RITS: a toolbox for assessing complex interventions via interrupted time series models.

BACKGROUND: Various interacting and interdependent components comprise complex interventions. These components create difficulty in assessing the true impact of interventions designed to improve patient-centered outcomes. Interrupted time series (ITS) designs borrow from case-crossover designs and serve as quasi-experimental methodology able to retrospectively assess the impact of an intervention while accounting for temporal correlation. While ITS designs are aptly situated for studying the impacts of large-scale public health policies, existing ITS software implement rigid ITS methodology that often assume the pre- and post-intervention phases are fully differentiated (by a known change-point or set of time points) and do not allow for changes in both the mean functions and correlation structure. RESULTS: This article describes the Robust Interrupted Time Series (RITS) toolbox, a stand-alone user-friendly application researchers can use to implement flexible ITS models that estimate the lagged effect of an intervention on an outcome, level and trend changes, and post-intervention changes in the correlation structure, for single and multiple ITS. The RITS toolbox incorporates a formal test for the existence of a change in the outcome and estimates a change-point over a set of possible change-points defined by the researcher. In settings with multiple ITS, RITS provides a global over-all units change-point and allows for unit-specific changes in the mean functions and correlation structures. CONCLUSIONS: The RITS toolbox is the first piece of software that allows researchers to use flexible ITS models that test for the existence of a change-point, estimate the change-point (if estimation is desired), and allow for changes in both the mean functions and correlation structures at the change point. RITS does not require any knowledge of a statistical (or otherwise) programming language, is freely available to the community, and may be downloaded and used on a local machine to ensure data protection.

Electrode Surface Area Impacts Measurement of High Frequency Oscillations in Human Intracranial EEG.

OBJECTIVE: High-frequency oscillations (HFOs) are a promising prognostic biomarker of surgical outcome in patients with epilepsy. Their rates of occurrence and morphology have been studied extensively using recordings from electrodes of various geometries. While electrode size is a potential confounding factor in HFO studies, it has largely been disregarded due to a lack of consistent evidence. Therefore, we designed an experiment to directly test the impact of electrode size on HFO measurement. METHODS: We first simulated HFO measurement using a lumped model of the electrode-tissue interaction. Then eight human subjects were each implanted with a high-density 8x8 grid of subdural electrodes. After implantation, the electrode sizes were altered using a technique recently developed by our group, enabling intracranial EEG recordings for three different electrode surface areas from a static brain location. HFOs were automatically detected in the data and their characteristics were calculated. RESULTS: The human subject measurements were consistent with the model. Specifically, HFO rate measured per area of tissue decreased significantly as electrode surface area increased. The smallest electrodes recorded more fast ripples than ripples. Amplitude of detected HFOs also decreased as electrode surface area increased, while duration and peak frequency were unaffected. CONCLUSION: These results suggest that HFO rates measured using electrodes of different surface areas cannot be compared directly. SIGNIFICANCE: This has significant implications for HFOs as a tool for surgical planning, particularly for individual patients implanted with electrodes of multiple sizes and comparisons of HFO rate made across patients and studies.

Inference on Long-Range Temporal Correlations in Human EEG Data.

Detrended Fluctuation Analysis (DFA) is a statistical estimation algorithm used to assess long-range temporal dependence in neural time series. The algorithm produces a single number, the DFA exponent, that reflects the strength of long-range temporal correlations in the data. No methods have been developed to generate confidence intervals for the DFA exponent for a single time series segment. Thus, we present a statistical measure of uncertainty for the DFA exponent in electroencephalographic (EEG) data via application of a moving-block bootstrap (MBB). We tested the effect of three data characteristics on the DFA exponent: (1) time series length, (2) the presence of artifacts, and (3) the presence of discontinuities. We found that signal lengths of ∼5 minutes produced stable measurements of the DFA exponent and that the presence of artifacts positively biased DFA exponent distributions. In comparison, the impact of discontinuities was small, even those associated with artifact removal. We show that it is possible to combine a moving block bootstrap with DFA to obtain an accurate estimate of the DFA exponent as well as its associated confidence intervals in both simulated data and human EEG data. We applied the proposed method to human EEG data to (1) calculate a time-varying estimate of long-range temporal dependence during a sleep-wake cycle of a healthy infant and (2) compare pre- and post-treatment EEG data within individual subjects with pediatric epilepsy. Our proposed method enables dynamic tracking of the DFA exponent across the entire recording period and permits within-subject comparisons, expanding the utility of the DFA algorithm by providing a measure of certainty and formal tests of statistical significance for the estimation of long-range temporal dependence in neural data.

Assessing the age- and gender-dependence of the severity and case fatality rates of COVID-19 disease in Spain.

Background: The assessment of the severity and case fatality rates of coronavirus disease 2019 (COVID-19) and the determinants of its variation is essential for planning health resources and responding to the pandemic. The interpretation of case fatality rates (CFRs) remains a challenge due to different biases associated with surveillance and reporting. For example, rates may be affected by preferential ascertainment of severe cases and time delay from disease onset to death. Using data from Spain, we demonstrate how some of these biases may be corrected when estimating severity and case fatality rates by age group and gender, and identify issues that may affect the correct interpretation of the results. Methods: Crude CFRs are estimated by dividing the total number of deaths by the total number of confirmed cases. CFRs adjusted for preferential ascertainment of severe cases are obtained by assuming a uniform attack rate in all population groups, and using demography-adjusted under-ascertainment rates. CFRs adjusted for the delay between disease onset and death are estimated by using as denominator the number of cases that could have a clinical outcome by the time rates are calculated. A sensitivity analysis is carried out to compare CFRs obtained using different levels of ascertainment and different distributions for the time from disease onset to death. Results: COVID-19 outcomes are highly influenced by age and gender. Different assumptions yield different CFR values but in all scenarios CFRs are higher in old ages and males. Conclusions: The procedures used to obtain the CFR estimates require strong assumptions and although the interpretation of their magnitude should be treated with caution, the differences observed by age and gender are fundamental underpinnings to inform decision-making.

Time-frequency spectral estimation of multichannel EEG using the Auto-SLEX method.

In this paper, we apply a new time-frequency spectral estimation method for multichannel data to epileptiform electroencephalography (EEG). The method is based on the smooth localized complex exponentials (SLEX) functions which are time-frequency localized versions of the Fourier functions and, hence, are ideal for analyzing nonstationary signals whose spectral properties evolve over time. The SLEX functions are simultaneously orthogonal and localized in time and frequency because they are obtained by applying a projection operator rather than a window or taper. In this paper, we present the Auto-SLEX method which is a statistical method that 1) computes the periodogram using the SLEX transform, 2) automatically segments the signal into approximately stationary segments using an objective criterion that is based on log energy, and 3) automatically selects the optimal bandwidth of the spectral smoothing window. The method is applied to the intracranial EEG from a patient with temporal lobe epilepsy. This analysis reveals a reduction in average duration of stationarity in preseizure epochs of data compared to baseline. These changes begin up to hours prior to electrical seizure onset in this patient.

Continuum of mammography use among US women: classification tree analysis.

OBJECTIVES: To identify women with low mammography utilization. METHODS: We used Classification Tree Analysis among women aged 42-80 from the 2008 Behavioral Risk Factor Surveillance System (N = 169,427) to identify sub-groups along a continuum of screening. RESULTS: Women with neither a primary care provider nor health insurance had the lowest utilization (33.9%) and were 2.8% of the sample. Non-smoking women aged 55-80, with a primary care provider, health insurance, and income of $75,000 or more had the highest utilization (90.7%) and comprised 5% of the sample. CONCLUSION: As access to primary care providers and health insurance increases with the Affordable Care act, classification tree analyses may help to identify women of high priority for intervention.

Classification of functional brain images with a spatio-temporal dissimilarity map.

Classification of subjects into predefined groups, such as patient vs. control, based on their functional MRI data is a potentially useful procedure for clinical diagnostic purposes. This paper presents an automated method for classifying subjects into groups based on their functional MRI data. The proposed methodology provides general framework using preprocessed time series for the whole brain volume. Using a training set of two groups of subjects, the new methodology identifies spatio-temporal features that distinguish the groups and uses these features to categorize new subjects. We demonstrate the method using simulations and a clinical application that classifies individuals into schizotypy and control groups.