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  1. Article ; Online: Discussion of "A formal causal interpretation of the case-crossover design" by Zach Shahn, Miguel A. Hernan, and James M. Robins.

    Pfeiffer, Ruth M / Gail, Mitchell H

    Biometrics

    2022  Volume 79, Issue 2, Page(s) 1346–1348

    Abstract: Shahn, Hernan, and Robins give conditions under which estimates from a case-crossover analysis converge to the desired causal relative risk times a bias factor, and they discuss conditions needed to have small bias. To simplify the problem, we discuss ... ...

    Abstract Shahn, Hernan, and Robins give conditions under which estimates from a case-crossover analysis converge to the desired causal relative risk times a bias factor, and they discuss conditions needed to have small bias. To simplify the problem, we discuss only two exposure times and rely on randomized exposure assignments, thereby avoiding the need for potential outcome notation. We identify many, but not all, of the conditions discussed by Shahn et al. in this simple analysis.
    MeSH term(s) Animals ; Cross-Over Studies ; Songbirds ; Causality ; Bias ; Research Design
    Language English
    Publishing date 2022-09-19
    Publishing country United States
    Document type Journal Article ; Comment
    ZDB-ID 213543-7
    ISSN 1541-0420 ; 0099-4987 ; 0006-341X
    ISSN (online) 1541-0420
    ISSN 0099-4987 ; 0006-341X
    DOI 10.1111/biom.13747
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  2. Article ; Online: Mediation analysis using incomplete information from publicly available data sources.

    Derkach, Andriy / Kantor, Elizabeth D / Sampson, Joshua N / Pfeiffer, Ruth M

    Statistics in medicine

    2024  

    Abstract: Our work was motivated by the question whether, and to what extent, well-established risk factors mediate the racial disparity observed for colorectal cancer (CRC) incidence in the United States. Mediation analysis examines the relationships between an ... ...

    Abstract Our work was motivated by the question whether, and to what extent, well-established risk factors mediate the racial disparity observed for colorectal cancer (CRC) incidence in the United States. Mediation analysis examines the relationships between an exposure, a mediator and an outcome. All available methods require access to a single complete data set with these three variables. However, because population-based studies usually include few non-White participants, these approaches have limited utility in answering our motivating question. Recently, we developed novel methods to integrate several data sets with incomplete information for mediation analysis. These methods have two limitations: (i) they only consider a single mediator and (ii) they require a data set containing individual-level data on the mediator and exposure (and possibly confounders) obtained by independent and identically distributed sampling from the target population. Here, we propose a new method for mediation analysis with several different data sets that accommodates complex survey and registry data, and allows for multiple mediators. The proposed approach yields unbiased causal effects estimates and confidence intervals with nominal coverage in simulations. We apply our method to data from U.S. cancer registries, a U.S.-population-representative survey and summary level odds-ratio estimates, to rigorously evaluate what proportion of the difference in CRC risk between non-Hispanic Whites and Blacks is mediated by three potentially modifiable risk factors (CRC screening history, body mass index, and regular aspirin use).
    Language English
    Publishing date 2024-04-12
    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.10076
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  3. Article ; Online: Discussion of “A formal causal interpretation of the case‐crossover design” by Zach Shahn, Miguel A. Hernan, and James M. Robins

    Pfeiffer, Ruth M. / Gail, Mitchell H.

    Biometrics. 2023 June, v. 79, no. 2 p.1346-1348

    2023  

    Abstract: Shahn, Hernan, and Robins give conditions under which estimates from a case‐crossover analysis converge to the desired causal relative risk times a bias factor, and they discuss conditions needed to have small bias. To simplify the problem, we discuss ... ...

    Abstract Shahn, Hernan, and Robins give conditions under which estimates from a case‐crossover analysis converge to the desired causal relative risk times a bias factor, and they discuss conditions needed to have small bias. To simplify the problem, we discuss only two exposure times and rely on randomized exposure assignments, thereby avoiding the need for potential outcome notation. We identify many, but not all, of the conditions discussed by Shahn et al. in this simple analysis.
    Keywords biometry ; exposure duration ; relative risk
    Language English
    Dates of publication 2023-06
    Size p. 1346-1348.
    Publishing place John Wiley & Sons, Ltd
    Document type Article ; Online
    Note JOURNAL ARTICLE
    ZDB-ID 213543-7
    ISSN 0099-4987 ; 0006-341X
    ISSN 0099-4987 ; 0006-341X
    DOI 10.1111/biom.13747
    Database NAL-Catalogue (AGRICOLA)

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  4. Article ; Online: Predicting absolute risk for a person with missing risk factors.

    Wang, Bang / Cheng, Yu / Gail, Mitchell H / Fine, Jason / Pfeiffer, Ruth M

    Statistical methods in medical research

    2024  Volume 33, Issue 4, Page(s) 557–573

    Abstract: We compared methods to project absolute risk, the probability of experiencing the outcome of interest in a given projection interval accommodating competing risks, for a person from the target population with missing predictors. Without missing data, a ... ...

    Abstract We compared methods to project absolute risk, the probability of experiencing the outcome of interest in a given projection interval accommodating competing risks, for a person from the target population with missing predictors. Without missing data, a perfectly calibrated model gives unbiased absolute risk estimates in a new target population, even if the predictor distribution differs from the training data. However, if predictors are missing in target population members, a reference dataset with complete data is needed to impute them and to estimate absolute risk, conditional only on the observed predictors. If the predictor distributions of the reference data and the target population differ, this approach yields biased estimates. We compared the bias and mean squared error of absolute risk predictions for seven methods that assume predictors are missing at random (MAR). Some methods imputed individual missing predictors, others imputed linear predictor combinations (risk scores). Simulations were based on real breast cancer predictor distributions and outcome data. We also analyzed a real breast cancer dataset. The largest bias for all methods resulted from different predictor distributions of the reference and target populations. No method was unbiased in this situation. Surprisingly, violating the MAR assumption did not induce severe biases. Most multiple imputation methods performed similarly and were less biased (but more variable) than a method that used a single expected risk score. Our work shows the importance of selecting predictor reference datasets similar to the target population to reduce bias of absolute risk predictions with missing risk factors.
    MeSH term(s) Humans ; Female ; Risk Factors ; Research Design ; Bias ; Data Interpretation, Statistical ; Breast Neoplasms
    Language English
    Publishing date 2024-03-01
    Publishing country England
    Document type Journal Article
    ZDB-ID 1136948-6
    ISSN 1477-0334 ; 0962-2802
    ISSN (online) 1477-0334
    ISSN 0962-2802
    DOI 10.1177/09622802241227945
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  5. Article ; Online: Frequentist model averaging for analysis of dose-response in epidemiologic studies with complex exposure uncertainty.

    Kwon, Deukwoo / Simon, Steven L / Hoffman, F Owen / Pfeiffer, Ruth M

    PloS one

    2023  Volume 18, Issue 12, Page(s) e0290498

    Abstract: In epidemiologic studies, association estimates of an exposure with disease outcomes are often biased when the uncertainties of exposure are ignored. Consequently, corresponding confidence intervals (CIs) will not have correct coverage. This issue is ... ...

    Abstract In epidemiologic studies, association estimates of an exposure with disease outcomes are often biased when the uncertainties of exposure are ignored. Consequently, corresponding confidence intervals (CIs) will not have correct coverage. This issue is particularly problematic when exposures must be reconstructed from physical measurements, for example, for environmental or occupational radiation doses that were received by a study population for which radiation doses cannot be measured directly. To incorporate complex uncertainties in reconstructed exposures, the two-dimensional Monte Carlo (2DMC) dose estimation method has been proposed and used in various dose reconstruction efforts. The 2DMC method generates multiple exposure realizations from dosimetry models that incorporate various sources of errors to reflect the uncertainty of the dose distribution as well as the uncertainties in individual doses in the exposed population. Traditional measurement-error model approaches, typically based on using mean doses in the dose-exposure analysis, do not fully account exposure uncertainties. A recently developed statistical approach that overcomes many of these limitations by analyzing multiple exposure realizations in relation to disease risk is Bayesian model averaging (BMA). The analytic advantage of the BMA is its ability to better accommodate complex exposure uncertainty in the risk estimation, but a practical. Drawback is its significant computational complexity. In this present paper, we propose a novel frequentist model averaging (FMA) approach which has all the analytical advantages of the BMA method but is much simpler to implement and computationally faster. We show in simulations that, like BMA, FMA yields 95% confidence intervals for association parameters that close to 95% coverage rate. In simulations, the FMA has shorter length of CIs than those of another frequentist approach, the corrected information matrix (CIM) method. We illustrate the similarities in performance of BMA and FMA from a study of exposures from radioactive fallout in Kazakhstan.
    MeSH term(s) Humans ; Uncertainty ; Bayes Theorem ; Radiometry/methods ; Epidemiologic Studies ; Monte Carlo Method
    Language English
    Publishing date 2023-12-14
    Publishing country United States
    Document type Journal Article
    ZDB-ID 2267670-3
    ISSN 1932-6203 ; 1932-6203
    ISSN (online) 1932-6203
    ISSN 1932-6203
    DOI 10.1371/journal.pone.0290498
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  6. Article ; Online: Structured time-dependent inverse regression (STIR).

    Song, Minsun / Bura, Efstathia / Parzer, Roman / Pfeiffer, Ruth M

    Statistics in medicine

    2023  Volume 42, Issue 9, Page(s) 1289–1307

    Abstract: We propose and study structured time-dependent inverse regression (STIR), a novel sufficient dimension reduction model, to analyze longitudinally measured, correlated biomarkers in relation to an outcome. The time structure is accommodated in an inverse ... ...

    Abstract We propose and study structured time-dependent inverse regression (STIR), a novel sufficient dimension reduction model, to analyze longitudinally measured, correlated biomarkers in relation to an outcome. The time structure is accommodated in an inverse regression model for the markers that can be applied both to equally and unequally spaced time points for each sample. The inverse regression structure also naturally accommodates retrospectively sampled markers, that is, markers measured in case-control studies. We estimate the corresponding linear combinations of the markers, the reduction, using least squares. We show that under additional distributional assumptions the reduction contains sufficient information about the outcome. In extensive simulations the STIR linear combinations perform well in predictive models based on samples of realistic size. A Wald-type test for association of a particular marker with outcome at any time point based on the STIR reduction has better power overall than assessing associations based on logistic or linear regression models that include all longitudinally measured markers as independent predictors. As illustrations we estimate the STIR reductions for a cohort study of diabetes and hyperlipidemia and a case-control study of brain cancer with multiple longitudinally measured biomarkers. We assess the STIR reductions' predictive performance and identify outcome-associated biomarkers.
    MeSH term(s) Humans ; Cohort Studies ; Case-Control Studies ; Retrospective Studies ; Least-Squares Analysis ; Biomarkers
    Chemical Substances Biomarkers
    Language English
    Publishing date 2023-03-14
    Publishing country England
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 843037-8
    ISSN 1097-0258 ; 0277-6715
    ISSN (online) 1097-0258
    ISSN 0277-6715
    DOI 10.1002/sim.9670
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  7. Article ; Online: Immune-related conditions and cancer-specific mortality among older adults with cancer in the United States.

    Wang, Jeanny H / Derkach, Andriy / Pfeiffer, Ruth M / Engels, Eric A

    International journal of cancer

    2022  Volume 151, Issue 8, Page(s) 1216–1227

    Abstract: Immunity may play a role in preventing cancer progression. We studied associations of immune-related conditions with cancer-specific mortality among older adults in the United States. We evaluated 1 229 443 patients diagnosed with 20 common cancer types ( ...

    Abstract Immunity may play a role in preventing cancer progression. We studied associations of immune-related conditions with cancer-specific mortality among older adults in the United States. We evaluated 1 229 443 patients diagnosed with 20 common cancer types (age 67-99, years 1993-2013) using Surveillance Epidemiology and End Results-Medicare data. With Medicare claims, we ascertained immune-related medical conditions diagnosed before cancer diagnosis (4 immunosuppressive conditions [n = 3380 affected cases], 32 autoimmune conditions [n = 155 766], 3 allergic conditions [n = 101 366]). For each cancer site, we estimated adjusted hazard ratios (aHRs) and 95% confidence intervals (CIs) for cancer-specific mortality associated with each condition, applying a Bonferroni cutoff for significance (P < 5.1 × 10
    MeSH term(s) Aged ; Aged, 80 and over ; Autoimmune Diseases/epidemiology ; Bayes Theorem ; Case-Control Studies ; Humans ; Hypersensitivity/complications ; Hypersensitivity/epidemiology ; Male ; Medicare ; Neoplasms ; SEER Program ; United States/epidemiology
    Language English
    Publishing date 2022-06-23
    Publishing country United States
    Document type Journal Article ; Meta-Analysis ; Research Support, N.I.H., Intramural
    ZDB-ID 218257-9
    ISSN 1097-0215 ; 0020-7136
    ISSN (online) 1097-0215
    ISSN 0020-7136
    DOI 10.1002/ijc.34140
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    Zs.MO 576: Show issues

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  8. Article ; Online: Accommodating population differences when validating risk prediction models.

    Pfeiffer, Ruth M / Chen, Yiyao / Gail, Mitchell H / Ankerst, Donna P

    Statistics in medicine

    2022  Volume 41, Issue 24, Page(s) 4756–4780

    Abstract: Validation of risk prediction models in independent data provides a more rigorous assessment of model performance than internal assessment, for example, done by cross-validation in the data used for model development. However, several differences between ...

    Abstract Validation of risk prediction models in independent data provides a more rigorous assessment of model performance than internal assessment, for example, done by cross-validation in the data used for model development. However, several differences between the populations that gave rise to the training and the validation data can lead to seemingly poor performance of a risk model. In this paper we formalize the notions of "similarity" or "relatedness" of the training and validation data, and define reproducibility and transportability. We address the impact of different distributions of model predictors and differences in verifying the disease status or outcome on measures of calibration, accuracy and discrimination of a model. When individual level information from both the training and validation data sets is available, we propose and study weighted versions of the validation metrics that adjust for differences in the risk factor distributions and in outcome verification between the training and validation data to provide a more comprehensive assessment of model performance. We provide conditions on the risk model and the populations that gave rise to the training and validation data that ensure a model's reproducibility or transportability, and show how to check these conditions using weighted and unweighted performance measures. We illustrate the method by developing and validating a model that predicts the risk of developing prostate cancer using data from two large prostate cancer screening trials.
    MeSH term(s) Early Detection of Cancer ; Humans ; Male ; Prognosis ; Prostate-Specific Antigen ; Prostatic Neoplasms/diagnosis ; Reproducibility of Results ; Risk Assessment
    Chemical Substances Prostate-Specific Antigen (EC 3.4.21.77)
    Language English
    Publishing date 2022-07-05
    Publishing country England
    Document type Journal Article ; Research Support, N.I.H., Extramural
    ZDB-ID 843037-8
    ISSN 1097-0258 ; 0277-6715
    ISSN (online) 1097-0258
    ISSN 0277-6715
    DOI 10.1002/sim.9447
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  9. Article ; Online: Subset testing and analysis of multiple phenotypes.

    Derkach, Andriy / Pfeiffer, Ruth M

    Genetic epidemiology

    2019  Volume 43, Issue 5, Page(s) 492–505

    Abstract: Meta-analysis of multiple genome-wide association studies (GWAS) is effective for detecting single- or multimarker associations with complex traits. We develop a flexible procedure (subset testing and analysis of multiple phenotypes [STAMP]) based on ... ...

    Abstract Meta-analysis of multiple genome-wide association studies (GWAS) is effective for detecting single- or multimarker associations with complex traits. We develop a flexible procedure (subset testing and analysis of multiple phenotypes [STAMP]) based on mixture models to perform a region-based meta-analysis of different phenotypes using data from different GWAS and identify subsets of associated phenotypes. Our model framework helps distinguish true associations from between-study heterogeneity. As a measure of association, we compute for each phenotype the posterior probability that the genetic region under investigation is truly associated. Extensive simulations show that STAMP is more powerful than standard approaches for meta-analyses when the proportion of truly associated outcomes is between 25% and 50%. For other settings, the power of STAMP is similar to that of existing methods. We illustrate our method on two examples, the association of a region on chromosome 9p21 with the risk of 14 cancers, and the associations of expression of quantitative trait loci from two genetic regions with their cis-single-nucleotide polymorphisms measured in 17 tissue types using data from The Cancer Genome Atlas.
    MeSH term(s) Case-Control Studies ; Computer Simulation ; Genome-Wide Association Study/methods ; Humans ; Models, Genetic ; Neoplasms/pathology ; Phenotype ; Polymorphism, Single Nucleotide/genetics ; Quantitative Trait Loci/genetics
    Language English
    Publishing date 2019-03-28
    Publishing country United States
    Document type Journal Article ; Meta-Analysis
    ZDB-ID 605785-8
    ISSN 1098-2272 ; 0741-0395
    ISSN (online) 1098-2272
    ISSN 0741-0395
    DOI 10.1002/gepi.22199
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  10. Article ; Online: Incorporating survival data into case-control studies with incident and prevalent cases.

    Mandal, Soutrik / Qin, Jing / Pfeiffer, Ruth M

    Statistics in medicine

    2021  Volume 40, Issue 28, Page(s) 6295–6308

    Abstract: Typically, case-control studies to estimate odds-ratios associating risk factors with disease incidence only include newly diagnosed cases. Recently proposed methods allow incorporating information on prevalent cases, individuals who survived from ... ...

    Abstract Typically, case-control studies to estimate odds-ratios associating risk factors with disease incidence only include newly diagnosed cases. Recently proposed methods allow incorporating information on prevalent cases, individuals who survived from disease diagnosis to sampling, into cross-sectionally sampled case-control studies under parametric assumptions for the survival time after diagnosis. Here we propose and study methods to additionally use prospectively observed survival times from prevalent and incident cases to adjust logistic models for the time between diagnosis and sampling, the backward time, for prevalent cases. This adjustment yields unbiased odds-ratio estimates from case-control studies that include prevalent cases. We propose a computationally simple two-step generalized method-of-moments estimation procedure. First, we estimate the survival distribution assuming a semiparametric Cox model using an expectation-maximization algorithm that yields fully efficient estimates and accommodates left truncation for prevalent cases and right censoring. Then, we use the estimated survival distribution in an extension of the logistic model to three groups (controls, incident, and prevalent cases), to adjust for the survival bias in prevalent cases. In simulations, under modest amounts of censoring, odds-ratios from the two-step procedure were equally efficient as those estimated from a joint logistic and survival data likelihood under parametric assumptions. This indicates that utilizing the cases' prospective survival data lessens model dependencies and improves precision of association estimates for case-control studies with prevalent cases. We illustrate the methods by estimating associations between single nucleotide polymorphisms and breast cancer risk using controls, and incident and prevalent cases sampled from the US Radiologic Technologists Study cohort.
    MeSH term(s) Bias ; Case-Control Studies ; Cohort Studies ; Humans ; Proportional Hazards Models ; Prospective Studies
    Language English
    Publishing date 2021-09-12
    Publishing country England
    Document type Journal Article ; Research Support, N.I.H., Intramural
    ZDB-ID 843037-8
    ISSN 1097-0258 ; 0277-6715
    ISSN (online) 1097-0258
    ISSN 0277-6715
    DOI 10.1002/sim.9183
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