Biostatistics and Health Data Science Works

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https://hdl.handle.net/1805/7162

Works authored by scholars from the Department of Biostatistics and Health Data Science, a dual department of the Richard M. Fairbanks School of Public Health and the IU School of Medicine.

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A Bayesian phase I/II biomarker-based design for identifying subgroup-specific optimal dose for immunotherapy
(Sage, 2022) Guo, Beibei; Zang, Yong; Biostatistics and Health Data Science, School of Medicine
Immunotherapy is an innovative treatment that enlists the patient’s immune system to battle tumors. The optimal dose for treating patients with an immunotherapeutic agent may differ according to their biomarker status. In this article, we propose a biomarker-based phase I/II dose-finding design for identifying subgroup-specific optimal dose for immunotherapy (BSOI) that jointly models the immune response, toxicity, and efficacy outcomes. We propose parsimonious yet flexible models to borrow information across different types of outcomes and subgroups. We quantify the desirability of the dose using a utility function and adopt a two-stage dose-finding algorithm to find the optimal dose for each subgroup. Simulation studies show that the BSOI design has desirable operating characteristics in selecting the subgroup-specific optimal doses and allocating patients to those optimal doses, and outperforms conventional designs.
A Contextual Approach to the Psychological Study of Identity Concealment: Examining Direct, Interactive, and Indirect Effects of Structural Stigma on Concealment Motivation Across Proximal and Distal Geographic Levels
(Sage, 2021) Lattanner, Micah R.; Ford, Jessie; Bo, Na; Tu, Wanzhu; Pachankis, John E.; Dodge, Brian; Hatzenbuehler, Mark L.; Biostatistics, School of Public Health
Psychological theories of identity concealment locate the ultimate source of concealment decisions within the social environment, yet most studies have not explicitly assessed stigmatizing environments beyond the immediate situation. We advanced the identity-concealment literature by objectively measuring structural forms of stigma related to sexual orientation (e.g., social policies) at proximal and distal geographic levels. We linked these measures to a new, population-based data set of 502 gay and bisexual men (residing in 44 states and Washington, DC; 269 counties; and 354 cities) who completed survey items about stigma, including identity-concealment motivation. Among gay men, the association between structural stigma and concealment motivation was (a) observed across three levels (city, county, and state), (b) conditional on one's exposure at another geographic level (participants reported the least motivations to conceal their identity if they resided in both cities and states that were lowest in structural stigma), and (c) mediated by subjective perceptions of greater structural stigma.
A Deep Language Model for Symptom Extraction From Clinical Text and its Application to Extract COVID-19 Symptoms From Social Media
(IEEE, 2022) Luo, Xiao; Gandhi, Priyanka; Storey, Susan; Huang, Kun; Biostatistics and Health Data Science, School of Medicine
Patients experience various symptoms when they have either acute or chronic diseases or undergo some treatments for diseases. Symptoms are often indicators of the severity of the disease and the need for hospitalization. Symptoms are often described in free text written as clinical notes in the Electronic Health Records (EHR) and are not integrated with other clinical factors for disease prediction and healthcare outcome management. In this research, we propose a novel deep language model to extract patient-reported symptoms from clinical text. The deep language model integrates syntactic and semantic analysis for symptom extraction and identifies the actual symptoms reported by patients and conditional or negation symptoms. The deep language model can extract both complex and straightforward symptom expressions. We used a real-world clinical notes dataset to evaluate our model and demonstrated that our model achieves superior performance compared to three other state-of-the-art symptom extraction models. We extensively analyzed our model to illustrate its effectiveness by examining each component’s contribution to the model. Finally, we applied our model on a COVID-19 tweets data set to extract COVID-19 symptoms. The results show that our model can identify all the symptoms suggested by CDC ahead of their timeline and many rare symptoms.
A descriptive study of the multidisciplinary healthcare experiences of inpatient resuscitation events
(Elsevier, 2023-01-06) Varner-Perez, Shelley E.; Shelley E., Kelly A. L.; Banks, Sarah K.; Burke, Emily S.; Slaven, James E.; Morse, Gregory J.; Whitaker, Myra K.; Cottingham, Ann H.; Ahmed, Rami A.; Biostatistics, School of Public Health
Background: In-hospital resuscitation events have complex and enduring effects on clinicians, with implications for job satisfaction, performance, and burnout. Ethically ambiguous cases are associated with increased moral distress. We aim to quantitatively describe the multidisciplinary resuscitation experience. Methods: Multidisciplinary in-hospital healthcare professionals at an adult academic health center in the Midwestern United States completed surveys one and six weeks after a resuscitation event. Surveys included demographic data, task load (NASA-TLX), overall and moral distress, anxiety, depression, and spiritual peace. Spearman's rank correlation was computed to assess task load and distress. Results: During the 5-month study period, the study included 12 resuscitation events across six inpatient units. Of 82 in-hospital healthcare professionals eligible for recruitment, 44 (53.7%) completed the one-week post-resuscitation event survey. Of those, 37 (84.1%) completed the six-week survey. Highest median task load burden at one week was seen for temporal demand, effort, and mental demand. Median moral distress scores were low, while "at peace" median scores tended to be high. There were no significant non-zero changes in task load or distress scores from weeks 1-6. Mental demand (r = 0.545, p < 0.001), physical demand (r = 0.464, p = 0.005), performance (r = -0.539, p < 0.001), and frustration (r = 0.545, p < 0.001) significantly correlated with overall distress. Performance (r = -0.371, p = 0.028) and frustration (r = 0.480, p = 0.004) also significantly correlated with moral distress. Conclusions: In-hospital healthcare professionals' experiences of resuscitation events are varied and complex. Aspects of task load burden including mental and physical demand, performance, and frustration contribute to overall and moral distress, deserving greater attention in clinical contexts.
A distinct symptom pattern emerges for COVID-19 long-haul: a nationwide study
(Springer Nature, 2022-09-23) Pinto, Melissa D.; Downs, Charles A.; Huang, Yong; El‑Azab, Sarah A.; Ramrakhiani, Nathan S.; Barisano, Anthony; Yu, Lu; Taylor, Kaitlyn; Esperanca, Alvaro; Abrahim, Heather L.; Hughes, Thomas; Giraldo Herrera, Maria; Rahamani, Amir M.; Dutt, Nikil; Chakraborty, Rana; Mendiola, Christian; Lambert, Natalie; Biostatistics, School of Public Health
Long-haul COVID-19, also called post-acute sequelae of SARS-CoV-2 (PASC), is a new illness caused by SARS-CoV-2 infection and characterized by the persistence of symptoms. The purpose of this cross-sectional study was to identify a distinct and significant temporal pattern of PASC symptoms (symptom type and onset) among a nationwide sample of PASC survivors (n = 5652). The sample was randomly sorted into two independent samples for exploratory (EFA) and confirmatory factor analyses (CFA). Five factors emerged from the EFA: (1) cold and flu-like symptoms, (2) change in smell and/or taste, (3) dyspnea and chest pain, (4) cognitive and visual problems, and (5) cardiac symptoms. The CFA had excellent model fit (x2 = 513.721, df = 207, p < 0.01, TLI = 0.952, CFI = 0.964, RMSEA = 0.024). These findings demonstrate a novel symptom pattern for PASC. These findings can enable nurses in the identification of at-risk patients and facilitate early, systematic symptom management strategies for PASC.
A framework for detecting noncoding rare-variant associations of large-scale whole-genome sequencing studies
(Springer Nature, 2022) Li, Zilin; Li, Xihao; Zhou, Hufeng; Gaynor, Sheila M.; Selvaraj, Margaret Sunitha; Arapoglou, Theodore; Quick, Corbin; Liu, Yaowu; Chen, Han; Sun, Ryan; Dey, Rounak; Arnett, Donna K.; Auer, Paul L.; Bielak, Lawrence F.; Bis, Joshua C.; Blackwell, Thomas W.; Blangero, John; Boerwinkle, Eric; Bowden, Donald W.; Brody, Jennifer A.; Cade, Brian E.; Conomos, Matthew P.; Correa, Adolfo; Cupples, L. Adrienne; Curran, Joanne E.; de Vries, Paul S.; Duggirala, Ravindranath; Franceschini, Nora; Freedman, Barry I.; Göring, Harald H. H.; Guo, Xiuqing; Kalyani, Rita R.; Kooperberg, Charles; Kral, Brian G.; Lange, Leslie A.; Lin, Bridget M.; Manichaikul, Ani; Manning, Alisa K.; Martin, Lisa W.; Mathias, Rasika A.; Meigs, James B.; Mitchell, Braxton D.; Montasser, May E.; Morrison, Alanna C.; Naseri, Take; O'Connell, Jeffrey R.; Palmer, Nicholette D.; Peyser, Patricia A.; Psaty, Bruce M.; Raffield, Laura M.; Redline, Susan; Reiner, Alexander P.; Reupena, Muagututi'a Sefuiva; Rice, Kenneth M.; Rich, Stephen S.; Smith, Jennifer A.; Taylor, Kent D.; Taub, Margaret A.; Vasan, Ramachandran S.; Weeks, Daniel E.; Wilson, James G.; Yanek, Lisa R.; Zhao, Wei; NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium; TOPMed Lipids Working Group; Rotter, Jerome I.; Willer, Cristen J.; Natarajan, Pradeep; Peloso, Gina M.; Lin, Xihong; Biostatistics and Health Data Science, School of Medicine
Large-scale whole-genome sequencing studies have enabled analysis of noncoding rare-variant (RV) associations with complex human diseases and traits. Variant-set analysis is a powerful approach to study RV association. However, existing methods have limited ability in analyzing the noncoding genome. We propose a computationally efficient and robust noncoding RV association detection framework, STAARpipeline, to automatically annotate a whole-genome sequencing study and perform flexible noncoding RV association analysis, including gene-centric analysis and fixed window-based and dynamic window-based non-gene-centric analysis by incorporating variant functional annotations. In gene-centric analysis, STAARpipeline uses STAAR to group noncoding variants based on functional categories of genes and incorporate multiple functional annotations. In non-gene-centric analysis, STAARpipeline uses SCANG-STAAR to incorporate dynamic window sizes and multiple functional annotations. We apply STAARpipeline to identify noncoding RV sets associated with four lipid traits in 21,015 discovery samples from the Trans-Omics for Precision Medicine (TOPMed) program and replicate several of them in an additional 9,123 TOPMed samples. We also analyze five non-lipid TOPMed traits.
A Highly Ordered, Nanostructured Fluorinated CaP-Coated Melt Electrowritten Scaffold for Periodontal Tissue Regeneration
(Wiley, 2021) Daghrery, Arwa; Ferreira, Jessica A.; de Souza Araújo, Isaac J.; Clarkson, Brian H.; Eckert, George J.; Bhaduri, Sarit B.; Malda, Jos; Bottino, Marco C.; Biostatistics, School of Public Health
Periodontitis is a chronic inflammatory, bacteria-triggered disorder affecting nearly half of American adults. Although some level of tissue regeneration is realized, its low success in complex cases demands superior strategies to amplify regenerative capacity. Herein, highly ordered scaffolds are engineered via Melt ElectroWriting (MEW), and the effects of strand spacing, as well as the presence of a nanostructured fluorinated calcium phosphate (F/CaP) coating on the adhesion/proliferation, and osteogenic differentiation of human-derived periodontal ligament stem cells, are investigated. Upon initial cell-scaffold interaction screening aimed at defining the most suitable design, MEW poly(𝝐-caprolactone) scaffolds with 500 µm strand spacing are chosen. Following an alkali treatment, scaffolds are immersed in a pre-established solution to allow for coating formation. The presence of a nanostructured F/CaP coating leads to a marked upregulation of osteogenic genes and attenuated bacterial growth. In vivo findings confirm that the F/CaP-coated scaffolds are biocompatible and lead to periodontal regeneration when implanted in a rat mandibular periodontal fenestration defect model. In aggregate, it is considered that this work can contribute to the development of personalized scaffolds capable of enabling tissue-specific differentiation of progenitor cells, and thus guide simultaneous and coordinated regeneration of soft and hard periodontal tissues, while providing antimicrobial protection.
A novel conceptual model of trauma-informed care for patients with post-acute sequelae of SARS-CoV-2 illness (PASC)
(Wiley, 2022) Burton, Candace W.; Downs, Charles A.; Hughes, Thomas; Lambert, Natalie; Abrahim, Heather L.; Giraldo Herrera, Maria; Huang, Yong; Rahmani, Amir; Lee, Jung-Ah; Chakraborty, Rana; Pinto, Melissa D.; Biostatistics, School of Public Health
Aim: This paper proposes a novel, trauma-informed, conceptual model of care for Post-Acute Sequelae of COVID-19 illness (PASC). Design: This paper describes essential elements, linkages and dimensions of the model that affect PASC patient experiences and the potential impact of trauma-informed care on outcomes. Data sources: PASC is a consequence of the global pandemic, and a new disease of which little is known. Our model was derived from the limited available studies, expert clinical experience specific to PASC survivors and publicly available social media narratives authored by PASC survivors. Implications for nursing: The model provides a critical and novel framework for the understanding and care of persons affected by PASC. This model is aimed at the provision of nursing care, with the intention of reducing the traumatic impacts of the uncertain course of this disease, a lack of defined treatment options and difficulties in seeking care. The use of a trauma-informed care approach to PASC patients can enhance nurses' ability to remediate and ameliorate both the traumatic burden of and the symptoms and experience of the illness. Conclusion: Applying a trauma-informed perspective to care of PASC patients can help to reduce the overall burden of this complex condition. Owing to the fundamentally holistic perspective of the nursing profession, nurses are best positioned to implement care that addresses multiple facets of the PASC experience. Impact: The proposed model specifically addresses the myriad ways in which PASC may affect physical as well as mental and psychosocial dimensions of health. The model particularly seeks to suggest means of supporting patients who have already experienced a life-threatening illness and are now coping with its long-term impact. Since the scope of this impact is not yet defined, trauma-informed care for PASC patients is likely to reduce the overall health and systems burdens of this complex condition.
A sequential Monte Carlo Gibbs coupled with stochastically approximated expectation-maximization algorithm for functional data
(International Press, 2022-01-11) Liu, Ziyue; Biostatistics and Health Data Science, School of Medicine
We develop an algorithm to overcome the curse of dimensionality in sequential Monte Carlo (SMC) for functional data. In the inner iterations of the algorithm for given parameter values, the conditional SMC is extended to obtain draws of the underlying state vectors. These draws in turn are used in the outer iterations to update the parameter values in the framework of stochastically approximated expectation-maximization to obtain maximum likelihood estimates of the parameters. Standard errors of the parameters are calculated using a stochastic approximation of Louis formula. Three numeric examples are used for illustration. They show that although the computational burden remains high, the algorithm produces reasonable results without exponentially increasing the particle numbers.
A simple two-step procedure using the Fellegi-Sunter model for frequency-based record linkage
(Taylor & Francis, 2021-05-04) Xu, Huiping; Li, Xiaochun; Grannis, Shaun; Biostatistics, School of Public Health
The widely used Fellegi-Sunter model for probabilistic record linkage does not leverage information contained in field values and consequently leads to identical classification of match status regardless of whether records agree on rare or common values. Since agreement on rare values is less likely to occur by chance than agreement on common values, records agreeing on rare values are more likely to be matches. Existing frequency-based methods typically rely on knowledge of error probabilities associated with field values and frequencies of agreed field values among matches, often derived using prior studies or training data. When such information is unavailable, applications of these methods are challenging. In this paper, we propose a simple two-step procedure for frequency-based matching using the Fellegi-Sunter framework to overcome these challenges. Matching weights are adjusted based on frequency distributions of the agreed field values among matches and non-matches, estimated by the Fellegi-Sunter model without relying on prior studies or training data. Through a real-world application and simulation, our method is found to produce comparable or better performance than the unadjusted method. Furthermore, frequency-based matching provides greater improvement in matching accuracy when using poorly discriminating fields with diminished benefit as the discriminating power of matching fields increases.

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