r in which the eyes can follow a target and how well one can pay attention to a task together help to pinpoint risk factors related to schizophrenia.
“I was able to integrate several threads of work that I had been developing over the prior 15 or 20 years,” he said. “I proposed the study in a way that tied together several methodological and theoretical threads in the way I would want to do it ideally, and they supported it. It was a high-risk funding decision, and I am grateful to NARSAD for getting behind the study.”
Lenzenweger collaborated with Geoff McLachlan at the University of Queensland in Australia and Donald B. Rubin of Harvard University, both leaders in the application of new statistical methods to health-related problems. They applied a technique called finite mixture modeling to separate the research subjects into the two groups.
For years, debate in the field has centered on whether risk for schizophrenia was graded or more binary in nature. Lenzenweger and his colleagues showed that people could be divided into two groups, those at risk and those not at risk.
In the new study, the smaller of the two groups contained individuals who displayed dilute forms of schizophrenia-like symptoms even though they had never had the illness. Study of these people revealed actual schizophrenia in their biological family members, but not other psychiatric illnesses.
“What was very exciting for us is that this method allows you to assign a probability to every person in the sample with respect to likelihood of risk for schizophrenia liability,” Lenzenweger said. “By doing this, one can generate very precise estimates of where individuals fall on the risk dimension.”
A second, mathematically independent model called taxometric analysis generated the same clean partitioning of the two groups, Lenzenweger noted.
Although the risk predictions can be made now, they are not yet ready for cPage: 1 2 3 Related medicine news :1
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