Department of Biostatistics and Health Data Science

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https://hdl.handle.net/1805/7161

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 Bayesian Phase I/II Design to Determine Subgroup-Specific Optimal Dose for Immunotherapy Sequentially Combined with Radiotherapy
(Wiley, 2023) Guo, Beibei; Zang, Yong; Lin, Li-Hsiang; Zhang, Rui; Biostatistics and Health Data Science, Richard M. Fairbanks School of Public Health
Sequential administration of immunotherapy following radiotherapy (immunoRT) has attracted much attention in cancer research. Due to its unique feature that radiotherapy upregulates the expression of a predictive biomarker for immunotherapy, novel clinical trial designs are needed for immunoRT to identify patient subgroups and the optimal dose for each subgroup. In this article, we propose a Bayesian phase I/II design for immunotherapy administered after standard-dose radiotherapy for this purpose. We construct a latent subgroup membership variable and model it as a function of the baseline and pre-post radiotherapy change in the predictive biomarker measurements. Conditional on the latent subgroup membership of each patient, we jointly model the continuous immune response and the binary efficacy outcome using plateau models, and model toxicity using the equivalent toxicity score approach to account for toxicity grades. During the trial, based on accumulating data, we continuously update model estimates and adaptively randomize patients to admissible doses. Simulation studies and an illustrative trial application show that our design has good operating characteristics in terms of identifying both patient subgroups and the optimal dose for each subgroup.
A Comparative Analysis of Oral Health and Self-Rated Health: ‘All of Us Research Program’ vs. ‘Health and Retirement Study’
(MDPI, 2024-09-13) Weintraub, Jane A.; Moss, Kevin L.; Finlayson, Tracy L.; Jones, Judith A.; Preisser, John S.; Biostatistics and Health Data Science, Richard M. Fairbanks School of Public Health
Poor oral health can impact overall health. This study assessed the association between dental factors (dentate status and dental utilization) and self-rated health (S-RH) among older adults in two cross-sectional datasets: (1) NIH "All of Us (AoU) Research Program" (May 2018-July 2022 release) and (2) U.S. nationally representative "Health and Retirement Study" (HRS) 2018 wave. Participants aged ≥ 51 years were included in these analyses if (1) from AoU, they had clinical dental and medical data from electronic health records (EHRs) and surveys (n = 5480), and (2) from HRS, they had dental and socio-demographic survey data (n = 14,358). S-RH was dichotomized (fair/poor vs. better) and analyzed with logistic regression. Sample survey weights for HRS and stratification and averaging AoU results used the weighted HRS race-ethnicity and age distribution standardized respective analyses to the U.S. population. Fair/poor S-RH was reported by 32.6% in AoU and 28.6% in HRS. Dentate status information was available from 7.7% of AoU EHRs. In population-standardized analyses, lack of dental service use increased odds of fair/poor S-RH in AoU, OR (95% CI) = 1.28 (1.11-1.48), and in HRS = 1.45 (1.09-1.94), as did having diabetes, less education, and ever being a smoker. Having no natural teeth was not statistically associated with fair/poor S-RH. Lack of dental service was positively associated with fair/poor S-RH in both datasets. More and better oral health information in AoU and HRS are needed.
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 generalized phase 1-2-3 design integrating dose optimization with confirmatory treatment comparison
(Oxford University Press, 2024) Zang, Yong; Thall, Peter F.; Yuan, Ying; Biostatistics and Health Data Science, Richard M. Fairbanks School of Public Health
A generalized phase 1-2-3 design, Gen 1-2-3, that includes all phases of clinical treatment evaluation is proposed. The design extends and modifies the design of Chapple and Thall (2019), denoted by CT. Both designs begin with a phase 1-2 trial including dose acceptability and optimality criteria, and both select an optimal dose for phase 3. The Gen 1-2-3 design has the following key differences. In stage 1, it uses phase 1-2 criteria to identify a set of candidate doses rather than 1 dose. In stage 2, which is intermediate between phase 1-2 and phase 3, it randomizes additional patients fairly among the candidate doses and an active control treatment arm and uses survival time data from both stage 1 and stage 2 patients to select an optimal dose. It then makes a Go/No Go decision of whether or not to conduct phase 3 based on the predictive probability that the selected optimal dose will provide a specified substantive improvement in survival time over the control. A simulation study shows that the Gen 1-2-3 design has desirable operating characteristics compared to the CT design and 2 conventional designs.
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.

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