1. Challenges in Evaluating Screening & Prevention Interventions
Jack Cuzick
Cancer Research UK

2. Screening & Prevention Trials
Large
Multicentre, International
Long
Compliance, contamination
Expensive
Consent
Trial Mechanics

3. Classical Approach Randomised Population Based Trial
Intent-to-Treat Analysis of Mortality
Strengths
Unbiased
Directly applicable to population
Weaknesses
Expensive follow-up
Requires high compliance, low contamination

4. Simplifications Make Trials More ‘Routine’ Surrogate Endpoints
Target High-Risk Individuals

5. Informed Consent Can require more time than intervention
Should be relaxed when comparing new intervention at least as effective as conventional
Need for ‘Community Consent’
Data Protection Act
Research vs Implementation Studies

6. Cluster Randomisation
Can minimize consent issues
Requires well-defined population
Good compliance essential
Preselect based on likely compliance if possible
Aim for >60 ‘matched’ cluster
Minimise between cluster variation

7. Reciprocal Trials Avoids issues of untreated controls
Two outcomes for same exposure
Ex-smokers CT screening for lung cancers vs Aggressive cardiovascular interventions (BP and cholesterol)
Two unlinked types of screening Colorectal vs (ovary/prostate)

8. Compliance (& Contamination)
Acceptance of other screening on offer
Pre-questionnaires
Run-in procedures
Contamination
Availability of intervention
Positive correlation with compliance

9. Selection of Compliant Population Before Randomisation
Strengths
Increase of power
Efficiency of trial resources
Weaknesses
Generalizability
Greater risk of contamination

10. Analysis allowing for non-compliance
Strengths
Estimate of screening effect in compliers
Extrapolation to different levels of compliance
Appropriate confidence intervals (larger)
Weakness
Auxillary to ITT population analysis

11. Randomised Option Contaminators
Treatment
Control
Insist
(Group A)
Contaminators
Accept
only if
offered
(Group B)
True
Comparison
Group
Patient’s behaviour regarding treatment
Refuse
(Group C)
Non - compliers

12. A Hypothetical Example–
Binary Outcome

13. Potentially Dangerous Modifications
Non-randomised comparisons
Historical Controls
Compliers vs Non-compliers
Case-Control Studies
Survival or stage changes in screen detected
cancers
Lead time bias
Length bias
Subgroup Analysis

14. Phased Introduction Group 1 Group 2 Group 3 Group 4 Group 5 x x x x x
Ignore
subsequent cancers
Group 1
Group 2
Group 3
Group 4
Group 5 x x x x x x x x
Phasing period > lead time
Prevaluated screen problematic if incidence age dependent
must include prevaluated screen
Ideally exit screen for all (e.g. groups 1, 3, 5)

15. Evaluating Service Screening Case-Control Audit
Focus on screening failures Cervix – stage Ib+ Breast – deaths (or stage 2) Colon – Duke’s B or greater
Compare screening histories of failures (cases) to programme in general (controls)
Require well-defined target population
Evaluate
Coverage
Screening interval
Follow-up
Misreading
True ‘false negatives’
Problems with screen-detected cancers

16. Evaluating Service Screening Modelling – Process Parameters
Need surrogate for mortality reduction
Previous trials for validation
Early (easy) vs late (hard) markers
High detection rate of early lesions
Reduced detection of advanced lesions on subsequent screens
Reduced interval cancer rate
Reduced overall rate of advanced lesions

17. Intermediate Endpoints/Biomarkers
Strengths
More power
Earlier results
Modelling of different strategies
Weaknesses
Need for validation
Treatments specifically armed at biomarker

18. Surrogates for Breast Cancer Risk
Mammographic Density
Oestradiol
Insulin-like growth factor II (?)
Cytology
Weight (loss)

19. Risk-Benefit Ratio Critical in Screening or Prevention –
well population
Side-effects early and patient-specific
Benefits late and non-individual specific
Costs
Individual - Travel, time off work, anxiety (reassurance)
Health System - Screening Test
Further Evaluation
Treatment - (Potential Cost & Reduction)
Programme Management & Evaluation