Lost (to follow-up)

Introduction

  • Topic: Loss in research, specifically loss to follow-up in randomized control trials (RCTs).

  • Importance: Understanding the impact of patients being lost to follow-up on study results.

Example Study

  • Fictitious study with 4 participants:

    • Control group: 2 participants (1 good outcome, 1 bad outcome)

    • Experimental group: 2 participants (1 good outcome, 1 lost to follow-up)

  • Control group good outcome probability: 50% (1 out of 2)

  • Experimental group good outcome probability: 100% (1 good outcome out of 1 known)

  • Issue: Participant's outcome who was lost to follow-up is unknown, leading to deceptive results.

Possible Scenarios for Missing Participant

  1. Good Outcome: If the missing participant had a good outcome, experimental group would show 100% success, better than control.

  2. Bad Outcome: If the missing participant had a bad outcome, experimental group would show 50%, equal to control.

  3. Negative Adverse Effect: If missing participant experienced adverse effects, control may be the better choice.

  • Conclusion: Missing participants complicate results and conclusions of studies significantly.

Identifying Loss to Follow-Up

  • Key tool: CONSORT Diagram

    • Displays:

      • Number approached for study

      • Number included and excluded

      • Randomization to study arms

      • Follow-up details for participants

  • Importance of scrutinizing results based on missing participants' possible outcomes.

Acceptable Loss to Follow-Up Levels

  • General Rule of Thumb:

    • <5% loss may be acceptable (good)

    • 20% loss is concerning (bad)

  • Concern: Even small samples going missing could skew results dramatically if those participants experienced negative outcomes.

  • Conclusion: No specific acceptable loss rate; each study must be assessed on its own merits.

Attrition Bias

  • Definition: Non-random loss of participants leads to attrition bias, affecting validity of study.

  • Implications: Results may misrepresent true effectiveness of interventions.

  • Advice: Check for bias indicators when evaluating studies.

Indicators of Concern

  1. Loss to follow-up rate exceeds effect size.

  2. Disparity in loss to follow-up between study arms.

Conclusion

  • Responsibility lies with readers to critically assess loss to follow-up in research.

  • Consider implications of missing participants’ outcomes (good, neutral, bad).

  • Attrition bias is detrimental; vigilant evaluation is necessary.

  • Final Thought: Always evaluate research and draw personal conclusions.