Network enrichment significance testing in brain-phenotype association studies.

Functional networks often guide our interpretation of spatial maps of brain-phenotype associations. However, methods for assessing enrichment of associations within networks of interest have varied in terms of both scientific rigor and underlying assumptions. While some approaches have relied on subjective interpretations, others have made unrealistic assumptions about spatial properties of imaging data, leading to inflated false positive rates. We seek to address this gap in existing methodology by borrowing insight from a method widely used in genetics research for testing enrichment of associations between a set of genes and a phenotype of interest. We propose network enrichment significance testing (NEST), a flexible framework for testing the specificity of brain-phenotype associations to functional networks or other sub-regions of the brain. We apply NEST to study enrichment of associations with structural and functional brain imaging data from a large-scale neurodevelopmental cohort study.

Penalized decomposition using residuals (PeDecURe) for feature extraction in the presence of nuisance variables.

Neuroimaging data are an increasingly important part of etiological studies of neurological and psychiatric disorders. However, mitigating the influence of nuisance variables, including confounders, remains a challenge in image analysis. In studies of Alzheimer's disease, for example, an imbalance in disease rates by age and sex may make it difficult to distinguish between structural patterns in the brain (as measured by neuroimaging scans) attributable to disease progression and those characteristic of typical human aging or sex differences. Concerningly, when not properly accounted for, nuisance variables pose threats to the generalizability and interpretability of findings from these studies. Motivated by this critical issue, in this work, we examine the impact of nuisance variables on feature extraction methods and propose Penalized Decomposition Using Residuals (PeDecURe), a new method for obtaining nuisance variable-adjusted features. PeDecURe estimates primary directions of variation which maximize covariance between partially residualized imaging features and a variable of interest (e.g., Alzheimer's diagnosis) while simultaneously mitigating the influence of nuisance variation through a penalty on the covariance between partially residualized imaging features and those variables. Using features derived using PeDecURe's first direction of variation, we train a highly accurate and generalizable predictive model, as evidenced by its robustness in testing samples with different underlying nuisance variable distributions. We compare PeDecURe to commonly used decomposition methods (principal component analysis (PCA) and partial least squares) as well as a confounder-adjusted variation of PCA. We find that features derived from PeDecURe offer greater accuracy and generalizability and lower correlations with nuisance variables compared with the other methods. While PeDecURe is primarily motivated by challenges that arise in the analysis of neuroimaging data, it is broadly applicable to data sets with highly correlated features, where novel methods to handle nuisance variables are warranted.

Performance of Multiple Imputation Using Modern Machine Learning Methods in Electronic Health Records Data.

BACKGROUND: Missing data are common in studies using electronic health records (EHRs)-derived data. Missingness in EHR data is related to healthcare utilization patterns, resulting in complex and potentially missing not at random missingness mechanisms. Prior research has suggested that machine learning-based multiple imputation methods may outperform traditional methods and may perform well even in settings of missing not at random missingness. METHODS: We used plasmode simulations based on a nationwide EHR-derived de-identified database for patients with metastatic urothelial carcinoma to compare the performance of multiple imputation using chained equations, random forests, and denoising autoencoders in terms of bias and precision of hazard ratio estimates under varying proportions of observations with missing values and missingness mechanisms (missing completely at random, missing at random, and missing not at random). RESULTS: Multiple imputation by chained equations and random forest methods had low bias and similar standard errors for parameter estimates under missingness completely at random. Under missingness at random, denoising autoencoders had higher bias than multiple imputation by chained equations and random forests. Contrary to results of prior studies of denoising autoencoders, all methods exhibited substantial bias under missingness not at random, with bias increasing in direct proportion to the amount of missing data. CONCLUSIONS: We found no advantage of denoising autoencoders for multiple imputation in the setting of an epidemiologic study conducted using EHR data. Results suggested that denoising autoencoders may overfit the data leading to poor confounder control. Use of more flexible imputation approaches does not mitigate bias induced by missingness not at random and can produce estimates with spurious precision.

Estimation and evaluation of individualized treatment rules following multiple imputation.

An individualized treatment rule (ITR) is a function that inputs patient-level information and outputs a recommended treatment. An important focus of precision medicine is to develop optimal ITRs that maximize a population-level distributional summary. However, guidance for estimating and evaluating optimal ITRs in the presence of missing data is limited. Our work is motivated by the Social Incentives to Encourage Physical Activity and Understand Predictors (STEP UP) study. Participants were randomized to a control or one of three interventions designed to increase physical activity and were given wearable devices to record daily steps as a measure of physical activity. Many participants were missing at least one daily step count during the study period. In the primary analysis of the STEP UP trial, multiple imputation (MI) was used to address missingness in daily step counts. Despite ubiquitous use of MI in practice, it has been given relatively little attention in the context of personalized medicine. We fill this gap by describing two frameworks for estimation and evaluation of an optimal ITR following MI and assessing their performance using simulated data. One framework relies on splitting the data into independent training and testing sets for estimation and evaluation, respectively. The other framework estimates an optimal ITR using the full data and constructs an m $$ m $$ -out-of- n $$ n $$ bootstrap confidence interval to evaluate its performance. Finally, we provide an illustrative analysis to estimate and evaluate an optimal ITR from the STEP UP data with a focus on practical considerations such as choosing the number of imputations.

Voxel-wise intermodal coupling analysis of two or more modalities using local covariance decomposition.

When individual subjects are imaged with multiple modalities, biological information is present not only within each modality, but also between modalities - that is, in how modalities covary at the voxel level. Previous studies have shown that local covariance structures between modalities, or intermodal coupling (IMCo), can be summarized for two modalities, and that two-modality IMCo reveals otherwise undiscovered patterns in neurodevelopment and certain diseases. However, previous IMCo methods are based on the slopes of local weighted linear regression lines, which are inherently asymmetric and limited to the two-modality setting. Here, we present a generalization of IMCo estimation which uses local covariance decompositions to define a symmetric, voxel-wise coupling coefficient that is valid for two or more modalities. We use this method to study coupling between cerebral blood flow, amplitude of low frequency fluctuations, and local connectivity in 803 subjects ages 8 through 22. We demonstrate that coupling is spatially heterogeneous, varies with respect to age and sex in neurodevelopment, and reveals patterns that are not present in individual modalities. As availability of multi-modal data continues to increase, principal-component-based IMCo (pIMCo) offers a powerful approach for summarizing relationships between multiple aspects of brain structure and function. An R package for estimating pIMCo is available at: https://github.com/hufengling/pIMCo.

A local group differences test for subject-level multivariate density neuroimaging outcomes.

A great deal of neuroimaging research focuses on voxel-wise analysis or segmentation of damaged tissue, yet many diseases are characterized by diffuse or non-regional neuropathology. In simple cases, these processes can be quantified using summary statistics of voxel intensities. However, the manifestation of a disease process in imaging data is often unknown, or appears as a complex and nonlinear relationship between the voxel intensities on various modalities. When the relevant pattern is unknown, summary statistics are often unable to capture differences between disease groups, and their use may encourage post hoc searches for the optimal summary measure. In this study, we introduce the multi-modal density testing (MMDT) framework for the naive discovery of group differences in voxel intensity profiles. MMDT operationalizes multi-modal magnetic resonance imaging (MRI) data as multivariate subject-level densities of voxel intensities and utilizes kernel density estimation to develop a local two-sample test for individual points within the density space. Through simulations, we show that this method controls type I error and recovers relevant differences when applied to a specified point. Additionally, we demonstrate the ability to maintain power while controlling the family-wise error rate and false discovery rate when applying the test over a grid of points within the density space. Finally, we apply this method to a study of subjects with either multiple sclerosis (MS) or conditions that tend to mimic MS on MRI, and find significant differences between the two groups in their voxel intensity profiles within the thalamus.

Structural brain measures linked to clinical phenotypes in major depression replicate across clinical centres.

Abnormalities in brain structural measures, such as cortical thickness and subcortical volumes, are observed in patients with major depressive disorder (MDD) who also often show heterogeneous clinical features. This study seeks to identify the multivariate associations between structural phenotypes and specific clinical symptoms, a novel area of investigation. T1-weighted magnetic resonance imaging measures were obtained using 3 T scanners for 178 unmedicated depressed patients at four academic medical centres. Cortical thickness and subcortical volumes were determined for the depressed patients and patients' clinical presentation was characterized by 213 item-level clinical measures, which were grouped into several large, homogeneous categories by K-means clustering. The multivariate correlations between structural and cluster-level clinical-feature measures were examined using canonical correlation analysis (CCA) and confirmed with both 5-fold and leave-one-site-out cross-validation. Four broad types of clinical measures were detected based on clustering: an anxious misery composite (composed of item-level depression, anxiety, anhedonia, neuroticism and suicidality scores); positive personality traits (extraversion, openness, agreeableness and conscientiousness); reported history of physical/emotional trauma; and a reported history of sexual abuse. Responses on the item-level anxious misery measures were negatively associated with cortical thickness/subcortical volumes in the limbic system and frontal lobe; reported childhood history of physical/emotional trauma and sexual abuse measures were negatively correlated with entorhinal thickness and left hippocampal volume, respectively. In contrast, the positive traits measures were positively associated with hippocampal and amygdala volumes and cortical thickness of the highly-connected precuneus and cingulate cortex. Our findings suggest that structural brain measures may reflect neurobiological mechanisms underlying MDD features.

Equitable implementation of S.A.F.E. Firearm: A multi-method pilot study.

Attention to health equity is critical in the implementation of firearm safety efforts. We present our operationalization of equity-oriented recommendations in preparation for launch of a hybrid effectiveness-implementation trial focused on firearm safety promotion in pediatric primary care as a universal suicide prevention strategy. In Step 1 of our process, pre-trial engagement with clinican partners and literature review alerted us that delivery of a firearm safety program may vary by patients' medical complexity, race, and ethnicity. In Step 2, we selected the Health Equity Implementation Framework to inform our understanding of contextual determinants (i.e., barriers and facilitators). In Step 3, we leveraged an implementation pilot across 5 pediatric primary care clinics in 2 health system sites to study signals of inequities. Eligible well-child visits for 694 patients and 47 clinicians were included. Our results suggested that medical complexity was not associated with program delivery. We did see potential signals of inequities by race and ethnicity but must interpret with caution. Though we did not initially plan to examine differences by sex assigned at birth, we discovered that clinicians may be more likely to deliver the program to parents of male than female patients. Seven qualitative interviews with clinicians provided additional context. In Step 4, we interrogated equity considerations (e.g., why and how do these inequities exist). In Step 5, we will develop a plan to probe potential inequities related to race, ethnicity, and sex in the fully powered trial. Our process highlights that prospective, rigorous, exploratory work is vital for equity-informed implementation trials.

Spillover Effects of Medicare's Voluntary Bundled Payments for Joint Replacement Surgery to Patients Insured by Commercial Health Plans.

BACKGROUND: Under the Bundled Payments for Care Improvement (BPCI) program, bundled paymtents for lower-extremity joint replacement (LEJR) are associated with 2% to 4% cost savings with stable quality among Medicare fee-for-service beneficiaries. However, BPCI may prompt practice changes that benefit all patients, not just fee-for-service beneficiaries. OBJECTIVE: To examine the association between hospital participation in BPCI and LEJR outcomes for patients with commercial insurance or Medicare Advantage (MA). DESIGN: Quasi-experimental study using Health Care Cost Institute claims from 2011 to 2016. SETTING: LEJR at 281 BPCI hospitals and 562 non-BPCI hospitals. PATIENTS: 184 922 patients with MA or commercial insurance. MEASUREMENTS: Differential changes in LEJR outcomes at BPCI hospitals versus at non-BPCI hospitals matched on propensity score were evaluated using a difference-in-differences (DID) method. Secondary analyses evaluated associations by patient MA status and hospital characteristics. Primary outcomes were changes in 90-day total spending on LEJR episodes and 90-day readmissions; secondary outcomes were postacute spending and discharge to postacute care providers. RESULTS: Average episode spending decreased more at BPCI versus non-BPCI hospitals (change, -2.2% [95% CI, -3.6% to -0.71%]; P = 0.004), but differences in changes in 90-day readmissions were not significant (adjusted DID, -0.47 percentage point [CI, -1.0 to 0.06 percentage point]; P = 0.084). Participation in BPCI was also associated with differences in decreases in postacute spending and discharge to institutional postacute care providers. Decreases in episode spending were larger for hospitals with high baseline spending but did not vary by MA status. LIMITATION: Nonrandomized studies are subject to residual confounding and selection. CONCLUSION: Participation in BPCI was associated with modest spillovers in episode savings. Bundled payments may prompt hospitals to implement broad care redesign that produces benefits regardless of insurance coverage. PRIMARY FUNDING SOURCE: Leonard Davis Institute of Health Economics at the University of Pennsylvania.

Childhood trauma history is linked to abnormal brain connectivity in major depression.

Patients with major depressive disorder (MDD) present with heterogeneous symptom profiles, while neurobiological mechanisms are still largely unknown. Brain network studies consistently report disruptions of resting-state networks (RSNs) in patients with MDD, including hypoconnectivity in the frontoparietal network (FPN), hyperconnectivity in the default mode network (DMN), and increased connection between the DMN and FPN. Using a large, multisite fMRI dataset (n = 189 patients with MDD, n = 39 controls), we investigated network connectivity differences within and between RSNs in patients with MDD and healthy controls. We found that MDD could be characterized by a network model with the following abnormalities relative to controls: (i) lower within-network connectivity in three task-positive RSNs [FPN, dorsal attention network (DAN), and cingulo-opercular network (CON)], (ii) higher within-network connectivity in two intrinsic networks [DMN and salience network (SAN)], and (iii) higher within-network connectivity in two sensory networks [sensorimotor network (SMN) and visual network (VIS)]. Furthermore, we found significant alterations in connectivity between a number of these networks. Among patients with MDD, a history of childhood trauma and current symptoms quantified by clinical assessments were associated with a multivariate pattern of seven different within- and between-network connectivities involving the DAN, FPN, CON, subcortical regions, ventral attention network (VAN), auditory network (AUD), VIS, and SMN. Overall, our study showed that traumatic childhood experiences and dimensional symptoms are linked to abnormal network architecture in MDD. Our results suggest that RSN connectivity may explain underlying neurobiological mechanisms of MDD symptoms and has the potential to serve as an effective diagnostic biomarker.

Resting fMRI-guided TMS results in subcortical and brain network modulation indexed by interleaved TMS/fMRI.

Traditional non-invasive imaging methods describe statistical associations of functional co-activation over time. They cannot easily establish hierarchies in communication as done in non-human animals using invasive methods. Here, we interleaved functional MRI (fMRI) recordings with non-invasive transcranial magnetic stimulation (TMS) to map causal communication between the frontal cortex and subcortical target structures including the subgenual anterior cingulate cortex (sgACC) and the amygdala. Seed-based correlation maps from each participant's resting fMRI scan determined individual stimulation sites with high temporal correlation to targets for the subsequent TMS/fMRI session(s). The resulting TMS/fMRI images were transformed to quantile responses, so that regions of high-/low-quantile response corresponded to the areas of the brain with the most positive/negative evoked response relative to the global brain response. We then modeled the average quantile response for a given region (e.g., structure or network) to determine whether TMS was effective in the relative engagement of the downstream targets. Both the sgACC and amygdala were differentially influenced by TMS. Furthermore, we found that the sgACC distributed brain network was modulated in response to fMRI-guided TMS. The amygdala, but not its distributed network, also responded to TMS. Our findings suggest that individual targeting and brain response measurements reflect causal circuit mapping to the sgACC and amygdala in humans. These results set the stage to further map circuits in the brain and link circuit pathway integrity to clinical intervention outcomes, especially when the intervention targets specific pathways and networks as is possible with TMS.

Longitudinal ComBat: A method for harmonizing longitudinal multi-scanner imaging data.

While aggregation of neuroimaging datasets from multiple sites and scanners can yield increased statistical power, it also presents challenges due to systematic scanner effects. This unwanted technical variability can introduce noise and bias into estimation of biological variability of interest. We propose a method for harmonizing longitudinal multi-scanner imaging data based on ComBat, a method originally developed for genomics and later adapted to cross-sectional neuroimaging data. Using longitudinal cortical thickness measurements from 663 participants in the Alzheimer's Disease Neuroimaging Initiative (ADNI) study, we demonstrate the presence of additive and multiplicative scanner effects in various brain regions. We compare estimates of the association between diagnosis and change in cortical thickness over time using three versions of the ADNI data: unharmonized data, data harmonized using cross-sectional ComBat, and data harmonized using longitudinal ComBat. In simulation studies, we show that longitudinal ComBat is more powerful for detecting longitudinal change than cross-sectional ComBat and controls the type I error rate better than unharmonized data with scanner included as a covariate. The proposed method would be useful for other types of longitudinal data requiring harmonization, such as genomic data, or neuroimaging studies of neurodevelopment, psychiatric disorders, or other neurological diseases.

Applying Machine Learning Algorithms to Segment High-Cost Patient Populations.

BACKGROUND: Efforts to improve the value of care for high-cost patients may benefit from care management strategies targeted at clinically distinct subgroups of patients. OBJECTIVE: To evaluate the performance of three different machine learning algorithms for identifying subgroups of high-cost patients. DESIGN: We applied three different clustering algorithms-connectivity-based clustering using agglomerative hierarchical clustering, centroid-based clustering with the k-medoids algorithm, and density-based clustering with the OPTICS algorithm-to a clinical and administrative dataset. We then examined the extent to which each algorithm identified subgroups of patients that were (1) clinically distinct and (2) associated with meaningful differences in relevant utilization metrics. PARTICIPANTS: Patients enrolled in a national Medicare Advantage plan, categorized in the top decile of spending (n = 6154). MAIN MEASURES: Post hoc discriminative models comparing the importance of variables for distinguishing observations in one cluster from the rest. Variance in utilization and spending measures. KEY RESULTS: Connectivity-based, centroid-based, and density-based clustering identified eight, five, and ten subgroups of high-cost patients, respectively. Post hoc discriminative models indicated that density-based clustering subgroups were the most clinically distinct. The variance of utilization and spending measures was the greatest among the subgroups identified through density-based clustering. CONCLUSIONS: Machine learning algorithms can be used to segment a high-cost patient population into subgroups of patients that are clinically distinct and associated with meaningful differences in utilization and spending measures. For these purposes, density-based clustering with the OPTICS algorithm outperformed connectivity-based and centroid-based clustering algorithms.

Subgroups of High-Cost Medicare Advantage Patients: an Observational Study.

BACKGROUND: There is a growing focus on improving the quality and value of health care delivery for high-cost patients. Compared to fee-for-service Medicare, less is known about the clinical composition of high-cost Medicare Advantage populations. OBJECTIVE: To describe a high-cost Medicare Advantage population and identify clinically and operationally significant subgroups of patients. DESIGN: We used a density-based clustering algorithm to group high-cost patients (top 10% of spending) according to 161 distinct demographic, clinical, and claims-based variables. We then examined rates of utilization, spending, and mortality among subgroups. PARTICIPANTS: Sixty-one thousand five hundred forty-six Medicare Advantage beneficiaries. MAIN MEASURES: Spending, utilization, and mortality. KEY RESULTS: High-cost patients (n = 6154) accounted for 55% of total spending. High-cost patients were more likely to be younger, male, and have higher rates of comorbid illnesses. We identified ten subgroups of high-cost patients: acute exacerbations of chronic disease (mixed); end-stage renal disease (ESRD); recurrent gastrointestinal bleed (GIB); orthopedic trauma (trauma); vascular disease (vascular); surgical infections and other complications (complications); cirrhosis with hepatitis C (liver); ESRD with increased medical and behavioral comorbidity (ESRD+); cancer with high-cost imaging and radiation therapy (oncology); and neurologic disorders (neurologic). The average number of inpatient days ranged from 3.25 (oncology) to 26.09 (trauma). Preventable spending (as a percentage of total spending) ranged from 0.8% (oncology) to 9.5% (complications) and the percentage of spending attributable to prescription medications ranged from 7.9% (trauma and oncology) to 77.0% (liver). The percentage of patients who were persistently high-cost ranged from 11.8% (trauma) to 100.0% (ESRD+). One-year mortality ranged from 0.0% (liver) to 25.8% (ESRD+). CONCLUSIONS: We identified clinically distinct subgroups of patients within a heterogeneous high-cost Medicare Advantage population using cluster analysis. These subgroups, defined by condition-specific profiles and illness trajectories, had markedly different patterns of utilization, spending, and mortality, holding important implications for clinical strategy.

Network changes associated with transdiagnostic depressive symptom improvement following cognitive behavioral therapy in MDD and PTSD.

Despite widespread use of cognitive behavioral therapy (CBT) in clinical practice, its mechanisms with respect to brain networks remain sparsely described. In this study, we applied tools from graph theory and network science to better understand the transdiagnostic neural mechanisms of this treatment for depression. A sample of 64 subjects was included in a study of network dynamics: 33 patients (15 MDD, 18 PTSD) received longitudinal fMRI resting state scans before and after 12 weeks of CBT. Depression severity was rated on the Montgomery-Asberg Depression Rating Scale (MADRS). Thirty-one healthy controls were included to determine baseline network roles. Univariate and multivariate regression analyses were conducted on the normalized change scores of within- and between-system connectivity and normalized change score of the MADRS. Penalized regression was used to select a sparse set of predictors in a data-driven manner. Univariate analyses showed greater symptom reduction was associated with an increased functional role of the Ventral Attention (VA) system as an incohesive provincial system (decreased between- and decreased within-system connectivity). Multivariate analyses selected between-system connectivity of the VA system as the most prominent feature associated with depression improvement. Observed VA system changes are interesting in light of brain controllability descriptions: attentional control systems, including the VA system, fall on the boundary between-network communities, and facilitate integration or segregation of diverse cognitive systems. Thus, increasing segregation of the VA system following CBT (decreased between-network connectivity) may result in less contribution of emotional attention to cognitive processes, thereby potentially improving cognitive control.

Statistical harmonization corrects site effects in functional connectivity measurements from multi-site fMRI data.

Acquiring resting-state functional magnetic resonance imaging (fMRI) datasets at multiple MRI scanners and clinical sites can improve statistical power and generalizability of results. However, multi-site neuroimaging studies have reported considerable nonbiological variability in fMRI measurements due to different scanner manufacturers and acquisition protocols. These undesirable sources of variability may limit power to detect effects of interest and may even result in erroneous findings. Until now, there has not been an approach that removes unwanted site effects. In this study, using a relatively large multi-site (4 sites) fMRI dataset, we investigated the impact of site effects on functional connectivity and network measures estimated by widely used connectivity metrics and brain parcellations. The protocols and image acquisition of the dataset used in this study had been homogenized using identical MRI phantom acquisitions from each of the neuroimaging sites; however, intersite acquisition effects were not completely eliminated. Indeed, in this study, we found that the magnitude of site effects depended on the choice of connectivity metric and brain atlas. Therefore, to further remove site effects, we applied ComBat, a harmonization technique previously shown to eliminate site effects in multi-site diffusion tensor imaging (DTI) and cortical thickness studies. In the current work, ComBat successfully removed site effects identified in connectivity and network measures and increased the power to detect age associations when using optimal combinations of connectivity metrics and brain atlases. Our proposed ComBat harmonization approach for fMRI-derived connectivity measures facilitates reliable and efficient analysis of retrospective and prospective multi-site fMRI neuroimaging studies.

Control-group feature normalization for multivariate pattern analysis of structural MRI data using the support vector machine.

Normalization of feature vector values is a common practice in machine learning. Generally, each feature value is standardized to the unit hypercube or by normalizing to zero mean and unit variance. Classification decisions based on support vector machines (SVMs) or by other methods are sensitive to the specific normalization used on the features. In the context of multivariate pattern analysis using neuroimaging data, standardization effectively up- and down-weights features based on their individual variability. Since the standard approach uses the entire data set to guide the normalization, it utilizes the total variability of these features. This total variation is inevitably dependent on the amount of marginal separation between groups. Thus, such a normalization may attenuate the separability of the data in high dimensional space. In this work we propose an alternate approach that uses an estimate of the control-group standard deviation to normalize features before training. We study our proposed approach in the context of group classification using structural MRI data. We show that control-based normalization leads to better reproducibility of estimated multivariate disease patterns and improves the classifier performance in many cases.

iqLearn: Interactive Q-Learning in R.

Chronic illness treatment strategies must adapt to the evolving health status of the patient receiving treatment. Data-driven dynamic treatment regimes can offer guidance for clinicians and intervention scientists on how to treat patients over time in order to bring about the most favorable clinical outcome on average. Methods for estimating optimal dynamic treatment regimes, such as Q-learning, typically require modeling nonsmooth, nonmonotone transformations of data. Thus, building well-fitting models can be challenging and in some cases may result in a poor estimate of the optimal treatment regime. Interactive Q-learning (IQ-learning) is an alternative to Q-learning that only requires modeling smooth, monotone transformations of the data. The R package iqLearn provides functions for implementing both the IQ-learning and Q-learning algorithms. We demonstrate how to estimate a two-stage optimal treatment policy with iqLearn using a generated data set bmiData which mimics a two-stage randomized body mass index reduction trial with binary treatments at each stage.

Comparative effectiveness of less commonly used systemic monotherapies and common combination therapies for moderate to severe psoriasis in the clinical setting.

BACKGROUND: The effectiveness of psoriasis therapies in real-world settings remains relatively unknown. OBJECTIVE: We sought to compare the effectiveness of less commonly used systemic therapies and commonly used combination therapies for psoriasis. METHODS: This was a multicenter cross-sectional study of 203 patients with plaque psoriasis receiving less common systemic monotherapy (acitretin, cyclosporine, or infliximab) or common combination therapies (adalimumab, etanercept, or infliximab and methotrexate) compared with 168 patients receiving methotrexate evaluated at 1 of 10 US outpatient dermatology sites participating in the Dermatology Clinical Effectiveness Research Network. RESULTS: In adjusted analyses, patients on acitretin (relative response rate 2.01; 95% confidence interval [CI] 1.18-3.41), infliximab (relative response rate 1.93; 95% CI 1.26-2.98), adalimumab and methotrexate (relative response rate 3.04; 95% CI 2.12-4.36), etanercept and methotrexate (relative response rate 2.22; 95% CI 1.25-3.94), and infliximab and methotrexate (relative response rate 1.72; 95% CI 1.10-2.70) were more likely to have clear or almost clear skin compared with patients on methotrexate. There were no differences among treatments when response rate was defined by health-related quality of life. LIMITATIONS: Single time point assessment may result in overestimation of effectiveness. CONCLUSIONS: The efficacy of therapies in clinical trials may overestimate their effectiveness as used in clinical practice. Although physician-reported relative response rates were different among therapies, absolute differences were small and did not correspond to differences in patient-reported outcomes.

Association of Electronic Self-Scheduling and Screening Mammogram Completion.

INTRODUCTION: The purpose of this study was to evaluate if an electronic health record (EHR) self-scheduling function was associated with changes in mammogram completion for primary care patients who were eligible for a screening mammogram using U.S. Preventive Service Task Force recommendations. METHODS: This was a retrospective cohort study (September 1, 2014-August 31, 2019, analyses completed in 2022) using a difference-in-differences design to examine mammogram completion before versus after the implementation of self-scheduling. The difference-in-differences estimate was the interaction between time (pre-versus post-implementation) and group (active EHR patient portal versus inactive EHR patient portal). The primary outcome was mammogram completion among all eligible patients, with completion defined as receiving a mammogram within 6 months post-visit. The secondary outcome was mammogram completion among patients who received a clinician order during their visit. RESULTS: The primary analysis included 35,257 patient visits. The overall mammogram completion rate in the pre-period was 22.2% and 49.7% in the post-period. EHR self-scheduling was significantly associated with increased mammogram completion among those with an active EHR portal, relative to patients with an inactive portal (adjusted difference 13.2 percentage points [95% CI 10.6-15.8]). For patients who received a clinician mammogram order at their eligible visit, self-scheduling was significantly associated with increased mammogram completion among patients with an active EHR portal account (adjusted difference 14.7 percentage points, [95% CI 10.9-18.5]). CONCLUSIONS: EHR-based self-scheduling was associated with a significant increase in mammogram completion among primary care patients. Self-scheduling can be a low-cost, scalable function for increasing preventive cancer screenings.