Josué Guzmán

Bio: Josué Guzmán is an academic researcher. The author has contributed to research in topics: Log-linear analysis & Regression analysis. The author has an hindex of 2, co-authored 2 publications receiving 2698 citations.

Effect of alendronate on bone mineral density in Puerto Rican males with osteoporosis.

Abstract: Osteoporosis in men was not recognized as a major health problem until recently, and increased research in this area resulted in the approval of alendronate for the treatment of osteoporosis in men at the beginning of this decade. Low bone mineral density (BMD) has been demonstrated to be a strong predictor of fracture in men as it is in women. The causes of osteoporosis in men are variable and can be classified as primary or secondary. The aim of these report is to present the response of BMD in 10 Puerto Rican men with secondary causes of osteoporosis treated with alendronate. A significant increase of BMD in spine, total hip, trochanter and intertrochanteric regions were noted. A non-significant increase in femoral neck was observed.

Using the Margins Command to Estimate and Interpret Adjusted Predictions and Marginal Effects

Abstract: As Long & Freese show, it can often be helpful to compute predicted/expected values for hypothetical or prototypical cases. Stata 11 introduced new tools for making such calculations – factor variables and the margins command. These can do many of the things that were previously done by Stata’s own adjust and mfx commands, as well as Long & Freese’s spost9 commands like prvalue. Unfortunately, the complexity of the margins syntax, the daunting 50 page reference manual entry that describes it, and a lack of understanding about what margins offers over older commands may have dissuaded researchers from using it. This paper therefore shows how margins can easily replicate analyses done by older commands. It demonstrates how margins provides a superior means for dealing with interdependent variables (e.g. X and X^2; X1, X2, and X1 * X2; multiple dummies created from a single categorical variable), and is also superior for data that are svyset. The paper explains how the new asobserved option works and the substantive reasons for preferring it over the atmeans approach used by older commands. The paper primarily focuses on the computation of adjusted predictions but also shows how margins has the same advantages for computing marginal effects.

Effective Messages in Vaccine Promotion: A Randomized Trial

Abstract: OBJECTIVES: To test the effectiveness of messages designed to reduce vaccine misperceptions and increase vaccination rates for measles-mumps-rubella (MMR). METHODS: A Web-based nationally representative 2-wave survey experiment was conducted with 1759 parents age 18 years and older residing in the United States who have children in their household age 17 years or younger (conducted June–July 2011). Parents were randomly assigned to receive 1 of 4 interventions: (1) information explaining the lack of evidence that MMR causes autism from the Centers for Disease Control and Prevention; (2) textual information about the dangers of the diseases prevented by MMR from the Vaccine Information Statement; (3) images of children who have diseases prevented by the MMR vaccine; (4) a dramatic narrative about an infant who almost died of measles from a Centers for Disease Control and Prevention fact sheet; or to a control group. RESULTS: None of the interventions increased parental intent to vaccinate a future child. Refuting claims of an MMR/autism link successfully reduced misperceptions that vaccines cause autism but nonetheless decreased intent to vaccinate among parents who had the least favorable vaccine attitudes. In addition, images of sick children increased expressed belief in a vaccine/autism link and a dramatic narrative about an infant in danger increased self-reported belief in serious vaccine side effects. CONCLUSIONS: Current public health communications about vaccines may not be effective. For some parents, they may actually increase misperceptions or reduce vaccination intention. Attempts to increase concerns about communicable diseases or correct false claims about vaccines may be especially likely to be counterproductive. More study of pro-vaccine messaging is needed.

Comparing Regression Coefficients Between Same-sample Nested Models Using Logit and Probit A New Method

Abstract: Logit and probit models are widely used in empirical sociological research. However, the common practice of comparing the coefficients of a given variable across differently specified models fitted...

Assessing Public Opinions of and Interest in New Vehicle Technologies: An Austin Perspective

Abstract: Technological advances are bringing connected and autonomous vehicles (CAVs) to the ever- evolving transportation system. Anticipating the public acceptance and adoption of these technologies is important. A recent internet-based survey was conducted polling 347 Austinites to understand their opinions on smart-car technologies and strategies. Ordered-probit and other model results indicate that respondents perceive fewer crashes to be the primary benefit of autonomous vehicles (AVs), with equipment failure being their top concern. Their average willingness to pay (WTP) for adding full (Level 4) automation ($7,253) appears to be much higher than that for adding partial (Level 3) automation ($3,300) to their current vehicles. This study estimates the impact of demographics, built-environment variables, and travel characteristics on Austinites’ WTP for adding such automations and connectivity to their current and coming vehicles. It also estimates adoption rates of shared autonomous vehicles (SAVs) under different pricing scenarios ($1, $2, and $3 per mile), choice dependence on friends’ and neighbors’ adoption rates, and home-location decisions after AVs and SAVs become a common mode of transport. Higher-income, technology-savvy males, living in urban areas, and those who have experienced more crashes have a greater interest in and higher WTP for the new technologies, with less dependence on others’ adoption rates. Such behavioral models are useful to simulate long-term adoption of CAV technologies under different vehicle pricing and demographic scenarios. These results can be used to develop smarter transportation systems for more efficient and sustainable travel.

Understanding and interpreting generalized ordered logit models

Abstract: When outcome variables are ordinal rather than continuous, the ordered logit model, aka the proportional odds model (ologit/po), is a popular analytical method. However, generalized ordered logit/partial proportional odds models (gologit/ppo) are often a superior alternative. Gologit/ppo models can be less restrictive than proportional odds models and more parsimonious than methods that ignore the ordering of categories altogether. However, the use of gologit/ppo models has itself been problematic or at least sub-optimal. Researchers typically note that such models fit better but fail to explain why the ordered logit model was inadequate or the substantive insights gained by using the gologit alternative. This paper uses both hypothetical examples and data from the 2012 European Social Survey to address these shortcomings.