Study to Evaluate the Efficacy and Tolerability of Debio 1562 in Combination With Rituximab in Participants With Relapsed and/or Refractory DLBCL and Other Forms of NHL: Complete Statistical Analysis

1. Trial at a Glance

This page covers the registry results for Study to Evaluate the Efficacy and Tolerability of Debio 1562 in Combination With Rituximab in Participants With Relapsed and/or Refractory DLBCL and Other Forms of NHL (NCT02564744), a non-randomized phase 2 study. The primary outcome is Number of Participants With Treatment-emergent Adverse Events (TEAEs).

2. Clinical Question

The study’s stated purpose was to assess safety and tolerability and the anti-tumor activity of proposed Debio 1562 dose regimens in combination with rituximab. That purpose does not make the cohort results a randomized estimate of treatment benefit. The non-randomized sequence and differences in cohort composition and dosing limit direct comparisons between cohorts. The registry record does not report a randomized comparator or a control treatment against which to estimate a causal treatment effect.

3. Trial Design

This was a Phase 2 study sponsored by Debiopharm International SA. The registry lists enrollment as 100 and the study status as completed. The recorded study start was 2016-06-05, and the primary completion date was 2021-01-13. Allocation was non-randomized, masking was absent, and the model was sequential.

The study had a safety run-in and several named cohorts. Part 1 included Cohort 1 for participants with relapsed and/or refractory DLBCL and Cohort 2 for participants with relapsed and/or refractory follicular lymphoma, marginal zone lymphoma/mucosa-associated lymphoid tissue lymphoma, mantle cell lymphoma, or other NHL with sponsor approval. Part 2/3 included Cohort A and Cohort B, both described for participants with relapsed DLBCL. The safety run-in description also covers DLBCL and other NHL, but its full eligibility wording is not reported in the registry record.

The schedules differed across cohorts. For example, the Part 1 Cohort 1 description lists Debio 1562 0.7 mg/kg followed by rituximab 375 mg/m² on Day 1 of 21-day cycles. Cohort B is described with Debio 1562 0.4 mg/kg followed by rituximab 375 mg/m² on Day 1, then Debio 1562 0.2 mg/kg on Days 8 and 15 of a 21-day cycle. The record describes treatment continuing for at least 6 cycles and/or until discontinuation criteria in the Part 2/3 cohorts; the Part 1 descriptions use discontinuation criteria. These are cohort-specific regimens, not randomized alternatives.

Because allocation was not randomized and the study was sequential, the safety counts describe the cohorts as studied. Differences between cohorts could reflect their different participants, regimens, or place in the study, so they do not isolate the effect of one regimen versus another. The registry record does not report the detailed sequence rationale or a statistical plan for comparing cohorts.

4. Participant Flow and Accounting

The registry reports how participants moved through each stage of the study. All counts below are exactly as posted; missing counts are not inferred from other stages.

Before assignment: A total of 127 participants were screened and 100 participants with relapsed or refractory (R/R) diffuse large B-cell lymphoma (DLBCL) and other forms of non-Hodgkin's lymphoma (NHL) were enrolled, 37 participants into Part 1 and 63 participants into Part 2/3.

Overall Study

Reasons for not completing this period:

Analysis population for the primary outcome, as defined in the registry: Safety population included all participants from the screened population who received at least one dose of Debio 1562.

5. Baseline Characteristics

The table reproduces the baseline characteristics posted in the registry, which describe the participants analysed at baseline and help show whether the randomized groups were comparable.

Population: Safety population included all participants from the screened population who received at least one dose of Debio 1562.

6. Primary Endpoint

The registry lists the primary outcome as Number of Participants With Treatment-emergent Adverse Events (TEAEs), measured over Up to 30 days after end of treatment (EOT) (Up to 38 months).

The primary endpoint was the number of participants with treatment-emergent adverse events (TEAEs), measured in participants. An adverse event was defined broadly as a noxious, pathological, or unintended change in function, including physical signs, symptoms, or laboratory changes, whether or not considered related to study drug. The definition also included worsening pre-existing conditions, intercurrent illnesses, drug interactions, relevant concomitant-medication events, clinically important laboratory changes, and clinically significant examination, vital-sign, or weight abnormalities.

The endpoint was assessed in the safety population, defined as screened participants who received at least one dose of Debio 1562. Its timeframe was up to 30 days after end of treatment, with the listed maximum period described as up to 38 months. Counting participants with events is a direct way to describe how many people experienced an event during the specified period. It does not, by itself, describe event severity, duration, recurrence, or whether the event was caused by treatment.

The registry also presents serious adverse event counts separately from other adverse event counts. Those categories give additional safety context, but they are not interchangeable with the primary endpoint’s overall TEAE count. The record does not separately identify a single overall TEAE count across all cohorts in the information available for interpretation. Nor does it provide a comparative estimate against a randomized control group.

7. Why No Re-analysis Is Shown

8. Statistical Interpretation

9. Statistical Methods Explained

The registry lists a participant-count primary outcome, rather than a continuous measurement. For a count outcome, the basic descriptive result is the number of participants meeting the event definition in the stated population and timeframe. A numerator without a clearly aligned denominator is incomplete for judging how common an event was. The registry supplies cohort at-risk counts for the listed serious and other adverse event summaries, but it does not report one overall primary-endpoint TEAE count across the cohorts in the information available for interpretation.

What does a participant count capture?

A participant count answers whether a person experienced an event at least once during the observation period. A person with repeated events can still contribute as one participant to a count of participants affected. This differs from counting all event episodes, measuring time to first event, or measuring event duration. The registry’s primary endpoint is explicitly framed as the number of participants with TEAEs. The broader event definition includes events whether or not considered study-drug related, so the endpoint is not limited to events judged to have been caused by treatment.

For safety summaries, the event definition and the population matter. The stated safety population includes screened participants who received at least one dose of Debio 1562. Participants who were screened but did not receive a dose are outside that definition. The recorded timeframe extends to 30 days after end of treatment, up to 38 months. Interpretation therefore depends on the observation period and treatment exposure, not just the number of participants affected.

Why does design affect interpretation?

General statistical principle: random allocation helps balance known and unknown participant characteristics between comparison groups, on average. In a non-randomized study, differences between groups can arise from who entered each cohort, the dose schedule, the time the cohort was studied, or other factors. This means a comparison of observed counts is not automatically an estimate of what would have happened if the same participants had received a different regimen.

That issue is particularly relevant because the study is described as sequential and includes cohorts with different disease descriptions and schedules. Cohort A and Cohort B are both described for relapsed DLBCL, but their dosing schedules differ. Part 1 includes cohorts with other disease descriptions. As a result, even cohort-level counts with at-risk denominators do not create like-for-like comparisons. The registry explicitly notes that this is not a randomized comparison and that arms are not a like-for-like control comparison.

What can uncertainty intervals tell a reader?

General statistical principle: a confidence interval gives a range of values compatible with the observed data under a specified statistical model and sampling process. It communicates uncertainty in an estimate; it does not remove bias from a non-randomized design or account for missing information unless those features are handled by the analysis. The registry record does not report confidence intervals for the safety counts or a comparative statistical analysis. Consequently, the precision of a difference between cohorts cannot be assessed from the posted counts alone.

For a binary outcome, a proportion and its confidence interval are often used to describe the fraction of participants with an event. No new proportion should be inferred from the counts without an explicit analysis, particularly when event definitions, follow-up, and analysis populations may differ. The serious adverse event terms include percentages, but their denominators are not reported in the registry record. Their precision and direct comparability are therefore unclear.

How should event categories be distinguished?

The primary outcome is TEAEs, while the posted safety summaries distinguish serious adverse events from other adverse events and list deaths separately. These categories answer different descriptive questions. Seriousness is a classification of an event; it does not establish that the event was caused by study treatment. A count of participants with other adverse events is not necessarily the same as the total number with any TEAE, especially when event categories may overlap. The registry record does not describe whether or how participants appearing in more than one category were handled in an overall TEAE result.

General statistical principle: in repeated-event data, event rates may be affected by over-dispersion, which occurs when variability is greater than a simple model assumes. Methods that account for repeated events or time at risk can be useful when the question concerns event frequency over exposure. However, the primary endpoint is a participant count, and the registry does not report an event-rate model, repeated-event analysis, or adjustment for time at risk. No such method should be assumed.

What do the planning illustrations mean?

General statistical principle: a sample-size illustration estimates the number of participants that might be needed under stated assumptions to detect a specified effect with a chosen significance level and power. The registry describes its planning illustration as based on the observed effect, with two-sided significance level 0.05, equal allocation between two arms, and a normal approximation. It describes the target effect as a stated fraction of the observed effect. For time-to-event outcomes, it specifies a total event count; for other outcomes, it specifies participants per arm.

Those assumptions describe a planning calculation, not the study’s observed power and not necessarily the design used to plan this trial. Specific sample-size illustration values are not reported in the registry record. The record also does not report a trial sample-size justification, the effect estimate used in the illustration, or the statistical details needed to reproduce it. Any such illustration should be treated as hypothetical planning information, not as evidence of efficacy or a substitute for uncertainty estimates from an observed analysis.

More generally, statistical significance and clinical importance are distinct concepts. A p-value, if reported, would address compatibility of data with a specified null model under assumptions; it would not establish causation or the importance of an outcome. No inferential analysis results are reported in the registry record. The available safety information supports a descriptive reading of the listed counts and terms, not a formal test of differences or a causal treatment conclusion.

10. Exact Estimates for Each Arm

No two-arm comparison is shown for this trial, but each arm's result can still be summarized with exact interval estimates computed from the posted counts. The Clopper-Pearson interval is exact and conservative; the Wilson interval is a common closer-to-nominal alternative; the Bayesian credible interval uses a uniform Beta(1, 1) prior. For small groups the three can differ noticeably.

R code and output

arm    <- c("Part 1: Safety Run-in", "Part 1: Cohort 1", "Part 1: Cohort 2", "Part 2/3: Cohort A", "Part 2/3: Cohort B")
n      <- c(17, 8, 12, 33, 30)
events <- c(16, 8, 12, 32, 29)
for (i in seq_along(n)) {
  ci <- binom.test(events[i], n[i])$conf.int          # Clopper-Pearson exact interval
  cr <- qbeta(c(0.025, 0.975), 1 + events[i], 1 + n[i] - events[i])   # Bayesian, uniform Beta(1,1) prior
  wi <- (function(x, n) { p <- x / n; z <- qnorm(0.975); d <- 1 + z^2 / n
    c((p + z^2 / (2 * n) - z * sqrt(p * (1 - p) / n + z^2 / (4 * n^2))) / d, (p + z^2 / (2 * n) + z * sqrt(p * (1 - p) / n + z^2 / (4 * n^2))) / d) })(events[i], n[i])
  cat(sprintf("ARM|%s|%d|%d|%.1f|%.1f|%.1f|%.1f|%.1f|%.1f|%.1f\n", arm[i], n[i], events[i], 100 * events[i] / n[i],
              100 * ci[1], 100 * ci[2], 100 * wi[1], 100 * wi[2], 100 * cr[1], 100 * cr[2]))
}

ARM  Part 1: Safety Run-in  17  16  94.1  71.3  99.9  73.0  99.0  72.7  98.6
ARM  Part 1: Cohort 1  8  8  100.0  63.1  100.0  67.6  100.0  66.4  99.7
ARM  Part 1: Cohort 2  12  12  100.0  73.5  100.0  75.8  100.0  75.3  99.8
ARM  Part 2/3: Cohort A  33  32  97.0  84.2  99.9  84.7  99.5  84.7  99.3
ARM  Part 2/3: Cohort B  30  29  96.7  82.8  99.9  83.3  99.4  83.3  99.2

11. Multiplicity, Missing Data and Analysis Populations

The registry lists 17 outcome measures, including the primary TEAE count and secondary pharmacokinetic measures and progression-free survival. When a study evaluates multiple outcomes, multiplicity can arise because repeated statistical tests increase the chance of at least one apparently notable result even when none reflects a real effect. General statistical approaches can address this by prespecifying a hierarchy, adjusting significance thresholds, or emphasizing estimation and uncertainty rather than isolated tests.

The registry record does not report a multiplicity strategy, a hierarchy among secondary outcomes, or a statistical analysis plan. It also does not report inferential analyses for the outcomes. It is therefore not possible to determine whether any multiplicity adjustment was planned or used, or how secondary outcomes were intended to support conclusions. The registry’s count of listed outcome measures does not establish that every measure was analyzed or that each had a reported result.

The analysis population for the primary safety endpoint is described as screened participants who received at least one dose of Debio 1562. A detailed account of missing data, exclusions after treatment began, or outcome-specific analysis populations is not reported in the registry record. For participants with incomplete follow-up, the treatment of unobserved events is also not reported. These details matter because an incomplete observation period can affect event ascertainment, especially when the timeframe is defined relative to end of treatment.

The at-risk counts and adverse event counts are reported by cohort, but the record does not give a full accounting of missing observations for each outcome. Nor does it state whether analyses adjusted for time on treatment or follow-up. The study is non-randomized and sequential, and no like-for-like control comparison is described. Together, these omissions limit both inferential assessment and interpretation of differences among cohorts. No missing-data method, multiplicity adjustment, or unreported analysis should be assumed.

12. Safety Results

The registry reports serious adverse events as affected participants divided by participants at risk.

Deaths

Other (non-serious) adverse events

Participants with at least one non-serious event at or above the registry's 5% reporting threshold.

Most frequent serious adverse event terms

Most frequent non-serious adverse event terms

Serious events with the highest arm-level rate: tumour pain (12.5%), back pain (12.5%), pneumonia (12.5%), influenza (12.5%), urinary tract infection (12.5%).

The safety summaries report serious adverse event counts and other adverse event counts separately for each cohort, alongside at-risk counts. For example, the Safety Run-in lists 5 participants with serious adverse events among 17 at risk, while Part 2/3: Cohort A lists 15 among 33 at risk. These are descriptive cohort summaries, not randomized comparisons. Cohorts differed in their place in the sequential study, participant descriptions, and/or dosing schedules, so contrasts between their counts do not isolate a treatment effect.

The record also lists deaths by cohort. A death count does not identify its cause or establish a relationship to treatment. The registry does not report that the listed deaths were treatment-related. Similarly, serious adverse event classification describes seriousness, not causation. The listed serious event terms include percentages, but their denominator is not reported in the registry record. Those percentages should therefore be read as reported labels, not converted to counts or compared directly with cohort-level counts.

Counts of participants affected are not counts of all event episodes; a participant may experience more than one event. Serious and other event categories should also not be assumed to be mutually exclusive unless the record says so. The registry does not report enough detail to establish overlap or to reconstruct an overall TEAE total from the separate summaries.

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

The posted results describe safety observations in a Phase 2 study of Debio 1562 with rituximab across a safety run-in and four named cohorts. They include cohort-level counts for serious adverse events, other adverse events, and deaths, as well as selected serious-event terms and percentages. These summaries indicate what was recorded in the study population under the registry’s stated definitions and observation period.

They do not, by themselves, show that a particular regimen caused an event or prevented one. Events were included whether or not considered study-drug related, and death counts are not identified as treatment-related. The absence of random allocation and a like-for-like control means that differences between cohorts cannot be interpreted as causal treatment effects. Participant characteristics, disease type, dose schedule, exposure, and time in the study may differ across cohorts.

The record’s stated purpose included assessment of anti-tumor activity, and it lists progression-free survival as a secondary outcome. However, the outcome estimates and comparative analyses for progression-free survival are not reported in the registry record. The safety summaries therefore do not establish an efficacy result. Nor do the available entries provide a complete uncertainty assessment for the cohort counts, since confidence intervals and inferential analyses are not reported.

The primary endpoint is the number of participants with TEAEs, but the available information does not identify a single overall TEAE count across cohorts. Serious and other adverse event summaries are useful descriptive information, but they should not be substituted for an overall endpoint result without the registry’s definitions and participant-level handling. A participant count also does not convey the number, duration, recurrence, or severity of all event episodes.

In short, these data support a descriptive account of reported safety events in the studied cohorts. They do not support a randomized comparison, establish causation, or resolve how the regimens compare in efficacy or safety. Those interpretations require outcome-specific results, transparent denominators and analysis populations, and methods appropriate to the study design.

14. Why This Trial Matters Statistically

This study illustrates why an outcome’s label and its design context both matter. A count of participants with TEAEs is readily understood, but its interpretation depends on the safety population, event definition, observation period, and completeness of follow-up. The registry defines TEAEs broadly and sets the safety population as screened participants who received at least one dose of Debio 1562. Those details determine what the count is intended to represent.

The sequential, non-randomized design is equally important. The five cohorts were not randomized treatment arms, and their disease descriptions and schedules were not identical. Even where two cohorts share a relapsed DLBCL description, different dosing schedules and study placement prevent a simple causal comparison. Statistical descriptions should preserve that distinction rather than present cohort differences as if they arose from a controlled trial.

The record also demonstrates the value of separating adverse event categories. Serious adverse events, other adverse events, and deaths are distinct summaries, and a count of participants affected is not the same as a count of event episodes. Percentages for selected serious-event terms have no reported denominator, while the cohort summaries give at-risk counts. Clear denominators and explicit category definitions are necessary for readers to assess frequency and comparability.

Finally, a study can list many outcome measures without reporting analyses that answer each outcome question. The registry identifies pharmacokinetic outcomes and progression-free survival, but their results and the analysis plan are not reported in the registry record. A transparent statistical account would make it possible to evaluate estimation, uncertainty, multiplicity, and missing data. Without those elements, the strongest interpretation remains descriptive: the posted cohort counts document events but do not establish comparative treatment effects.

15. Limitations

• The posted summary statistics are the only data these results rest on; patient-level data, full protocols and statistical analysis plans are not available.
• Recalculated results are simple, unadjusted calculations from posted summaries. They do not replace the sponsor's prespecified analysis.

Several trial-specific limitations constrain interpretation. First, the study was non-randomized, unmasked, and sequential. The registry explicitly cautions that the cohorts are not a like-for-like control comparison. Cohort differences therefore cannot be attributed solely to the regimens. Second, the cohorts include different disease descriptions, and the recorded schedules vary. This limits direct comparison even before considering possible differences in follow-up and exposure.

Third, the registry record does not provide a single overall primary-endpoint TEAE result across the cohorts in the information available for interpretation. It reports serious and other adverse event counts separately, alongside deaths, but does not specify how those categories relate to an overall TEAE count or whether participants can appear in more than one category. The denominator for the percentages attached to selected serious-event terms is also not reported in the registry record.

Fourth, confidence intervals, comparative estimates, inferential analyses, and a statistical analysis plan are not reported in the registry record. The handling of missing outcome data, incomplete follow-up, and differences in time at risk is also not reported. Those omissions limit assessment of precision and may matter for safety outcomes observed over different lengths of exposure.

Finally, the registry lists progression-free survival and pharmacokinetic outcomes but does not report their outcome estimates in the available record. The stated aim included anti-tumor activity, but the safety counts cannot answer that efficacy question. Taken together, the record supports descriptive interpretation of the listed events, while leaving important details about analysis, uncertainty, and outcome reporting unresolved.

16. Related Tutorials

17. Related Statistical Calculators

18. Sources

• ClinicalTrials.gov: Study to Evaluate the Efficacy and Tolerability of Debio 1562 in Combination With Rituximab in Participants With Relapsed and/or Refractory DLBCL and Other Forms of NHL — NCT02564744.
• Registry record last updated 2024-06-12; status last verified 2024-05.
• Results first posted 2024-05-17.
• Data retrieved from ClinicalTrials.gov on 2026-11-09.
• Group sizes, means, event counts and sponsor-reported p-values are reproduced as posted. Effect estimates, confidence intervals and p-values not attributed to the sponsor are independently calculated from the posted summary statistics.

19. Record Summary

NCT02564744 is a completed Phase 2, non-randomized, unmasked, sequential study sponsored by Debiopharm International SA. It evaluated Debio 1562 in combination with rituximab in relapsed and/or refractory DLBCL and other forms of NHL. The registry lists enrollment as 100, a start date of 2016-06-05, and a primary completion date of 2021-01-13.

The primary outcome was the number of participants with TEAEs in the safety population, assessed up to 30 days after end of treatment, up to 38 months. The registry reports serious and other adverse event counts and deaths by cohort. It also lists pharmacokinetic outcomes and progression-free survival as secondary outcomes. Detailed comparative analyses, outcome estimates for the listed secondary outcomes, a statistical analysis plan, and missing-data methods are not reported in the registry record.