Multiple Imputation Based on Conditional Quantile Estimation

Multiple imputation is a simulation-based approach for the analysis of data with missing observations. It is widely utilized in many set- tings and preeminent among general approaches when the analytical method does not involve a likelihood function or this is too complex. We consider a multiple imputation method based on the estimation of conditional quantiles of missing observations given the observed data. The method does not require modeling a likelihood and has desirable features that may be useful in some practical settings. It can also be applied to impute dependent, bounded, censored and count data. In a simulation study it shows some advantage over the alternative meth- ods considered in terms of mean squared error across all scenarios except when the data arise from a normal distribution where all meth- ods considered perform equally well. We present an application to the estimation of percentiles of body mass index conditional on physical activity assessed by accelerometers

Confidence Regions Near Singular Information and Boundary Points With Applications to Mixed Models

We propose confidence regions with asymptotically correct uniform coverage probability of parameters whose Fisher information matrix can be singular at important points of the parameter set. Our work is motivated by the need for reliable inference on scale parameters close or equal to zero in mixed models, which is obtained as a special case. The confidence regions are constructed by inverting a continuous extension of the score test statistic standardized by expected information, which we show exists at points of singular information under regularity conditions. Similar results have previously only been obtained for scalar parameters, under conditions stronger than ours, and applications to mixed models have not been considered. In simulations our confidence regions have near-nominal coverage with as few as $n = 20$ independent observations, regardless of how close to the boundary the true parameter is. It is a corollary of our main results that the proposed test statistic has an asymptotic chi-square distribution with degrees of freedom equal to the number of tested parameters, even if they are on the boundary of the parameter set

Multiplicative models for survival percentiles: estimating percentile ratios and multiplicative interaction in the metric of time

Evaluating percentiles of survival was proposed as a possible method to analyze time-to-event outcomes. This approach sets the cumulative risk of the event of interest to a specific proportion and evaluates the time by which this proportion is attainedIn this context, exposure-outcome associations can be expressed in terms of differences in survival percentiles, expressing the difference in survival time by which different subgroups of the study population experience the same proportion of events, or in terms of percentile ratios, expressing the strength of the exposure in accelerating the time to the event. Additive models for conditional survival percentiles have been introduced, and their use to estimate multivariable-adjusted percentile differences, and additive interaction on the metric of time has been described. On the other hand, the percentile ratio has never been fully described, neither statistical methods have been presented for its models-based estimation. To bridge this gap, we provide a detailed presentation of the percentile ratio as a relative measure to assess exposure-outcome associations in the context of time-to-event analysis, discussing its interpretation and advantages. We then introduce multiplicative statistical models for conditional survival percentiles, and present their use in estimating percentile ratios and multiplicative interactions in the metric of time. The introduction of multiplicative models for survival percentiles allows researchers to apply this approach in a large variety of context where multivariable adjustment is required, enriching the potentials of the percentile approach as a flexible and valuable tool to evaluate time-to-event outcomes in medical research

Modeling sign concordance of quantile regression residuals with multiple outcomes

Quantile regression permits describing how quantiles of a scalar response vari- able depend on a set of predictors. Because a unique de nition of multivariate quantiles is lacking, extending quantile regression to multivariate responses is somewhat complicated. In this paper, we describe a simple approach based on a two-step procedure: in the  rst step, quantile regression is applied to each re- sponse separately; in the second step, the joint distribution of the signs of the residuals is modeled through multinomial regression. The described approach does not require a multidimensional de nition of quantiles, and can be used to capture important features of a multivariate response and assess the e ects of co- variates on the correlation structure. We apply the proposed method to analyze two di erent datasets

2000 Roster

2000 Women\u27s Basketball Roster, George Fox Universit

Correlates of total physical activity among middle-aged and elderly women

Information on correlates of total physical activity (PA) levels among middle-aged and elderly women is limited. This article aims to investigate whether total daily PA levels are associated with age, body mass index, smoking, drinking status, and sociodemographic factors

Differential Age-Related Declines in Cardiorespiratory Fitness Between People With and Without Type 2 Diabetes Mellitus

Objective To assess the extent to which the established age-related decline in cardiorespiratory fitness (CRF) is augmented in adult men with type 2 diabetes mellitus (T2DM). Participants and Methods This study used data from the Aerobics Center Longitudinal Study, conducted between September 18, 1974, and August 3, 2006, in primarily non-Hispanic white, middle-to-upper class adults. The analyses were restricted to adult men with complete data on age, CRF, and T2DM (35,307 participants). Quantile regression models were used to estimate age-related differences in CRF, estimated using a maximal treadmill test, between persons with and without T2DM. Smoking status and birth cohort served as covariates. Results Age-related declines in CRF were observed in men with and without T2DM. For men younger than 60 years, at low-mid percentiles of the CRF distribution the magnitude of the age-related decline in CRF was significantly higher (P-values=.00, .02) in men with T2DM than in those without T2DM. At upper percentiles, the decline with age between the 2 groups was virtually identical. Significant declines in CRF in men 45 years or younger were observed only at high levels of CRF for those without T2DM and at low levels of CRF for those with T2DM (P-values .00, .04). Conclusion This study reported that men younger than 60 years with T2DM at the low-mid CRF percentiles experience an accelerated age-related decline in CRF. Men younger than 60 years with T2DM exhibiting high levels of CRF experienced a decline in CRF comparable to men without T2DM. This study highlights the importance of incorporating sufficient levels of exercise or activity to maintain high CRF in men with T2DM

Maximum Agreement Linear Prediction via the Concordance Correlation Coefficient

This paper examines distributional properties and predictive performance of the estimated maximum agreement linear predictor (MALP) introduced in Bottai, Kim, Lieberman, Luta, and Pena (2022) paper in The American Statistician, which is the linear predictor maximizing Lin's concordance correlation coefficient (CCC) between the predictor and the predictand. It is compared and contrasted, theoretically and through computer experiments, with the estimated least-squares linear predictor (LSLP). Finite-sample and asymptotic properties are obtained, and confidence intervals are also presented. The predictors are illustrated using two real data sets: an eye data set and a bodyfat data set. The results indicate that the estimated MALP is a viable alternative to the estimated LSLP if one desires a predictor whose predicted values possess higher agreement with the predictand values, as measured by the CCC