous hazard of progression or death that is approximately 2% lower in the pembrolizumab group than in the control group.
The estimate does not mean that 2% fewer participants progressed or died, and it is not an absolute risk difference. It is a relative time-to-event measure from the Cox model.
The two-sided 95% confidence interval of 0.81–1.19 spans 1. This indicates substantial uncertainty around the point estimate and includes values compatible with either a lower or higher estimated hazard under the model. The p-value of 0.41648 is a measure of evidence against the specified null hypothesis; it is not a measure of the magnitude of the treatment effect.
Because this is a Cox-model result, interpretation also depends on the proportional-hazards framework and on censoring and follow-up patterns. The ClinicalTrials.gov record does not provide median PFS, event counts, or Kaplan-Meier estimates, so those quantities are not inferred here.
Overall Survival
OS hazard ratio
95% CI: 0.59–0.91 · P = 0.00224
Primary analysis cutoff: 07-Sep-2016; up to approximately 20 months
| Endpoint | Effect estimate | 95% CI | P-value |
|---|---|---|---|
| OS — all participants | HR 0.73 | 0.59–0.91 | 0.00224 |
The OS hazard ratio of 0.73 means that, under the fitted Cox model and over the analyzed follow-up, the estimated instantaneous hazard of death was approximately 27% lower in the pembrolizumab group than in the control group.
This does not mean that 27% of participants benefited, that 27% of participants were prevented from dying, or that each individual participant experienced a 27% reduction in risk. The hazard ratio is a model-based relative measure of the event rate over time.
The 95% confidence interval of 0.59–0.91 quantifies uncertainty around the estimated hazard ratio. Because the entire interval is below 1, the interval is consistent with a lower estimated hazard under the fitted model. It does not describe the range of individual treatment effects.
The p-value of 0.00224 quantifies statistical evidence under the specified testing framework; it does not measure the size or clinical importance of the effect. The analysis was a superiority analysis, and the Cox model incorporated the registry-specified stratification structure.
7. Primary Results: PD-L1-Positive Tumors
The registry prespecified PFS and OS analyses among randomized participants with PD-L1-positive tumors, defined in the study as PD-L1 CPS ≥1%.
| Primary endpoint | Hazard ratio | 95% CI | P-value |
|---|---|---|---|
| PFS — PD-L1-positive tumors | 0.91 | 0.68–1.24 | 0.26443 |
| OS — PD-L1-positive tumors | 0.61 | 0.43–0.86 | 0.00239 |
PD-L1-Positive PFS
PFS hazard ratio
95% CI: 0.68–1.24 · P = 0.26443
The estimated PFS hazard ratio of 0.91 corresponds to an estimated 9% lower instantaneous hazard of progression or death in the pembrolizumab group under the fitted model.
The 95% CI of 0.68–1.24 is relatively broad and crosses 1. Thus, the point estimate alone should not be treated as evidence of a definitive treatment effect in this population. The p-value of 0.26443 provides evidence against the superiority null hypothesis according to the reported analysis framework, but it does not quantify effect size or establish that the two treatment effects are identical.
As with the all-participant PFS analysis, the hazard ratio is not a median survival difference or a percentage of participants who remained progression-free.
PD-L1-Positive OS
OS hazard ratio
95% CI: 0.43–0.86 · P = 0.00239
The OS hazard ratio of 0.61 corresponds to an estimated instantaneous hazard of death approximately 39% lower in the pembrolizumab group under the fitted Cox model.
The 95% CI of 0.43–0.86 remains below 1, indicating that the uncertainty interval for the model-based estimate is entirely on the lower-hazard side of the null value. The p-value of 0.00239 is evidence against the superiority null within the reported analysis; it is not a measure of the clinical magnitude of benefit.
The population is restricted to PD-L1-positive participants, so this estimate should not be silently substituted for the all-participant OS estimate of 0.73. These are different analysis populations and answer related but distinct statistical questions.
8. Primary Results: Strongly PD-L1-Positive Tumors
The strongly PD-L1-positive population was defined by PD-L1 CPS ≥10%. The registry reports separate PFS and OS primary analyses for this population.
| Primary endpoint | Hazard ratio | 95% CI | P-value |
|---|---|---|---|
| PFS — strongly PD-L1-positive tumors | 0.89 | 0.61–1.28 | 0.23958 |
| OS — strongly PD-L1-positive tumors | 0.57 | 0.37–0.88 | 0.00483 |
Strongly PD-L1-Positive PFS
PFS hazard ratio
95% CI: 0.61–1.28 · P = 0.23958
The estimated hazard ratio of 0.89 corresponds to an approximately 11% lower estimated instantaneous hazard of progression or death in the pembrolizumab group under the fitted model.
The 95% CI of 0.61–1.28 crosses 1, so the interval is compatible with both a lower and a higher hazard relative to control. The p-value of 0.23958 does not measure the size of the observed HR and should not be interpreted as the probability that the treatment has no effect.
Strongly PD-L1-Positive OS
OS hazard ratio
95% CI: 0.37–0.88 · P = 0.00483
The OS hazard ratio of 0.57 corresponds to an approximately 43% lower estimated instantaneous hazard of death in the pembrolizumab group under the fitted model.
The 95% CI of 0.37–0.88 is entirely below 1, while the p-value of 0.00483 provides statistical evidence against the superiority null under the reported analysis framework.
The estimate should nevertheless be interpreted as a model-based relative treatment effect in the strongly PD-L1-positive population, not as an absolute survival probability or a guarantee of benefit for an individual participant.
9. Primary Endpoint Results Side by Side
| Population | PFS HR | PFS 95% CI | PFS P-value | OS HR | OS 95% CI | OS P-value |
|---|---|---|---|---|---|---|
| All participants | 0.98 | 0.81–1.19 | 0.41648 | 0.73 | 0.59–0.91 | 0.00224 |
| PD-L1-positive, CPS ≥1% | 0.91 | 0.68–1.24 | 0.26443 | 0.61 | 0.43–0.86 | 0.00239 |
| Strongly PD-L1-positive, CPS ≥10% | 0.89 | 0.61–1.28 | 0.23958 | 0.57 | 0.37–0.88 | 0.00483 |
This table illustrates why endpoint definition and analysis population matter. The six primary analyses are not six measurements of exactly the same quantity. They represent two different time-to-event outcomes evaluated in three nested or differently restricted populations.
10. Secondary Endpoint Results
The registry contains nine secondary statistical analyses. These include objective response rate under RECIST 1.1 and modified RECIST, as well as modified-RECIST PFS. The response analyses use risk differences, while the PFS analyses use Cox hazard ratios.
Objective Response Rate — RECIST 1.1
| Population | Risk difference | 95% CI | P-value | Analysis cutoff |
|---|---|---|---|---|
| All participants | 10.0 | 3.9–16.2 | 0.00068 | 26-Oct-2017 |
| PD-L1-positive tumors | 15.6 | 6.5–25.7 | 0.00049 | 26-Oct-2017 |
| Strongly PD-L1-positive tumors | 17.2 | 6.8–29.4 | 0.00061 | 26-Oct-2017 |
The registry reports these effects as differences in percentages. Thus, the all-participant estimate of 10.0 is a 10.0-percentage-point difference in objective response rate under the specified analysis, rather than a 10.0% relative increase.
The 95% confidence interval for the all-participant RECIST 1.1 risk difference is 3.9–16.2. The interval therefore quantifies uncertainty around a positive percentage-point difference, rather than uncertainty around a hazard ratio.
The p-value of 0.00068 addresses statistical evidence under the reported superiority analysis. It does not tell us that the response difference is clinically important, nor does it establish how long responses lasted.
The PD-L1-positive and strongly PD-L1-positive estimates are 15.6 and 17.2, respectively, but their larger numerical values should not by themselves be interpreted as proof of effect modification. Formal comparison of treatment effects across populations requires an appropriate interaction analysis.
Progression-Free Survival — Modified RECIST
| Population | Hazard ratio | 95% CI | P-value | Analysis cutoff |
|---|---|---|---|---|
| All participants | 0.86 | 0.71–1.04 | 0.05328 | 26-Oct-2017 |
| PD-L1-positive tumors | 0.82 | 0.60–1.10 | 0.08745 | 26-Oct-2017 |
| Strongly PD-L1-positive tumors | 0.77 | 0.53–1.11 | 0.07066 | 26-Oct-2017 |
The modified-RECIST PFS estimates are all below 1: 0.86 for all participants, 0.82 for PD-L1-positive participants, and 0.77 for strongly PD-L1-positive participants.
However, each corresponding 95% confidence interval includes 1. The all-participant interval is 0.71–1.04, the PD-L1-positive interval is 0.60–1.10, and the strongly PD-L1-positive interval is 0.53–1.11. These intervals communicate more uncertainty than the point estimates alone.
The associated p-values—0.05328, 0.08745, and 0.07066—should not be converted into claims about effect size. In particular, a p-value just above or below a conventional threshold is not a scientifically meaningful discontinuity in the underlying evidence.
Objective Response Rate — Modified RECIST
| Population | Risk difference | 95% CI | P-value | Analysis cutoff |
|---|---|---|---|---|
| All participants | 13.8 | 7.4–20.3 | 0.00001 | 26-Oct-2017 |
| PD-L1-positive tumors | 21.0 | 11.1–31.5 | 0.00002 | 26-Oct-2017 |
| Strongly PD-L1-positive tumors | 21.5 | 10.1–34.2 | 0.00009 | 26-Oct-2017 |
The modified-RECIST response analyses estimate positive differences in response percentages in all three populations. For all participants, the reported risk difference is 13.8 with a 95% CI of 7.4–20.3.
The PD-L1-positive and strongly PD-L1-positive analyses report risk differences of 21.0 and 21.5, respectively. Their confidence intervals are 11.1–31.5 and 10.1–34.2.
These are percentage-point contrasts, not hazard ratios. They also arise from a different response assessment framework than the primary RECIST 1.1 time-to-event endpoints, so the estimates should be kept conceptually separate.
11. Safety: Serious Adverse Events
The ClinicalTrials.gov record reports serious adverse events by treatment group as affected participants over participants at risk.
| Group | Serious adverse events affected / at risk |
|---|---|
| Control | 104/255 |
| Pembrolizumab | 107/266 |
| Control switched over to pembrolizumab | 8/13 |
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 an unsupported inferential comparison.
12. Statistical Methods Explained
Why was a Cox proportional-hazards model used?
PFS and OS are time-to-event endpoints. Some participants can remain alive or progression-free at the analysis cutoff, creating right-censored observations. A Cox model can use the timing of observed events while appropriately retaining information from participants whose event has not yet occurred at their last assessment.
What does an OS hazard ratio of 0.73 mean?
A hazard ratio of 0.73 means that the estimated instantaneous hazard of death in the pembrolizumab group was approximately 27% lower than in the control group under the fitted model. It does not mean that 27% of participants survived, that survival probability increased by 27 percentage points, or that every individual experienced the same reduction.
Why does the confidence interval matter?
A point estimate is only one estimate from the observed trial data. The 95% confidence interval communicates the statistical uncertainty surrounding that estimate under the analysis framework. For example, the all-participant OS HR of 0.73 has a 95% CI of 0.59–0.91, while the all-participant PFS HR of 0.98 has a 95% CI of 0.81–1.19.
Why are some PFS confidence intervals compatible with both directions?
The PFS estimates for all participants, PD-L1-positive participants, and strongly PD-L1-positive participants have confidence intervals of 0.81–1.19, 0.68–1.24, and 0.61–1.28, respectively. Because each interval contains 1, the data represented by those intervals are compatible with both lower and higher hazards under the fitted model.
What is different about the response analyses?
Objective response is a binary endpoint rather than a time-to-event endpoint. The registry reports Miettinen & Nurminen methods and a difference in percentages as the effect measure. Therefore, the result is expressed in percentage points rather than as a hazard ratio.
Why are PD-L1 subgroup estimates not automatically interaction tests?
Comparing 0.73 in all participants with 0.61 or 0.57 in PD-L1-defined populations is descriptive. A difference between numerical estimates does not establish that the treatment effect changes according to PD-L1 status. A formal interaction test is needed to evaluate effect modification, and no such result is included in the ClinicalTrials.gov record.
Why does randomization matter?
Randomization assigns participants to treatment groups before the outcome is observed, providing the basis for a causal comparison of the randomized strategies under the trial design. The primary analysis population preserved this assignment principle by analyzing randomized participants according to their randomized group.
13. Understanding the Cox Model More Deeply
The Cox proportional-hazards model separates the baseline hazard from the relative treatment effect. Conceptually, the treatment coefficient changes the hazard multiplicatively:
An HR below 1 corresponds to a lower modeled instantaneous event rate in the treatment group; an HR above 1 corresponds to a higher modeled instantaneous event rate.
For KEYNOTE-045, the primary analyses incorporated treatment as a covariate and stratified by ECOG Performance Status, presence or absence of liver metastases, hemoglobin, and time from completion of the most recent chemotherapy. Stratification allows the baseline hazard to differ across the specified strata while estimating the treatment contrast within the Cox framework.
This is an important distinction from simply comparing two crude event proportions. A time-to-event model uses the timing of events and censoring information, whereas a binary endpoint analysis summarizes whether an event occurred within a defined framework.
Relative measure
HR 0.73 is a relative model-based treatment effect for the instantaneous hazard of death.
Absolute measure
A risk difference such as 10.0 is a percentage-point contrast in response proportions.
Time-to-event measure
PFS and OS account for when progression, death, or censoring occurs.
Binary measure
ORR classifies participants according to whether they meet the response definition.
14. Censoring and the Proportional-Hazards Assumption
Because PFS and OS are time-to-event endpoints, participants who have not experienced the endpoint by the analysis cutoff can contribute information until the point at which their follow-up ends. This is the role of censoring in survival analysis.
The Cox model additionally relies on the proportional-hazards framework for its usual interpretation of a single hazard ratio. Under proportional hazards, the relative hazard associated with treatment is treated as stable over time after accounting for the model structure. If that assumption is inappropriate, a single HR can summarize the treatment contrast incompletely.
15. Primary Endpoint Interpretation
| Question | What the ClinicalTrials.gov record tells us | What it does not tell us |
|---|---|---|
| How large is the modeled relative effect? | The HR provides a relative time-to-event estimate. | It does not provide an absolute risk difference. |
| How precise is the estimate? | The 95% CI quantifies statistical uncertainty around the estimate. | It does not describe individual-level treatment-effect variability. |
| How much statistical evidence is present? | The p-value summarizes evidence under the specified hypothesis-testing framework. | It does not measure clinical importance or effect size. |
| Is the result generalizable? | The result describes the randomized trial population and analysis framework. | It does not establish effects in populations not represented by the trial. |
The most direct statistical contrast in the all-participant primary analyses is between PFS and OS. The PFS estimate is 0.98 with a 95% CI of 0.81–1.19, whereas the OS estimate is 0.73 with a 95% CI of 0.59–0.91. These should not be collapsed into a single efficacy statistic because they represent different endpoints with different event definitions.
Similarly, the PD-L1-positive and strongly PD-L1-positive estimates describe restricted populations. The OS estimates are 0.61 and 0.57, respectively, while the corresponding PFS estimates are 0.91 and 0.89. A descriptive difference between OS and PFS effects does not itself identify why the endpoints differ; interpretation depends on follow-up, censoring, subsequent therapy, event definitions, and the underlying disease process.
16. Risk Difference and Response Analysis
The secondary response analyses use a different statistical language from the primary survival analyses. Instead of a hazard ratio, the registry reports a difference in percentages, normalized as a risk difference.
The bars above are visual representations of the reported point estimates and are not estimates of response rates themselves. The underlying registry data report the treatment-group difference in percentages, not the two response proportions from which the difference arose.
The same distinction applies to modified RECIST response. The reported risk differences are 13.8, 21.0, and 21.5 for all participants, PD-L1-positive participants, and strongly PD-L1-positive participants, respectively.
17. Multiplicity and the Six Primary Analyses
The trial registry identifies six primary endpoints and six corresponding formal statistical analyses. Each has a reported estimate, two-sided 95% confidence interval, p-value, and superiority hypothesis.
| Feature | Registry-supported interpretation |
|---|---|
| Primary endpoint count | 6 |
| Formal primary analyses | 6 |
| Primary analyses with estimate + CI | 6 |
| Hypothesis type | Superiority |
| CI type | 95%, two-sided |
| Primary time frame | Through 07-Sep-2016, up to approximately 20 months |
The ClinicalTrials.gov record establishes that all six primary endpoint analyses were formally analyzed, but they do not provide an alpha-allocation scheme, multiplicity-adjustment procedure, or hierarchy linking the six tests. Therefore, this page does not impose a multiplicity interpretation that is not contained in the ClinicalTrials.gov record.
18. Missing Data, Censoring, and Analysis Populations
The primary efficacy analysis population is explicitly described as all randomized participants, regardless of whether or not they received study treatment, with participants retained in the group to which they were randomized. This is the key analysis-population principle supported by the ClinicalTrials.gov record.
The time-to-event endpoints necessarily involve censoring because the outcome is defined as occurring over time rather than at a single fixed binary assessment. However, the ClinicalTrials.gov record does not describe a specific missing-data imputation procedure, sensitivity analysis, or censoring-rule sensitivity analysis beyond the endpoint definitions themselves.
What is reported
Randomized analysis populations, Cox models, stratification variables, estimates, confidence intervals, and p-values.
What is not reported here
No specific imputation strategy or missing-data sensitivity analysis is reported in the ClinicalTrials.gov record.
For this reason, the absence of an imputation method on this page should not be interpreted as evidence that no missing-data procedures existed in the underlying protocol or statistical analysis plan. It means only that the ClinicalTrials.gov record does not specify one.
19. Crossover and Interpretation of Randomized Comparisons
The serious-adverse-event data include a distinct group labeled Control Switched Over to Pembrolizumab: 8/13. This indicates that treatment switching occurred and that a switched-over population was reported separately.
From a statistical perspective, treatment switching creates an important distinction between the effect of assignment to a randomized strategy and the effect of receiving a particular treatment exposure. An intention-to-treat comparison preserves the randomized assignment contrast, while crossover can make the observed treatment-group trajectories more similar than they would have been without switching.
20. Timeline of the Trial Record
Trial start
KEYNOTE-045 began enrollment under a randomized phase 3 design.
Primary completion and primary analysis cutoff
The primary completion date and the primary analysis database cutoff were 07-Sep-2016. The registered primary endpoint time frame extended up to approximately 20 months.
Final-analysis secondary endpoint cutoff
The reported secondary ORR and modified-RECIST analyses used a final analysis database cutoff of 26-Oct-2017, up to approximately 34 months.
21. Statistical Interpretation of the Primary Results
The PFS HR of 0.98 indicates a modeled relative hazard close to the null value of 1. Its 95% CI of 0.81–1.19 spans 1, and the p-value is 0.41648.
The OS HR of 0.73 corresponds to an approximately 27% lower estimated instantaneous hazard of death under the Cox model. Its 95% CI of 0.59–0.91 is below 1, with a p-value of 0.00224.
The OS HR of 0.61 corresponds to an approximately 39% lower estimated instantaneous hazard of death under the fitted model. The 95% CI is 0.43–0.86, with a p-value of 0.00239.
The OS HR of 0.57 corresponds to an approximately 43% lower estimated instantaneous hazard of death under the fitted model. The 95% CI is 0.37–0.88, with a p-value of 0.00483.
These four results illustrate a central principle of clinical-trial statistics: the point estimate, confidence interval, and p-value answer different questions. The HR describes the estimated relative treatment effect; the confidence interval describes statistical uncertainty around that estimate; and the p-value describes evidence against the specified null hypothesis.
22. Limitations
- Registry-derived scope: this page is restricted to the ClinicalTrials.gov-derived trial data and the five permitted PubMed records. Additional numerical results have not been imported from other publications.
- No median survival values reported: the ClinicalTrials.gov record does not report median PFS or median OS, so none are presented.
- No subgroup forest estimates beyond the registered populations: the ClinicalTrials.gov record does not provide additional baseline or clinical subgroup HRs, so no such subgroup analysis is constructed.
- No event counts for primary endpoints: the registry-reported primary analyses provide hazard ratios, confidence intervals, and p-values but not event counts by randomized arm.
- No Kaplan-Meier estimates reported: the registry data do not provide survival probabilities at specific time points. No Kaplan-Meier curve or reconstructed median is inferred.
- No formal interaction tests reported: differences among PD-L1-defined treatment estimates cannot be treated as formal evidence of treatment-effect heterogeneity.
- No multiplicity procedure reported: the data identify six primary analyses but do not provide an alpha-allocation or hierarchical testing procedure.
- No imputation procedure reported: the data do not specify a missing-data or imputation method for these analyses.
- Proportional-hazards assumption: Cox-model interpretation depends on the proportional-hazards framework, but the ClinicalTrials.gov record does not report an assumption diagnostic.
- Switching: the presence of control participants who switched to pembrolizumab is relevant to interpretation of randomized treatment comparisons, but the ClinicalTrials.gov record does not provide a crossover-adjusted effect estimate.
23. Why This Trial Matters Statistically
KEYNOTE-045 is a useful statistical teaching case because the registry data combine randomized treatment comparison, multiple time-to-event endpoints, biomarker-defined analysis populations, Cox regression, stratification, response-rate analysis, and treatment switching.
| Concept | How it appears in KEYNOTE-045 |
|---|---|
| Randomization | The trial used randomized allocation in a parallel-group phase 3 design. |
| Intention-to-treat principle | Primary efficacy populations consisted of randomized participants analyzed according to randomized treatment group. |
| Time-to-event analysis | PFS and OS were primary time-to-event endpoints. |
| Cox proportional-hazards model | Used for all six primary analyses and the modified-RECIST PFS secondary analyses. |
| Hazard ratio | Used as the effect measure for PFS and OS. |
| Confidence interval | All six primary analyses report two-sided 95% confidence intervals. |
| Stratified analysis | Primary analyses were stratified by ECOG Performance Status, liver metastasis status, hemoglobin, and time from completion of the most recent chemotherapy. |
| Risk difference | RECIST 1.1 and modified-RECIST ORR analyses report differences in percentages. |
| Score-based CI | Response analyses used the Miettinen & Nurminen method. |
| Biomarker-defined populations | Primary endpoints were evaluated in all participants, PD-L1-positive participants, and strongly PD-L1-positive participants. |
| Treatment switching | A separate control-switched-to-pembrolizumab safety group is reported as 8/13. |
24. A Practical Reading Strategy for KEYNOTE-045
A statistically disciplined reading of the trial starts with the endpoint rather than the p-value. First identify whether the endpoint is time-to-event or binary. Then identify the analysis population, the effect measure, the confidence interval, and the analysis method.
For example, the all-participant OS result is HR 0.73 with a 95% CI of 0.59–0.91 and p = 0.00224. The correct first interpretation is that the fitted model estimates a lower hazard of death in the pembrolizumab group, with the confidence interval lying below 1. Only after understanding that relative effect should the p-value be considered.
For all-participant RECIST 1.1 ORR, the effect is a risk difference of 10.0 with a 95% CI of 3.9–16.2 and p = 0.00068. This is a fundamentally different statistical object: it is a percentage-point contrast in a binary response endpoint, not a relative hazard over time.
25. Related Tutorials
Learn more about the methods used in this trial:
26. Related Calculators
27. Sources
- ClinicalTrials.gov: NCT02256436 — KEYNOTE-045.
- PubMed: PMID 40037029.
- PubMed: PMID 36494006.
- PubMed: PMID 35247908.
- PubMed: PMID 35101941.
- PubMed: PMID 31395089.
Continue with the statistical methods
Explore the survival-analysis, confidence-interval, and clinical-trial methods that underlie the KEYNOTE-045 analysis.
28. Record Summary
KEYNOTE-045 provides a useful example of how a randomized phase 3 trial can generate several related but distinct statistical questions. The six primary analyses evaluate PFS and OS in all participants, PD-L1-positive participants, and strongly PD-L1-positive participants. Each primary analysis uses a Cox proportional-hazards model with treatment as a covariate and stratification by ECOG Performance Status, liver metastasis status, hemoglobin, and time from completion of the most recent chemotherapy.
The reported all-participant PFS hazard ratio was 0.98 with a 95% CI of 0.81–1.19 and p = 0.41648, while the all-participant OS hazard ratio was 0.73 with a 95% CI of 0.59–0.91 and p = 0.00224. The PD-L1-positive and strongly PD-L1-positive OS analyses reported hazard ratios of 0.61 and 0.57, respectively, with corresponding 95% confidence intervals of 0.43–0.86 and 0.37–0.88.
The secondary analyses demonstrate a second statistical framework. Objective response rate was analyzed using the Miettinen & Nurminen method, with risk difference as the effect measure, while modified-RECIST PFS was analyzed with Cox regression. Keeping these effect measures separate is essential: a hazard ratio describes a modeled relative event rate over time, whereas a risk difference describes an absolute percentage-point difference in a binary outcome.