This page separates reported registry information from statistical interpretation. The numerical trial information on this page is restricted to the ClinicalTrials.gov record. This page provides an independent statistical analysis and educational interpretation of publicly reported results. ClinicalTrials.gov provides the official trial registry record.
1. Trial at a Glance
TROPION-Lung01 is a randomized, parallel-group phase 3 treatment trial evaluating DS-1062a versus docetaxel in previously treated advanced or metastatic non-small cell lung cancer with or without actionable genomic alterations.
| Feature | TROPION-Lung01 |
|---|---|
| Trial name | TROPION-Lung01 |
| ClinicalTrials.gov identifier | NCT04656652 |
| Phase | Phase 3 |
| Status | Active, not recruiting |
| Condition | Non-small cell lung cancer |
| Population | Previously treated advanced or metastatic non-small cell lung cancer with or without actionable genomic alterations |
| Allocation | Randomized |
| Design model | Parallel |
| Masking | None |
| Primary purpose | Treatment |
| Enrollment | 605.0 |
| Interventions | DS-1062a and docetaxel |
| Primary endpoints | Progression-free survival and overall survival |
| Results posted | Yes |
| Outcome measures posted | 5 |
| Lead sponsor | Daiichi Sankyo |
2. Clinical Question
The central statistical question is whether treatment assignment to DS-1062a produces a different time-to-event outcome than assignment to docetaxel in patients with previously treated advanced or metastatic non-small cell lung cancer.
Population
Patients with previously treated advanced or metastatic non-small cell lung cancer, with or without actionable genomic alterations.
Intervention
DS-1062a at 6.0 mg/kg, as represented in the ClinicalTrials.gov record.
Comparator
Docetaxel at 75 mg/m2, as represented in the ClinicalTrials.gov record.
Primary question
How does DS-1062a compare with docetaxel for the registered progression-free survival and overall survival endpoints?
3. Trial Design
DS-1062a
- DS-1062a
- Supplied safety population: 6.0 mg/kg
Docetaxel
- Docetaxel
- Supplied safety population: 75 mg/m2
4. Trial Timeline
Trial start
The ClinicalTrials.gov record identifies 2020-12-21 as the trial start date.
Primary completion
The ClinicalTrials.gov record identifies 2024-05-10 as the primary completion date.
Active, not recruiting
The trial is listed as ACTIVE_NOT_RECRUITING in the ClinicalTrials.gov record.
5. Primary Endpoints
| Endpoint | Registered definition | Time frame | Endpoint type |
|---|---|---|---|
| Progression-free survival (PFS) | Time from randomization to the earlier of the dates of the first documentation of radiographic progressive disease or death due to any cause. | From randomization until disease progression or death (whichever occurs first), up to approximately 27 months. | Time-to-event |
| Overall survival (OS) | Time from randomization to the date of death due to any cause. | From randomization until date of death due to any cause, up to approximately 38 months. | Time-to-event |
Why these endpoints are statistically related
Both registered primary endpoints begin at the same origin: randomization. Both are therefore naturally analyzed as time-to-event outcomes, but they define the event differently. PFS reaches its event at the earlier of radiographic progression or death, whereas OS reaches its event only at death.
This distinction matters because a patient can experience progression without immediately experiencing death. Consequently, PFS and OS can provide different information about the treatment comparison even though both are measured from randomization.
6. Statistical Methodology
The ClinicalTrials.gov record identifies both primary endpoints as time-to-event outcomes. The ClinicalTrials.gov record does not provide formal statistical-analysis entries, hazard-ratio estimates, confidence intervals, p-values, stratification factors, interim-analysis rules, multiplicity procedures, or missing-data specifications. Those details should therefore not be inferred as trial-specific facts.
Typical analysis framework for PFS
For a randomized trial with the registered PFS definition, a standard analysis would estimate the time-to-progression-or-death distribution using the Kaplan-Meier method. Treatment groups can then be compared with a time-to-event hypothesis test, commonly a log-rank test, and the relative treatment effect can commonly be summarized using a Cox proportional-hazards model and its hazard ratio.
Typical analysis framework for OS
OS has the same time-to-event structure, but the event is death from any cause. Kaplan-Meier estimation can describe survival over time, while a log-rank comparison and Cox model are commonly used to compare randomized groups.
The survival function represents the probability that the event time T exceeds time t. For PFS, the event is the earlier of documented radiographic progression or death. For OS, the event is death due to any cause.
Censoring
Time-to-event trials commonly include participants whose event has not occurred by the last available assessment. Such observations are right-censored rather than treated as if the event occurred at the last assessment. Correct handling of censoring is important because simply excluding patients without observed events can bias the estimated event-time distribution.
Hazard ratios
A Cox model can express the relative instantaneous event rate between randomized treatment groups through a hazard ratio. A hazard ratio below 1 would conventionally indicate a lower estimated instantaneous event rate in the numerator treatment group, while a hazard ratio above 1 would indicate a higher estimated instantaneous event rate.
This ratio compares modeled instantaneous event rates. It is not the same as a risk ratio, an absolute survival difference, a median-survival difference, or the percentage of patients who benefit.
7. Planned Analysis
Results are posted for the trial at the registry level, but the ClinicalTrials.gov record contains no formal statistical-analysis records for the two primary endpoints. Accordingly, there are no registry-reported primary-effect estimates, confidence intervals, or p-values that can be reported on this page.
Progression-free survival
The registry defines PFS as the time from randomization to the earlier of first documentation of radiographic progressive disease or death due to any cause, with the stated time frame extending from randomization until disease progression or death, up to approximately 27 months.
For an endpoint with this definition, a typical statistical analysis would use Kaplan-Meier estimation to describe PFS, a between-group time-to-event comparison such as a log-rank test, and a Cox proportional-hazards model to estimate a treatment hazard ratio with a confidence interval. The ClinicalTrials.gov record does not report a formal comparison for this endpoint.
Overall survival
The registry defines OS as the time from randomization to death due to any cause, with the stated time frame extending from randomization until death due to any cause, up to approximately 38 months.
For this endpoint, a typical analysis would likewise use Kaplan-Meier estimation and a treatment-group comparison of the time-to-death distribution, with a hazard ratio commonly obtained from a Cox model. The ClinicalTrials.gov record does not report a formal comparison for this endpoint.
8. Safety Results
The ClinicalTrials.gov record provides serious adverse events by treatment arm as affected participants divided by participants at risk. These figures are descriptive safety counts; the ClinicalTrials.gov record does not provide a formal statistical comparison.
| Treatment arm | Serious adverse events | Interpretation of registry-reported count |
|---|---|---|
| DS-1062a 6.0 mg/kg | 92/297 | 92 affected participants among 297 at risk. |
| Docetaxel 75 mg/m2 | 111/290 | 111 affected participants among 290 at risk. |
DS-1062a
The registry safety entry reports 92 serious-adverse-event-affected participants among 297 at risk.
Docetaxel
The registry safety entry reports 111 serious-adverse-event-affected participants among 290 at risk.
The denominators in the registry-reported safety entries differ from the overall enrollment of 605.0. That distinction is important: an exposure-specific safety denominator should not automatically be treated as the randomized population denominator without additional trial documentation.
9. How to Read the Primary Endpoints
PFS combines two possible events: radiographic progression and death. Because the registered definition takes whichever occurs first, PFS is not simply a measurement of tumor shrinkage. A participant can contribute a PFS event without experiencing death.
OS has a single event definition: death due to any cause. It is therefore conceptually simpler than PFS, although the longer time required to observe deaths can create different follow-up and censoring considerations.
A hazard ratio, if reported, would describe a relative difference in modeled instantaneous event rates. It would not directly state how many additional months an individual patient lives, the probability of progression by a particular date, or the fraction of patients who benefit.
10. Statistical Methods Explained
Why is randomization important in this trial?
Randomization assigns participants prospectively to the two treatment groups rather than allowing treatment choice to determine group membership. In expectation, randomization balances measured and unmeasured prognostic factors across groups. This is what makes the treatment-group contrast a randomized comparison rather than a simple observational comparison.
Why are PFS and OS treated as time-to-event endpoints?
The key information is not only whether an event occurs, but when it occurs. PFS records time to the earlier of progression or death, while OS records time to death. Participants may also have incomplete follow-up, making censoring part of the statistical structure.
Why would Kaplan-Meier estimation be appropriate?
Kaplan-Meier estimation is designed for time-to-event data with right censoring. It estimates the event-free probability over time without requiring every participant to have experienced the event before analysis.
What would a hazard ratio mean if one were reported?
A hazard ratio would compare the estimated instantaneous event rates between the two randomized groups under a specified survival model. For example, an HR below 1 would indicate a lower estimated instantaneous event rate for the treatment group in the modeled comparison. It would not mean that the same percentage of patients avoid the event.
Why does the confidence interval matter?
A confidence interval communicates statistical uncertainty around an estimated treatment effect. A narrow interval indicates greater precision than a wide interval, all else equal. The interval is not a range containing the treatment effect for individual patients, nor does it describe the full distribution of patient-level outcomes.
Why does the p-value not measure effect size?
A p-value addresses the compatibility of the observed data with a specified null hypothesis under the statistical testing framework. It does not quantify the magnitude of the treatment effect. Effect size and uncertainty are better described with measures such as hazard ratios and confidence intervals, together with absolute event-time estimates when available.
Why must the Cox proportional-hazards assumption be considered?
A conventional Cox proportional-hazards interpretation assumes that the relative hazard is reasonably represented by a common hazard ratio over time. If treatment effects vary substantially over follow-up, a single hazard ratio may compress a more complex pattern into one summary number. The ClinicalTrials.gov record does not state whether proportional-hazards diagnostics were performed.
11. Analysis Population and Interpretation
The ClinicalTrials.gov record identifies randomization as the allocation method but do not specify the detailed analysis populations used for the primary efficacy or safety analyses.
| Population / concept | What is established by the ClinicalTrials.gov record | Statistical relevance |
|---|---|---|
| Randomized population | The trial is randomized and has enrollment of 605.0. | Randomization provides the foundation for the primary treatment comparison. |
| Primary efficacy population | Not specified in the ClinicalTrials.gov record. | The precise analysis population should be taken from the protocol or statistical analysis plan rather than inferred. |
| Safety population | Serious-adverse-event counts are reported with arm-specific denominators of 297 and 290. | Safety interpretation should use the denominators associated with the reported safety measure. |
12. Missing Data and Censoring
The ClinicalTrials.gov record does not specify a missing-data or imputation strategy for PFS or OS. For time-to-event endpoints, the principal statistical issue is often incomplete event observation rather than conventional missing-value imputation. Participants who remain event-free at their last relevant assessment can be censored under the prespecified rules.
For PFS in particular, the timing and assessment of radiographic progression can affect the observed event time. The registry definition explicitly identifies the first documentation of radiographic progressive disease or death as the endpoint event, but the ClinicalTrials.gov record does not specify the detailed censoring rules surrounding missed or incomplete assessments.
For OS, death due to any cause is the event, while participants without an observed death by the relevant analysis point require appropriate follow-up and censoring rules. The ClinicalTrials.gov record does not provide those detailed rules.
13. Multiplicity and Interim Analysis
The ClinicalTrials.gov record identifies 2 primary endpoints, both time-to-event outcomes. They do not provide a formal alpha allocation, multiplicity hierarchy, interim-analysis schedule, stopping boundary, or type I error-control strategy.
| Design topic | What the ClinicalTrials.gov record establishes | What is not reported |
|---|---|---|
| Primary endpoints | 2 primary endpoints: PFS and OS. | The formal multiplicity strategy. |
| Interim analysis | Not specified in the ClinicalTrials.gov record. | Timing, information fraction, stopping boundaries, and alpha spending. |
| Multiple testing | Two primary endpoints are registered. | Whether a hierarchical, alpha-splitting, gatekeeping, or other confirmatory strategy was used. |
This distinction is important because two primary endpoints can create a multiple-testing problem if each is tested independently at the same nominal significance level. A confirmatory trial generally requires a prespecified strategy for controlling the familywise type I error rate. The ClinicalTrials.gov record does not establish which strategy, if any, was used for TROPION-Lung01.
14. Non-Inferiority, Crossover, Factorial, and Bayesian Methods
Non-inferiority margin
No non-inferiority margin is reported. The registered primary endpoints are described as time-to-event outcomes, but the ClinicalTrials.gov record does not characterize the trial as a non-inferiority study.
Crossover
No crossover procedure is reported in the ClinicalTrials.gov record.
Factorial design
The trial is identified as a parallel design. No factorial structure is reported.
Bayesian methods
No Bayesian analysis is reported in the provided statistical-analysis data.
15. What the Registry Results Do Tell Us
The registry data establish several important structural facts even though the registry-reported statistical-analysis array contains no formal treatment-effect estimates.
| Question | Registry information reported |
|---|---|
| Was the trial randomized? | Yes. |
| How many treatment arms? | 2. |
| What was the design model? | Parallel. |
| Was the trial masked? | No. |
| What was enrollment? | 605.0. |
| What were the primary endpoints? | PFS and OS. |
| What type were the primary endpoints? | Time-to-event. |
| Were results posted? | Yes. |
| How many outcome measures were posted? | 5. |
| Are formal statistical-analysis records reported? | No. |
| Are serious adverse-event counts reported? | Yes. |
The absence of formal statistical-analysis entries in the ClinicalTrials.gov recordset is itself statistically important. A rigorous evidence summary should distinguish between a registry having posted outcome measures and the registry providing the numerical inferential analysis needed to quantify treatment effects.
16. Limitations
- No formal statistical analyses reported: no formal statistical analyses were posted. Consequently, no hazard ratios, confidence intervals, p-values, median PFS, median OS, or event counts can be attributed to this page.
- Limited endpoint detail: the registry definitions specify the event and time frame, but the ClinicalTrials.gov record does not include the complete statistical analysis plan.
- Unknown stratification: no randomization stratification factors are reported, so none are inferred here.
- Unknown multiplicity strategy: two primary endpoints are registered, but the ClinicalTrials.gov record does not establish how familywise type I error was controlled.
- Unknown interim-analysis strategy: the ClinicalTrials.gov record does not identify whether interim efficacy analyses were conducted or how they were controlled.
- Unknown censoring rules: the endpoint definitions are provided, but detailed rules for censoring and handling incomplete assessments are not included in the ClinicalTrials.gov recordset.
- Safety denominators differ from total enrollment: serious-adverse-event data use arm-specific denominators of 297 and 290, rather than the overall enrollment of 605.0.
- Unmasked design: the registry identifies masking as none. For endpoints involving radiographic assessment, knowledge of treatment assignment can be a potential source of assessment bias, although the ClinicalTrials.gov record does not provide enough detail to characterize its magnitude.
- Generalizability: the population is defined by the trial's eligibility framework for previously treated advanced or metastatic non-small cell lung cancer, but the ClinicalTrials.gov record does not provide a detailed baseline-characteristics table.
17. Why This Trial Matters Statistically
TROPION-Lung01 is a useful statistical teaching case because it combines randomized allocation with two primary time-to-event endpoints and an unmasked parallel-group design. The trial illustrates how the same randomized comparison can produce multiple endpoint definitions that answer related but distinct questions.
| Concept | How it appears in TROPION-Lung01 |
|---|---|
| Randomization | The allocation is explicitly randomized. |
| Parallel-group design | The design model is parallel with 2 treatment arms. |
| Time-to-event analysis | Both registered primary endpoints are time-to-event outcomes. |
| Kaplan-Meier estimation | A standard method for describing PFS and OS distributions with censoring. |
| Hazard ratio | A standard relative effect measure for randomized time-to-event comparisons, although no trial-specific HR is reported here. |
| Cox regression | A standard model for estimating a treatment hazard ratio, subject to model assumptions. |
| Endpoint definition | PFS uses progression or death; OS uses death from any cause. |
| Multiplicity | Two primary endpoints create a need to understand the prespecified error-control strategy. |
| Censoring | Time-to-event interpretation depends on appropriate handling of incomplete event observation. |
| Safety denominators | Serious adverse events are reported using arm-specific denominators rather than total enrollment. |
18. Statistical Interpretation vs Clinical Interpretation
Statistical interpretation
The trial is a randomized phase 3 parallel-group comparison with 2 registered primary time-to-event endpoints. The ClinicalTrials.gov record defines the endpoints but do not provide the formal inferential statistics required to quantify the treatment effect.
Clinical interpretation
The trial addresses whether DS-1062a differs from docetaxel in progression-free survival and overall survival in previously treated advanced or metastatic non-small cell lung cancer. The ClinicalTrials.gov record alone do not establish the magnitude or direction of those endpoint differences.
19. Interpreting a Future Hazard Ratio
If a formal analysis reports a hazard ratio for either primary endpoint, the number should be interpreted together with its confidence interval and the endpoint definition.
A hazard ratio would compare the modeled instantaneous rate of the composite PFS event—radiographic progression or death—between the randomized groups. It would not represent the percentage of patients whose tumors progressed, nor would it directly equal a difference in median PFS.
A hazard ratio would compare the modeled instantaneous rate of death between the randomized groups. It would not mean that the same proportion of patients experienced a particular percentage reduction in their individual risk.
The confidence interval should be considered alongside the point estimate. A wider interval indicates more uncertainty around the estimated treatment effect than a narrower interval, all else equal. It does not describe the range of outcomes expected for an individual patient.
If a p-value is reported, it should be interpreted as evidence relative to the prespecified null hypothesis and testing framework. It is not a measure of effect magnitude, clinical importance, or individual treatment benefit.
20. Related Statistical Concepts
Learn more about the methods used in this trial:
21. Related Statistical Calculators
22. Sources
- ClinicalTrials.gov: NCT04656652 — TROPION-Lung01.
- Linked publication: PubMed PMID 41268710.
- Linked publication: PubMed PMID 40516821.
- Linked publication: PubMed PMID 39250535.
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23. Record Summary
TROPION-Lung01 is a randomized phase 3, parallel-group treatment trial with 605.0 enrolled participants and 2 treatment arms comparing DS-1062a with docetaxel in previously treated advanced or metastatic non-small cell lung cancer with or without actionable genomic alterations. Its 2 registered primary endpoints are progression-free survival and overall survival, both defined as time-to-event outcomes.
The ClinicalTrials.gov record supports a detailed statistical framework based on randomization, time-to-event estimation, censoring, Kaplan-Meier methods, and conventional survival-model interpretation. However, no formal statistical analyses were posted. Therefore, this page deliberately does not report numerical efficacy estimates, confidence intervals, p-values, median survival times, event counts, subgroup estimates, or other inferential results that are not present in the ClinicalTrials.gov record.