arXiv subjects

Jonathan S. Schildcrout

Publications and source records attributed to Jonathan S. Schildcrout.

3 recordsLinked to original sources

arXiv2026

Two-Phase Sampling Designs and Analysis Approaches for Ordinal Outcomes

Modern clinical trials and cohort studies gather low-cost data on all participants but may have limited resources to assess expensive exposures such as biomarkers or genomic data. When interest lies in associations involving expensive exposures, two-phase designs provide a cost-effective framework by using information available on all participants to guide the targeted selection of a subset for additional measurements. We extend this framework to studies with ordinal outcomes, a common yet previously unexplored setting. We propose three outcome-informed phase 2 sampling designs -- outcome-dependent sampling (ODS), covariate-stratified ODS, and residual-dependent sampling -- that leverage phase 1 data to enrich phase 2 selection with informative subjects. We then develop analysis methods for valid and efficient estimation/inference, including conditional likelihood methods with ascertainment-corrected maximum likelihood estimation, multiple imputation, and a full likelihood method using sieve maximum likelihood estimation. Across a range of scenarios, simulation studies show that the proposed methods substantially improve efficiency over simple random sampling with standard maximum likelihood estimation. We further demonstrate their practical utility by examining the association between interleukin-6 and a four-level clinical status outcome -- discharged, hospitalized but not in the ICU, hospitalized in the ICU, and death -- 14 days after randomization into the Crystalloid Liberal or Vasopressors Early Resuscitation in Sepsis trial.

stat.ME↗

arXiv2020

Generalized Linear Models for Longitudinal Data with Biased Sampling Designs: A Sequential Offsetted Regressions Approach

Biased sampling designs can be highly efficient when studying rare (binary) or low variability (continuous) endpoints. We consider longitudinal data settings in which the probability of being sampled depends on a repeatedly measured response through an outcome-related, auxiliary variable. Such auxiliary variable- or outcome-dependent sampling improves observed response and possibly exposure variability over random sampling, {even though} the auxiliary variable is not of scientific interest. {For analysis,} we propose a generalized linear model based approach using a sequence of two offsetted regressions. The first estimates the relationship of the auxiliary variable to response and covariate data using an offsetted logistic regression model. The offset hinges on the (assumed) known ratio of sampling probabilities for different values of the auxiliary variable. Results from the auxiliary model are used to estimate observation-specific probabilities of being sampled conditional on the response and covariates, and these probabilities are then used to account for bias in the second, target population model. We provide asymptotic standard errors accounting for uncertainty in the estimation of the auxiliary model, and perform simulation studies demonstrating substantial bias reduction, correct coverage probability, and improved design efficiency over simple random sampling designs. We illustrate the approaches with two examples.

stat.ME↗

arXiv2015

Biased sampling designs to improve research efficiency: Factors influencing pulmonary function over time in children with asthma

Substudies of the Childhood Asthma Management Program [Control. Clin. Trials 20 (1999) 91-120; N. Engl. J. Med. 343 (2000) 1054-1063] seek to identify patient characteristics associated with asthma symptoms and lung function. To determine if genetic measures are associated with trajectories of lung function as measured by forced vital capacity (FVC), children in the primary cohort study retrospectively had candidate loci evaluated. Given participant burden and constraints on financial resources, it is often desirable to target a subsample for ascertainment of costly measures. Methods that can leverage the longitudinal outcome on the full cohort to selectively measure informative individuals have been promising, but have been restricted in their use to analysis of the targeted subsample. In this paper we detail two multiple imputation analysis strategies that exploit outcome and partially observed covariate data on the nonsampled subjects, and we characterize alternative design and analysis combinations that could be used for future studies of pulmonary function and other outcomes. Candidate predictor (e.g., IL10 cytokine polymorphisms) associations obtained from targeted sampling designs can be estimated with very high efficiency compared to standard designs. Further, even though multiple imputation can dramatically improve estimation efficiency for covariates available on all subjects (e.g., gender and baseline age), relatively modest efficiency gains were observed in parameters associated with predictors that are exclusive to the targeted sample. Our results suggest that future studies of longitudinal trajectories can be efficiently conducted by use of outcome-dependent designs and associated full cohort analysis.

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