Sequential Monte Carlo methods: applications to disease surveillance and fMRI data

We present contributions to epidemic tracking and analysis of fMRI data using sequential Monte Carlo methods within a state-space modeling framework. Using a model for tracking and prediction of a disease outbreak via a syndromic surveillance system, we compare the performance of several particle filtering algorithms in terms of their abilities to efficiently estimate disease states and unknown fixed parameters governing disease transmission. In this context, we demonstrate that basic particle filters may fail due to degeneracy when estimating fixed parameters, and we suggest the use of an algorithm developed by Liu and West (2001), which incorporates a kernel density approximation to the filtered distribution of the fixed parameters to allow for their regeneration. In addition, we show that seemingly uninformative uniform priors on fixed parameters can affect posterior inferences, and we suggest the use of priors bounded only by the support of the parameter. We demonstrate the negative impact of using multinomial resampling and suggest the use of either stratified or residual resampling within the particle filter. We also run a particle MCMC algorithm and show that the performance of the Liu and West (2001) particle filter is competitive with particle MCMC in this particular syndromic surveillance model setting. Finally, the improved performance of the Liu and West (2001) particle filter enables us to relax prior assumptions on model parameters, yet still provide reasonable estimates for model parameters and disease states.We also analyze real and simulated fMRI data using a state-space formulation of a regression model with autocorrelated error structure. We demonstrate via simulation that analyzing autocorrelated fMRI data using a model with independent error structure can inflate the false positive rate of concluding significant neural activity, and we compare methods of accounting for autocorrelation in fMRI data by examining ROC curves. In addition, we show that comparing models with different autocorrelated error structures on the basis of the independence of fitted model residuals can produce misleading results. Using data collected from an fMRI experiment featuring an episodic word recognition task, we estimate parameters in dynamic regression models using maximum likelihood and identify clusters of low and high activation in specific brain regions. We compare alternative models for fMRI time series from these brain regions by approximating the marginal likelihood of the data using particle learning. Our results suggest that a regression model with a dynamic intercept is the preferred model for most fMRI time series in the episodic word recognition experiment within the brain regions we considered, while a model with a dynamic slope is preferred for a small percentage of voxels in these brain regions

Sequential Monte Carlo methods: applications to disease surveillance and fMRI data

We present contributions to epidemic tracking and analysis of fMRI data using sequential Monte Carlo methods within a state-space modeling framework. Using a model for tracking and prediction of a disease outbreak via a syndromic surveillance system, we compare the performance of several particle filtering algorithms in terms of their abilities to efficiently estimate disease states and unknown fixed parameters governing disease transmission. In this context, we demonstrate that basic particle filters may fail due to degeneracy when estimating fixed parameters, and we suggest the use of an algorithm developed by Liu and West (2001), which incorporates a kernel density approximation to the filtered distribution of the fixed parameters to allow for their regeneration. In addition, we show that seemingly uninformative uniform priors on fixed parameters can affect posterior inferences, and we suggest the use of priors bounded only by the support of the parameter. We demonstrate the negative impact of using multinomial resampling and suggest the use of either stratified or residual resampling within the particle filter. We also run a particle MCMC algorithm and show that the performance of the Liu and West (2001) particle filter is competitive with particle MCMC in this particular syndromic surveillance model setting. Finally, the improved performance of the Liu and West (2001) particle filter enables us to relax prior assumptions on model parameters, yet still provide reasonable estimates for model parameters and disease states.We also analyze real and simulated fMRI data using a state-space formulation of a regression model with autocorrelated error structure. We demonstrate via simulation that analyzing autocorrelated fMRI data using a model with independent error structure can inflate the false positive rate of concluding significant neural activity, and we compare methods of accounting for autocorrelation in fMRI data by examining ROC curves. In addition, we show that comparing models with different autocorrelated error structures on the basis of the independence of fitted model residuals can produce misleading results. Using data collected from an fMRI experiment featuring an episodic word recognition task, we estimate parameters in dynamic regression models using maximum likelihood and identify clusters of low and high activation in specific brain regions. We compare alternative models for fMRI time series from these brain regions by approximating the marginal likelihood of the data using particle learning. Our results suggest that a regression model with a dynamic intercept is the preferred model for most fMRI time series in the episodic word recognition experiment within the brain regions we considered, while a model with a dynamic slope is preferred for a small percentage of voxels in these brain regions

Five-Year Patterns of Diabetic Retinopathy Progression in US Clinical Practice

To characterize the natural course of diabetic retinopathy (DR) in contemporary clinical practice. This was a retrospective analysis of US claims data collected between January 1, 2006, and April 30, 2017. Patients aged ≥18 years with continuous medical and prescription insurance coverage for 18 months before DR diagnosis (index date) and for a follow-up period of 5 years were included (N=14,490). The time and risk of progressing to severe nonproliferative DR (NPDR) or proliferative DR (PDR) and of developing diabetic macular edema (DME) were evaluated over 5 years in patients stratified by DR severity at initial diagnosis. The estimated probability of progressing to severe NPDR or PDR within 5 years of diagnosis was 17.6% for patients with moderate NPDR versus 5.8% for mild NPDR. The probability of developing DME within 5 years was 62.6%, 44.6%, and 28.4% for patients diagnosed with severe NPDR, moderate NPDR, and PDR, respectively, versus 15.6% for mild NPDR. Among those observed to progress, median time to severe NPDR or PDR was approximately 2.0 years in patients with moderate NPDR, whereas median time to DME was approximately 0.5 years in patients with severe NPDR, 1.3 years in moderate NPDR, and 1.6 years in PDR. Relative to mild NPDR, adjusted hazard ratios (95% confidence interval) for progression to severe NPDR or PDR within 5 years were 3.12 (2.61-3.72) in patients with moderate NPDR, and for incident DME were 5.92 (5.13-6.82), 3.54 (3.22-3.91), and 1.96 (1.80-2.14) in patients with severe NPDR, moderate NPDR, and PDR, respectively. The risk of DR progression and DME over 5 years was highest among patients diagnosed with moderate and severe NPDR, respectively. Our findings reinforce the importance of close monitoring for these patients to avoid unobserved disease progression toward PDR and/or DME

Treatment Patterns by Race and Ethnicity in Newly Diagnosed Persons with Multiple Sclerosis

Abstract Background Non-Hispanic Black and Hispanic persons with MS (pwMS) are more likely to experience rapid disease progression and severe disability than non-Hispanic White pwMS; however, it is unknown how the initiation of high-efficacy disease-modifying therapies (DMTs) differs by race/ethnicity. This real-world study describes DMT treatment patterns in newly diagnosed pwMS in the United States (US) overall and by race/ethnicity. Methods This retrospective analysis used the US Optum Market Clarity claims/electronic health records database (January 2015–September 2020). pwMS who were first diagnosed in 2016 or later and initiated any DMT in the two years following diagnosis were included. Continuous enrollment in the claims data for ≥ 12 months before and ≥ 24 months after diagnosis was required. Treatment patterns 2 years after diagnosis were analyzed descriptively overall and by race/ethnicity. Results The sample included 682 newly diagnosed and treated pwMS (non-Hispanic Black, n = 99; non-Hispanic White, n = 479; Hispanic, n = 35; other/unknown race/ethnicity, n = 69). The mean time from diagnosis to DMT initiation was 4.9 months in all pwMS. Glatiramer acetate and dimethyl fumarate were the most common first-line DMTs in non-Hispanic Black (28% and 20% respectively) and Hispanic pwMS (31%, 29%); however, glatiramer acetate and ocrelizumab were the most common in non-Hispanic White pwMS (33%, 18%). Use of first-line high-efficacy DMTs was limited across all race/ethnicity subgroups (11-29%), but uptake increased in non-Hispanic Black and White pwMS over the study period. Conclusion Use of high-efficacy DMTs was low across all race/ethnicity subgroups of newly diagnosed pwMS in the US, including populations at a greater risk of experiencing rapid disease progression and severe disability

TGFβ attenuates tumour response to PD-L1 blockade by contributing to exclusion of T cells

Therapeutic antibodies that block the programmed death-1 (PD-1)-programmed death-ligand 1 (PD-L1) pathway can induce robust and durable responses in patients with various cancers, including metastatic urothelial cancer. However, these responses only occur in a subset of patients. Elucidating the determinants of response and resistance is key to improving outcomes and developing new treatment strategies. Here we examined tumours from a large cohort of patients with metastatic urothelial cancer who were treated with an anti-PD-L1 agent (atezolizumab) and identified major determinants of clinical outcome. Response to treatment was associated with CD8 + T-effector cell phenotype and, to an even greater extent, high neoantigen or tumour mutation burden. Lack of response was associated with a signature of transforming growth factor β (TGFβ) signalling in fibroblasts. This occurred particularly in patients with tumours, which showed exclusion of CD8 + T cells from the tumour parenchyma that were instead found in the fibroblast-and collagen-rich peritumoural stroma; a common phenotype among patients with metastatic urothelial cancer. Using a mouse model that recapitulates this immune-excluded phenotype, we found that therapeutic co-Administration of TGFβ-blocking and anti-PD-L1 antibodies reduced TGFβ signalling in stromal cells, facilitated T-cell penetration into the centre of tumours, and provoked vigorous anti-Tumour immunity and tumour regression. Integration of these three independent biological features provides the best basis for understanding patient outcome in this setting and suggests that TGFβ shapes the tumour microenvironment to restrain anti-Tumour immunity by restricting T-cell infiltration