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Cervical Cancer Randomized Trial Statistical Analysis NCT00614211

LACC: Complete Statistical Analysis of Laparoscopic Radical Hysterectomy in Cervical Cancer

An independent statistical review of the LACC randomized trial comparing total abdominal radical hysterectomy with total laparoscopic or robotic radical hysterectomy for cervical cancer.

ClinicalTrials.gov record: NCT00614211

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

LACC was a randomized parallel-group clinical trial evaluating surgical approaches for cervical cancer. The registry reports an enrollment of 636 participants and a primary endpoint of disease-free survival at 5 years from surgery.

636
Enrollment
2
Arms
5 years
Primary endpoint timeframe
Randomized
Allocation
FeatureLACC
NCT IDNCT00614211
TitleLaparoscopic Approach to Cervical Cancer
StatusCOMPLETED
PhaseNA
ConditionCervical Cancer
DesignRandomized, parallel assignment, open-label
Primary purposeTreatment

2. Clinical Question

Population

Patients with cervical cancer enrolled in the LACC study.

Intervention

Total laparoscopic or robotic radical hysterectomy.

Comparator

Total abdominal radical hysterectomy.

Primary question

Does the laparoscopic or robotic surgical approach produce comparable disease-free survival to abdominal radical hysterectomy?

3. Trial Design

Randomize
636 patients
Arm 1
Abdominal surgery
Arm 2
Laparoscopic/robotic surgery
Follow-up
5-year DFS

Total Abdominal Radical Hysterectomy

Comparator surgical procedure.

Total Laparoscopic or Robotic Radical Hysterectomy

Intervention surgical procedure.

4. Endpoints

EndpointDefinition
Disease free survival5 years from surgery; compare treatment equivalence.

5. Planned Analysis

The primary endpoint was disease-free survival at 5 years from surgery. For randomized time-to-event endpoints such as disease-free survival, statistical analysis commonly involves estimation of event-free survival over time, comparison between randomized groups, and estimation of a relative treatment effect with confidence intervals.

The ClinicalTrials.gov record does not report posted statistical analyses or endpoint results for this trial.

6. Statistical Methodology

Time-to-event analysis

Disease-free survival is a time-to-event outcome. Analysis typically accounts for patients who have not experienced an event by the end of follow-up through censoring, allowing follow-up information from all eligible participants to contribute to the estimate.

Equivalence interpretation

The registry describes the primary endpoint as comparing treatment equivalence. Equivalence questions differ from superiority questions because the goal is not simply to show one approach has fewer events. Instead, the analysis must evaluate whether the difference between approaches falls within a prespecified acceptable range.

Conceptual distinction

Superiority: Is one treatment better than another?

Equivalence: Are two treatments sufficiently similar within predefined limits?

7. Statistical Methods Explained

Why is disease-free survival analyzed as a time-to-event endpoint?

Disease-free survival incorporates both whether an event occurs and when it occurs. This provides more information than simply comparing the percentage of patients with events at one fixed time point.

Why does randomization matter?

Random assignment helps balance known and unknown patient characteristics between groups, allowing differences in outcomes to be attributed more directly to the assigned surgical approaches.

What makes equivalence analysis different?

An equivalence analysis requires a predefined margin describing how much difference between approaches is considered clinically acceptable.

Why are confidence intervals important?

Confidence intervals describe the uncertainty around an estimated treatment difference and help determine whether the observed difference is compatible with the prespecified equivalence framework.

Why can missing follow-up matter?

Loss to follow-up and censoring assumptions can influence time-to-event analyses. The validity of conclusions depends on appropriate handling of follow-up information.

8. Limitations

9. Why This Trial Matters Statistically

LACC illustrates several important concepts in clinical trial methodology.

ConceptApplication
RandomizationComparison of two surgical approaches using randomized allocation.
Parallel designParticipants assigned to one of two treatment groups.
Time-to-event analysisDisease-free survival measured over time.
Equivalence testingPrimary question focused on treatment equivalence.
CensoringImportant consideration for survival-type endpoints.

10. Related Tutorials

11. Related Calculators

12. Sources