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Cervical Cancer Phase 3 Randomized NCT01566240

INTERLACE: Complete Statistical Analysis of Induction Chemotherapy in Locally Advanced Cervical Cancer

An independent statistical review of the randomized phase 3 INTERLACE trial evaluating induction chemotherapy plus chemoradiation as first line treatment for locally advanced cervical cancer.

ClinicalTrials.gov registration: NCT01566240
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

INTERLACE is a phase 3 randomized trial evaluating induction chemotherapy followed by chemoradiation as first line treatment for locally advanced cervical cancer.

500
Enrollment
2
Arms
Phase 3
Trial Phase
5 years
Primary Endpoint Time Frame
FeatureINTERLACE
ConditionCervical Cancer
AllocationRandomized
Design modelParallel
MaskingNone
Primary purposeTreatment
Lead sponsorUniversity College, London
StatusActive, not recruiting
Start date2012-11-08
Primary completion date2026-02

2. Clinical Question

Population

Patients with locally advanced cervical cancer.

Intervention

Induction chemotherapy using paclitaxel and carboplatin followed by chemoradiation.

Comparator

Chemoradiation treatment without the induction chemotherapy sequence.

Primary question

Does induction chemotherapy plus chemoradiation improve overall survival compared with the comparator approach?

3. Trial Design

01
Randomization
500 participants
02
Treatment
Two parallel arms
03
Follow-up
Outcome assessment
04
Analysis
Overall survival
Allocation

Randomized allocation.

Masking

No masking.

Arms

Two treatment groups.

Interventions

Paclitaxel, carboplatin, radiotherapy, and cisplatin.

4. Primary Endpoint

EndpointDefinition / Time Frame
Overall Survival5 years

5. Planned Analysis

The registry identifies overall survival as the primary endpoint with a time frame of 5 years. Overall survival is typically analyzed as a time-to-event endpoint, where the event is death from any cause and patients without an observed event at the analysis cutoff contribute follow-up information through censoring.

Time-to-event analysis

Common statistical approaches for overall survival include Kaplan-Meier estimation to describe survival over time, log-rank testing to compare randomized groups, and Cox proportional-hazards modeling to estimate relative treatment effects.

Registry status: The ClinicalTrials.gov record does not report posted statistical analyses or outcome estimates for the primary endpoint.

6. Statistical Methodology

Overall survival as a time-to-event endpoint

Overall survival differs from a simple proportion because each participant contributes information over a period of observation. Some participants may still be alive when follow-up ends, creating censored observations.

Conceptual survival function

S(t) = Probability of surviving beyond time t

The Kaplan-Meier estimator uses observed event times to estimate survival while retaining information from censored participants.

Hazard ratios

If a Cox proportional-hazards model is used, the hazard ratio summarizes the relative event rate between randomized groups over the analyzed follow-up. A hazard ratio below 1 indicates a lower estimated hazard in the treatment group; it does not directly represent the percentage of patients who survive or the absolute number of deaths prevented.

Randomization

Randomization creates the framework for comparing treatment groups while reducing systematic differences in measured and unmeasured baseline characteristics.

7. Statistical Methods Explained

Why is overall survival analyzed differently from a simple response rate?

Overall survival includes both the timing of events and censoring. A response rate measures whether an event occurred by a defined assessment, while survival analysis evaluates the distribution of time until the event occurs.

What does Kaplan-Meier estimation contribute?

Kaplan-Meier methods allow researchers to estimate survival probabilities over time when not every participant has experienced the event by the analysis cutoff.

Why is randomization important?

Randomization allows the treatment groups to be compared under a design intended to balance prognostic factors between groups.

What does a confidence interval represent?

A confidence interval describes uncertainty around an estimated treatment effect. It reflects statistical precision rather than the range of outcomes experienced by individual patients.

Why does follow-up duration matter?

Time-to-event outcomes depend on how long participants are observed. Longer follow-up can provide additional information about events occurring later after treatment.

8. Limitations

9. Why This Trial Matters Statistically

ConceptHow it appears in INTERLACE
RandomizationRandomized parallel-group phase 3 design
Time-to-event analysisOverall survival measured over 5 years
CensoringParticipants without observed events at analysis contribute follow-up information
Hazard modelingPotential framework for comparing survival distributions
Clinical trial designTwo-arm treatment comparison

10. Sources