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At least 1,099 records · Page 61Linked to original sources
PubMedDate not supplied

Sensitivity analysis in economic evaluation: a review of published studies.

A structured methodological review of journal articles published in 1992 was undertaken to determine whether recently published economic evaluation studies deal systematically and comprehensively with uncertainty. Ninety three journal articles were identified from a range of searches including a computerised search of the MEDLINE CD-Rom database. Articles were reviewed to determine how they had handled uncertainty in: a) data sources; b) generalisability; c) extrapolation; and d) analytic method. Articles were subsequently assessed to determine how they had represented this uncertainty in terms of the overall results of their analysis. Finally, studies were rated on the basis of their overall performance with respect to dealing systematically and comprehensively with uncertainty. Despite the numerous books and articles devoted to the appropriate methods to be employed by analysts conducting economic evaluation, 22 (24%) studies failed to consider uncertainty at all and 35 (38%) studies employed sensitivity analysis in a manner judged as inadequate. In all, 36 (39%) studies were judged to have given at least an adequate account of uncertainty with 13 (14%) of those judged to have provided a good account of uncertainty. Such disappointing results may reflect a general lack of detail in much of the methods literature concerning how sensitivity analysis should be applied and how results should be presented. Journal editors and readers of economic evaluation articles should acquaint themselves with the methods for handling uncertainty in order that they can critically evaluate the extent to which authors have allowed for uncertainties inherent in their analysis.

Cost-Benefit Analysis↗
PubMedDate not supplied

Confidence intervals for cost/effectiveness ratios.

The reduction of costs is becoming increasingly important in the medical field. The relevant topic of many clinical trials is not effectiveness per se, but rather cost-effectiveness ratios. Surprisingly, no statistical tools for analyzing cost-effectiveness ratios have been provided in the medical literature yet. This paper explains the gap in the literature, and provides a first technique for obtaining confidence intervals for cost-effectiveness ratios. The technique does not use sophisticated tools to achieve maximal optimality, but seeks for tractability and ease of application while still satisfying all formal statistical requirements.

Clinical Trials as Topic↗
PubMed2002

Cost data for individual patients included in clinical studies: no amount of statistical analysis can compensate for inadequate costing methods.

This work examines the quality of the cost methods used to derive patient level costs in 45 economic evaluations conducted alongside randomised controlled trials. The perspective of the cost analysis, the methods used to determine quantities and values of resources and how the cost data were reported are examined. The reported costing methods were found to be of poor quality, highlighting the need for greater rigour. Researchers to date appear more concerned with whether cost data have been subjected to the appropriate statistical analysis. For the results of clinical studies to be valid both cost methods and the methods used for the statistical analysis of cost data should be of a high quality.

Cost-Benefit Analysis↗
PubMed2005

Drug utilization statistics for individual-level pharmacy dispensing data.

The emergence of large, computerized pharmacoepidemiologic databases has enabled us to study drug utilization with the individual user as the unit of analysis. A recurrent problem in such analyses, however, is the overwhelming volume and complexity of data. This paper reviews the methods to estimate basic epidemiologic measures of drug utilization and some methods to screen for aberrant prescribing patterns. It also presents the basis and application of the waiting time distributions that can provide information about the period prevalence, point prevalence, incidence, duration of use, seasonality and rate of prescription renewal or relapse for specific drugs. If analyzed regularly, these measures can disclose subtle trends in clinical drug use that would not be evident from the wholesale figures. In specific situations, pharmacy dispensing data without diagnostic information can be used to assess the association between drug use and adverse effects in a prescription symmetry design.

Age Factors↗
PubMed2002

Longitudinal profiles of health care providers.

Provider profiling is the activity of collecting, comparing and reporting quality of care measures for individuals, groups, agencies and institutions that provide health care services. Univariate provider profiles, such as hospital-specific mortality rates, have been constructed using cross-sectional data based on posterior summaries or maximum likelihood estimates. As data continue to be collected over time, the construction and interpretation of longitudinal profiles of health care providers will become increasingly important. Longitudinal series can be used to improve the precision of estimates - a feature that is particularly important for providers who treat a small number of patients per year. We extend and apply hierarchical models to examine and classify provider performance over time using two examples, one in the area of cardiology and the other in mental health. Performance is evaluated using the squared Mahalanobis distance and posterior probabilities based on this distance. By comparing providers based on level and temporal trend simultaneously, conservative but comprehensive assessments of performance are possible. Furthermore, the longitudinal profiles developed are easily interpreted and flexible, making them of practical use to policy-makers.

Cardiovascular Diseases↗
PubMed2003

Likelihood-based confidence intervals for a log-normal mean.

To construct a confidence interval for the mean of a log-normal distribution in small samples, we propose likelihood-based approaches - the signed log-likelihood ratio and modified signed log-likelihood ratio methods. Extensive Monte Carlo simulation results show the advantages of the modified signed log-likelihood ratio method over the signed log-likelihood ratio method and other methods. In particular, the modified signed log-likelihood ratio method produces a confidence interval with a nearly exact coverage probability and highly accurate and symmetric error probabilities even for extremely small sample sizes. We then apply the methods to two sets of real-life data.

Biomedical Research↗
PubMed1996

Bivariate frailty model for the analysis of multivariate survival time.

Because of limitations of the univariate frailty model in analysis of multivariate survival data, a bivariate frailty model is introduced for the analysis of bivariate survival data. This provides tremendous flexibility especially in allowing negative associations between subjects within the same cluster. The approach involves incorporating into the model two possibly correlated frailties for each cluster. The bivariate lognormal distribution is used as the frailty distribution. The model is then generalized to multivariate survival data with two distinguished groups and also to alternating process data. A modified EM algorithm is developed with no requirement of specification of the baseline hazards. The estimators are generalized maximum likelihood estimators with subject-specific interpretation. The model is applied to a mental health study on evaluation of health policy effects for inpatient psychiatric care.

Algorithms↗
PubMed1995

Methods and problems in measuring quality of life.

The US health-care transition demands increased accountability for medical care. This has contributed to increased interest in documenting valued medical outcomes, including improvements in health-related quality of life and treatment satisfaction. These data can only be obtained validly by asking patients directly about their current health state, perception of well-being, and satisfaction with care. A core set of well-validated instruments have been developed to measure health-related quality of life in patients with cancer. As these are employed with increasing frequency, rigorous quality assurance of data collection is critical. Because of the necessity of quality control, patient-reported data collection can be labor-intensive and prohibitively costly. However, time and cost-saving methods, such as centralized telephone survey methods or on-site direct data entry via interactive computer, can guarantee high-quality data while minimizing costs. Justification of the need for these methods and a brief description are provided.

Data Interpretation, Statistical↗
PubMed1987

Software for health care analysts: a modular approach.

The many concerns about the cost and quality of health care suggest the need to facilitate planners' using existing data bases for utilization review, program evaluation, and technology assessment. Despite both the availability of relevant data and widespread improvements in computing power, integrated computer software to permit analyses by nonspecialists has not previously been developed. This paper discusses the features of a health policy information system which aids working with hospital discharge abstracts, medical claims, cancer registries, and vital statistics files. Analyses of small area utilization, length of stay, in-hospital mortality, and readmissions are facilitated by this package. This information system, named the Health Applications System, includes an analysis module, three information management modules, and a set of record linkage modules. The modules were developed using the macroprocessor in the fourth-generation SAS system. Features of the software and their implications for data analysis are discussed.

Data Interpretation, Statistical↗
PubMed2001

Introduction: risk-adjustment issues in mental health services.

State mental health authorities and other public and private entities are developing outcome measures and comparing results across providers, programs, and systems. To make comparisons equitable, outcomes must be risk adjusted. This article provides an introduction to mental health risk adjustment and outlines issues involved in the selection of outcome and risk variables, data collection protocols, and analytic methods. It stresses the importance of proper identification of risk-adjustment variables and models. The article concludes with the next steps necessary to develop a valid approach to the risk-adjustment methodology.

Benchmarking↗
PubMed1999

Long-term effects of a system of care on children and adolescents.

This study evaluates an exemplary system of care designed to provide comprehensive mental health services to children and adolescents. It was believed that the system would lead to more improvement in the functioning and symptoms of clients compared to those receiving care as usual. The project employed a randomized experimental five-wave longitudinal design with 350 families. While access to care, type of care, and the amount of care were better in the system of care, there were no differences in clinical outcomes compared to care received outside the system. In addition, children who did not receive any services, regardless of experimental condition, improved at the same rate as treated children. Similar to the Fort Bragg results, the effects of systems of care are primarily limited to system-level outcomes but do not appear to affect individual outcomes such as functioning and symptomatology.

Adolescent↗
PubMedDate not supplied

The unit of analysis error in studies about physicians' patient care behavior.

OBJECTIVE: To estimate the frequency with which patients are incorrectly used as the unit of analysis among statistical calculations in published studies of physicians' patient care behavior. DESIGN: Retrospective review of studies published during 1980-1990. ARTICLES: 54 articles retrieved by a computerized search using medical subject headings for physicians and study characteristics. Article selection criteria included the requirement that the physician should have been the correct unit of analysis. INTERVENTION: Presence of the error was determined by consensus using published criteria. MAIN RESULTS: The error was present in 38 articles (70%). The number of study physicians was reported in 35 articles (65%). The error was found in 57% of articles that reported the number of study physicians and in 95% of those that did not. The error rate was not lower among articles published more recently nor among those published in journals with higher rates of article citations in the medical literature. CONCLUSION: The unit of analysis error occurs frequently and can generate artificially low p values. Failure to report the number of study physicians can be a clue that this type of error has been made.

Data Interpretation, Statistical↗
PubMed2005

[Interdisciplinary emergency room management of trauma patients from the standpoint of coworkers].

INTRODUCTION: The purpose of this study was to examine whether staff questionnaire evaluation is useful for quality control in the emergency room (ER) setting. METHODS: Consecutive anonymous questionnaires (Likert scale 1-5) were filled out by the involved medical staff in all ER trauma cases in a university hospital from July 2002 to December 2003 (analysis of variance, P<0.05). RESULTS: In 171 ER cases, 844 staff members responded. Main criticisms concerned time management or satisfaction with personal ER training (Likert <4). Consultants rated the quality of their training significantly higher than younger doctors, two thirds of consultants vs one third of residents having passed an Advanced Trauma and Life Support course (P<0.001). Depending on responders' professional specialties and whether the situation concerned multiple trauma (Injury Severity Score >15), a significant systematic difference resulted. CONCLUSION: Our standardized staff questionnaire evaluation was revealed to be a discriminative instrument for quality management of trauma cases in the ER. To confirm these findings, correlation with clinical outcome data and further validation of the method are needed.

Attitude of Health Personnel↗
PubMed2006

Are there distinctive trajectory groups in substance abuse remission over 10 years? An application of the group-based modeling approach.

This paper introduces to mental health services and evaluation researchers a method for analyzing longitudinal data: a group-based modeling approach for applied research. We present the basic formulations of the model and illustrate its application by analyzing the 10-year longitudinal data on substance abuse remission from the New Hampshire Dual Disorders Study. The basic features of the approach are: identifying latent groups with distinctive trajectories, estimating the proportion of the population in each group, linking individual-level covariates to group membership, assigning individuals to different groups, and creating group profiles based on posterior probabilities. We also discuss cautions and the controversies surrounding this approach. The major findings include four groups with distinct trajectories of remission from substance abuse.

Female↗
PubMed2004

Bayes and health care research.

Bayes' rule shows how one might rationally change one's beliefs in the light of evidence. It is the foundation of a statistical method called Bayesianism. In health care research, Bayesianism has its advocates but the dominant statistical method is frequentism. There are at least two important philosophical differences between these methods. First, Bayesianism takes a subjectivist view of probability (i.e. that probability scores are statements of subjective belief, not objective fact) whilst frequentism takes an objectivist view. Second, Bayesianism is explicitly inductive (i.e. it shows how we may induce views about the world based on partial data from it) whereas frequentism is at least compatible with non-inductive views of scientific method, particularly the critical realism of Popper. Popper and others detail significant problems with induction. Frequentism's apparent ability to avoid these, plus its ability to give a seemingly more scientific and objective take on probability, lies behind its philosophical appeal to health care researchers. However, there are also significant problems with frequentism, particularly its inability to assign probability scores to single events. Popper thus proposed an alternative objectivist view of probability, called propensity theory, which he allies to a theory of corroboration; but this too has significant problems, in particular, it may not successfully avoid induction. If this is so then Bayesianism might be philosophically the strongest of the statistical approaches. The article sets out a number of its philosophical and methodological attractions. Finally, it outlines a way in which critical realism and Bayesianism might work together.

Bayes Theorem↗