On Determining the BMD from Multiple Outcomes in Developmental Toxicity Studies when One Outcome is Intentionally Missing

Authors

  • Julie S. Najita,

    Corresponding author
    1. Department of Biostatistics, Harvard School of Public Health, Boston, MA, USA
    • Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA, USA
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  • Paul J. Catalano

    1. Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA, USA
    2. Department of Biostatistics, Harvard School of Public Health, Boston, MA, USA
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Address correspondence to Julie S. Najita, Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, MS CLS11007, 450 Brookline Avenue, Boston, MA 02215, USA; tel: 617-582-8377; fax: 617-632-24444; jnajita@jimmy.harvard.edu.

Abstract

Public health concerns over the occurrence of developmental abnormalities that can occur as a result of prenatal exposure to drugs, chemicals, and other environmental factors has led to a number of developmental toxicity studies and the use of the benchmark dose (BMD) for risk assessment. To characterize risk from multiple sources, more recent analytic methods involve a joint modeling approach, accounting for multiple dichotomous and continuous outcomes. For some continuous outcomes, evaluating all subjects may not be feasible, and only a subset may be evaluated due to limited resources. The subset can be selected according to a prespecified probability model and the unobserved data can be viewed as intentionally missing in the sense that subset selection results in missingness that is experimentally planned. We describe a subset selection model that allows for sampling pups with malformations and healthy pups at different rates, and includes the well-known simple random sample (SRS) as a special case. We were interested in understanding how sampling rates that are selected beforehand influence the precision of the BMD. Using simulations we show how improvements over the SRS can be obtained by oversampling malformations, and how some sampling rates can yield precision that is substantially worse than the SRS. We also illustrate the potential for cost saving with oversampling. Simulations are based on a joint mixed effects model, and to account for subset selection, use of case weights to obtain valid dose-response estimates.

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