Abstracts of the 4th Biennial Schizophrenia International Research Conference / Schizophrenia Research 153, Supplement 1 (2014) S1–S384 S87

SLEEP EEG STUDIES IN SCHIZOPHRENIA: METHODOLOGICAL

CONSIDERATIONS

Matcheri Keshavan

Beth Israel Deaconess Medical Center and Massachusetts Mental Health

Center, Harvard University, Boston, MA, Boston, USA

Alterations in electroencephalographic (EEG) sleep architecture are com-

mon in psychotic disorders, but the literature is limited to inconsistent

replications and lack of diagnostic specificity, issues common to other

electrophysiological assessments such as ERP and eye tracking studies. This

presentation will review potential methodological reasons for the lack of

consistency in the literature. A review of the literature on EEG biomarkers

in psychotic disorders was done with the following questions in mind: how

robustly do these measures differ between patients and controls, and how

diagnostically specific are they? How are these tests traditionally admin-

istered and standardized, and what are their advantages and limitations?

What are the issues that need to be addressed? What are the best ways to

maximize reliability and validity, while keeping costs and subject burden

to a minimum? We focused on sleep biomarkers, but also considered other

EEG biomarkers (evoked response potentials, resting EEG) in the review.

While there are robust differences between patients and controls on several

sleep EEG measures such as slow wave sleep and REM proportions and

densities, few show effects that discriminate between DSM diagnostic cat-

egories. At least in part these limitations are related to variations between

studies, patient populations, small sample sizes and small effect sizes, labor

intensive procedures and patient burden, factors affecting physiological

states such as smoking and activity levels, differences in instrumentation,

data processing approaches and scoring procedures. Establishing group

differences will require reduction of both instrumentation and biological

noise. Automated detection algorithms (e.g. delta counts, spindle density,

etc.) and artifact rejection procedures are likely to increase sensitivity and

specificity. Ambulatory studies might help cost and subject burden. Making

centralized, blinded processing of data, clear quality control procedures and

standardized calibration of equipment are critical.

CLINICIAN-BASED ASSESSMENT OF PSYCHIATRIC DIAGNOSIS AND

SYMPTOM SEVERITY

Janet B.W. Williams

MedAvante, Hamilton, USA

FDA puts the estimate of failed trials in schizophrenia at 25%, with placebo

response growing steadily. In psychiatric clinical trials, most procedures

are conducted by site-based clinicians whose diagnoses and assessments

may be influenced by various forms of bias, and whose individual findings

may contribute to variability of results when combined with those of

other raters and sites. Bias and variability are two key contributors to trial

failure and a number of strategies have attempted to address them. On a

systems level, dramatically increasing the sample size of a study has been

attempted as a way to improve signal detection, but larger samples require

more raters, sites and countries which adds to the problem of variability

and signal noise and may decrease effect size. Others have tried to be more

selective in choosing sites; however, personnel turnover can affect success

rates if a site does not have consistent training, and good performance

in one trial may have a very low correlation with good performance in

the next study at that same site. Finally, despite efforts to conduct trials

in certain countries that demonstrated good signal detection in previous

studies, placebo response seems to be increasing nonetheless. At the rater

level, strategies to avoid bias and reduce variability have included limit-

ing participation to very experienced raters, and increasing rater training.

Unfortunately, clinical experience alone does not result in good interrater

reliability within a cohort of raters; experienced raters must be carefully

calibrated with each other to achieve this. In addition, studies have shown

that even intensive group rater training at the beginning of a study does not

improve interrater reliability significantly, even in the short term. Thus, the

variability that results from combining ratings from many different sites

remains. Recent methodological approaches to improving trial failure rates

include supplementing site ratings with patients’ self-reports of symptoms,

dual assessments by site-based and independent raters, and providing

feedback to site raters based on reviews of their recorded interviews.

Each of these methods has advantages and disadvantages, which will be

reviewed. Another particularly powerful method replaces a large number

of site-based raters with a much smaller cohort of remote centralized

raters who are geographically and financially independent from the sites.

These experienced raters can be trained and calibrated to a single standard,

continuously monitored to avoid rater drift, and blinded to protocol de-

tails (e.g., inclusion criteria) and visit number (e.g., baseline vs. endpoint)

to avoid expectation bias. This method has met considerable resistance

from site-based clinicians who believe they can yield the most informed

ratings based on strong relationships with study subjects. However, a

growing body of evidence indicates that centralization to ensure blinding

and independence of raters improves signal detection or lowers placebo

response.