Adaptive staircase research papers

k-up 1-dn staircase

• Adaptive procedures in psychophysical research (Leek, 2001)

• Determining thresholds using adaptive procedures and psychometric fits: evaluating efficiency using theory, simulations, and human experiments (Karmali et al., 2016)

  • To find efficient adaptive algorithms for psychometric threshold (“sigma”) estimation, we combined analytic approaches, Monte Carlo simulations and human experiments for a one-interval, binary forced-choice, direction-recognition task. To our knowledge, this is the first time analytic results have been combined and compared with either simulation or human results. Human performance was consistent with theory and not significantly different from simulation predictions.

• Psychophysics with children: Investigating the effects of attentional lapses on threshold estimates (Manning et al., 2018)

  • When assessing the perceptual abilities of children, researchers tend to use psychophysical techniques designed for use with adults. However, children’s poorer attentiveness might bias the threshold estimates obtained by these methods.

    We estimated inattentiveness using responses to “easy” catch trials. As expected, children had higher threshold estimates and made more errors on catch trials than adults. Lower threshold estimates were obtained from psychometric functions fit to the data in the QUEST condition than the MCS and Levitt staircases, and the threshold estimates obtained when fitting a psychometric function to the QUEST data were also lower than when using the QUEST mode. This suggests that threshold estimates cannot be compared directly across methods.

    Simulations indicated that inattentiveness biased threshold estimates particularly when threshold estimates were computed as the QUEST mode or the average of staircase reversals. In contrast, thresholds estimated by post-hoc psychometric function fitting were less biased by attentional lapses.

• The k-in-a-row up-and-down design, revisited (Oron & Hoff, 2009)

  • The percentile-finding experimental design known variously as ‘forced-choice fixed-staircase’, ‘geometric up-and-down’ or ‘k-in-a-row’ (KR) was introduced by Wetherill four decades ago.

    However, its statistical properties have not been fully documented, and the existence of a unique mode in its asymptotic treatment distribution has been recently disputed.Here we revisit the KR design and its basic properties. We find that KR does generate a unique stationary mode near its target percentile.

    A recent experimental example from anesthesiology serves to highlight some of the ‘up-and-down’ design family’s properties and advantages.

i3+3 design

• The i3+3 Design for Phase I Clinical Trials (Liu et al., 2019)

  • One major criticism of the 3+3 design is that it is based on simple rules, does not depend on statistical models for inference, and leads to unsafe and unreliable operating characteristics. On the other hand, being rule-based allows 3+3 to be easily understood and implemented in practice, making it the first choice among clinicians. Is it possible to have a rule-based design with great performance? Methods: We propose a new rule-based design called i3+3, where the letter “i” represents the word “interval”. The i3+3 design is based on simple but more advanced rules that account for the variabilities in the observed data.

    The i3+3 design is far superior than the 3+3 design in trial safety and the ability to identify the true MTD. Compared with model-based phase I designs, i3+3 also demonstrates comparable performances. In other words, the i3+3 design possesses both the simplicity and transparency of the rule-based approaches, and the superior operating characteristics seen in model-based approaches.