Additive Functional Cox Model

Cui, Erjia; Crainiceanu, Ciprian M.; Leroux, Andrew

doi:10.1080/10618600.2020.1853550

Cited by 24 publications

(26 citation statements)

References 43 publications

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“…Indeed, functional data analysis has been successfully applied to accelerometry data in studying the epidemiology of physical activity, aging, and health status [133] as well as predicting the risk of future mortality [134]. These methods range from modelling minute level accelerometry data directly [133][134][135][136] to identifying interpretable lower dimensional summaries that capture key features of the sensor data [134,137]. Combining methods from functional data analysis with analysis of EMA of pain remains a key methodologic challenge.…”

Section: Pain and Wearable Devices: The Promising Road Aheadmentioning

confidence: 99%

Wearable Devices: Current Status and Opportunities in Pain Assessment and Management

Leroux

Rzasa-Lynn²,

Crainiceanu

et al. 2021

Digit Biomark

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Introduction: We investigated the possibilities and opportunities for using wearable devices that measure physical activity and physiometric signals in conjunction with ecological momentary assessment (EMA) data to improve the assessment and treatment of pain. Methods: We considered studies with cross-sectional and longitudinal designs as well as interventional or observational studies correlating pain scores with measures derived from wearable devices. A search was also performed on studies that investigated physical activity and physiometric signals among patients with pain. Results: Few studies have assessed the possibility of incorporating wearable devices as objective tools for contextualizing pain and physical function in free-living environments. Of the studies that have been conducted, most focus solely on physical activity and functional outcomes as measured by a wearable accelerometer. Several studies report promising correlations between pain scores and signals derived from wearable devices, objectively measured physical activity, and physical function. In addition, there is a known association between physiologic signals that can be measured by wearable devices and pain, though studies using wearable devices to measure these signals and associate them with pain in free-living environments are limited. Conclusion: There exists a great opportunity to study the complex interplay between physiometric signals, physical function, and pain in a real-time fashion in free-living environments. The literature supports the hypothesis that wearable devices can be used to develop reproducible biosignals that correlate with pain. The combination of wearable devices and EMA will likely lead to the development of clinically meaningful endpoints that will transform how we understand and treat pain patients.

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Section: Pain and Wearable Devices: The Promising Road Aheadmentioning

confidence: 99%

Wearable Devices: Current Status and Opportunities in Pain Assessment and Management

Leroux

Rzasa-Lynn²,

Crainiceanu

et al. 2021

Digit Biomark

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“…The aforementioned functions can be considered as summarized features capturing spatial interaction between individual cells for each subject. In order to investigate the association between cell-level spatial 6/22 effect and patient survival, we leverage a previously published model, the Additive Functional Cox Model (AFCM) [29]. This model allows us to incorporate each subject's spatial summary function as functional covariates in addition to other scalar clinical variables such as age, sex, and disease stage.…”

Section: Modelmentioning

confidence: 99%

“…In our setting, X i will be a spatial summary for subject i, such as a mark connection function or Moran's I function. Following Cui et al [29], we jointly model the effect of cell-level spatial interactions with clinical variables on a subject's risk of mortality as follows…”

Section: Modelmentioning

confidence: 99%

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SPF: A Spatial and Functional Data Analytic Approach to cell Imaging data

Wrobel

Bitler

et al. 2021

Preprint

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The tumor microenvironment (TME), which characterizes the tumor and its surroundings, plays a critical role in understanding cancer development and progression. Recent advances in imaging techniques enable researchers to study spatial structure of the TME at a single-cell level. Investigating spatial patterns and interactions of cell subtypes within the TME provides useful insights into how cells with different biological purposes behave, which may consequentially impact a subject's clinical outcomes. We utilize a class of well-known spatial summary statistics, the K-function and its variants, to explore inter-cell dependence as a function of distances between cells. Using techniques from functional data analysis, we introduce an approach to model the association between these summary spatial functions and subject-level outcomes, while controlling for other clinical scalar predictors such as age and disease stage. In particular, we leverage the additive functional Cox regression model (AFCM) to study the nonlinear impact of spatial interaction between tumor and stromal cells on overall survival in patients with non-small cell lung cancer, using multiplex immunohistochemistry (mIHC) data. The applicability of our approach is further validated using a publicly available Multiplexed Ion beam Imaging (MIBI) triple-negative breast cancer dataset.

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“…An alternative approach involves analyzing the entire 24 h acceleration profiles in conjunction with health outcomes. Despite evidence that specific patterns of timing and magnitudes of physical activity over the 24 h day are associated with aging [11] and mortality [7,16], analytic approaches that use the full acceleration profiles have been underutilized in the literature, perhaps because of the computational and methodological challenges of working with high dimensional, correlated, structured data.…”

Section: Introductionmentioning

confidence: 99%

Diurnal Physical Activity Patterns across Ages in a Large UK Based Cohort: The UK Biobank Study

Wrobel

Muschelli

Leroux

2021

Sensors

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The ability of individuals to engage in physical activity is a critical component of overall health and quality of life. However, there is a natural decline in physical activity associated with the aging process. Establishing normative trends of physical activity in aging populations is essential to developing public health guidelines and informing clinical perspectives regarding individuals’ levels of physical activity. Beyond overall quantity of physical activity, patterns regarding the timing of activity provide additional insights into latent health status. Wearable accelerometers, paired with statistical methods from functional data analysis, provide the means to estimate diurnal patterns in physical activity. To date, these methods have been only applied to study aging trends in populations based in the United States. Here, we apply curve registration and functional regression to 24 h activity profiles for 88,793 men (N = 39,255) and women (N = 49,538) ages 42–78 from the UK Biobank accelerometer study to understand how physical activity patterns vary across ages and by gender. Our analysis finds that daily patterns in both the volume of physical activity and probability of being active change with age, and that there are marked gender differences in these trends. This work represents the largest-ever population analyzed using tools of this kind, and suggest that aging trends in physical activity are reproducible in different populations across countries.

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Additive Functional Cox Model

Cited by 24 publications

References 43 publications

Wearable Devices: Current Status and Opportunities in Pain Assessment and Management

Wearable Devices: Current Status and Opportunities in Pain Assessment and Management

SPF: A Spatial and Functional Data Analytic Approach to cell Imaging data

Diurnal Physical Activity Patterns across Ages in a Large UK Based Cohort: The UK Biobank Study

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