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1. Double Robust Variance Estimation with Parametric Working Models

Author

Shook-Sa, Bonnie E., Zivich, Paul N., Lee, Chanhwa, Xue, Keyi, Ross, Rachael K., Edwards, Jessie K., Stringer, Jeffrey S. A., and Cole, Stephen R.

Subjects
Statistics - Methodology, Statistics - Applications
Abstract

Doubly robust estimators have gained popularity in the field of causal inference due to their ability to provide consistent point estimates when either an outcome or exposure model is correctly specified. However, for nonrandomized exposures the influence function based variance estimator frequently used with doubly robust estimators of the average causal effect is only consistent when both working models (i.e., outcome and exposure models) are correctly specified. Here, the empirical sandwich variance estimator and the nonparametric bootstrap are demonstrated to be doubly robust variance estimators. That is, they are expected to provide valid estimates of the variance leading to nominal confidence interval coverage when only one working model is correctly specified. Simulation studies illustrate the properties of the influence function based, empirical sandwich, and nonparametric bootstrap variance estimators in the setting where parametric working models are assumed. Estimators are applied to data from the Improving Pregnancy Outcomes with Progesterone (IPOP) study to estimate the effect of maternal anemia on birth weight among women with HIV., Comment: 32 pages, 14 figures, 13 tables

Published
2024

2. Associations between pain-management treatments and opioid use disorder risk among Medicaid patients

Author

Rudolph, Kara E., Williams, Nicholas T., Diaz, Ivan, Forrest, Sarah, Hoffman, Katherine L., Samples, Hillary, Olfson, Mark, Doan, Lisa, Cerda, Magdalena, and Ross, Rachael

Subjects
Statistics - Applications
Abstract

Introduction: Chronic pain patients are at increased risk of opioid-misuse. Less is known about the unique risk conferred by each pain-management treatment, as treatments are typically implemented together, confounding their independent effects. We estimated the extent to which pain-management strategies were associated with risk of incident opioid use disorder (OUD) for those with chronic pain, controlling for baseline demographic and clinical confounding variables and holding other pain-management treatments at their observed levels. Methods: We used data from two chronic pain subgroups within a cohort of non-pregnant Medicaid patients aged 35-64 years, 2016-2019, from 25 states: 1) those with a chronic pain condition co-morbid with physical disability (N=6,133) or 2) those with chronic pain without disability (N=67,438). We considered 9 pain-management treatments: prescription opioid i) dose and ii) duration; iii) number of opioid prescribers; opioid co-prescription with iv) benzodiazepines, v) muscle relaxants, and vi) gabapentinoids; vii) non-opioid pain prescription, viii) physical therapy, and ix) other pain treatment modality. Our outcome was incident OUD. Results: Having an opioid and gabapentin co-prescription or an opioid and benzodiazepine co-prescription was statistically significantly associated with a 16-46% increased risk of OUD. Opioid dose and duration also were significantly associated with increased risk of OUD. Physical therapy was significantly associated with an 11% decreased risk of OUD in the subgroup with chronic pain but no disability. Conclusions: Co-prescription of opioids with either gabapentin or benzodiazepines may substantially increase risk of OUD. More positively, physical therapy may be a relatively accessible and safe pain-management strategy.

Published
2024

3. Validity of Complete Case Analysis Depends on the Target Population

Author

Webster-Clark, Michael and Ross, Rachael K

Subjects
Statistics - Methodology
Abstract

Missing data is a pernicious problem in epidemiologic research. Research on the validity of complete case analysis for missing data has typically focused on estimating the average treatment effect (ATE) in the whole population. However, other target populations like the treated (ATT) or external targets can be of substantive interest. In such cases, whether missing covariate data occurs within or outside the target population may impact the validity of complete case analysis. We sought to assess bias in complete case analysis when covariate data is missing outside the target (e.g., missing covariate data among the untreated when estimating the ATT). We simulated a study of the effect of a binary treatment X on a binary outcome Y in the presence of 3 confounders C1-C3 that modified the risk difference (RD). We induced missingness in C1 only among the untreated under 4 scenarios: completely randomly (similar to MCAR); randomly based on C2 and C3 (similar to MAR); randomly based on C1 (similar to MNAR); or randomly based on Y (similar to MAR). We estimated the ATE and ATT using weighting and averaged results across the replicates. We conducted a parallel simulation transporting trial results to a target population in the presence of missing covariate data in the trial. In the complete case analysis, estimated ATE was unbiased only when C1 was MCAR among the untreated. The estimated ATT, on the other hand, was unbiased in all scenarios except when Y caused missingness. The parallel simulation of generalizing and transporting trial results saw similar bias patterns. If missing covariate data is only present outside the target population, complete case analysis is unbiased except when missingness is associated with the outcome., Comment: 24 pages, 4 tables, 2 figures, presented at SER 2023 in Portland and ICPE 2023 in Halifax

Published
2024

4. Who Are We Missing? A Principled Approach to Characterizing the Underrepresented Population

Author

Parikh, Harsh, Ross, Rachael, Stuart, Elizabeth, and Rudolph, Kara

Subjects
Statistics - Methodology, Computer Science - Computers and Society, Computer Science - Machine Learning, Statistics - Applications
Abstract

Randomized controlled trials (RCTs) serve as the cornerstone for understanding causal effects, yet extending inferences to target populations presents challenges due to effect heterogeneity and underrepresentation. Our paper addresses the critical issue of identifying and characterizing underrepresented subgroups in RCTs, proposing a novel framework for refining target populations to improve generalizability. We introduce an optimization-based approach, Rashomon Set of Optimal Trees (ROOT), to characterize underrepresented groups. ROOT optimizes the target subpopulation distribution by minimizing the variance of the target average treatment effect estimate, ensuring more precise treatment effect estimations. Notably, ROOT generates interpretable characteristics of the underrepresented population, aiding researchers in effective communication. Our approach demonstrates improved precision and interpretability compared to alternatives, as illustrated with synthetic data experiments. We apply our methodology to extend inferences from the Starting Treatment with Agonist Replacement Therapies (START) trial -- investigating the effectiveness of medication for opioid use disorder -- to the real-world population represented by the Treatment Episode Dataset: Admissions (TEDS-A). By refining target populations using ROOT, our framework offers a systematic approach to enhance decision-making accuracy and inform future trials in diverse populations., Comment: MOUD Analysis Included

Published
2024

5. Empirical sandwich variance estimator for iterated conditional expectation g-computation

Author

Zivich, Paul N, Ross, Rachael K, Shook-Sa, Bonnie E, Cole, Stephen R, and Edwards, Jessie K

Subjects
Statistics - Methodology, Statistics - Computation
Abstract

Iterated conditional expectation (ICE) g-computation is an estimation approach for addressing time-varying confounding for both longitudinal and time-to-event data. Unlike other g-computation implementations, ICE avoids the need to specify models for each time-varying covariate. For variance estimation, previous work has suggested the bootstrap. However, bootstrapping can be computationally intense. Here, we present ICE g-computation as a set of stacked estimating equations. Therefore, the variance for the ICE g-computation estimator can be consistently estimated using the empirical sandwich variance estimator. Performance of the variance estimator was evaluated empirically with a simulation study. The proposed approach is also demonstrated with an illustrative example on the effect of cigarette smoking on the prevalence of hypertension. In the simulation study, the empirical sandwich variance estimator appropriately estimated the variance. When comparing runtimes between the sandwich variance estimator and the bootstrap for the applied example, the sandwich estimator was substantially faster, even when bootstraps were run in parallel. The empirical sandwich variance estimator is a viable option for variance estimation with ICE g-computation., Comment: 18 pages, 1 figure, 6 tables

Published
2023

7. Letter

Author

Cole, Stephen R, primary, Zivich, Paul N, additional, Edwards, Jessie K, additional, Ross, Rachael K, additional, and Shook-Sa, Bonnie E, additional

Published
2023
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8. Adjunctive Diagnostic Studies Completed Following Detection of Candidemia in Children: Secondary Analysis of Observed Practice From a Multicenter Cohort Study Conducted by the Pediatric Fungal Network

Author

Wattier, Rachel L, primary, Bucayu, Robert F T, additional, Boge, Craig L K, additional, Ross, Rachael K, additional, Yildirim, Inci, additional, Zaoutis, Theoklis E, additional, Palazzi, Debra L, additional, Vora, Surabhi B, additional, Castagnola, Elio, additional, Avilés-Robles, Martha, additional, Danziger-Isakov, Lara, additional, Tribble, Alison C, additional, Sharma, Tanvi S, additional, Arrieta, Antonio C, additional, Maron, Gabriela, additional, Berman, David M, additional, Yin, Dwight E, additional, Sung, Lillian, additional, Green, Michael, additional, Roilides, Emmanuel, additional, Belani, Kiran, additional, Romero, José, additional, Soler-Palacin, Pere, additional, López-Medina, Eduardo, additional, Nolt, Dawn, additional, Bin Hussain, Ibrahim Zaid, additional, Muller, William J, additional, Hauger, Sarmistha B, additional, Halasa, Natasha, additional, Dulek, Daniel, additional, Pong, Alice, additional, Gonzalez, Blanca E, additional, Abzug, Mark J, additional, Carlesse, Fabianne, additional, Huppler, Anna R, additional, Rajan, Sujatha, additional, Aftandilian, Catherine, additional, Ardura, Monica I, additional, Chakrabarti, Arunaloke, additional, Hanisch, Benjamin, additional, Salvatore, Christine M, additional, Klingspor, Lena, additional, Knackstedt, Elizabeth D, additional, Lutsar, Irja, additional, Santolaya, Maria E, additional, Shuster, Sydney, additional, Johnson, Sarah K, additional, Steinbach, William J, additional, and Fisher, Brian T, additional

Published
2023
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11. Missing Outcome Data in Epidemiologic Studies

Author

Cole, Stephen R, primary, Zivich, Paul N, additional, Edwards, Jessie K, additional, Ross, Rachael K, additional, Shook-Sa, Bonnie E, additional, Price, Joan T., additional, and Stringer, Jeffrey S A, additional

Published
2022
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17. Missing Outcome Data in Epidemiologic Studies.

Author

Cole, Stephen R, Zivich, Paul N, Edwards, Jessie K, Ross, Rachael K, Shook-Sa, Bonnie E, Price, Joan T., and Stringer, Jeffrey S A

Subjects
STATISTICS, DATABASE evaluation, DATA analysis, RESEARCH bias, ELECTRONIC publications, EPIDEMIOLOGICAL research
Abstract

Missing data are pandemic and a central problem for epidemiology. Missing data reduce precision and can cause notable bias. There remain too few simple published examples detailing types of missing data and illustrating their possible impact on results. Here we take an example randomized trial that was not subject to missing data and induce missing data to illustrate 4 scenarios in which outcomes are 1) missing completely at random, 2) missing at random with positivity, 3) missing at random without positivity, and 4) missing not at random. We demonstrate that accounting for missing data is generally a better strategy than ignoring missing data, which unfortunately remains a standard approach in epidemiology. [ABSTRACT FROM AUTHOR]

Published
2023
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19. Comparative Effectiveness of Echinocandins vs Triazoles or Amphotericin B Formulations as Initial Directed Therapy for Invasive Candidiasis in Children and Adolescents

Author

Fisher, Brian T, primary, Zaoutis, Theoklis E, additional, Xiao, Rui, additional, Wattier, Rachel L, additional, Castagnola, Elio, additional, Pana, Zoi Dorothea, additional, Fullenkamp, Allison, additional, Boge, Craig L K, additional, Ross, Rachael K, additional, Yildirim, Inci, additional, Palazzi, Debra L, additional, Danziger-Isakov, Lara, additional, Vora, Surabhi B, additional, Arrieta, Antonio, additional, Yin, Dwight E, additional, Avilés-Robles, Martha, additional, Sharma, Tanvi, additional, Tribble, Alison C, additional, Maron, Gabriela, additional, Berman, David, additional, Green, Michael, additional, Sung, Lillian, additional, Romero, José, additional, Hauger, Sarmistha B, additional, Roilides, Emmanuel, additional, Belani, Kiran, additional, Nolt, Dawn, additional, Soler-Palacin, Pere, additional, López-Medina, Eduardo, additional, Muller, William J, additional, Halasa, Natasha, additional, Dulek, Daniel, additional, Hussain, Ibrahim Zaid Bin, additional, Pong, Alice, additional, Hoffman, Jill, additional, Rajan, Sujatha, additional, Gonzalez, Blanca E, additional, Hanisch, Benjamin, additional, Aftandilian, Catherine, additional, Carlesse, Fabianne, additional, Abzug, Mark J, additional, Huppler, Anna R, additional, Salvatore, Christine M, additional, Ardura, Monica I, additional, Chakrabarti, Arunaloke, additional, Santolaya, Maria E, additional, Localio, A Russell, additional, and Steinbach, William J, additional

Published
2021
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20. Validation of a five-year mortality prediction model among US Medicare beneficiaries

Author

Ross, Rachael K., Kuo, Tzy-Mey, Webster-Clark, Michael, Lewis, Carmen L., Kistler, Christine E., Funk, Michele Jonsson, and Lund, Jennifer L.

Subjects
Aged, 80 and over, Male, Models, Statistical, Frailty, Comorbidity, Medicare, Article, United States, Risk Factors, North Carolina, Humans, Disabled Persons, Female, Mortality, Aged, Retrospective Studies
Abstract

BACKGROUND/OBJECTIVES: A claims-based model predicting five-year mortality (Lund-Lewis) was developed in a 2008 cohort of North Carolina (NC) Medicare beneficiaries and included indicators of comorbid conditions, frailty, disability, and functional impairment. The objective of this study was to externally validate the Lund-Lewis model within a nationwide sample of Medicare beneficiaries. DESIGN: Retrospective validation study SETTING: United States Medicare population PARTICIPANTS (INCL SAMPLE SIZE): From a random sample of Medicare beneficiaries, we created four annual cohorts from 2008 to 2011 of individuals aged 66 years and older with an office visit in that year. The annual cohorts ranged from 1.13 to 1.18 million beneficiaries. MEASUREMENTS: The outcome was five-year all-cause mortality. We assessed clinical indicators in the 12 months prior to the qualifying office visit and estimated predicted five-year mortality for each beneficiary in the nationwide sample by applying estimates derived in the original NC cohort. Model performance was assessed by quantifying discrimination, calibration, and reclassification metrics compared to a model fit on a comorbidity score. RESULTS: Across the annual cohorts, five-year mortality ranged from 24.4% to 25.5%. The model had strong discrimination (C-statistics ranged across cohorts from 0.823-0.826). Reclassification measures showed improvement over a comorbidity score model for beneficiaries who died but reduced performance among beneficiaries who survived. The calibration slope ranged from 0.83 to 0.86; the model generally predicted a higher risk than observed. CONCLUSION: The Lund-Lewis model showed strong and consistent discrimination in a national US Medicare sample though calibration indicated slight overfitting. Future work should investigate methods for improving model calibration and evaluating performance within specific disease settings.

Published
2020

24. Meta-Analysis and Sparse-Data Bias

Author

Richardson, David B, primary, Cole, Stephen R, additional, Ross, Rachael K, additional, Poole, Charles, additional, Chu, Haitao, additional, and Keil, Alexander P, additional

Published
2020
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26. Reflection on modern methods: combining weights for confounding and missing data.

Author

Ross, Rachael K, Breskin, Alexander, Breger, Tiffany L, and Westreich, Daniel

Abstract

Inverse probability weights are increasingly used in epidemiological analysis, and estimation and application of weights to address a single bias are well discussed in the literature. Weights to address multiple biases simultaneously (i.e. a combination of weights) have almost exclusively been discussed related to marginal structural models in longitudinal settings where treatment weights (estimated first) are combined with censoring weights (estimated second). In this work, we examine two examples of combined weights for confounding and missingness in a time-fixed setting in which outcome or confounder data are missing, and the estimand is the marginal expectation of the outcome under a time-fixed treatment. We discuss the identification conditions, construction of combined weights and how assumptions of the missing data mechanisms affect this construction. We use a simulation to illustrate the estimation and application of the weights in the two examples. Notably, when only outcome data are missing, construction of combined weights is straightforward; however, when confounder data are missing, we show that in general we must follow a specific estimation procedure which entails first estimating missingness weights and then estimating treatment probabilities from data with missingness weights applied. However, if treatment and missingness are conditionally independent, then treatment probabilities can be estimated among the complete cases. [ABSTRACT FROM AUTHOR]

Published
2022
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27. Initiator Types and the Causal Question of the Prevalent New-User Design: A Simulation Study.

Author

Webster-Clark, Michael, Ross, Rachael K, and Lund, Jennifer L

Subjects
*EXPERIMENTAL design, *RELATIVE medical risk, *SAMPLE size (Statistics), *CONFIDENCE intervals, *MORTALITY, *MULTIVARIATE analysis, *REGRESSION analysis, *TREATMENT delay (Medicine), *RISK assessment, *MATHEMATICAL variables, *DESCRIPTIVE statistics, *RESEARCH funding, *DATA analysis, *STATISTICAL correlation, *LOGISTIC regression analysis, *PROBABILITY theory
Abstract

New-user designs restricting to treatment initiators have become the preferred design for studying drug comparative safety and effectiveness using nonexperimental data. This design reduces confounding by indication and healthy-adherer bias at the cost of smaller study sizes and reduced external validity, particularly when assessing a newly approved treatment compared with standard treatment. The prevalent new-user design includes adopters of a new treatment who switched from or previously used standard treatment (i.e. the comparator), expanding study sample size and potentially broadening the study population for inference. Previous work has suggested the use of time-conditional propensity-score matching to mitigate prevalent user bias. In this study, we describe 3 "types" of initiators of a treatment: new users, direct switchers, and delayed switchers. Using these initiator types, we articulate the causal questions answered by the prevalent new-user design and compare them with those answered by the new-user design. We then show, using simulation, how conditioning on time since initiating the comparator (rather than full treatment history) can still result in a biased estimate of the treatment effect. When implemented properly, the prevalent new-user design estimates new and important causal effects distinct from the new-user design. [ABSTRACT FROM AUTHOR]

Published
2021
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28. Outpatient Fluoroquinolone Use in Children, 2000–2018.

Author

Ross, Rachael K, Gerber, Jeffrey S, Willis, Zachary I, Hersh, Adam L, and Kinlaw, Alan C

Subjects
*ANTIBIOTICS, *CONFIDENCE intervals, *AGE distribution, *URINARY tract infections, *FLUOROQUINOLONES, *TIME series analysis, *DESCRIPTIVE statistics, *LONGITUDINAL method, *CHILDREN
Abstract

Background While fluoroquinolones are commonly used in adults, the use in children has been low. Since 2000, there were 3 US Food and Drug Administration (FDA) Boxed warnings regarding fluoroquinolones (2008, 2013, and 2016). Our objective was to describe the use of fluoroquinolones in children and assess the impact of 3 recent FDA warnings on fluoroquinolone use. Methods From 2000 to 2018, we assessed claims for all outpatient prescription fills to measure the use of systemic fluoroquinolones and other broad-spectrum antibiotics in children  less than 18 years old in the MarketScan Commercial Claims and Encounters database. We describe demographics, indication for antibiotic, and clinical characteristics. To assess the impact of FDA warnings on fill rates, we conducted an interrupted time-series analysis. Results The cohort included 34.6 million unique beneficiaries less than 18 years old with 441 062 fluoroquinolone fills (5.5 fills per 1000 person-years). The fluoroquinolone fill rate was highest among children > 11 years old. Urinary tract infection was the most common associated diagnosis (21.8%). Since 2008, the fluoroquinolone fill rate has declined. By the end of the study period in December 2018, in the (counterfactual) absence of the FDA warnings, fluoroquinolone fill rate would have been 7.5 (95% confidence interval [CI]: 5.2-9.7); however, the corresponding rate in observed data was 2.8 (95% CI: 1.7-3.9). Conclusions Fluoroquinolone use was low compared with other common broad-spectrum antibiotics and declining trends over time were associated with FDA warnings, even though these warnings were not pediatric specific. Future work should assess the adverse events at issue in these warnings in children. [ABSTRACT FROM AUTHOR]

Published
2021
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29. Meta-Analysis and Sparse-Data Bias.

Author

Richardson, David B, Cole, Stephen R, Ross, Rachael K, Poole, Charles, Chu, Haitao, and Keil, Alexander P

Subjects
CONFIDENCE intervals, LONGITUDINAL method, META-analysis, LOGISTIC regression analysis, RESEARCH bias, STATISTICAL models, ODDS ratio
Abstract

Meta-analyses are undertaken to combine information from a set of studies, often in settings where some of the individual study-specific estimates are based on relatively small study samples. Finite sample bias may occur when maximum likelihood estimates of associations are obtained by fitting logistic regression models to sparse data sets. Here we show that combining information from small studies by undertaking a meta-analytical summary of logistic regression estimates can propagate such sparse-data bias. In simulations, we illustrate 2 challenges encountered in meta-analyses of logistic regression results in settings of sparse data: 1) bias in the summary meta-analytical result and 2) confidence interval coverage that can worsen rather than improve, in terms of being less than nominal, as the number of studies in the meta-analysis increases. [ABSTRACT FROM AUTHOR]

Published
2021
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43. Failure to Validate a Multivariable Clinical Prediction Model to Identify Pediatric Intensive Care Unit Patients at High Risk for Candidemia

Author

Fisher, Brian T., primary, Ross, Rachael K., primary, Roilides, Emmanuel, primary, Palazzi, Debra L., primary, Abzug, Mark J., primary, Hoffman, Jill A., primary, Berman, David M., primary, Prasad, Priya A., primary, Localio, A. Russell, primary, Steinbach, William J., primary, Vogiatzi, Lambrini, primary, Dutta, Ankhi, primary, and Zaoutis, Theoklis E., primary

Published
2015
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49. 1449Clinical Prediction Model for Candidemia in Pediatric ICU Patients: Failure to Validate

Author

Fisher, Brian, primary, Ross, Rachael, additional, Roilides, Emmanuel, additional, Palazzi, Debra, additional, Abzug, Mark J, additional, Hoffman, Jill, additional, Berman, David, additional, Prasad, Priya, additional, Localio, Russell, additional, Steinbach, William, additional, Vogiatzi, Lambrini, additional, Dutta, Ankhi, additional, and Zaoutis, Theoklis, additional

Published
2014
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