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Article ; Online: Efficient designs and analysis of two-phase studies with longitudinal binary data.

Di Gravio, Chiara / Schildcrout, Jonathan S / Tao, Ran

2024 Volume 80, Issue 1

Abstract: Researchers interested in understanding the relationship between a readily available longitudinal binary outcome and a novel biomarker exposure can be confronted with ascertainment costs that limit sample size. In such settings, two-phase studies can be ... ...

Abstract	Researchers interested in understanding the relationship between a readily available longitudinal binary outcome and a novel biomarker exposure can be confronted with ascertainment costs that limit sample size. In such settings, two-phase studies can be cost-effective solutions that allow researchers to target informative individuals for exposure ascertainment and increase estimation precision for time-varying and/or time-fixed exposure coefficients. In this paper, we introduce a novel class of residual-dependent sampling (RDS) designs that select informative individuals using data available on the longitudinal outcome and inexpensive covariates. Together with the RDS designs, we propose a semiparametric analysis approach that efficiently uses all data to estimate the parameters. We describe a numerically stable and computationally efficient EM algorithm to maximize the semiparametric likelihood. We examine the finite sample operating characteristics of the proposed approaches through extensive simulation studies, and compare the efficiency of our designs and analysis approach with existing ones. We illustrate the usefulness of the proposed RDS designs and analysis method in practice by studying the association between a genetic marker and poor lung function among patients enrolled in the Lung Health Study (Connett et al, 1993).
MeSH term(s)	Humans ; Computer Simulation ; Sample Size ; Probability ; Data Interpretation, Statistical ; Sampling Studies ; Models, Statistical ; Longitudinal Studies
Language	English
Publishing date	2024-02-15
Publishing country	United States
Document type	Journal Article
ZDB-ID	213543-7
ISSN	1541-0420 ; 0099-4987 ; 0006-341X
ISSN (online)	1541-0420
ISSN	0099-4987 ; 0006-341X
DOI	10.1093/biomtc/ujad010
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Article ; Online: Model misspecification and robust analysis for outcome-dependent sampling designs under generalized linear models.

Maronge, Jacob M / Schildcrout, Jonathan S / Rathouz, Paul J

Statistics in medicine

2023 Volume 42, Issue 9, Page(s) 1338–1352

Abstract: Outcome-dependent sampling (ODS) is a commonly used class of sampling designs to increase estimation efficiency in settings where response information (and possibly adjuster covariates) is available, but the exposure is expensive and/or cumbersome to ... ...

Abstract	Outcome-dependent sampling (ODS) is a commonly used class of sampling designs to increase estimation efficiency in settings where response information (and possibly adjuster covariates) is available, but the exposure is expensive and/or cumbersome to collect. We focus on ODS within the context of a two-phase study, where in Phase One the response and adjuster covariate information is collected on a large cohort that is representative of the target population, but the expensive exposure variable is not yet measured. In Phase Two, using response information from Phase One, we selectively oversample a subset of informative subjects in whom we collect expensive exposure information. Importantly, the Phase Two sample is no longer representative, and we must use ascertainment-correcting analysis procedures for valid inferences. In this paper, we focus on likelihood-based analysis procedures, particularly a conditional-likelihood approach and a full-likelihood approach. Whereas the full-likelihood retains incomplete Phase One data for subjects not selected into Phase Two, the conditional-likelihood explicitly conditions on Phase Two sample selection (ie, it is a "complete case" analysis procedure). These designs and analysis procedures are typically implemented assuming a known, parametric model for the response distribution. However, in this paper, we approach analyses implementing a novel semi-parametric extension to generalized linear models (SPGLM) to develop likelihood-based procedures with improved robustness to misspecification of distributional assumptions. We specifically focus on the common setting where standard GLM distributional assumptions are not satisfied (eg, misspecified mean/variance relationship). We aim to provide practical design guidance and flexible tools for practitioners in these settings.
MeSH term(s)	Humans ; Linear Models ; Models, Statistical ; Likelihood Functions
Language	English
Publishing date	2023-02-09
Publishing country	England
Document type	Journal Article ; Research Support, N.I.H., Extramural ; 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.9673
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Article ; Online: Design and analysis of two-phase studies with multivariate longitudinal data.

Di Gravio, Chiara / Tao, Ran / Schildcrout, Jonathan S

Biometrics

2022 Volume 79, Issue 2, Page(s) 1420–1432

Abstract: Two-phase studies are crucial when outcome and covariate data are available in a first-phase sample (e.g., a cohort study), but costs associated with retrospective ascertainment of a novel exposure limit the size of the second-phase sample, in whom the ... ...

Abstract	Two-phase studies are crucial when outcome and covariate data are available in a first-phase sample (e.g., a cohort study), but costs associated with retrospective ascertainment of a novel exposure limit the size of the second-phase sample, in whom the exposure is collected. For longitudinal outcomes, one class of two-phase studies stratifies subjects based on an outcome vector summary (e.g., an average or a slope over time) and oversamples subjects in the extreme value strata while undersampling subjects in the medium-value stratum. Based on the choice of the summary, two-phase studies for longitudinal data can increase efficiency of time-varying and/or time-fixed exposure parameter estimates. In this manuscript, we extend efficient, two-phase study designs to multivariate longitudinal continuous outcomes, and we detail two analysis approaches. The first approach is a multiple imputation analysis that combines complete data from subjects selected for phase two with the incomplete data from those not selected. The second approach is a conditional maximum likelihood analysis that is intended for applications where only data from subjects selected for phase two are available. Importantly, we show that both approaches can be applied to secondary analyses of previously conducted two-phase studies. We examine finite sample operating characteristics of the two approaches and use the Lung Health Study (Connett et al. (1993), Controlled Clinical Trials, 14, 3S-19S) to examine genetic associations with lung function decline over time.
MeSH term(s)	Humans ; Cohort Studies ; Models, Statistical ; Longitudinal Studies ; Retrospective Studies ; Research Design
Language	English
Publishing date	2022-01-28
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Extramural
ZDB-ID	213543-7
ISSN	1541-0420 ; 0099-4987 ; 0006-341X
ISSN (online)	1541-0420
ISSN	0099-4987 ; 0006-341X
DOI	10.1111/biom.13616
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Article ; Online: Analyzing clustered continuous response variables with ordinal regression models

Tian, Yuqi / Shepherd, Bryan E. / Li, Chun / Zeng, Donglin / Schildcrout, Jonathan S.

Biometrics. 2023 Dec., v. 79, no. 4 p.3764-3777

2023

Abstract: Continuous response data are regularly transformed to meet regression modeling assumptions. However, approaches taken to identify the appropriate transformation can be ad hoc and can increase model uncertainty. Further, the resulting transformations ... ...

Abstract	Continuous response data are regularly transformed to meet regression modeling assumptions. However, approaches taken to identify the appropriate transformation can be ad hoc and can increase model uncertainty. Further, the resulting transformations often vary across studies leading to difficulties with synthesizing and interpreting results. When a continuous response variable is measured repeatedly within individuals or when continuous responses arise from clusters, analyses have the additional challenge caused by within‐individual or within‐cluster correlations. We extend a widely used ordinal regression model, the cumulative probability model (CPM), to fit clustered, continuous response data using generalized estimating equations for ordinal responses. With the proposed approach, estimates of marginal model parameters, cumulative distribution functions , expectations, and quantiles conditional on covariates can be obtained without pretransformation of the response data. While computational challenges arise with large numbers of distinct values of the continuous response variable, we propose feasible and computationally efficient approaches to fit CPMs under commonly used working correlation structures. We study finite sample operating characteristics of the estimators via simulation and illustrate their implementation with two data examples. One studies predictors of CD4:CD8 ratios in a cohort living with HIV, and the other investigates the association of a single nucleotide polymorphism and lung function decline in a cohort with early chronic obstructive pulmonary disease.
Keywords	cumulative distribution ; lung function ; model uncertainty ; models ; regression analysis ; respiratory tract diseases ; single nucleotide polymorphism
Language	English
Dates of publication	2023-12
Size	p. 3764-3777.
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.13904
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Article ; Online: Design and analysis of two‐phase studies with multivariate longitudinal data

Di Gravio, Chiara / Tao, Ran / Schildcrout, Jonathan S.

Biometrics. 2023 June, v. 79, no. 2 p.1420-1432

2023

Abstract: Two‐phase studies are crucial when outcome and covariate data are available in a first‐phase sample (e.g., a cohort study), but costs associated with retrospective ascertainment of a novel exposure limit the size of the second‐phase sample, in whom the ... ...

Abstract	Two‐phase studies are crucial when outcome and covariate data are available in a first‐phase sample (e.g., a cohort study), but costs associated with retrospective ascertainment of a novel exposure limit the size of the second‐phase sample, in whom the exposure is collected. For longitudinal outcomes, one class of two‐phase studies stratifies subjects based on an outcome vector summary (e.g., an average or a slope over time) and oversamples subjects in the extreme value strata while undersampling subjects in the medium‐value stratum. Based on the choice of the summary, two‐phase studies for longitudinal data can increase efficiency of time‐varying and/or time‐fixed exposure parameter estimates. In this manuscript, we extend efficient, two‐phase study designs to multivariate longitudinal continuous outcomes, and we detail two analysis approaches. The first approach is a multiple imputation analysis that combines complete data from subjects selected for phase two with the incomplete data from those not selected. The second approach is a conditional maximum likelihood analysis that is intended for applications where only data from subjects selected for phase two are available. Importantly, we show that both approaches can be applied to secondary analyses of previously conducted two‐phase studies. We examine finite sample operating characteristics of the two approaches and use the Lung Health Study (Connett et al. (1993), Controlled Clinical Trials, 14, 3S–19S) to examine genetic associations with lung function decline over time.
Keywords	cohort studies ; lung function ; lungs ; probability analysis
Language	English
Dates of publication	2023-06
Size	p. 1420-1432.
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.13616
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Article ; Online: Generalized case‐control sampling under generalized linear models

Maronge, Jacob M. / Tao, Ran / Schildcrout, Jonathan S. / Rathouz, Paul J.

Biometrics. 2023 Mar., v. 79, no. 1 p.332-343

2023

Abstract: A generalized case‐control (GCC) study, like the standard case‐control study, leverages outcome‐dependent sampling (ODS) to extend to nonbinary responses. We develop a novel, unifying approach for analyzing GCC study data using the recently developed ... ...

Abstract	A generalized case‐control (GCC) study, like the standard case‐control study, leverages outcome‐dependent sampling (ODS) to extend to nonbinary responses. We develop a novel, unifying approach for analyzing GCC study data using the recently developed semiparametric extension of the generalized linear model (GLM), which is substantially more robust to model misspecification than existing approaches based on parametric GLMs. For valid estimation and inference, we use a conditional likelihood to account for the biased sampling design. We describe analysis procedures for estimation and inference for the semiparametric GLM under a conditional likelihood, and we discuss problems with estimation and inference under a conditional likelihood when the response distribution is misspecified. We demonstrate the flexibility of our approach over existing ones through extensive simulation studies, and we apply the methodology to an analysis of the Asset and Health Dynamics Among the Oldest Old study, which motives our research. The proposed approach yields a simple yet versatile solution for handling ODS in a wide variety of possible response distributions and sampling schemes encountered in practice.
Keywords	assets ; case-control studies ; linear models
Language	English
Dates of publication	2023-03
Size	p. 332-343.
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.13571
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Article ; Online: Analyzing clustered continuous response variables with ordinal regression models.

Tian, Yuqi / Shepherd, Bryan E / Li, Chun / Zeng, Donglin / Schildcrout, Jonathan S

Biometrics

2023 Volume 79, Issue 4, Page(s) 3764–3777

Abstract	Continuous response data are regularly transformed to meet regression modeling assumptions. However, approaches taken to identify the appropriate transformation can be ad hoc and can increase model uncertainty. Further, the resulting transformations often vary across studies leading to difficulties with synthesizing and interpreting results. When a continuous response variable is measured repeatedly within individuals or when continuous responses arise from clusters, analyses have the additional challenge caused by within-individual or within-cluster correlations. We extend a widely used ordinal regression model, the cumulative probability model (CPM), to fit clustered, continuous response data using generalized estimating equations for ordinal responses. With the proposed approach, estimates of marginal model parameters, cumulative distribution functions , expectations, and quantiles conditional on covariates can be obtained without pretransformation of the response data. While computational challenges arise with large numbers of distinct values of the continuous response variable, we propose feasible and computationally efficient approaches to fit CPMs under commonly used working correlation structures. We study finite sample operating characteristics of the estimators via simulation and illustrate their implementation with two data examples. One studies predictors of CD4:CD8 ratios in a cohort living with HIV, and the other investigates the association of a single nucleotide polymorphism and lung function decline in a cohort with early chronic obstructive pulmonary disease.
MeSH term(s)	Humans ; Models, Statistical ; Computer Simulation ; Probability ; Uncertainty
Language	English
Publishing date	2023-07-17
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't ; Research Support, N.I.H., Extramural
ZDB-ID	213543-7
ISSN	1541-0420 ; 0099-4987 ; 0006-341X
ISSN (online)	1541-0420
ISSN	0099-4987 ; 0006-341X
DOI	10.1111/biom.13904
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Article ; Online: Survey design and analysis considerations when utilizing misclassified sampling strata.

Mitani, Aya A / Mercaldo, Nathaniel D / Haneuse, Sebastien / Schildcrout, Jonathan S

BMC medical research methodology

2021 Volume 21, Issue 1, Page(s) 145

Abstract: Background: A large multi-center survey was conducted to understand patients' perspectives on biobank study participation with particular focus on racial and ethnic minorities. In order to enrich the study sample with racial and ethnic minorities, ... ...

Abstract	Background: A large multi-center survey was conducted to understand patients' perspectives on biobank study participation with particular focus on racial and ethnic minorities. In order to enrich the study sample with racial and ethnic minorities, disproportionate stratified sampling was implemented with strata defined by electronic health records (EHR) that are known to be inaccurate. We investigate the effect of sampling strata misclassification in complex survey design. Methods: Under non-differential and differential misclassification in the sampling strata, we compare the validity and precision of three simple and common analysis approaches for settings in which the primary exposure is used to define the sampling strata. We also compare the precision gains/losses observed from using a disproportionate stratified sampling scheme compared to using a simple random sample under varying degrees of strata misclassification. Results: Disproportionate stratified sampling can result in more efficient parameter estimates of the rare subgroups (race/ethnic minorities) in the sampling strata compared to simple random sampling. When sampling strata misclassification is non-differential with respect to the outcome, a design-agnostic analysis was preferred over model-based and design-based analyses. All methods yielded unbiased parameter estimates but standard error estimates were lowest from the design-agnostic analysis. However, when misclassification is differential, only the design-based method produced valid parameter estimates of the variables included in the sampling strata. Conclusions: In complex survey design, when the interest is in making inference on rare subgroups, we recommend implementing disproportionate stratified sampling over simple random sampling even if the sampling strata are misclassified. If the misclassification is non-differential, we recommend a design-agnostic analysis. However, if the misclassification is differential, we recommend using design-based analyses.
MeSH term(s)	Electronic Health Records ; Ethnicity ; Humans ; Minority Groups ; Research Design ; Surveys and Questionnaires
Language	English
Publishing date	2021-07-11
Publishing country	England
Document type	Journal Article ; Research Support, N.I.H., Extramural
ZDB-ID	2041362-2
ISSN	1471-2288 ; 1471-2288
ISSN (online)	1471-2288
ISSN	1471-2288
DOI	10.1186/s12874-021-01332-8
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Generalized case-control sampling under generalized linear models.

Maronge, Jacob M / Tao, Ran / Schildcrout, Jonathan S / Rathouz, Paul J

Biometrics

2021 Volume 79, Issue 1, Page(s) 332–343

Abstract: A generalized case-control (GCC) study, like the standard case-control study, leverages outcome-dependent sampling (ODS) to extend to nonbinary responses. We develop a novel, unifying approach for analyzing GCC study data using the recently developed ... ...

Abstract	A generalized case-control (GCC) study, like the standard case-control study, leverages outcome-dependent sampling (ODS) to extend to nonbinary responses. We develop a novel, unifying approach for analyzing GCC study data using the recently developed semiparametric extension of the generalized linear model (GLM), which is substantially more robust to model misspecification than existing approaches based on parametric GLMs. For valid estimation and inference, we use a conditional likelihood to account for the biased sampling design. We describe analysis procedures for estimation and inference for the semiparametric GLM under a conditional likelihood, and we discuss problems with estimation and inference under a conditional likelihood when the response distribution is misspecified. We demonstrate the flexibility of our approach over existing ones through extensive simulation studies, and we apply the methodology to an analysis of the Asset and Health Dynamics Among the Oldest Old study, which motives our research. The proposed approach yields a simple yet versatile solution for handling ODS in a wide variety of possible response distributions and sampling schemes encountered in practice.
MeSH term(s)	Linear Models ; Likelihood Functions ; Case-Control Studies ; Data Interpretation, Statistical ; Computer Simulation ; Models, Statistical
Language	English
Publishing date	2021-10-12
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't ; Research Support, N.I.H., Extramural
ZDB-ID	213543-7
ISSN	1541-0420 ; 0099-4987 ; 0006-341X
ISSN (online)	1541-0420
ISSN	0099-4987 ; 0006-341X
DOI	10.1111/biom.13571
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Article ; Online: Vulnerability to functional decline is associated with noncardiovascular cause of 90-day readmission in hospitalized patients with heart failure.

Trochez, Ricardo J / Barrett, Jennifer B / Shi, Yaping / Schildcrout, Jonathan S / Rick, Chelsea / Nair, Devika / Welch, Sarah A / Kumar, Anupam A / Bell, Susan P / Kripalani, Sunil

Journal of hospital medicine

2024 Volume 19, Issue 5, Page(s) 386–393

Abstract: Background: Hospital readmission is common among patients with heart failure. Vulnerability to decline in physical function may increase the risk of noncardiovascular readmission for these patients, but the association between vulnerability and the ... ...

Abstract	Background: Hospital readmission is common among patients with heart failure. Vulnerability to decline in physical function may increase the risk of noncardiovascular readmission for these patients, but the association between vulnerability and the cause of unplanned readmission is poorly understood, inhibiting the development of effective interventions. Objectives: We examined the association of vulnerability with the cause of readmission (cardiovascular vs. noncardiovascular) among hospitalized patients with acute decompensated heart failure. Designs, settings, and participants: This prospective longitudinal study is part of the Vanderbilt Inpatient Cohort Study. Main outcome and measures: The primary outcome was the cause of unplanned readmission (cardiovascular vs. noncardiovascular). The primary independent variable was vulnerability, measured using the Vulnerable Elders Survey (VES-13). Results: Among 804 hospitalized patients with acute decompensated heart failure, 315 (39.2%) experienced an unplanned readmission within 90 days of discharge. In a multinomial logistic model with no readmission as the reference category, higher vulnerability was associated with readmission for noncardiovascular causes (relative risk ratio [RRR] = 1.36, 95% confidence interval [CI]: 1.06-1.75) in the first 90 days after discharge. The VES-13 score was not associated with readmission for cardiovascular causes (RRR = 0.94, 95% CI: 0.75-1.17). Conclusions: Vulnerability to functional decline predicted noncardiovascular readmission risk among hospitalized patients with heart failure. The VES-13 is a brief, validated, and freely available tool that should be considered in planning care transitions. Additional work is needed to examine the efficacy of interventions to monitor and mitigate noncardiovascular concerns among vulnerable patients with heart failure being discharged from the hospital.
MeSH term(s)	Humans ; Heart Failure ; Patient Readmission/statistics & numerical data ; Male ; Female ; Aged ; Prospective Studies ; Longitudinal Studies ; Aged, 80 and over ; Risk Factors ; Middle Aged ; Hospitalization
Language	English
Publishing date	2024-02-24
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
ZDB-ID	2233783-0
ISSN	1553-5606 ; 1553-5592
ISSN (online)	1553-5606
ISSN	1553-5592
DOI	10.1002/jhm.13316
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