We present statistical methods for big data arising from online analytical processing, where large amounts of data arrive in streams and require fast analysis without storage/access to the historical data. In particular, we develop iterative estimating algorithms and statistical inferences for linear models and estimating equations that update as new data arrive. These algorithms are computationally efficient, minimally storage-intensive, and allow for possible rank deficiencies in the subset design matrices due to rare-event covariates. Within the linear model setting, the proposed online-updating framework leads to predictive residual tests that can be used to assess the goodness of fit of the hypothesized model. We also propose a new online-updating estimator under the estimating equation setting. Theoretical properties of the goodness-of-fit tests and proposed estimators are examined in detail. In simulation studies and real data applications, our estimator compares favorably with competing approaches under the estimating equation setting. Supplementary materials for this article are available online.

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Yishu Xue, and Elizabeth D. Schifano. CSAM 2017;24:583-604. https://doi.org/10.29220/CSAM.2017.24.6.583

Research on the transition to adult care for young adults with type 1 diabetes (T1D) emphasizes transition readiness, with less emphasis on transition outcomes. The relatively few studies that focus on outcomes use a wide variety of measures with little reliance on stakeholder engagement for measure selection This study engaged multiple stakeholders (i.e., young adults with T1D, parents, pediatric and adult health care providers, and experts) in qualitative interviews to identify the content domain for developing a multidimensional measure of health care transition (HCT) outcomes The following constructs were identified for a planned measure of HCT outcomes: biomedical markers of T1D control; T1D knowledge/skills; navigation of a new health care system; integration of T1D into emerging adult roles; balance of parental involvement with autonomy; and “ownership” of T1D self-management The results can guide creation of an initial item pool for a multidimensional profile of HCT outcomes.