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Challenges in Evaluating Screening & Prevention Interventions

Challenges in Evaluating Screening & Prevention Interventions. Jack Cuzick Cancer Research UK. Screening & Prevention Trials. Large Multicentre, International Long Compliance, contamination Expensive Consent Trial Mechanics. Classical Approach.

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Challenges in Evaluating Screening & Prevention Interventions

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  1. Challenges in EvaluatingScreening & 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) Compliance • Acceptance of other screening on offer • Pre-questionnaires • Run-in procedures Contamination • Availability of intervention • Positive correlation with compliance

  9. Selection of Compliant PopulationBefore 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 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. Ignore subsequent cancers x Group 1 Group 2 Group 3 Group 4 Group 5 x x x Phased Introduction 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 ScreeningCase-Control Audit • Focus on screening failuresCervix – 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

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