Download or read book Accounting for Selection Bias in Observational Studies written by Rebecca D. Koperski and published by . This book was released on 2000 with total page 57 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Developing a Protocol for Observational Comparative Effectiveness Research A User s Guide written by Agency for Health Care Research and Quality (U.S.) and published by Government Printing Office. This book was released on 2013-02-21 with total page 236 pages. Available in PDF, EPUB and Kindle. Book excerpt: This User’s Guide is a resource for investigators and stakeholders who develop and review observational comparative effectiveness research protocols. It explains how to (1) identify key considerations and best practices for research design; (2) build a protocol based on these standards and best practices; and (3) judge the adequacy and completeness of a protocol. Eleven chapters cover all aspects of research design, including: developing study objectives, defining and refining study questions, addressing the heterogeneity of treatment effect, characterizing exposure, selecting a comparator, defining and measuring outcomes, and identifying optimal data sources. Checklists of guidance and key considerations for protocols are provided at the end of each chapter. The User’s Guide was created by researchers affiliated with AHRQ’s Effective Health Care Program, particularly those who participated in AHRQ’s DEcIDE (Developing Evidence to Inform Decisions About Effectiveness) program. Chapters were subject to multiple internal and external independent reviews. More more information, please consult the Agency website: www.effectivehealthcare.ahrq.gov)

Download or read book Finding What Works in Health Care written by Institute of Medicine and published by National Academies Press. This book was released on 2011-07-20 with total page 267 pages. Available in PDF, EPUB and Kindle. Book excerpt: Healthcare decision makers in search of reliable information that compares health interventions increasingly turn to systematic reviews for the best summary of the evidence. Systematic reviews identify, select, assess, and synthesize the findings of similar but separate studies, and can help clarify what is known and not known about the potential benefits and harms of drugs, devices, and other healthcare services. Systematic reviews can be helpful for clinicians who want to integrate research findings into their daily practices, for patients to make well-informed choices about their own care, for professional medical societies and other organizations that develop clinical practice guidelines. Too often systematic reviews are of uncertain or poor quality. There are no universally accepted standards for developing systematic reviews leading to variability in how conflicts of interest and biases are handled, how evidence is appraised, and the overall scientific rigor of the process. In Finding What Works in Health Care the Institute of Medicine (IOM) recommends 21 standards for developing high-quality systematic reviews of comparative effectiveness research. The standards address the entire systematic review process from the initial steps of formulating the topic and building the review team to producing a detailed final report that synthesizes what the evidence shows and where knowledge gaps remain. Finding What Works in Health Care also proposes a framework for improving the quality of the science underpinning systematic reviews. This book will serve as a vital resource for both sponsors and producers of systematic reviews of comparative effectiveness research.

Download or read book Methods to Control for Bias in Observational Studies written by Anne P. Ehlers and published by . This book was released on 2016 with total page 41 pages. Available in PDF, EPUB and Kindle. Book excerpt: Observational studies often suffer from the problem of confounding, where observed results are biased due to the presence of factors that are strongly associated with both the exposure of interest and the outcome. Typical sources of confounding include factors such as age, sex, and medical comorbidities. The failure to account for confounding in the analytic framework can lead to biased results and ultimately an incorrect inference. Arguably the most common method of accounting for confounding is through the use of regression based approaches, although other methods such as propensity score matching are described. Beyond confounding, an additional source of bias that must be accounted for is the fact that observational data often is sampled from specified groups of individuals. For example, there may be clusters of individuals who are enrolled in the same health plan or are treated at the same hospital. The effect of this sampling framework is that patient outcomes from one health plan, hospital, etc are correlated. The correlation must be accounted for in the model to account in order to make a correct inference. Models that include multiple levels of analysis (such as patient and hospital) are call multilevel or hierarchical. As with the case of confounding discussed above, there are multiple well described methods to account for unmeasured factors that are contained at the cluster level. This thesis contains two observational studies that were completed by the author during her course of study in the Master’s in Public Health Program. Both studies have been accepted for publication by peer-reviewed journals and this this information is copyrighted. These studies will highlight two separate methods to account for confounding, as well as two approaches for hierarchical data analysis.

Download or read book Selection Bias in Economic Evaluation With Observational Data written by Daniel Polsky and published by . This book was released on 2007 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Economic evaluation of medical interventions aims to aid decision makers to achieve the goal of efficient use of health care resources at a community level by quantifying the tradeoffs between resources for medical care and the resulting health outcomes. Ideally, these evaluations would lead to the adoption of treatment options that provide value for money and the elimination of those that do not. Unless these evaluations express the causal relationship between the economic endpoint and the treatment, interventions that do not provide sufficient value may be adopted and treatments that do may be eliminated. Hence, it is essential that estimates of differences between treatments reflect the causal effect of treatment on outcomes. When observational data rather than data from randomized clinical trials are used for the estimates of economic endpoints, there is always a possibility that selection bias may limit an investigator's ability to generate an unbiased estimate of the causal relationship between treatment and the economic outcome. The treatment option delivered in a clinical setting is typically the result of decisions made by patients and physicians. Selection bias arises when factors that can influence the treatment choice such as patient health and provider skills also influence outcomes. Adequately accounting for these factors is necessary if observational data are to be used in economic evaluations. The primary objective of this talk will be to describe how observational data can be used to estimate the causal relationship between treatment and outcomes. We will define the parameters of interest for economic evaluations, describe observational study designs, explain selection bias and the mechanism that generates selection of treatment, and then review and compare the various methods introduced in the literature that attempt to address selection bias in observational studies. We will compare instrumental variable (IV) analysis and propensity scores to correct for observational data bias in a cost-effectiveness analysis of two treatments for localized breast cancer (breast conserving surgery with radiation therapy (BCSRT) versus mastectomy (MST)). The data source was the Medicare claims for a national random sample of 2,907 women (age 67 or older) with localized breast cancer who were treated between 1992 and 1994. We constructed instrumental variables for treatment received from a linear probability model of the effects of economic factors and patient characteristics on actual treatment. We then estimated a linear probability model of three-year survival with both observational data (actual treatment) and the instrumental variables for treatment. We used 5 propensity score stratum, these strata were validated and then treatment effects were estimated within each strata. Contrary to the results of randomized clinical trials that found no difference in survival, analysis with the observational data found highly significant differences in survival among the three treatment alternatives: 79.2% survival for BCSO, 85.3% for MST, and 93.0% for BCSRT. The IV results and the propensity score results, in contrast, were consistent with the clinical trial results in that survival rates were not significantly different from each other. Observational data on health outcomes of alternative treatments for localized breast cancer should not be used for cost-effectiveness studies without appropriate adjustment. Both the instrumental variable method and propensity score methods produce results that are consistent with randomized clinical trials. The appropriate method depends on whether there is more potential for bias for non-linearity or unobserved variables.

Download or read book Statistical Problems Arising in Observational Studies written by Binbing Yu and published by . This book was released on 2001 with total page 328 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Applying Quantitative Bias Analysis to Epidemiologic Data written by Timothy L. Lash and published by Springer Science & Business Media. This book was released on 2011-04-14 with total page 200 pages. Available in PDF, EPUB and Kindle. Book excerpt: Bias analysis quantifies the influence of systematic error on an epidemiology study’s estimate of association. The fundamental methods of bias analysis in epi- miology have been well described for decades, yet are seldom applied in published presentations of epidemiologic research. More recent advances in bias analysis, such as probabilistic bias analysis, appear even more rarely. We suspect that there are both supply-side and demand-side explanations for the scarcity of bias analysis. On the demand side, journal reviewers and editors seldom request that authors address systematic error aside from listing them as limitations of their particular study. This listing is often accompanied by explanations for why the limitations should not pose much concern. On the supply side, methods for bias analysis receive little attention in most epidemiology curriculums, are often scattered throughout textbooks or absent from them altogether, and cannot be implemented easily using standard statistical computing software. Our objective in this text is to reduce these supply-side barriers, with the hope that demand for quantitative bias analysis will follow.

Download or read book Handling Missing Data Selection Bias and Measurement Error in Observational Studies written by and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Cochrane Handbook for Systematic Reviews of Interventions written by Julian P. T. Higgins and published by Wiley. This book was released on 2008-11-24 with total page 672 pages. Available in PDF, EPUB and Kindle. Book excerpt: Healthcare providers, consumers, researchers and policy makers are inundated with unmanageable amounts of information, including evidence from healthcare research. It has become impossible for all to have the time and resources to find, appraise and interpret this evidence and incorporate it into healthcare decisions. Cochrane Reviews respond to this challenge by identifying, appraising and synthesizing research-based evidence and presenting it in a standardized format, published in The Cochrane Library (www.thecochranelibrary.com). The Cochrane Handbook for Systematic Reviews of Interventions contains methodological guidance for the preparation and maintenance of Cochrane intervention reviews. Written in a clear and accessible format, it is the essential manual for all those preparing, maintaining and reading Cochrane reviews. Many of the principles and methods described here are appropriate for systematic reviews applied to other types of research and to systematic reviews of interventions undertaken by others. It is hoped therefore that this book will be invaluable to all those who want to understand the role of systematic reviews, critically appraise published reviews or perform reviews themselves.

Download or read book The Performance of Genetic Matching to Reduce Selection Bias in Observational Studies written by Seyfullah Tingir and published by . This book was released on 2013 with total page 57 pages. Available in PDF, EPUB and Kindle. Book excerpt: The results showed that 1-to-many GM performs better than OFPSM. 1-to-1 GM had the worst performance in terms of relative bias of ATT estimates and standard error of the ATT estimates. Increasing sample size improved both power and covariate balance. Also, increasing the Pseudo R-squared factor had increased bias of ATT and reduced covariate balance; but improved standard error estimates. However, increasing the R-squared levels produced bias standard error of the ATT estimates.

Download or read book Measuring Racial Discrimination written by National Research Council and published by National Academies Press. This book was released on 2004-07-24 with total page 335 pages. Available in PDF, EPUB and Kindle. Book excerpt: Many racial and ethnic groups in the United States, including blacks, Hispanics, Asians, American Indians, and others, have historically faced severe discriminationâ€"pervasive and open denial of civil, social, political, educational, and economic opportunities. Today, large differences among racial and ethnic groups continue to exist in employment, income and wealth, housing, education, criminal justice, health, and other areas. While many factors may contribute to such differences, their size and extent suggest that various forms of discriminatory treatment persist in U.S. society and serve to undercut the achievement of equal opportunity. Measuring Racial Discrimination considers the definition of race and racial discrimination, reviews the existing techniques used to measure racial discrimination, and identifies new tools and areas for future research. The book conducts a thorough evaluation of current methodologies for a wide range of circumstances in which racial discrimination may occur, and makes recommendations on how to better assess the presence and effects of discrimination.

Download or read book Cohort Studies in Health Sciences written by René Mauricio Barría and published by . This book was released on 2018-09 with total page 73 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Data Collection and Analysis written by Roger Sapsford and published by SAGE. This book was released on 2006-03-29 with total page 358 pages. Available in PDF, EPUB and Kindle. Book excerpt: In simple and non-technical terms, this text illustrates a wide range of techniques and approaches used in social research projects.

Download or read book The Role of Pretest and Proxy Pretest Measures of the Outcome for Removing Selection Bias in Observational Studies written by Kelly Hallberg and published by . This book was released on 2011 with total page 9 pages. Available in PDF, EPUB and Kindle. Book excerpt: The purpose of this paper is threefold. The first is to test whether the pretest plays a greater role in bias reduction than any other single covariate, which the authors predict it will. The second is to examine the marginal improvement in bias reduction offered by having two pretest measurement waves. The authors predict that there will be some marginal gain in bias reduction as a result of including an additional pretest wave. The third purpose is to examine the extent to which a proxy pretest measure can substitute for a real pretest whose form is invariant between pretest and posttest. (Contains 3 tables.).

Download or read book Matched Sampling for Causal Effects written by Donald B. Rubin and published by Cambridge University Press. This book was released on 2006-09-04 with total page 5 pages. Available in PDF, EPUB and Kindle. Book excerpt: Matched sampling is often used to help assess the causal effect of some exposure or intervention, typically when randomized experiments are not available or cannot be conducted. This book presents a selection of Donald B. Rubin's research articles on matched sampling, from the early 1970s, when the author was one of the major researchers involved in establishing the field, to recent contributions to this now extremely active area. The articles include fundamental theoretical studies that have become classics, important extensions, and real applications that range from breast cancer treatments to tobacco litigation to studies of criminal tendencies. They are organized into seven parts, each with an introduction by the author that provides historical and personal context and discusses the relevance of the work today. A concluding essay offers advice to investigators designing observational studies. The book provides an accessible introduction to the study of matched sampling and will be an indispensable reference for students and researchers.

Download or read book Epidemiology in Medicine written by Julie E. Buring and published by Lippincott Williams & Wilkins. This book was released on 1987 with total page 408 pages. Available in PDF, EPUB and Kindle. Book excerpt: Harvard Medical School, Boston. Textbook for medical and public health students.

Download or read book Selection Bias and Econometric Remedies in Accounting and Finance Research written by Jenny Wu Tucker and published by . This book was released on 2019 with total page 37 pages. Available in PDF, EPUB and Kindle. Book excerpt: While managers' accounting and financial decisions are, for many, fascinating topics, selection bias poses a serious challenge to researchers estimating the decisions' effects using non-experimental data. Selection bias potentially occurs because managers' decisions are non-random and the outcomes of choices not made are never observable. “Selection bias due to observables” arises from sample differences that researchers can observe but fail to control. “Selection bias due to unobservables” arises from the unobservable and thus uncontrolled sample differences that affect managers' decisions and their consequences. In this article I review two econometric tools developed to mitigate these biases - the propensity score matching (PSM) method to mitigate selection bias due to observables and the Heckman inverse-Mills-ratio (IMR) method to address selection bias due to unobservables - and discuss their applications in accounting and finance research. The article has four takeaways. First, researchers should select the correct method to alleviate potential selection bias: the PSM method mitigates selection bias due to observables, but does not alleviate selection bias due to unobservables. Second, in applying PSM researchers are advised to restrict their inferences to firms whose characteristics can be found in both the sample and control groups. Third, the IMR method, though popular, is limited to situations in which the choices are binary, the outcomes of choices are modeled in a linear regression, and the unobservables in the choice and outcome models follow a multivariate normal distribution. Researchers can overcome these constraints by using full information maximum likelihood estimation. Last, when the IMR method is used, special attention should be paid to the formulas in calculating IMRs. The article also calls for researchers' attention to other approaches to evaluating the effects of managers' decisions.