Pechlivanoglou, P. | Search bioRxiv

bioRxiv2021

Obsessive Compulsive Disorder and Response Inhibition: Meta-analysis of the Stop-Signal Task

This systematic review and meta-analysis updates evidence pertaining to deficient response inhibition in obsessive-compulsive disorder (OCD) as measured by the stop-signal task (SST). We conducted a meta-analysis of the literature to compare response inhibition in patients with OCD and healthy controls, meta-regressions to determine relative influences of age and sex on response inhibition impairment, and a risk of bias assessment for included studies using the Newcastle-Ottawa Scale (NOS). Stop-signal reaction time (SSRT), which estimates the latency of the stopping process deficit, was significantly longer in OCD samples than in controls, reflecting inferior inhibitory control (Raw mean difference = 23.43ms; p = <0.001; 95% CI = [17.42, 29.45]). We did not observe differences in mean reaction time (MRT) in OCD compared to controls (Raw mean difference = 2.51ms; p = 0.755; 95% CI = [-13.27, 18.30]). Age impacted effect size of SSRT, indicating a greater deficit in older patients than younger ones. We did not observe a significant effect of sex on SSRT or MRT scores. General Scientific SummaryDifficulty inhibiting responses is an hypothesized deficit in Obsessive-Compulsive Disorder (OCD). The results of this systematic review and meta-analysis of studies using the Stop Signal Task support the notion of deficient response inhibition in OCD and indicate that older individuals with OCD show greater impairments than younger ones.

pathology↗

bioRxiv2019

A Multidimensional Array Representation of State-Transition Model Dynamics

Cost-effectiveness analyses often rely on cohort state-transition models (cSTMs). The cohort trace is the primary outcome of cSTMs, which captures the proportion of the cohort in each health state over time (state occupancy). However, the cohort trace is an aggregated measure that does not capture the information about the specific transitions among the health states (transition dynamics). In practice, these transition dynamics are crucial in many applications, such as incorporating transition rewards or computing various epidemiological outcomes that could be used for model calibration and validation (e.g. disease incidence and lifetime risk). In this manuscript we propose modifying the transitional cSTMs cohort trace computation to compute and store cSTMs dynamics that capture both state occupancy and transition dynamics. This approach produces a multidimensional matrix from which both the state occupancy and the transition dynamics can be recovered. We highlight the advantages and potential applications of this approach with an example coded in R to facilitate the implementation of our method.

epidemiology↗