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Updated: Nov 9, 2025

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Optimising assessment of dark adaptation data using time to event analysis.

Bethany E Higgins1, Giovanni Montesano1,2, Alison M Binns1

  • 1Optometry and Visual Sciences, School of Health Sciences, City, University of London Northampton Square, London, EC1V 0HB, UK.

Scientific Reports
|April 16, 2021
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Summary

Time-to-event analysis improves dark adaptation measurements in age-related macular degeneration (AMD) research. This method efficiently analyzes rod-intercept time data, reducing required sample sizes for studies.

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Area of Science:

  • Ophthalmology
  • Biostatistics
  • Medical Imaging

Background:

  • Dark adaptation is a key functional measurement in age-related macular degeneration (AMD) research.
  • Severe AMD cases may yield dark adaptation data exceeding the standard 20-30 minute test time.
  • Existing data handling methods include censored data points or estimated recovery times, which may not be statistically robust.

Purpose of the Study:

  • To introduce and validate time-to-event analysis for rod-intercept time data in AMD research.
  • To demonstrate the statistical power and efficiency of time-to-event analysis compared to standard methods.
  • To reduce sample size requirements for studies utilizing dark adaptation as an outcome measure.

Main Methods:

  • Application of time-to-event analysis to rod-intercept time data.
  • Comparison of sample size requirements between time-to-event analysis and standard t-tests.
  • Evaluation of the method's ability to handle skewed and censored data.

Main Results:

  • Time-to-event analysis significantly reduces sample size needs: estimated sizes of 12 and 38 for uncapped and capped data, respectively, versus 20 and 61 for t-tests at 80% power (α=0.05).
  • The proposed method effectively accommodates both skewed and censored dark adaptation data.
  • Demonstrated increased statistical power for analyzing dark adaptation measurements.

Conclusions:

  • Time-to-event analysis is a more powerful and efficient statistical method for analyzing dark adaptation data in AMD research.
  • This approach accommodates data complexities like censoring and skewness, common in severe AMD cases.
  • Reduced sample size requirements facilitate more efficient clinical trial design and faster evaluation of new treatments for AMD.