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  1. Article ; Online: Causal inference methods for vaccine sieve analysis with effect modification.

    Yang, Guandong / Balzer, Laura B / Benkeser, David

    Statistics in medicine

    2022  Volume 41, Issue 8, Page(s) 1513–1524

    Abstract: The protective effects of vaccines may vary depending on individual characteristics, such as age. Traditionally, such effect modification has been examined with subgroup analyses or inclusion of cross-product terms in regression frameworks. However, in ... ...

    Abstract The protective effects of vaccines may vary depending on individual characteristics, such as age. Traditionally, such effect modification has been examined with subgroup analyses or inclusion of cross-product terms in regression frameworks. However, in many vaccine settings, effect modification may also depend on the infecting pathogen's characteristics, which are measured postrandomization. Sieve analysis examines whether such effects are present by combining pathogen genetic sequence information with individual-level data and can generate new hypotheses on the pathways whereby vaccines provide protection. In this article, we develop a causal framework for evaluating effect modification in the context of sieve analysis. Our approach can be used to assess the magnitude of sieve effects and, in particular, whether these effects are modified by individual-level characteristics. Our method accounts for difficulties occurring in real-world data analysis, such as competing risks, nonrandomized treatments, and differential dropout. Our approach also integrates modern machine learning techniques. We demonstrate the validity and efficiency of our approach in simulation studies and apply the methodology to a malaria vaccine study.
    MeSH term(s) Causality ; Computer Simulation ; Humans ; Machine Learning ; Malaria Vaccines ; Research Design
    Chemical Substances Malaria Vaccines
    Language English
    Publishing date 2022-01-19
    Publishing country England
    Document type Journal Article ; Research Support, N.I.H., Extramural ; Research Support, U.S. Gov't, Non-P.H.S.
    ZDB-ID 843037-8
    ISSN 1097-0258 ; 0277-6715
    ISSN (online) 1097-0258
    ISSN 0277-6715
    DOI 10.1002/sim.9302
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  2. Book ; Online: A Huber loss-based super learner with applications to healthcare expenditures

    Wu, Ziyue / Benkeser, David

    2022  

    Abstract: Complex distributions of the healthcare expenditure pose challenges to statistical modeling via a single model. Super learning, an ensemble method that combines a range of candidate models, is a promising alternative for cost estimation and has shown ... ...

    Abstract Complex distributions of the healthcare expenditure pose challenges to statistical modeling via a single model. Super learning, an ensemble method that combines a range of candidate models, is a promising alternative for cost estimation and has shown benefits over a single model. However, standard approaches to super learning may have poor performance in settings where extreme values are present, such as healthcare expenditure data. We propose a super learner based on the Huber loss, a "robust" loss function that combines squared error loss with absolute loss to down-weight the influence of outliers. We derive oracle inequalities that establish bounds on the finite-sample and asymptotic performance of the method. We show that the proposed method can be used both directly to optimize Huber risk, as well as in finite-sample settings where optimizing mean squared error is the ultimate goal. For this latter scenario, we provide two methods for performing a grid search for values of the robustification parameter indexing the Huber loss. Simulations and real data analysis demonstrate appreciable finite-sample gains in cost prediction and causal effect estimation using our proposed method.
    Keywords Statistics - Machine Learning ; Computer Science - Machine Learning
    Subject code 006
    Publishing date 2022-05-13
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  3. Article ; Online: Efficient estimation of pathwise differentiable target parameters with the undersmoothed highly adaptive lasso.

    van der Laan, Mark J / Benkeser, David / Cai, Weixin

    The international journal of biostatistics

    2022  Volume 19, Issue 1, Page(s) 261–289

    Abstract: We consider estimation of a functional parameter of a realistically modeled data distribution based on observing independent and identically distributed observations. The highly adaptive lasso estimator of the functional parameter is defined as the ... ...

    Abstract We consider estimation of a functional parameter of a realistically modeled data distribution based on observing independent and identically distributed observations. The highly adaptive lasso estimator of the functional parameter is defined as the minimizer of the empirical risk over a class of cadlag functions with finite sectional variation norm, where the functional parameter is parametrized in terms of such a class of functions. In this article we establish that this HAL estimator yields an asymptotically efficient estimator of any smooth feature of the functional parameter under a global undersmoothing condition. It is formally shown that the
    MeSH term(s) Likelihood Functions ; Employment
    Language English
    Publishing date 2022-07-15
    Publishing country Germany
    Document type Journal Article ; Research Support, N.I.H., Extramural
    ISSN 1557-4679
    ISSN (online) 1557-4679
    DOI 10.1515/ijb-2019-0092
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  4. Article: A two-stage super learner for healthcare expenditures.

    Wu, Ziyue / Berkowitz, Seth A / Heagerty, Patrick J / Benkeser, David

    Health services & outcomes research methodology

    2022  Volume 22, Issue 4, Page(s) 435–453

    Abstract: Objective: To improve the estimation of healthcare expenditures by introducing a novel method that is well-suited to situations where data exhibit strong skewness and zero-inflation.: Data sources: Simulations, and two real-world datasets: the 2016- ... ...

    Abstract Objective: To improve the estimation of healthcare expenditures by introducing a novel method that is well-suited to situations where data exhibit strong skewness and zero-inflation.
    Data sources: Simulations, and two real-world datasets: the 2016-2017 Medical Expenditure Panel Survey (MEPS); the Back Pain Outcomes using Longitudinal Data (BOLD).
    Study design: Super learner is an ensemble machine learning approach that can combine several algorithms to improve estimation. We propose a two-stage super learner that is well suited for healthcare expenditure data by separately estimating the probability of any healthcare expenditure and the mean amount of healthcare expenditure conditional on having healthcare expenditures. These estimates can then be combined to yield a single estimate of expenditures for each observation. The analytical strategy can flexibly incorporate a range of individual estimation approaches for each stage of estimation, including both regression-based approaches and machine learning algorithms such as random forests. We compare the performance of the two-stage super learner with a one-stage super learner, and with multiple individual algorithms for estimation of healthcare cost under a broad range of data settings in simulated and real data. The predictive performance was compared using Mean Squared Error and R
    Conclusions: Our results indicate that the two-stage super learner has better performance compared with a one-stage super learner and individual algorithms, for healthcare cost estimation under a wide variety of settings in simulations and in empirical analyses. The improvement of the two-stage super learner over the one-stage super learner was particularly evident in settings when zero-inflation is high.
    Language English
    Publishing date 2022-06-06
    Publishing country Netherlands
    Document type Journal Article
    ZDB-ID 1454630-9
    ISSN 1387-3741
    ISSN 1387-3741
    DOI 10.1007/s10742-022-00275-x
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  5. Article ; Online: Four statistical frameworks for assessing an immune correlate of protection (surrogate endpoint) from a randomized, controlled, vaccine efficacy trial.

    Gilbert, Peter B / Fong, Youyi / Hejazi, Nima S / Kenny, Avi / Huang, Ying / Carone, Marco / Benkeser, David / Follmann, Dean

    Vaccine

    2024  Volume 42, Issue 9, Page(s) 2181–2190

    Abstract: A central goal of vaccine research is to characterize and validate immune correlates of protection (CoPs). In addition to helping elucidate immunological mechanisms, a CoP can serve as a valid surrogate endpoint for an infectious disease clinical outcome ...

    Abstract A central goal of vaccine research is to characterize and validate immune correlates of protection (CoPs). In addition to helping elucidate immunological mechanisms, a CoP can serve as a valid surrogate endpoint for an infectious disease clinical outcome and thus qualifies as a primary endpoint for vaccine authorization or approval without requiring resource-intensive randomized, controlled phase 3 trials. Yet, it is challenging to persuasively validate a CoP, because a prognostic immune marker can fail as a reliable basis for predicting/inferring the level of vaccine efficacy against a clinical outcome, and because the statistical analysis of phase 3 trials only has limited capacity to disentangle association from cause. Moreover, the multitude of statistical methods garnered for CoP evaluation in phase 3 trials renders the comparison, interpretation, and synthesis of CoP results challenging. Toward promoting broader harmonization and standardization of CoP evaluation, this article summarizes four complementary statistical frameworks for evaluating CoPs in a phase 3 trial, focusing on the frameworks' distinct scientific objectives as measured and communicated by distinct causal vaccine efficacy parameters. Advantages and disadvantages of the frameworks are considered, dependent on phase 3 trial context, and perspectives are offered on how the frameworks can be applied and their results synthesized.
    MeSH term(s) Vaccine Efficacy ; Vaccines ; Research Design ; Biomarkers/analysis ; Causality ; Randomized Controlled Trials as Topic
    Chemical Substances Vaccines ; Biomarkers
    Language English
    Publishing date 2024-03-08
    Publishing country Netherlands
    Document type Journal Article ; Review
    ZDB-ID 605674-x
    ISSN 1873-2518 ; 0264-410X
    ISSN (online) 1873-2518
    ISSN 0264-410X
    DOI 10.1016/j.vaccine.2024.02.071
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  6. Article: Assessing trends in vaccine efficacy by pathogen genetic distance.

    Benkeser, David / Juraska, Michal / Gilbert, Peter B

    Journal de la Societe francaise de statistique (2009)

    2020  Volume 161, Issue 1, Page(s) 164–175

    Abstract: Preventive vaccines are an effective public health intervention for reducing the burden of infectious diseases, but have yet to be developed for several major infectious diseases. Vaccine sieve analysis studies whether and how the efficacy of a vaccine ... ...

    Abstract Preventive vaccines are an effective public health intervention for reducing the burden of infectious diseases, but have yet to be developed for several major infectious diseases. Vaccine sieve analysis studies whether and how the efficacy of a vaccine varies with the genetics of the infectious pathogen, which may help guide future vaccine development and deployment. A standard statistical approach to sieve analysis compares the effect of the vaccine to prevent infection and disease caused by pathogen types defined dichotomously as genetically near or far from a reference pathogen strain inside the vaccine construct. For example, near may be defined by amino acid identity at all amino acid positions considered in a multiple alignment and far defined by at least one amino acid difference. An alternative approach is to study the efficacy of the vaccine as a function of genetic distance from a pathogen to a reference vaccine strain where the distance cumulates over the set of amino acid positions. We propose a nonparametric method for estimating and testing the trend in the effect of a vaccine across genetic distance. We illustrate the operating characteristics of the estimator via simulation and apply the method to a recent preventive malaria vaccine efficacy trial.
    Language English
    Publishing date 2020-11-26
    Publishing country France
    Document type Journal Article
    ZDB-ID 2506006-5
    ISSN 2102-6238 ; 2102-6238 ; 0037-914X ; 1962-5197
    ISSN (online) 2102-6238
    ISSN 2102-6238 ; 0037-914X ; 1962-5197
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  7. Article ; Online: Accounting for motion in resting-state fMRI: What part of the spectrum are we characterizing in autism spectrum disorder?

    Nebel, Mary Beth / Lidstone, Daniel E / Wang, Liwei / Benkeser, David / Mostofsky, Stewart H / Risk, Benjamin B

    NeuroImage

    2022  Volume 257, Page(s) 119296

    Abstract: The exclusion of high-motion participants can reduce the impact of motion in functional Magnetic Resonance Imaging (fMRI) data. However, the exclusion of high-motion participants may change the distribution of clinically relevant variables in the study ... ...

    Abstract The exclusion of high-motion participants can reduce the impact of motion in functional Magnetic Resonance Imaging (fMRI) data. However, the exclusion of high-motion participants may change the distribution of clinically relevant variables in the study sample, and the resulting sample may not be representative of the population. Our goals are two-fold: 1) to document the biases introduced by common motion exclusion practices in functional connectivity research and 2) to introduce a framework to address these biases by treating excluded scans as a missing data problem. We use a study of autism spectrum disorder in children without an intellectual disability to illustrate the problem and the potential solution. We aggregated data from 545 children (8-13 years old) who participated in resting-state fMRI studies at Kennedy Krieger Institute (173 autistic and 372 typically developing) between 2007 and 2020. We found that autistic children were more likely to be excluded than typically developing children, with 28.5% and 16.1% of autistic and typically developing children excluded, respectively, using a lenient criterion and 81.0% and 60.1% with a stricter criterion. The resulting sample of autistic children with usable data tended to be older, have milder social deficits, better motor control, and higher intellectual ability than the original sample. These measures were also related to functional connectivity strength among children with usable data. This suggests that the generalizability of previous studies reporting naïve analyses (i.e., based only on participants with usable data) may be limited by the selection of older children with less severe clinical profiles because these children are better able to remain still during an rs-fMRI scan. We adapt doubly robust targeted minimum loss based estimation with an ensemble of machine learning algorithms to address these data losses and the resulting biases. The proposed approach selects more edges that differ in functional connectivity between autistic and typically developing children than the naïve approach, supporting this as a promising solution to improve the study of heterogeneous populations in which motion is common.
    MeSH term(s) Adolescent ; Autism Spectrum Disorder/diagnostic imaging ; Autistic Disorder ; Brain/diagnostic imaging ; Brain Mapping/methods ; Child ; Cognition ; Humans ; Magnetic Resonance Imaging/methods
    Language English
    Publishing date 2022-05-10
    Publishing country United States
    Document type Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
    ZDB-ID 1147767-2
    ISSN 1095-9572 ; 1053-8119
    ISSN (online) 1095-9572
    ISSN 1053-8119
    DOI 10.1016/j.neuroimage.2022.119296
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  8. Article ; Online: Estimation of the Average Causal Effect in Longitudinal Data With Time-Varying Exposures: The Challenge of Nonpositivity and the Impact of Model Flexibility.

    Rudolph, Jacqueline E / Benkeser, David / Kennedy, Edward H / Schisterman, Enrique F / Naimi, Ashley I

    American journal of epidemiology

    2022  Volume 191, Issue 11, Page(s) 1962–1969

    Abstract: There are important challenges to the estimation and identification of average causal effects in longitudinal data with time-varying exposures. Here, we discuss the difficulty in meeting the positivity condition. Our motivating example is the per- ... ...

    Abstract There are important challenges to the estimation and identification of average causal effects in longitudinal data with time-varying exposures. Here, we discuss the difficulty in meeting the positivity condition. Our motivating example is the per-protocol analysis of the Effects of Aspirin in Gestation and Reproduction (EAGeR) Trial. We estimated the average causal effect comparing the incidence of pregnancy by 26 weeks that would have occurred if all women had been assigned to aspirin and complied versus the incidence if all women had been assigned to placebo and complied. Using flexible targeted minimum loss-based estimation, we estimated a risk difference of 1.27% (95% CI: -9.83, 12.38). Using a less flexible inverse probability weighting approach, the risk difference was 5.77% (95% CI: -1.13, 13.05). However, the cumulative probability of compliance conditional on covariates approached 0 as follow-up accrued, indicating a practical violation of the positivity assumption, which limited our ability to make causal interpretations. The effects of nonpositivity were more apparent when using a more flexible estimator, as indicated by the greater imprecision. When faced with nonpositivity, one can use a flexible approach and be transparent about the uncertainty, use a parametric approach and smooth over gaps in the data, or target a different estimand that will be less vulnerable to positivity violations.
    MeSH term(s) Pregnancy ; Female ; Humans ; Causality ; Probability ; Incidence ; Aspirin ; Models, Statistical
    Chemical Substances Aspirin (R16CO5Y76E)
    Language English
    Publishing date 2022-07-18
    Publishing country United States
    Document type Journal Article ; Research Support, N.I.H., Extramural
    ZDB-ID 2937-3
    ISSN 1476-6256 ; 0002-9262
    ISSN (online) 1476-6256
    ISSN 0002-9262
    DOI 10.1093/aje/kwac136
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  9. Article ; Online: Sexual Mixing by HIV Status and Pre-exposure Prophylaxis Use Among Men Who Have Sex With Men: Addressing Information Bias.

    Maloney, Kevin M / Benkeser, David / Sullivan, Patrick S / Kelley, Colleen / Sanchez, Travis / Jenness, Samuel M

    Epidemiology (Cambridge, Mass.)

    2022  Volume 33, Issue 6, Page(s) 808–816

    Abstract: Background: Population-level estimates of sexual network mixing for parameterizing prediction models of pre-exposure prophylaxis (PrEP) effectiveness are needed to inform prevention of HIV transmission among men who have sex with men (MSM). Estimates ... ...

    Abstract Background: Population-level estimates of sexual network mixing for parameterizing prediction models of pre-exposure prophylaxis (PrEP) effectiveness are needed to inform prevention of HIV transmission among men who have sex with men (MSM). Estimates obtained by egocentric sampling are vulnerable to information bias due to incomplete respondent knowledge.
    Methods: We estimated patterns of serosorting and PrEP sorting among MSM in the United States using data from a 2017-2019 egocentric sexual network study. Respondents served as proxies to report the HIV status and PrEP use of recent sexual partners. We contrasted results from a complete-case analysis (unknown HIV and PrEP excluded) versus a bias analysis with respondent-reported data stochastically reclassified to simulate unobserved self-reported data from sexual partners.
    Results: We found strong evidence of preferential partnering across analytical approaches. The bias analysis showed concordance between sexual partners of HIV diagnosis and PrEP use statuses for MSM with diagnosed HIV (39%; 95% simulation interval: 31, 46), MSM who used PrEP (32%; 21, 37), and MSM who did not use PrEP (83%; 79, 87). The fraction of partners with diagnosed HIV was higher among MSM who used PrEP (11%; 9, 14) compared with MSM who did not use PrEP (4%; 3, 5). Comparatively, across all strata of respondents, the complete-case analysis overestimated the fractions of partners with diagnosed HIV or PrEP use.
    Conclusions: We found evidence consistent with HIV and PrEP sorting among MSM, which may decrease the population-level effectiveness of PrEP. Bias analyses can improve mixing estimates for parameterization of transmission models.
    MeSH term(s) Anti-HIV Agents/therapeutic use ; HIV Infections/drug therapy ; HIV Infections/epidemiology ; HIV Infections/prevention & control ; HIV Serosorting ; Homosexuality, Male ; Humans ; Male ; Pre-Exposure Prophylaxis/methods ; Sexual Behavior ; Sexual Partners ; Sexual and Gender Minorities ; United States/epidemiology
    Chemical Substances Anti-HIV Agents
    Language English
    Publishing date 2022-07-27
    Publishing country United States
    Document type Journal Article ; Research Support, N.I.H., Extramural
    ZDB-ID 1053263-8
    ISSN 1531-5487 ; 1044-3983
    ISSN (online) 1531-5487
    ISSN 1044-3983
    DOI 10.1097/EDE.0000000000001525
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  10. Article ; Online: Application of the SLAPNAP statistical learning tool to broadly neutralizing antibody HIV prevention research.

    Williamson, Brian D / Magaret, Craig A / Karuna, Shelly / Carpp, Lindsay N / Gelderblom, Huub C / Huang, Yunda / Benkeser, David / Gilbert, Peter B

    iScience

    2023  Volume 26, Issue 9, Page(s) 107595

    Abstract: Combination monoclonal broadly neutralizing antibody (bnAb) regimens are in clinical development for HIV prevention, necessitating additional knowledge of bnAb neutralization potency/breadth against circulating viruses. Williamson et al. (2021) described ...

    Abstract Combination monoclonal broadly neutralizing antibody (bnAb) regimens are in clinical development for HIV prevention, necessitating additional knowledge of bnAb neutralization potency/breadth against circulating viruses. Williamson et al. (2021) described a software tool, Super LeArner Prediction of NAb Panels (SLAPNAP), with application to any HIV bnAb regimen with sufficient neutralization data against a set of viruses in the Los Alamos National Laboratory's Compile, Neutralize, and Tally Nab Panels repository. SLAPNAP produces a proteomic antibody resistance (PAR) score for Env sequences based on predicted neutralization resistance and estimates variable importance of Env amino acid features. We apply SLAPNAP to compare HIV bnAb regimens undergoing clinical testing, finding improved power for downstream sieve analyses and increased precision for comparing neutralization potency/breadth of bnAb regimens due to the inclusion of PAR scores of Env sequences with much larger sample sizes available than for neutralization outcomes. SLAPNAP substantially improves bnAb regimen characterization, ranking, and down-selection.
    Language English
    Publishing date 2023-08-09
    Publishing country United States
    Document type Journal Article
    ISSN 2589-0042
    ISSN (online) 2589-0042
    DOI 10.1016/j.isci.2023.107595
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