Biomedical subjects

Melanie M Wall

Publications and source records attributed to Melanie M Wall.

6 recordsLinked to original sources

PubMed2003

Comparison of multiple regression to two latent variable techniques for estimation and prediction.

In the areas of epidemiology, psychology, sociology, and other social and behavioural sciences, researchers often encounter situations where there are not only many variables contributing to a particular phenomenon, but there are also strong relationships among many of the predictor variables of interest. By using the traditional multiple regression on all the predictor variables, it is possible to have problems with interpretation and multicollinearity. As an alternative to multiple regression, we explore the use of a latent variable model that can address the relationship among the predictor variables. We consider two different methods for estimation and prediction for this model: one that uses multiple regression on factor score estimates and the other that uses structural equation modelling. The first method uses multiple regression but on a set of predicted underlying factors (i.e. factor scores), and the second method is a full-information maximum-likelihood technique that incorporates the complete covariance structure of the data. In this tutorial, we will explain the model and each estimation method, including how to carry out prediction. A data example will be used for demonstration, where respiratory disease death rates by county in Minnesota are predicted by five county-level census variables. A simulation study is performed to evaluate the efficiency of prediction using the two latent variable modelling techniques compared to multiple regression.

Censuses↗

PubMed2003

Correlates of unhealthy weight-control behaviors among adolescents: implications for prevention programs.

This study aimed to identify correlates of unhealthy weight-control behaviors in adolescents to guide the development of programs aimed at the primary prevention of disordered eating. A model explaining unhealthy weight-control behaviors was tested among 4,746 adolescents using structural equation modeling. Models fit the data well and explained 76% of the variance in unhealthy weight-control behaviors among girls and 63% among boys. Weight-body concerns were a strong correlate of unhealthy weight-control behaviors in both girls and boys. Models also emphasized the importance of weight-specific social norms within the adolescent's proximal environment. Findings suggest the importance of addressing weight-body concerns within prevention programs and extending interventions beyond classroom settings to ensure changes in weight-related norms among peer groups and family members.

Adolescent↗

PubMed2003

Frailty modeling for spatially correlated survival data, with application to infant mortality in Minnesota.

The use of survival models involving a random effect or 'frailty' term is becoming more common. Usually the random effects are assumed to represent different clusters, and clusters are assumed to be independent. In this paper, we consider random effects corresponding to clusters that are spatially arranged, such as clinical sites or geographical regions. That is, we might suspect that random effects corresponding to strata in closer proximity to each other might also be similar in magnitude. Such spatial arrangement of the strata can be modeled in several ways, but we group these ways into two general settings: geostatistical approaches, where we use the exact geographic locations (e.g. latitude and longitude) of the strata, and lattice approaches, where we use only the positions of the strata relative to each other (e.g. which counties neighbor which others). We compare our approaches in the context of a dataset on infant mortality in Minnesota counties between 1992 and 1996. Our main substantive goal here is to explain the pattern of infant mortality using important covariates (sex, race, birth weight, age of mother, etc.) while accounting for possible (spatially correlated) differences in hazard among the counties. We use the GIS ArcView to map resulting fitted hazard rates, to help search for possible lingering spatial correlation. The DIC criterion (Spiegelhalter et al., Journal of the Royal Statistical Society, Series B 2002, to appear) is used to choose among various competing models. We investigate the quality of fit of our chosen model, and compare its results when used to investigate neonatal versus post-neonatal mortality. We also compare use of our time-to-event outcome survival model with the simpler dichotomous outcome logistic model. Finally, we summarize our findings and suggest directions for future research.

Adult↗

PubMed2003

Generalized common spatial factor model.

There are often two types of correlations in multivariate spatial data: correlations between variables measured at the same locations, and correlations of each variable across the locations. We hypothesize that these two types of correlations are caused by a common spatially correlated underlying factor. Under this hypothesis, we propose a generalized common spatial factor model. The parameters are estimated using the Bayesian method and a Markov chain Monte Carlo computing technique. Our main goals are to determine which observed variables share a common underlying spatial factor and also to predict the common spatial factor. The model is applied to county-level cancer mortality data in Minnesota to find whether there exists a common spatial factor underlying the cancer mortality throughout the state.

Bayes Theorem↗

PubMed2003

A method of moments technique for fitting interaction effects in structural equation models.

The desire to fit structural equation models containing an interaction term has received much methodological attention in the social science literature. This paper presents a technique for the cross-product structural model that utilizes factor score estimates and results in closed-form moments-type estimators. The technique, which does not require normality for the underlying factors, was originally introduced in a very general form by Wall and Amemiya (2000) for any polynomial structural model. In this paper, the practical implementation of this method, including standard error estimation, is presented specifically for the cross-product model. The procedure is applied to an example from social/behavioural epidemiology where the flexibility of the cross-product model provides a useful description of the underlying theory. A simulation study is also presented comparing the method of moments for the cross-product model with three other procedures.

Humans↗

PubMed2002

Small-sample adjustments in using the sandwich variance estimator in generalized estimating equations.

The generalized estimating equation (GEE) approach is widely used in regression analyses with correlated response data. Under mild conditions, the resulting regression coefficient estimator is consistent and asymptotically normal with its variance being consistently estimated by the so-called sandwich estimator. Statistical inference is thus accomplished by using the asymptotic Wald chi-squared test. However, it has been noted in the literature that for small samples the sandwich estimator may not perform well and may lead to much inflated type I errors for the Wald chi-squared test. Here we propose using an approximate t- or F-test that takes account of the variability of the sandwich estimator. The level of type I error of the proposed t- or F-test is guaranteed to be no larger than that of the Wald chi-squared test. The satisfactory performance of the proposed new tests is confirmed in a simulation study. Our proposal also has some advantages when compared with other new approaches based on direct modifications of the sandwich estimator, including the one that corrects the downward bias of the sandwich estimator. In addition to hypothesis testing, our result has a clear implication on constructing Wald-type confidence intervals or regions.

Adult↗