Understanding Scientific Inquiry

Science is a process of inquiry: a process of asking and answering questions.  However, a good question is more than an interrogatory, and a good answer is more than information: there are logical constraints that dictate when a question is answerable and what qualifies as an answer.  This paper will provide an understanding of (1) when a question is answerable, (2) when a question is not ready to be asked, (3) when a question is trivial, (4) what is required for a response to be an answer, and (5) what sequence of inquiry is required to identify an answer.  Equipped with this understanding, a scientist can better determine an appropriate sequence of study for a research program as well as identify the necessary arguments to warrant claims of understanding, funding, and the publication of research findings

How to define and test explanations in populations

Solving applied social, economic, psychological, health care and public health problems can require an understanding of facts or phenomena related to populations of interest.  Therefore, it can be useful to test whether an explanation of a phenomenon holds in a population.  However, different definitions for the phrase “explain in a population” lead to different interpretations and methods of testing.  In this paper, I present two definitions:  The first is based on the number of members in the population that conform to the explanation’s implications; the second is based on the total magnitude of explanation-consistent effects in the population.  I show that claims based on either definition can be tested using random coefficient models, but claims based on the second definition can also be tested using the more common, and simpler, population-level regression models.   Consequently, this paper provides an understanding of the type of explanatory claims these common methods can test

A simple goodness-of-fit test for continuous conditional distributions

This paper presents a pragmatic specification test for conditional continuous distributions with uncensored data.  We employ Monte Carlo (MC) experiments and the 2011 Medical Expenditure Panel Survey data to examine coverage and power to discern deviations from correct model specification in distribution and parameterization. We carry out MC experiments using 2000 runs for sample sizes 500 and 1000. The experiments show that the test has accurate coverage under correct specification, and that the test can discern deviations from correct specification in both the distributional family and parameterization. The power increases as sample size increases. The empirical example shows the test’s ability to identify specific distributions from other candidates using real cost data. Although the test can be used as a goodness-of-fit test for marginal distributions, it is particularly useful as an easy-to-use test for conditional continuous distributions, even those with one observation per pattern of explanatory variables

Understanding Statistical Testing

Statistical hypothesis testing is common in research, but a conventional understanding sometimes leads to mistaken application and misinterpretation. The logic of hypothesis testing presented in this article provides for a clearer understanding, application, and interpretation. Key conclusions are that (a) the magnitude of an estimate on its raw scale (i.e., not calibrated by the standard error) is irrelevant to statistical testing; (b) which statistical hypotheses are tested cannot generally be known a priori; (c) if an estimate falls in a hypothesized set of values, that hypothesis does not require testing; (d) if an estimate does not fall in a hypothesized set, that hypothesis requires testing; (e) the point in a hypothesized set that produces the largest p value is used for testing; and (f) statistically significant results constitute evidence, but insignificant results do not and must not be interpreted as evidence for or against the hypothesis being tested

How older persons structure information in the decision to seek medical care

Typical models of the decision to seek care consider information as a single conceptual object. This paper presents an alternative that allows multiple objects. For older persons seeking care, results support this alternative. Older decision-makers that segregate information into multiple conceptual objects assessed separately are characterized by socio-demographic (younger age, racial category, non-Hispanic, higher education, higher income, and not married), health status (better general health for men and worse general health for women, fewer known illnesses), and neuropsychological (less memory loss for men, trouble concentrating and trouble making decisions for men) factors. Results of this study support the conclusion that older persons are more likely to integrate information, and individuals with identifiable characteristics are more likely to do so than others. The theory tested in this study implies a potential explanation for misutilization of care (either over or under-utilization)

Least Squared Simulated Errors

Estimation by minimizing the sum of squared residuals is a common method for parameters of regression functions; however, regression functions are not always known or of interest. Maximizing the likelihood function is an alternative if a distribution can be properly specified. However, cases can arise in which a regression function is not known, no additional moment conditions are indicated, and we have a distribution for the random quantities, but maximum likelihood estimation is difficult to implement. In this article, we present the least squared simulated errors (LSSE) estimator for such cases. The conditions for consistency and asymptotic normality are given. Finite sample properties are investigated via Monte Carlo experiments on two examples. Results suggest LSSE can perform well in finite samples. We discuss the estimator’s limitations and conclude that the estimator is a viable option. We recommend Monte Carlo investigation of any given model to judge bias for a particular finite sample size of interest and discern whether asymptotic approximations or resampling techniques are preferable for the construction of tests or confidence intervals

Drug-Eluting Stents in New York State: Utilization, Disparities, Cost,Outcomes and Practice Variation

Thesis (Ph.D.)--University of Rochester. School of Medicine & Dentistry. Dept. of Community and Preventive Medicine, 2010.Coronary heart disease (CHD) has been the leading cause of death in both men and women in the United States for over 80 years and is a major cause of disability. Innovative medical technologies have been adopted continuously in treating CHD. The drug-eluting stent (DES), the newest medical device in percutaneous coronary intervention (PCI), has been adopted quickly since the Food and Drug Administration (FDA)’s approval in 2003. But, disparities and practice variations in DES use were reported during the initial years. Evidence suggests that both institutional and physician level factors can affect practice pattern. The safety of DES use was called into question in 2006. However, few studies have been conducted to answer the following questions: What is the relationship between DES adoption and revascularization pattern change? How did the disparity in DES use respond to the DES safety concern? What is the relationship between DES adoption and hospitalization cost for PCI procedures? What is the relationship between DES adoption and patient outcomes? How do institutional and physician level factors explain DES adoption? To answer above questions, this dissertation uses three preexisting data sources (New York State Department of Health Statewide Planning and Research Cooperative System (SPARCS), 2000 Census File, and New York State Hospital Profile) and a survey of New York State interventional cardiologists. Conceptual models are applied, and statistical models are used to test theory-derived hypotheses. This study finds that DES adoption changed the coronary revascularization utilization pattern. Practice variation in DES use was found. DES utilization profiles were affected by the DES safety concern and they differed by race, payer, hospital type and region. Racial, payer, and regional disparities emerged and changed during the course of the DES safety concern development. To the best knowledge of the author, this study is the first to report “re-adoption” disparity following the safety concern of a new medical technology adoption. As to the hospitalization cost of primary isolated PCI procedures, huge variation across hospitals was found. As to the outcomes of primary isolated PCI procedures, no significant difference in in-hospital death was detected between the DES group and the BMS group. Both hospital behavior theory and regulatory focus theory had explanatory power in explaining DES use. Hospital behavior theory was found to have explanatory power when DES utilization remained stable whereas regulatory focus theory was found to have explanatory power when DES utilization experienced dramatic changes. Physicians with stronger prevention focus were less likely to adopt DES when DES was introduced to the market as the newest technology whereas physicians with stronger promotion focus were more likely to stick with DES use when the DES safety concern developed. The findings from this study suggest policy implications in allocating financial, material, and personnel resources at hospitals and medical education and training systems. To mitigate disparities, attention should be given to black patients, self pay patients during the periods of safety concern development and technology re-adoption. Better quality of care and lower hospitalization cost in revascularization care might be achieved by reforming hospital’s structure and management. Actionable strategies based on hospital behavior theory and regulatory focus theory can better motivate hospitals and physicians to provide optimal quality of care to patients