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Bias correction via outcome reassignment for cross-sectional data with binary disease outcome

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  • Mei-Cheng Wang

    (Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health)

  • Yuxin Zhu

    (Department of Neurology, Johns Hopkins School of Medicine)

Abstract

Cross-sectionally sampled data with binary disease outcome are commonly analyzed in observational studies to identify the relationship between covariates and disease outcome. A cross-sectional population is defined as a population of living individuals at the sampling or observational time. It is generally understood that binary disease outcome from cross-sectional data contains less information than longitudinally collected time-to-event data, but there is insufficient understanding as to whether bias can possibly exist in cross-sectional data and how the bias is related to the population risk of interest. Wang and Yang (2021) presented the complexity and bias in cross-sectional data with binary disease outcome with detailed analytical explorations into the data structure. As the distribution of the cross-sectional binary outcome is quite different from the population risk distribution, bias can arise when using cross-sectional data analysis to draw inference for population risk. In this paper we argue that the commonly adopted age-specific risk probability is biased for the estimation of population risk and propose an outcome reassignment approach which reassigns a portion of the observed binary outcome, 0 or 1, to the other disease category. A sign test and a semiparametric pseudo-likelihood method are developed for analyzing cross-sectional data using the OR approach. Simulations and an analysis based on Alzheimer’s Disease data are presented to illustrate the proposed methods.

Suggested Citation

  • Mei-Cheng Wang & Yuxin Zhu, 2022. "Bias correction via outcome reassignment for cross-sectional data with binary disease outcome," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 28(4), pages 659-674, October.
    • Handle: RePEc:spr:lifeda:v:28:y:2022:i:4:d:10.1007_s10985-022-09559-3
    DOI: 10.1007/s10985-022-09559-3
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    References listed on IDEAS

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    3. M. Mandel & R. Fluss, 2009. "Nonparametric estimation of the probability of illness in the illness-death model under cross-sectional sampling," Biometrika, Biometrika Trust, vol. 96(4), pages 861-872.
    4. Ho, Daniel & Imai, Kosuke & King, Gary & Stuart, Elizabeth A., 2011. "MatchIt: Nonparametric Preprocessing for Parametric Causal Inference," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 42(i08).
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