The scientific integrity of the study described in the JAMA article is highly suspect at best. As it stands, the article is based on a study that uses erroneously coded data and underestimates both the prevalence of serial rapists in the data and the percentage of rapes those serial rapists report committing. Further, the models used in this study are based on untenable assumptions and ill-considered constraints. No reasonable and scientifically grounded debate over the "serial (campus) rapist assumption" can depend on this study.
Being assigned to a "latent class" of "increasing," "decreasing" or "low/time-limited" rape -- based on potentially flawed data and flawed statistical modeling -- is NOT the same as ACTUALLY BEING a man who had a pattern of increasing, decreasing or low/time-limited rape over time. (That includes when "rape" is reduced to a yes-or-no variable that obscures serial rape.)
If Swartout et al. would provide the "derivation dataset" with the R (rape) variables AND the ID numbers matching those in the public dataset, everyone could see just how many rapes and attempted rapes were committed, and when, by each of the men that their complex (and invalid) analysis assigned to the categories of "increasing," "decreasing" and "low/time-limited" rape. No knowledge of complex statistics is necessary; Swartout and colleagues need only provide the ID numbers for the subjects in the derivation dataset they used for their analyses and a variable indicating the "latent class" to which each subject was assigned by their analysis.
Similarly, with sufficient data from co-author Martie Thompson's "validation dataset," any researcher with rudimentary knowledge of statistics can conduct the same analyses described above and see just how many rapes and attempted rapes were committed, and when, by each of the men assigned to the "increasing," "decreasing" and "low/time-limited" rape categories. Critically, this would NOT involve disclosure of information in any way that is inconsistent with ethical management of such data, just as there are no such problems with the long-available public version of the "derivation dataset" of which co-author Jacquelyn White is the principle investigator.
"All of this is a troubling development for science, a field that wants the public to believe that transparency is one of its guiding principles. We'd like to believe that, too, but when researchers refuse to share data, and how they came up with it, they lose the right to call what they do science. The ability of other researchers -- including competitors -- to try to poke holes in an analysis is a bedrock of the scientific method" (bold added; http://www.statnews.com/2015/12/23/sharing-data-science/).
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