Patients' preferences for a prostate cancer consultation, a discrete choice experiment

Four basic medical decision making models are commonly discussed in the literature in reference to physician-patient interactions. All fall short in their attempt to capture the nuances of physician-patient interactions, and none satisfactorily address patients' preferences for communication and other attributes of care. Prostate cancer consultations are one setting where preferences matter and are likely to vary among patients. Fortunately, discrete choice experiments are capable of casting light on patients' preferences for communication and other attributes of value that make up a consultation before the consultation occurs, which is crucial if patients are to derive the most utility from the process of reaching a decision as well as the decision itself. The results of my dissertation provide strong support to the notion that patients, at least in the hypothetical setting of a DCE, have identifiable preferences for the attributes of a prostate cancer consultation and that those preferences are capable of being elicited before a consultation takes place. Further, patients' willingness-to-pay for the non-cost attributes of the consultation is surprisingly robust to a variety of individual level variables of interest. ^

FACTORS INFLUENCING CHOICE OF CONTRACEPTIVE METHOD AMONG MARRIED FECUND WOMEN WHO INTEND NO ADDITIONAL BIRTHS: HEALTH BELIEF MODEL AND ECONOMIC PERSPECTIVES

This research examined to what extent Health Belief Model (HBM) and socioeconomic variables were useful in explaining the choice whether or not more effective contraceptive methods were used among married fecund women intending no additional births. The source of the data was the 1976 National Survey of Family Growth conducted under the auspices of the National Center for Health Statistics. Using the HBM as a framework for multivariate analyses limited support was found (using available measures) that the HBM components of motivation and perceived efficacy influence the likelihood of more effective contraceptive method use. Support was also found that modifying variables suggested by the HBM can influence the effects of HBM components on the likelihood of more effective method use. Socioeconomic variables were found, using all cases and some subgroups, to have a significant additional influence on the likelihood of use of more effective methods. Limited support was found for the concept that the greater the opportunity costs of an unwanted birth the greater the likelihood of use of more effective contraceptive methods. This research supports the use of HBM and socioeconomic variables to explain the likelihood of a protective health behavior, use of more effective contraception if no additional births are intended.^

Genetic analysis of tumor progression susceptibility in the mouse skin model

In the field of chemical carcinogenesis the use of animal models has proved to be a useful tool in dissecting the multistage process of tumor formation. In this regard the outbred SENCAR mouse has been the strain of choice in the analysis of skin carcinogenesis given its high sensitivity to the chemically induced acquisition of premalignant lesions, papillomas, and the later progression of these lesions into squamous cell carcinomas (SCC).^ The derivation of an inbred strain from the SENCAR stock called SSIN, that in spite of a high sensitivity to the development of papillomas lack the ability to transform these premalignant lesions into SCC, suggested that tumor promotion and progression were under the genetic control of different sets of genes.^ In the present study the nature of susceptibility to tumor progression was investigated. Analysis of F1 hybrids between the outbred SENCAR and SSIN mice suggested that there is at least one dominant gene responsible for susceptibility to tumor progression.^ Later development of another inbred strain from the outbred SENCAR stock, that had sensitivity to both tumor promotion and progression, allowed the formulation of a more accurate genetic model. Using this newly derived line, SENCAR B/Pt. and SSIN it was determined that there is one dominant tumor progression susceptibility gene. Linkage analysis showed that this gene maps to mouse chromosome 14 and it was possible to narrow the region to a 16 cM interval.^ In order to better characterize the nature of the progression susceptibility differences between these two strains, their proliferative pattern was investigated. It was found that SENCAR B/Pt, have an enlarged proliferative compartment with overexpression of cyclin D1, p16 and p21. Further studies showed an aberrant overexpression of TGF-$\beta$ in the susceptible strain, an increase in apoptosis, p53 protein accumulation and early loss of connexin 26. These results taken together suggest that papillomas in the SENCAR B/Pt. mice have higher proliferation and may have an increase in genomic instability, these two factors would contribute to a higher sensitivity to tumor progression. ^

A model of factors determining levels of public support for health care policy in the United States

Public preferences for policy are formed in a little-understood process that is not adequately described by traditional economic theory of choice. In this paper I suggest that U.S. aggregate support for health reform can be modeled as tradeoffs among a small number of behavioral values and the stage of policy development. The theory underlying the model is based on Samuelson, et al.'s (1986) work and Wilke's (1991) elaboration of it as the Greed/Efficiency/Fairness (GEF) hypothesis of motivation in the management of resource dilemmas, and behavioral economics informed by Kahneman and Thaler's prospect theory. ^ The model developed in this paper employs ordered probit econometric techniques applied to data derived from U.S. polls taken from 1990 to mid-2003 that measured support for health reform proposals. Outcome data are four-tiered Likert counts; independent variables are dummies representing the presence or absence of operationalizations of each behavioral variable, along with an integer representing policy process stage. Marginal effects of each independent variable predict how support levels change on triggering that variable. Model estimation results indicate a vanishingly small likelihood that all coefficients are zero and all variables have signs expected from model theory. ^ Three hypotheses were tested: support will drain from health reform policy as it becomes increasingly well-articulated and approaches enactment; reforms appealing to fairness through universal health coverage will enjoy a higher degree of support than those targeted more narrowly; health reforms calling for government operation of the health finance system will achieve lower support than those that do not. Model results support the first and last hypotheses. Contrary to expectations, universal health care proposals did not provide incremental support beyond those targeted to “deserving” populations—children, elderly, working families. In addition, loss of autonomy (e.g. restrictions on choice of care giver) is found to be the “third rail” of health reform with significantly-reduced support. When applied to a hypothetical health reform in which an employer-mandated Medical Savings Account policy is the centerpiece, the model predicts support that may be insufficient to enactment. These results indicate that the method developed in the paper may prove valuable to health policy designers. ^

A multiplicative model for standardizing vital rates without using external standards

Standardization is a common method for adjusting confounding factors when comparing two or more exposure category to assess excess risk. Arbitrary choice of standard population in standardization introduces selection bias due to healthy worker effect. Small sample in specific groups also poses problems in estimating relative risk and the statistical significance is problematic. As an alternative, statistical models were proposed to overcome such limitations and find adjusted rates. In this dissertation, a multiplicative model is considered to address the issues related to standardized index namely: Standardized Mortality Ratio (SMR) and Comparative Mortality Factor (CMF). The model provides an alternative to conventional standardized technique. Maximum likelihood estimates of parameters of the model are used to construct an index similar to the SMR for estimating relative risk of exposure groups under comparison. Parametric Bootstrap resampling method is used to evaluate the goodness of fit of the model, behavior of estimated parameters and variability in relative risk on generated sample. The model provides an alternative to both direct and indirect standardization method. ^

DISCHARGE PLANNING: A PROCESS MODEL

As the requirements for health care hospitalization have become more demanding, so has the discharge planning process become a more important part of the health services system. A thorough understanding of hospital discharge planning can, then, contribute to our understanding of the health services system. This study involved the development of a process model of discharge planning from hospitals. Model building involved the identification of factors used by discharge planners to develop aftercare plans, and the specification of the roles of these factors in the development of the discharge plan. The factors in the model were concatenated in 16 discrete decision sequences, each of which produced an aftercare plan.^ The sample for this study comprised 407 inpatients admitted to the M. D. Anderson Hospital and Tumor Institution at Houston, Texas, who were discharged to any site within Texas during a 15 day period. Allogeneic bone marrow donors were excluded from the sample. The factors considered in the development of discharge plans were recorded by discharge planners and were used to develop the model. Data analysis consisted of sorting the discharge plans using the plan development factors until for some combination and sequence of factors all patients were discharged to a single site. The arrangement of factors that led to that aftercare plan became a decision sequence in the model.^ The model constructs the same discharge plans as those developed by hospital staff for every patient in the study. Tests of the validity of the model should be extended to other patients at the MDAH, to other cancer hospitals, and to other inpatient services. Revisions of the model based on these tests should be of value in the management of discharge planning services and in the design and development of comprehensive community health services.^

CHARACTERIZATION AND TREATMENT OF A NOVEL MOUSE MODEL OF TSC-ASSOCIATED AUTISM

Tuberous sclerosis complex (TSC) is a dominant tumor suppressor disorder caused by mutations in either TSC1 or TSC2. The proteins of these genes form a complex to inhibit the mammalian target of rapamycin complex 1 (mTORC1), which controls protein translation and cell growth. TSC causes substantial neuropathology, often leading to autism spectrum disorders (ASDs) in up to 60% of patients. The anatomic and neurophysiologic links between these two disorders are not well understood. However, both disorders share cerebellar abnormalities. Therefore, we have characterized a novel mouse model in which the Tsc2 gene was selectively deleted from cerebellar Purkinje cells (Tsc2f/-;Cre). These mice exhibit progressive Purkinje cell degeneration. Since loss of Purkinje cells is a well-reported postmortem finding in patients with ASD, we conducted a series of behavior tests to assess if Tsc2f/-;Cre mice displayed autistic-like deficits. Using the three chambered social choice assay, we found that Tsc2f/-;Cre mice showed behavioral deficits, exhibiting no preference between a stranger mouse and an inanimate object, or between a novel and a familiar mouse. Tsc2f/-;Cre mice also demonstrated increased repetitive behavior as assessed with marble burying activity. Altogether, these results demonstrate that loss of Tsc2 in Purkinje cells in a haploinsufficient background lead to behavioral deficits that are characteristic of human autism. Therefore, Purkinje cells loss and/or dysfunction may be an important link between TSC and ASD. Additionally, we have examined some of the cellular mechanisms resulting from mutations in Tsc2 leading to Purkinje cell death. Loss of Tsc2 led to upregulation of mTORC1 and increased cell size. As a consequence of increased protein synthesis, several cellular stress pathways were upregulated. Principally, these included altered calcium signaling, oxidative stress, and ER stress. Likely as a consequence of ER stress, there was also upregulation of ubiquitin and autophagy. Excitingly, treatment with an mTORC1 inhibitor, rapamycin attenuated mTORC1 activity and prevented Purkinje cell death by reducing of calcium signaling, the ER stress response, and ubiquitin. Remarkably, rapamycin treatment also reversed the social behavior deficits, thus providing a promising potential therapy for TSC-associated ASD.

Mixture modeling for joint analysis of survival, discrete, and continuous data

Mixture modeling is commonly used to model categorical latent variables that represent subpopulations in which population membership is unknown but can be inferred from the data. In relatively recent years, the potential of finite mixture models has been applied in time-to-event data. However, the commonly used survival mixture model assumes that the effects of the covariates involved in failure times differ across latent classes, but the covariate distribution is homogeneous. The aim of this dissertation is to develop a method to examine time-to-event data in the presence of unobserved heterogeneity under a framework of mixture modeling. A joint model is developed to incorporate the latent survival trajectory along with the observed information for the joint analysis of a time-to-event variable, its discrete and continuous covariates, and a latent class variable. It is assumed that the effects of covariates on survival times and the distribution of covariates vary across different latent classes. The unobservable survival trajectories are identified through estimating the probability that a subject belongs to a particular class based on observed information. We applied this method to a Hodgkin lymphoma study with long-term follow-up and observed four distinct latent classes in terms of long-term survival and distributions of prognostic factors. Our results from simulation studies and from the Hodgkin lymphoma study demonstrated the superiority of our joint model compared with the conventional survival model. This flexible inference method provides more accurate estimation and accommodates unobservable heterogeneity among individuals while taking involved interactions between covariates into consideration.^