New estimation techniques for ordinal sensitive variables

Methods to analyze multicategorical variables are extensively used in sociological, medical and educational research. Nonetheless, they have a very sparse presence in finite population sampling when sensitive topics are investigated and data are obtained by means of the randomized response technique (RRT), a survey method based on the principle that sensitive questions must not be asked directly to the respondents. The RRT is used with the aim of reducing social desirability bias, which is defined as the respondent tendency to release personal information according to what is socially acceptable. This nonstandard data-collection approach was originally developed to deal with dichotomous responses to sensitive questions. Later, the idea has been extended to multicategory responses. In this paper we consider ordinal variables with more than two response categories. In particular, we first discuss the theoretical framework for estimating the frequency of ordinal categories when data are subjected to misclassification due to the use of a particular RRT. Then, we show how it is possible to improve the efficiency of the inferential process by employing auxiliary information at the estimation stage through the calibration approach. Finally, we assess the performance of the proposed estimators in a Monte Carlo simulation study.Ministerio de Econom´ıa y Competitividad of Spai

Tobacco Use: Prevention, Cessation, and Control

Practice Center (RTI-UNC EPC) systematically reviewed the evidence on (a) the effectiveness of community- and population-based interventions to prevent tobacco use and to increase consumer demand for and implementation of effective cessation interventions; (b) the impacts of smokeless tobacco marketing on smoking, use of those products, and population harm; and (c) the directions for future research. Data Sources: We searched MEDLINE®, Cumulative Index to Nursing and Applied Health (CINAHL), Cochrane libraries, Cochrane Clinical Trials Register, Psychological Abstracts, and Sociological Abstracts from January 1980 through June 10, 2005. We included English-language randomized controlled trials, other trials, and observational studies, with sample size and followup restrictions. We used 15 Cochrane Collaboration systematic reviews, 5 prior systematic reviews, and 2 meta-analyses as the foundation for this report. Review Methods: Trained reviewers abstracted detailed data from included articles into evidence tables and completed quality assessments; other senior reviewers confirmed accuracy and resolved disagreements. Results: We identified 1,288 unique abstracts; 642 did not meet inclusion criteria, 156 overlapped with prior reviews, and 2 were not published articles. Of 488 full-text articles retrieved and reviewed, we excluded 298 for several reasons, marked 88 as background, and retained 102. Evidence (consistent with previous reviews) showed that (a) school-based prevention interventions have short-term (but not long-term) effects on adolescents; (b) multicomponent approaches, including telephone counseling, increase the number of users who attempt to quit; (c) self-help strategies alone are ineffective, but counseling and pharmacotherapy used either alone or in combination can improve success rates of quit attempts; and (d) provide training and academic detailing improve provider delivery of cessation treatments, but evidence is insufficient to show that these approaches yield higher quit rates. Recent evidence on the following topics was insufficient to change prior review findings: (a) effectiveness of population-based prevention interventions; (b) effectiveness of providerbased interventions to reduce tobacco initiation; (c) effectiveness of community- and providerbased interventions to increase use of proven cessation strategies; (d) effectiveness of marketing campaigns to switch tobacco users from smoking to smokeless tobacco products; and (e) effectiveness of interventions in populations with comorbidities and risk behaviors (e.g., depression, substance and alcohol abuse). No evidence was available on the way in which smokeless tobacco product marketing affects population harm. Conclusions: The evidence base has notable gaps and numerous study deficiencies. We found little information to address some of the issues that previous authoritative reviews had not covered, some information to substantiate earlier conclusions and recommendations from those reviews, and no evidence that would overturn any previous recommendations

Investigating the criterion validity of contingent valuation-willingness to pay methods

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonWith theoretical foundations in welfare theory, the cost benefit analysis (CBA) technique is a powerful tool for assessing benefits particularly where markets do not exist or would fail (for example due to the existence of public goods) or have become potentially politically excluded (such as the health sector). Unlike other economic evaluation techniques, costs and benefits are measured in monetary terms allowing for comparisons within and between different sectors of the economy for resource allocation decisions. Using contingent valuation (CV) techniques, people’s preferences for goods are determined by finding out what they would be willing to pay (WTP) for specified benefits or improvements; or accept (WTA), as compensation for withdrawal or loss of benefit. While the use of WTP methods has grown in other sectors, the uptake in health has been limited. A long-standing criticism is that stated WTP estimates may be poor indicators of actual WTP, calling into question their validity and the use of such estimates for welfare valuation. The aim of this thesis is to investigate the criterion validity of CV-WTP studies. A four-pronged approach including critical appraisals of the available literature and evidence on criterion validity and empirical analyses was adopted. The thesis established the scarcity in criterion validity assessments, particularly in the health sector and that evidence on the criterion validity of CV-WTP is more varied than authors are presenting. The variety in the methods used to assess and report criterion validity assessments is demonstrated. Further, the impact of the analysis of hypothetical WTP on criterion validity assessments and conclusions thereof is demonstrated. The empirical analyses further demonstrate the differences in predictions and predictors of WTP analyses, discussing the effect of these on criterion validity assessments and conclusions. Finally, the thesis offers suggestions for the reporting of criterion validity assessments, in efforts to improve the method

Statistical Analysis and Design of Crowdsourcing Applications

This thesis develops methods for the analysis and design of crowdsourced experiments and crowdsourced labeling tasks. Much of this document focuses on applications including running natural field experiments, estimating the number of objects in images and collecting labels for word sense disambiguation. Observed shortcomings of the crowdsourced experiments inspired the development of methodology for running more powerful experiments via matching on-the-fly. Using the label data to estimate response functions inspired work on non-parametric function estimation using Bayesian Additive Regression Trees (BART). This work then inspired extensions to BART such as incorporation of missing data as well as a user-friendly R package

The effect of occupational exposure to solar ultraviolet radiation on malignant skin melanoma and non- melanoma skin cancer: a systematic review and meta-analysis from the WHO/ILO Joint Estimates of the Work-related Burden of Disease and Injury

A systematic review and meta-analysis of studies were conducted reporting on the association between occupational exposure to solar ultraviolet radiation (UVR) and both malignant skin melanoma (melanoma) and non-melanoma skin cancer (NMSC), with the aim of enabling the estimation of the numbers of deaths and disability-adjusted life years from melanoma and NMSC attributable to occupational exposure to solar UVR, for the development of the World Health Organization (WHO)/International Labour Organization (ILO) Joint Estimates of the Work-related Burden of Disease and Injury (WHO/ILO Joint Estimates). A protocol was developed and published, applying the Navigation Guide as an organizing systematic review framework where feasible. Electronic bibliographic databases were searched for potentially relevant records; electronic grey literature databases and organizational websites were also searched, reference lists of previous systematic reviews and included study records were hand-searched, and additional experts were consulted. Randomized controlled trials and cohort, case–control and other non-randomized studies were included that estimated the effect of any occupational exposure to solar UVR, compared with no occupational exposure to solar UVR, on melanoma (excluding melanoma of the lip or eye) or NMSC prevalence, incidence or mortality. At least two reviewers independently screened titles and abstracts against the eligibility criteria at a first stage and full texts of potentially eligible records at a second stage. Adjusted relative risks were combined using random-effects meta-analysis. Two or more reviewers assessed the risk of bias, quality of evidence and strength of evidence. Fifty-three (48 case–control, three case–case and two cohort) eligible studies were found, published in 62 study records, including over 457 000 participants in 26 countries of three WHO regions (Region of the Americas, European Region and Western Pacific Region), reporting on the effect on melanoma or NMSC incidence or mortality. No studies on the prevalence of melanoma or NMSC were found. In most studies, exposure was self-reported in questionnaires during interviews and the health outcome was assessed via physician diagnosis based on biopsy and histopathological confirmation. The risk of bias of the body of evidence was judged to be generally “probably low”, although there were some concerns regarding risks of exposure misclassification bias, detection bias and confounding. The main meta-analyses of relevant case–control studies revealed a relative risk (RR) of melanoma and NMSC incidence of 1.45 (95% confidence interval (CI): 1.08–1.94; I2 = 81%) and 1.60 (95% CI: 1.21–2.11; I2 = 91%), respectively. No statistically significant differences in risk of melanoma and NMSC incidence were found when conducting subgroup analyses by WHO region, and no differences in risk of NMSC incidence in a subgroup analysis by sex. However, in a subgroup analysis by NMSC subtype, the increased risk of basal cell carcinoma (RR: 1.50; 95% CI: 1.10–2.04; 15 studies) was probably lower (P = 0.05 for subgroup differences) than the increased risk for squamous cell carcinoma (RR: 2.42; 95% CI: 1.66–3.53; 6 studies). The sensitivity analyses found that effect estimates of NMSC incidence were significantly higher in studies with any risk of bias domain rated as “high” or “probably high” compared with studies with only a “low” or “probably low” risk of bias, and in studies not reporting the health outcome by International Statistical Classification of Diseases and Related Health Problems (ICD) code compared with the two studies reporting ICD codes. The quality of available evidence of the effect of any occupational exposure to solar UVR on melanoma incidence and mortality and on NMSC mortality was rated as “low”, and the quality of evidence for NMSC incidence was rated as “moderate”. The strength of the existing bodies of evidence reporting on occupational exposure to solar UVR was judged as “inadequate evidence for harmfulness” for melanoma mortality and NMSC mortality. For the health outcome of melanoma incidence, the strength of evidence was judged as “limited evidence for harmfulness”, that is, a positive relationship was observed between exposure and outcome where chance, bias and confounding cannot be ruled out with reasonable confidence. For the health outcome of NMSC incidence, the strength of evidence was judged as “sufficient evidence of harmfulness”, that is, a positive relationship is observed between exposure and outcome where chance, bias and confounding can be ruled out with reasonable confidence. The 2009 International Agency for Research on Cancer classification of solar UVR as a Group 1 carcinogen that causes cutaneous melanoma and NMSC is a compelling attribute for the strength of evidence on occupational exposure to solar UVR and skin cancer incidence. Producing estimates for the burden of NMSC attributable to occupational exposure to solar UVR appears evidence-based (while acknowledging the limitations of the bodies of evidence), and the pooled effect estimates can be used as input data for the WHO/ILO Joint Estimates

Application of modern statistical methods in worldwide health insurance

With the increasing availability of internal and external data in the (health) insurance industry, the demand for new data insights from analytical methods is growing. This dissertation presents four examples of the application of advanced regression-based prediction techniques for claims and network management in health insurance: patient segmentation for and economic evaluation of disease management programs, fraud and abuse detection and medical quality assessment. Based on different health insurance datasets, it is shown that tailored models and newly developed algorithms, like Bayesian latent variable models, can optimize the business steering of health insurance companies. By incorporating and structuring medical and insurance knowledge these tailored regression approaches can at least compete with machine learning and artificial intelligence methods while being more transparent and interpretable for the business users. In all four examples, methodology and outcomes of the applied approaches are discussed extensively from an academic perspective. Various comparisons to analytical and market best practice methods allow to also judge the added value of the applied approaches from an economic perspective.Mit der wachsenden Verfügbarkeit von internen und externen Daten in der (Kranken-) Versicherungsindustrie steigt die Nachfrage nach neuen Erkenntnissen gewonnen aus analytischen Verfahren. In dieser Dissertation werden vier Anwendungsbeispiele für komplexe regressionsbasierte Vorhersagetechniken im Schaden- und Netzwerkmanagement von Krankenversicherungen präsentiert: Patientensegmentierung für und ökonomische Auswertung von Gesundheitsprogrammen, Betrugs- und Missbrauchserkennung und Messung medizinischer Behandlungsqualität. Basierend auf verschiedenen Krankenversicherungsdatensätzen wird gezeigt, dass maßgeschneiderte Modelle und neu entwickelte Algorithmen, wie bayesianische latente Variablenmodelle, die Geschäftsteuerung von Krankenversicherern optimieren können. Durch das Einbringen und Strukturieren von medizinischem und versicherungstechnischem Wissen können diese maßgeschneiderten Regressionsansätze mit Methoden aus dem maschinellen Lernen und der künstlichen Intelligenz zumindest mithalten. Gleichzeitig bieten diese Ansätze dem Businessanwender ein höheres Maß an Transparenz und Interpretierbarkeit. In allen vier Beispielen werden Methodik und Ergebnisse der angewandten Verfahren ausführlich aus einer akademischen Perspektive diskutiert. Verschiedene Vergleiche mit analytischen und marktüblichen Best-Practice-Methoden erlauben es, den Mehrwert der angewendeten Ansätze auch aus einer ökonomischen Perspektive zu bewerten

Causal Inference Methods For Addressing Positivity Violations And Bias In Observational And Cluster-Randomized Studies

Observational data are increasingly used to evaluate the effects of treatments on health outcomes. Causal inference provides a framework for formulating estimands of interest in this setting; however, identifiability of these estimands relies on certain assumptions. One assumption is called positivity, which requires the probability of treatment to be bounded away from 0 and 1. That is, for every covariate combination, we should observe both treated and control subjects. If the positivity assumption is violated, population-level causal inference necessarily involves some extrapolation. Ideally, a greater amount of uncertainty around the causal effect estimate is reflected in areas of non-overlap. With that goal in mind, we construct a Gaussian process model for estimating treatment effects in the presence of practical violations of positivity. Our method does not rely on strong parametric assumptions, provides a cohesive model for estimating treatment effects, and incorporates more uncertainty in areas of the covariate space where there is less overlap. Our work also focuses on the causal analysis of cluster randomized trials (CRTs) with a small number of clusters and a rare binary outcome. While estimation and covariate adjustment via generalized estimating equations (GEE) can account for chance imbalances and increase statistical power, analytical challenges frequently arise in such settings. For example, traditional GEE models tend to produce negatively biased standard error estimates, and regression adjustment often fails to converge with a rare outcome. We evaluate the utility of propensity score weighting and regression adjustment both in conjunction with bias-corrected sandwich variance estimators to precisely estimate a causal odds ratio and to obtain valid inference. In each project, we assess the proposed approaches and compare with alternative methods through simulation studies and then demonstrate their implementation with real use cases, including an observational study of right heart catheterization in female patients and a CRT that tests a sedation protocol in 31 pediatric intensive care units