Ovarian CancerRandomized TrialDESKTOP III

DESKTOP III: Complete Statistical Analysis of Tumor Debulking Surgery in Recurrent Ovarian Cancer

An independent statistical review of the randomized DESKTOP III study comparing tumor debulking surgery versus chemotherapy alone in patients with recurrent platinum-sensitive ovarian cancer with a positive AGO Score.

Scope of this 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

408
Enrollment
2
Arms
Randomized
Allocation
36 months
Primary endpoint timeframe
FeatureDESKTOP III
ClinicalTrials.gov IDNCT01166737
StatusCOMPLETED
Start date2010-07
Primary completion date2020-01-17
Lead sponsorAGO Study Group
ConditionsFallopian Tube Cancer; Ovarian Cancer; Peritoneal Cavity Cancer
Design modelFACTORIAL
MaskingNONE

2. Clinical Question

Population

Patients with platinum-sensitive recurrent ovarian cancer with a positive AGO Score.

Intervention

Tumor Debulking Surgery in recurrent ovarian disease.

Comparator

Chemotherapy alone.

Primary question

Does tumor debulking surgery improve overall survival compared with chemotherapy alone?

3. Trial Design

DESKTOP III was a randomized study with two arms. The registry describes the design model as factorial and the masking as none. The study enrolled 408 participants.

Design elementDescription
AllocationRANDOMIZED
Number of arms2
Primary purposeOTHER
InterventionTumor Debulking Surgery (surgery in recurrent ovarian disease)

4. Endpoints

EndpointDefinition / timeframe
Overall survivalApproximately 36 months after last patient randomized and observation of 244 events

The registry defines overall survival as the primary endpoint in patients with platinum-sensitive recurrent ovarian cancer with a positive AGO Score.

5. Statistical Methodology

The primary outcome is a time-to-event endpoint. Overall survival is typically evaluated by estimating survival distributions, comparing randomized groups, and estimating relative differences between groups.

Kaplan-Meier estimation

Kaplan-Meier methods estimate the probability of remaining event-free over time while accounting for participants whose follow-up ends before the event occurs.

Log-rank comparison

A log-rank test is commonly used to compare survival curves between randomized groups because it evaluates differences over the observed follow-up period.

Cox proportional-hazards modeling

A Cox model is commonly used for estimating a hazard ratio, which summarizes the relative instantaneous event rate between groups under the proportional-hazards assumption.

6. Planned Analysis

The registry identifies overall survival as the primary endpoint with assessment approximately 36 months after the last patient randomized and observation of 244 events.

For an endpoint of this type, a typical analysis would include Kaplan-Meier survival estimates and a comparison of randomized treatment groups using a time-to-event statistical framework. Results have not been posted on ClinicalTrials.gov.

7. Statistical Methods Explained

Why is overall survival a time-to-event endpoint?

Overall survival records not only whether an event occurs, but also when it occurs. This allows analysis of differences in survival experience over time.

Why does randomization matter?

Randomization balances known and unknown patient characteristics on average, allowing differences observed between groups to be interpreted within the randomized comparison.

What does censoring mean?

Censoring occurs when a participant's event status is not observed through the end of follow-up. Statistical survival methods incorporate available follow-up information without assuming the exact event time is known.

What does a hazard ratio represent?

A hazard ratio compares estimated instantaneous event rates between groups. It is not the same as the percentage of patients who experience an event or a direct probability difference.

Why is follow-up time important?

Survival estimates depend on the duration and completeness of observation. Longer follow-up can provide additional information about treatment effects over time.

8. Limitations

9. Why This Trial Matters Statistically

ConceptHow it appears
RandomizationRandomized comparison of two treatment strategies
Survival analysisOverall survival as the primary endpoint
Event-driven designPrimary endpoint assessed after observation of 244 events
Clinical trial designRegistry-reported factorial design model

10. Related Tutorials

Learn more about the methods used in this trial:

11. Related Calculators

12. Sources