SearcharxivSearch

arXiv · 2411.07399

Cumulative differences between subpopulations versus body mass index in the Behavioral Risk Factor Surveillance System data

Abstract

Prior works have demonstrated many advantages of cumulative statistics over the classical methods of reliability diagrams, ECEs (empirical, estimated, or expected calibration errors), and ICIs (integrated calibration indices). The advantages pertain to assessing calibration of predicted probabilities, comparison of responses from a subpopulation to the responses from the full population, and comparison of responses from one subpopulation to those from a separate subpopulation. The cumulative statistics include graphs of cumulative differences as a function of the scalar covariate, as well as metrics due to Kuiper and to Kolmogorov and Smirnov that summarize the graphs into single scalar statistics and associated P-values (also known as "attained significance levels" for significance tests). However, the prior works have not yet treated data from biostatistics. Fortunately, the advantages of the cumulative statistics extend to the Behavioral Risk Factor Surveillance System (BRFSS) of the Centers for Disease Control and Prevention. This is unsurprising, since the mathematics is the same as in earlier works. Nevertheless, detailed analysis of the BRFSS data is revealing and corroborates the findings of earlier works. Two methodological extensions beyond prior work that facilitate analysis of the BRFSS are (1) empirical estimators of uncertainty for graphs of the cumulative differences between two subpopulations, such that the estimators are valid for any real-valued responses, and (2) estimators of the weighted average treatment effect for the differences in the responses between the subpopulations. Both of these methods concern the case in which none of the covariate's observed values for one subpopulation is equal to any of the covariate's values for the other subpopulation. The data analysis presented reports results for this case as well as several others.

Explore related subjects

Keep this discovery

BibTeXRIS

Mark Tygert. 2024-11-11. Cumulative differences between subpopulations versus body mass index in the Behavioral Risk Factor Surveillance System data. https://arxiv.org/abs/2411.07399

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Surprise Reduction and Nullification in Bayesian and Inverse Bayesian Inference under Ambiguous Prediction-Error Attribution

In non-stationary environments, prediction errors may signal environmental change or transient outliers, and adaptive systems must track such changes without overreacting to outliers. We distinguish surprise reduction, which updates beliefs to fit observations, from surprise nullification, which weakens constraints imposed by the predictive structure, and formalize both within Bayesian and inverse Bayesian (BIB) inference. Belief and likelihood updates are derived from variational objectives sharing a nullification strength, determined endogenously by minimizing surprise under the candidate post-update predictive distribution. In the Gaussian case, nullification expands belief and likelihood variances by a common factor relative to standard Bayesian updating, leaving the ratio unchanged. BIB thus defers attribution of the prediction error, committing to neither latent-state change nor observation-process uncertainty. The nullification strength is carried over as a candidate and is maintained or released according to the predictive surprise of the next observation. In a mean estimation task with outliers and changepoints, no scanned parameter setting of a Sage-Husa-type adaptive Kalman filter, fixed-strength BIB variant, or belief-forgetting-only variant outperforms BIB in both changepoint tracking and post-outlier stability. An oracle-informed reduced Bayesian model tracks changepoints better but is less stable after outliers. Although BIB maintains no explicit hypotheses about changepoints or outliers, it generates event-dependent dynamics. The learning rate increases after changepoints, whereas after outliers, nullification is released, and this increase is suppressed. Deferring attribution and letting subsequent observations differentiate the responses may constitute a principle of adaptive inference in non-stationary environments.

stat.ME

Generalized Ridge Refitting for the Lasso and Prediction Improvement Bounds

We study a class of Lasso based estimators obtained by applying a quadratic correction on the Lasso equicorrelation set. The penalty matrix determines both the magnitude and geometry of the correction and contains, among other cases, the isotropic Lasso--Ridge correction, least squares refitting, Gram proportional interpolation between the Lasso and least squares, and coordinate specific penalties. We first derive a closed form representation and isolate the positive gain component of the resulting prediction improvement. We then control the remaining stochastic linear term in expectation by localizing the random signed equicorrelation model around a deterministic reference support. This yields a finite sample expectation bound that explicitly accounts for the randomness induced by Lasso model selection. The resulting decomposition provides a unified framework for understanding when Lasso based quadratic corrections can improve prediction.

stat.ME

Discretization in covariate-adaptive randomization: gains and losses

Covariate-adaptive randomization(CAR) is widely implemented in clinical trials to balance prognostic covariates across treatment arms. Continuous covariates are often discretized into strata in practice, yet their consequences are not clearly understood. This paper provides a comprehensive study of the impact of discretization on both the CAR design process and the inferential results thereafter. We establish the asymptotic properties of both imbalance measures and treatment effect estimators under discretized and non-discretized settings. Practical recommendations are given on when and how discretization should be employed. We show that discretization in design is generally recommended, as it enhances robustness against model misspecification. However, if the true model is known, the most efficient strategy is to balance covariates according to that model in the design. The theoretical results are corroborated by extensive simulation studies and an empirical application to a diabetes trial dataset. Together, the results clarify the gains and losses of discretization in CAR and pave the way for learning impact of discretization to other designs and beyond.

stat.ME