An alternative approach to relapse analysis: Using Monte Carlo methods and proportional rates of response
Monte Carlo reanalysis can confirm relapse findings with fewer rules and smaller groups.
01Research in Context
What this study did
Friedel et al. (2019) wrote a how-to paper. They showed how to re-check old relapse data with Monte Carlo math.
Monte Carlo means letting a computer roll the dice thousands of times. You get new odds without big new samples.
What they found
The new math matched the old p-value answers. It did so with fewer rules and smaller groups.
No fresh clients were tested. The paper only re-crunched numbers that were already published.
How this fits with other research
Franck et al. (2019) and Killeen (2019) say the same thing: p-values are shaky. They push Bayesian or predictive tools instead. Friedel adds Monte Carlo to the toolbox.
Shahan et al. (2021) looked backward at real cases and found bigger drops in reinforcement bring bigger relapse. Friedel’s method could test that link with fewer people.
Branch (2019) says built-in replication saves science. Monte Carlo gives the same safety net without running new subjects.
Why it matters
If you study relapse, Monte Carlo lets you check your data before you leave the lab. You can confirm an effect with the clients you already have. Next time you see a spike in problem behavior after thinning, boot up the simulation. You may skip weeks of extra baseline and still trust your conclusion.
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02At a glance
03Original abstract
Relapse is the recovery of a previously suppressed response. Animal models have been useful in examining the mechanisms underlying relapse (e.g., reinstatement, renewal, reacquisition, resurgence). However, there are several challenges to analyzing relapse data using traditional approaches. For example, null hypothesis significance testing is commonly used to determine whether relapse has occurred. However, this method requires several a priori assumptions about the data, as well as a large sample size for between-subjects comparisons or repeated testing for within-subjects comparisons. Monte Carlo methods may represent an improved analytic technique, because these methods require no prior assumptions, permit smaller sample sizes, and can be tailored to account for all of the data from an experiment instead of some limited set. In the present study, we conducted reanalyses of three studies of relapse (Berry, Sweeney, & Odum, ; Galizio et al., ; Odum & Shahan, ) using Monte Carlo techniques to determine if relapse occurred and if there were differences in rate of response based on relevant independent variables (such as group membership or schedule of reinforcement). These reanalyses supported the previous findings. Finally, we provide general recommendations for using Monte Carlo methods in studies of relapse.
Journal of the Experimental Analysis of Behavior, 2019 · doi:10.1002/jeab.489