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Article ; Online: Computer Algebra and Algorithms for Unbiased Moment Estimation of Arbitrary Order.

Gerlovina, Inna / Hubbard, Alan E

Cogent mathematics & statistics

2019 Volume 6, Issue 1

Abstract: While unbiased central moment estimators of lower orders (such as a sample variance) are easily obtainable and often used in practice, derivation of unbiased estimators of higher orders might be more challenging due to long math and tricky combinatorics. ...

Abstract	While unbiased central moment estimators of lower orders (such as a sample variance) are easily obtainable and often used in practice, derivation of unbiased estimators of higher orders might be more challenging due to long math and tricky combinatorics. Moreover, higher orders necessitate calculation of estimators of powers and products that also amount to these orders. We develop a software algorithm that allows the user to obtain unbiased estimators of an arbitrary order and provide results up to the 6th order, including powers and products of lower orders. The method also extends to finding pooled estimates of higher central moments of several different populations (
Language	English
Publishing date	2019-12-21
Publishing country	England
Document type	Journal Article
ISSN	2574-2558
ISSN (online)	2574-2558
DOI	10.1080/25742558.2019.1701917
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Article: Development of a Machine Learning Model of Postoperative Acute Kidney Injury Using Non-Invasive Time-Sensitive Intraoperative Predictors.

Zamirpour, Siavash / Hubbard, Alan E / Feng, Jean / Butte, Atul J / Pirracchio, Romain / Bishara, Andrew

Bioengineering (Basel, Switzerland)

2023 Volume 10, Issue 8

Abstract: Acute kidney injury (AKI) is a major postoperative complication that lacks established intraoperative predictors. Our objective was to develop a prediction model using preoperative and high-frequency intraoperative data for postoperative AKI. In this ... ...

Abstract	Acute kidney injury (AKI) is a major postoperative complication that lacks established intraoperative predictors. Our objective was to develop a prediction model using preoperative and high-frequency intraoperative data for postoperative AKI. In this retrospective cohort study, we evaluated 77,428 operative cases at a single academic center between 2016 and 2022. A total of 11,212 cases with serum creatinine (sCr) data were included in the analysis. Then, 8519 cases were randomly assigned to the training set and the remainder to the validation set. Fourteen preoperative and twenty intraoperative variables were evaluated using elastic net followed by hierarchical group least absolute shrinkage and selection operator (LASSO) regression. The training set was 56% male and had a median [IQR] age of 62 (51-72) and a 6% AKI rate. Retained model variables were preoperative sCr values, the number of minutes meeting cutoffs for urine output, heart rate, perfusion index intraoperatively, and the total estimated blood loss. The area under the receiver operator characteristic curve was 0.81 (95% CI, 0.77-0.85). At a score threshold of 0.767, specificity was 77% and sensitivity was 74%. A web application that calculates the model score is available online. Our findings demonstrate the utility of intraoperative time series data for prediction problems, including a new potential use of the perfusion index. Further research is needed to evaluate the model in clinical settings.
Language	English
Publishing date	2023-08-05
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2746191-9
ISSN	2306-5354
ISSN	2306-5354
DOI	10.3390/bioengineering10080932
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Article ; Online: A generalization of moderated statistics to data adaptive semiparametric estimation in high-dimensional biology.

Hejazi, Nima S / Boileau, Philippe / van der Laan, Mark J / Hubbard, Alan E

Statistical methods in medical research

2022 Volume 32, Issue 3, Page(s) 539–554

Abstract: The widespread availability of high-dimensional biological data has made the simultaneous screening of many biological characteristics a central problem in computational and high-dimensional biology. As the dimensionality of datasets continues to grow, ... ...

Abstract	The widespread availability of high-dimensional biological data has made the simultaneous screening of many biological characteristics a central problem in computational and high-dimensional biology. As the dimensionality of datasets continues to grow, so too does the complexity of identifying biomarkers linked to exposure patterns. The statistical analysis of such data often relies upon parametric modeling assumptions motivated by convenience, inviting opportunities for model misspecification. While estimation frameworks incorporating flexible, data adaptive regression strategies can mitigate this, their standard variance estimators are often unstable in high-dimensional settings, resulting in inflated Type-I error even after standard multiple testing corrections. We adapt a shrinkage approach compatible with parametric modeling strategies to semiparametric variance estimators of a family of efficient, asymptotically linear estimators of causal effects, defined by counterfactual exposure contrasts. Augmenting the inferential stability of these estimators in high-dimensional settings yields a data adaptive approach for robustly uncovering stable causal associations, even when sample sizes are limited. Our generalized variance estimator is evaluated against appropriate alternatives in numerical experiments, and an open source R/Bioconductor package, biotmle, is introduced. The proposal is demonstrated in an analysis of high-dimensional DNA methylation data from an observational study on the epigenetic effects of tobacco smoking.
MeSH term(s)	Research Design ; Sample Size ; Causality ; Biology
Language	English
Publishing date	2022-12-26
Publishing country	England
Document type	Observational Study ; Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't ; Research Support, U.S. Gov't, Non-P.H.S.
ZDB-ID	1136948-6
ISSN	1477-0334 ; 0962-2802
ISSN (online)	1477-0334
ISSN	0962-2802
DOI	10.1177/09622802221146313
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Zs.A 3564: Show issues

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Article ; Online: Sex differences in ascending aortic size reporting and growth on chest computed tomography and magnetic resonance imaging.

Zamirpour, Siavash / Boskovski, Marko T / Pirruccello, James P / Pace, William A / Hubbard, Alan E / Leach, Joseph R / Ge, Liang / Tseng, Elaine E

Clinical imaging

2023 Volume 105, Page(s) 110021

Abstract: Purpose: Diameter-based guidelines for prophylactic repair of ascending aortic aneurysms have led to routine aortic evaluation in chest imaging. Despite sex differences in aneurysm outcomes, there is little understanding of sex-specific aortic growth ... ...

Abstract	Purpose: Diameter-based guidelines for prophylactic repair of ascending aortic aneurysms have led to routine aortic evaluation in chest imaging. Despite sex differences in aneurysm outcomes, there is little understanding of sex-specific aortic growth rates. Our objective was to evaluate sex-specific temporal changes in radiologist-reported aortic size as well as sex differences in aortic reporting. Method: In this cohort study, we queried radiology reports of chest computed tomography or magnetic resonance imaging at an academic medical center from 1994 to 2022, excluding type A dissection. Aortic diameter was extracted using a custom text-processing algorithm. Growth rates were estimated using mixed-effects modeling with fixed terms for sex, age, and imaging modality, and patient-level random intercepts. Sex, age, and modality were evaluated as predictors of aortic reporting by logistic regression. Results: This study included 89,863 scans among 46,622 patients (median [interquartile range] age, 64 [52-73]; 22,437 women [48%]). Aortic diameter was recorded in 14% (12,722/89,863 reports). Temporal trends were analyzed in 7194 scans among 1998 patients (age, 68 [60-75]; 677 women [34%]) with ≥2 scans. Aortic growth rate was significantly higher in women (0.22 mm/year [95% confidence interval 0.17-0.28] vs. 0.09 mm/year [0.06-0.13], respectively). Aortic reporting was significantly less common in women (odds ratio, 0.54; 95% CI, 0.52-0.56; p < 0.001). Conclusions: While aortic growth rates were small overall, women had over twice the growth rate of men. Aortic dimensions were much less frequently reported in women than men. Sex-specific standardized assessment of aortic measurements may be needed to address sex differences in aneurysm outcomes.
MeSH term(s)	Humans ; Male ; Female ; Middle Aged ; Aged ; Cohort Studies ; Sex Characteristics ; Tomography, X-Ray Computed/methods ; Magnetic Resonance Imaging ; Aneurysm ; Aortic Aneurysm, Thoracic/diagnostic imaging ; Risk Factors
Language	English
Publishing date	2023-11-16
Publishing country	United States
Document type	Journal Article
ZDB-ID	1028123-x
ISSN	1873-4499 ; 0899-7071
ISSN (online)	1873-4499
ISSN	0899-7071
DOI	10.1016/j.clinimag.2023.110021
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Zs.A 1469: Show issues

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Article ; Online: Personalized online ensemble machine learning with applications for dynamic data streams.

Malenica, Ivana / Phillips, Rachael V / Chambaz, Antoine / Hubbard, Alan E / Pirracchio, Romain / van der Laan, Mark J

Statistics in medicine

2023 Volume 42, Issue 7, Page(s) 1013–1044

Abstract: In this work we introduce the personalized online super learner (POSL), an online personalizable ensemble machine learning algorithm for streaming data. POSL optimizes predictions with respect to baseline covariates, so personalization can vary from ... ...

Abstract	In this work we introduce the personalized online super learner (POSL), an online personalizable ensemble machine learning algorithm for streaming data. POSL optimizes predictions with respect to baseline covariates, so personalization can vary from completely individualized, that is, optimization with respect to subject ID, to many individuals, that is, optimization with respect to common baseline covariates. As an online algorithm, POSL learns in real time. As a super learner, POSL is grounded in statistical optimality theory and can leverage a diversity of candidate algorithms, including online algorithms with different training and update times, fixed/offline algorithms that are not updated during POSL's fitting procedure, pooled algorithms that learn from many individuals' time series, and individualized algorithms that learn from within a single time series. POSL's ensembling of the candidates can depend on the amount of data collected, the stationarity of the time series, and the mutual characteristics of a group of time series. Depending on the underlying data-generating process and the information available in the data, POSL is able to adapt to learning across samples, through time, or both. For a range of simulations that reflect realistic forecasting scenarios and in a medical application, we examine the performance of POSL relative to other current ensembling and online learning methods. We show that POSL is able to provide reliable predictions for both short and long time series, and it's able to adjust to changing data-generating environments. We further cultivate POSL's practicality by extending it to settings where time series dynamically enter and exit.
MeSH term(s)	Humans ; Machine Learning ; Algorithms
Language	English
Publishing date	2023-01-16
Publishing country	England
Document type	Journal Article
ZDB-ID	843037-8
ISSN	1097-0258 ; 0277-6715
ISSN (online)	1097-0258
ISSN	0277-6715
DOI	10.1002/sim.9655
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Zs.A 1756: Show issues

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Article ; Online: Estimating the effect of realistic improvements of metformin adherence on COVID-19 mortality using targeted machine learning.

Qiu, Sky / Hubbard, Alan E / Gutiérrez, Juan Pablo / Pimpale, Ganesh / Juárez-Flores, Arturo / Ghosh, Rakesh / de Jesús Ascencio-Montiel, Iván / Bertozzi, Stefano M

Global epidemiology

2024 Volume 7, Page(s) 100142

Abstract: Background: Type 2 diabetes elevates the risk of severe outcomes in COVID-19 patients, with multiple studies reporting higher case fatality rates. Metformin is a widely used medication for glycemic management. We hypothesize that improved adherence to ... ...

Abstract	Background: Type 2 diabetes elevates the risk of severe outcomes in COVID-19 patients, with multiple studies reporting higher case fatality rates. Metformin is a widely used medication for glycemic management. We hypothesize that improved adherence to metformin may lower COVID-19 post-infection mortality risk in this group. Utilizing data from the Mexican Social Security Institute (IMSS), we investigate the relationship between metformin adherence and mortality following COVID-19 infection in patients with chronic metformin prescriptions. Methods: This is a retrospective cohort study consisting of 61,180 IMSS beneficiaries who received a positive polymerase chain reaction (PCR) or rapid test for SARS-CoV-2 and had at least two consecutive months of metformin prescriptions prior to the positive test. The hypothetical intervention is improved adherence to metformin, measured by proportion of days covered (PDC), with the comparison being the observed metformin adherence values. The primary outcome is all-cause mortality following COVID-19 infection. We defined the causal parameter using shift intervention, an example of modified treatment policies. We used the targeted learning framework for estimation of the target estimand. Findings: Among COVID-19 positive patients with chronic metformin prescriptions, we found that a 5% and 10% absolute increase in metformin adherence is associated with a respective 0.26% (95% CI: -0.28%, 0.79%) and 1.26% (95% CI: 0.72%, 1.80%) absolute decrease in mortality risk. Interpretation: Subject to the limitations of a real-world data study, our results indicate a causal association between improved metformin adherence and reduced COVID-19 post-infection mortality risk.
Language	English
Publishing date	2024-03-30
Publishing country	United States
Document type	Journal Article
ISSN	2590-1133
ISSN (online)	2590-1133
DOI	10.1016/j.gloepi.2024.100142
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Article ; Online: Improved Child Feces Management Mediates Reductions in Childhood Diarrhea from an On-Site Sanitation Intervention: Causal Mediation Analysis of a Cluster-Randomized Trial in Rural Bangladesh.

Contreras, Jesse D / Islam, Mahfuza / Mertens, Andrew / Pickering, Amy J / Arnold, Benjamin F / Benjamin-Chung, Jade / Hubbard, Alan E / Rahman, Mahbubur / Unicomb, Leanne / Luby, Stephen P / Colford, John M / Ercumen, Ayse

Journal of epidemiology and global health

2024

Abstract: Background: The WASH benefits Bangladesh trial multi-component sanitation intervention reduced diarrheal disease among children < 5 years. Intervention components included latrine upgrades, child feces management tools, and behavioral promotion. It ... ...

Abstract	Background: The WASH benefits Bangladesh trial multi-component sanitation intervention reduced diarrheal disease among children < 5 years. Intervention components included latrine upgrades, child feces management tools, and behavioral promotion. It remains unclear which components most impacted diarrhea. Methods: We conducted mediation analysis within a subset of households (n = 720) from the sanitation and control arms. Potential mediators were categorized into indicators of latrine quality, latrine use practices, and feces management practices. We estimated average causal mediation effects (ACME) as prevalence differences (PD), defined as the intervention's effect on diarrhea through its effect on the mediator. Results: The intervention improved all indicators compared to controls. We found significant mediation through multiple latrine use and feces management practice indicators. The strongest mediators during monsoon seasons were reduced open defecation among children aged < 3 and 3-8 years, and increased disposal of child feces into latrines. The strongest mediators during dry seasons were access to a flush/pour-flush latrine, reduced open defecation among children aged 3-8 years, and increased disposal of child feces into latrines. Individual mediation effects were small (PD = 0.5-2 percentage points) compared to the overall intervention effect but collectively describe significant mediation pathways. Discussion: The effect of the WASH Benefits Bangladesh sanitation intervention on diarrheal disease was mediated through improved child feces management and reduced child open defecation. Although the intervention significantly improved latrine quality, relatively high latrine quality at baseline may have limited benefits from additional improvements. Targeting safe child feces management may increase the health benefits of rural sanitation interventions.
Language	English
Publishing date	2024-03-20
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2645324-1
ISSN	2210-6014 ; 2210-6014
ISSN (online)	2210-6014
ISSN	2210-6014
DOI	10.1007/s44197-024-00210-y
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Article ; Online: Ensemble machine learning for the prediction of patient-level outcomes following thyroidectomy.

Seib, Carolyn D / Roose, James P / Hubbard, Alan E / Suh, Insoo

American journal of surgery

2020 Volume 222, Issue 2, Page(s) 347–353

Abstract: Background: Accurate prediction of thyroidectomy complications is necessary to inform treatment decisions. Ensemble machine learning provides one approach to improve prediction.: Methods: We applied the Super Learner (SL) algorithm to the 2016-2018 ... ...

Abstract	Background: Accurate prediction of thyroidectomy complications is necessary to inform treatment decisions. Ensemble machine learning provides one approach to improve prediction. Methods: We applied the Super Learner (SL) algorithm to the 2016-2018 thyroidectomy-specific NSQIP database to predict complications following thyroidectomy. Cross-validation was used to assess model discrimination and precision. Results: For the 17,987 patients undergoing thyroidectomy, rates of recurrent laryngeal nerve injury, post-operative hypocalcemia prior to discharge or within 30 days, and neck hematoma were 6.1%, 6.4%, 9.0%, and 1.8%, respectively. SL improved prediction of thyroidectomy-specific outcomes when compared with benchmark logistic regression approaches. For postoperative hypocalcemia prior to discharge, SL improved the cross-validated AUROC to 0.72 (95%CI 0.70-0.74) compared to 0.70 (95%CI 0.68-0.72; p < 0.001) when using a manually curated logistic regression algorithm. Conclusion: Ensemble machine learning modestly improves prediction for thyroidectomy-specific outcomes. SL holds promise to provide more accurate patient-level risk prediction to inform treatment decisions.
MeSH term(s)	Adult ; Aged ; Algorithms ; Female ; Humans ; Logistic Models ; Machine Learning ; Male ; Middle Aged ; Postoperative Complications/diagnosis ; Postoperative Complications/epidemiology ; Predictive Value of Tests ; ROC Curve ; Risk Factors ; Thyroid Diseases/complications ; Thyroid Diseases/diagnosis ; Thyroid Diseases/surgery ; Thyroidectomy/adverse effects ; Treatment Outcome
Language	English
Publishing date	2020-12-03
Publishing country	United States
Document type	Journal Article
ZDB-ID	2953-1
ISSN	1879-1883 ; 0002-9610
ISSN (online)	1879-1883
ISSN	0002-9610
DOI	10.1016/j.amjsurg.2020.11.055
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Article ; Online: Acute myocardial infarction associated with abacavir and tenofovir based antiretroviral drug combinations in the United States.

Dorjee, Kunchok / Desai, Manisha / Choden, Tsering / Baxi, Sanjiv M / Hubbard, Alan E / Reingold, Arthur L

AIDS research and therapy

2021 Volume 18, Issue 1, Page(s) 57

Abstract: Introduction: Although individual antiretroviral drugs have been shown to be associated with elevated cardiovascular disease (CVD) risk, data are limited on the role of antiretroviral drug combinations. Therefore, we sought to investigate CVD risk ... ...

Abstract	Introduction: Although individual antiretroviral drugs have been shown to be associated with elevated cardiovascular disease (CVD) risk, data are limited on the role of antiretroviral drug combinations. Therefore, we sought to investigate CVD risk associated with antiretroviral drug combinations. Methods: Using an administrative health-plan dataset, risk of acute myocardial infarction (AMI) associated with current exposure to antiretroviral drug combinations was assessed among persons living with HIV receiving antiretroviral therapy (ART) across the U.S. from October 2009 through December 2014. To account for confounding-by-indication and for factors simultaneously acting as causal mediators and confounders, we applied inverse probability of treatment weighted marginal structural models to longitudinal data of patients. Results: Over 114,417 person-years (n = 73,071 persons) of ART exposure, 602 cases of AMI occurred at an event rate of 5.26 (95% CI: 4.86, 5.70)/1000 person-years. Of the 14 antiretroviral drug combinations studied, persons taking abacavir-lamivudine-darunavir had the highest incidence rate (IR: 11/1000; 95% CI: 7.4-16.0) of AMI. Risk (HR; 95% CI) of AMI was elevated for current exposure to abacavir-lamivudine-darunavir (1.91; 1.27-2.88), abacavir-lamivudine-atazanavir (1.58; 1.08-2.31), and tenofovir-emtricitabine-raltegravir (1.35; 1.07-1.71). Tenofovir-emtricitabine-efavirenz was associated with reduced risk (0.65; 0.54-0.78). Abacavir-lamivudine-darunavir was associated with increased risk of AMI beyond that expected of abacavir alone, likely attributable to darunavir co-administration. We did not find an elevated risk of AMI when abacavir-lamivudine was combined with efavirenz or raltegravir. Conclusion: The antiretroviral drug combinations abacavir-lamivudine-darunavir, abacavir-lamivudine-atazanavir and tenofovir-emtricitabine-raltegravir were found to be associated with elevated risk of AMI, while tenofovir-emtricitabine-efavirenz was associated with a lower risk. The AMI risk associated with abacavir-lamivudine-darunavir was greater than what was previously described for abacavir, which could suggest an added risk from darunavir. The results should be confirmed in additional studies.
MeSH term(s)	Anti-HIV Agents/adverse effects ; Dideoxynucleosides/adverse effects ; Drug Combinations ; HIV Infections/drug therapy ; HIV Infections/epidemiology ; HIV-1 ; Humans ; Lamivudine/therapeutic use ; Myocardial Infarction/chemically induced ; Myocardial Infarction/drug therapy ; Myocardial Infarction/epidemiology ; Tenofovir/adverse effects ; United States/epidemiology
Chemical Substances	Anti-HIV Agents ; Dideoxynucleosides ; Drug Combinations ; Lamivudine (2T8Q726O95) ; Tenofovir (99YXE507IL) ; abacavir (WR2TIP26VS)
Language	English
Publishing date	2021-09-06
Publishing country	England
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
ISSN	1742-6405
ISSN (online)	1742-6405
DOI	10.1186/s12981-021-00383-7
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Article ; Online: The impact of prenatal and early-life arsenic exposure on epigenetic age acceleration among adults in Northern Chile.

Bozack, Anne K / Boileau, Philippe / Hubbard, Alan E / Sillé, Fenna C M / Ferreccio, Catterina / Steinmaus, Craig M / Smith, Martyn T / Cardenas, Andres

Environmental epigenetics

2022 Volume 8, Issue 1, Page(s) dvac014

Abstract: Exposure to arsenic affects millions of people globally. Changes in the epigenome may be involved in pathways linking arsenic to health or serve as biomarkers of exposure. This study investigated associations between prenatal and early-life arsenic ... ...

Abstract	Exposure to arsenic affects millions of people globally. Changes in the epigenome may be involved in pathways linking arsenic to health or serve as biomarkers of exposure. This study investigated associations between prenatal and early-life arsenic exposure and epigenetic age acceleration (EAA) in adults, a biomarker of morbidity and mortality. DNA methylation was measured in peripheral blood mononuclear cells (PBMCs) and buccal cells from 40 adults (median age = 49 years) in Chile with and without high prenatal and early-life arsenic exposure. EAA was calculated using the Horvath, Hannum, PhenoAge, skin and blood, GrimAge, and DNA methylation telomere length clocks. We evaluated associations between arsenic exposure and EAA using robust linear models. Participants classified as with and without arsenic exposure had a median drinking water arsenic concentration at birth of 555 and 2 μg/l, respectively. In PBMCs, adjusting for sex and smoking, exposure was associated with a 6-year PhenoAge acceleration [
Language	English
Publishing date	2022-06-01
Publishing country	England
Document type	Journal Article
ZDB-ID	2831217-X
ISSN	2058-5888 ; 2058-5888
ISSN (online)	2058-5888
ISSN	2058-5888
DOI	10.1093/eep/dvac014
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