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P.Mean: Interesting articles, books, quotes,
or websites added to this site for 2011 (created 2011-01-01).
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This page lists interesting outside resources that others have shown me or that I have discovered while wandering around on the web.
Please also consult the page for other years:
November 2011
October 2011
136. Failure to report protocol violations in clinical trials: A threat to internal validity?
135. Application of Latent Semantic Analysis for Open-Ended Responses in a Large, Epidemiologic Study
134. Bayesian Probit Regression Model for the Diagnosis of Pulmonary Fibrosis: Proof-of-Principle
132. Rasch analysis of the Hospital Anxiety and Depression Scale (HADS) for use in motor neurone disease
September 2011
131. Disclosure of Clinical Trial Results When Product Development Is Abandoned
130. When psychologists "go wrong"
129. Obstacles to the accrual of patients to clinical trials in the community setting
127. How to do a grounded theory study: a worked example of a study of dental practices
126. Case-control and two-gate designs in diagnostic accuracy studies
125. survey: analysis of complex survey samples
124. Estimating popularity based on Google searches: Why it's a bad idea - The DO Loop
122. Questions asked and answered in pilot and feasibility randomized controlled trials
August 2011
121. Surrogates under scrutiny: fallible correlations, fatal consequences
120. No shortcuts when collaborating
118. Researchers don't mean to exaggerate, but lots of things can distort findings
117. Pre-validation methods for developing a patient reported outcome instrument
June 2011
115. Novel study designs to investigate the placebo response
114. Using e-mail recruitment and an online questionnaire to establish effect size: A worked example
113. Competencies for Conducting Safe Human Subjects Research
May 2011
112. Evaluating treatments in health care: The instability of a one-legged stool
109. Research During Fellowship: Ten Commandments
107. Calculating unreported confidence intervals for paired data
104. The Role of a Wine Pricing Competition in Teaching Data Mining at Stanford
April 2011
101. A field guide to misunderstandings about open access (SPARC)
100. Is a subgroup effect believable? Updating criteria to evaluate the credibility of subgroup analyses
99. When are randomised trials unnecessary? Picking signal from noise
March 2011
98. An Introduction to R for Windows
97. How to write consistently boring scientific literaturen
January 2011
94. "Is Cybermedicine Killing You?" - The Story of a Cochrane Disaster
93. "Summary Page": a novel tool that reduces omitted data in research databases
92. "Anecdotal Evidence": Why Narratives Matter to Medical Practice
91. Tim Friede. Sample Size Recalculation in Internal Pilot Study Designs: A Review. Description: This is a PowerPoint presentation showing some examples of blinded versus unblinded sample size reviews with both continuous and binary outcomes and superiority and noninferiority hypotheses. [Accessed December 20, 2010]. Available at: http://www.biopharmnet.com/doc/2008_08_08_presentation.pdf.
90. E Michael Foster, Michael Hosking, Serhan Ziya. A Spoonful of Math Helps the Medicine Go Down: An Illustration of How Healthcare can Benefit from Mathematical Modeling and Analysis. BMC Medical Research Methodology. 2010;10(1):60. Abstract: "OBJECTIVES: A recent joint report from the Institute of Medicine and the National Academy of Engineering, highlights the benefits of--indeed, the need for--mathematical analysis of healthcare delivery. Tools for such analysis have been developed over decades by researchers in Operations Research (OR). An OR perspective typically frames a complex problem in terms of its essential mathematical structure. This article illustrates the use and value of the tools of operations research in healthcare. It reviews one OR tool, queueing theory, and provides an illustration involving a hypothetical drug treatment facility. METHOD: Queueing Theory (QT) is the study of waiting lines. The theory is useful in that it provides solutions to problems of waiting and its relationship to key characteristics of healthcare systems. More generally, it illustrates the strengths of modeling in healthcare and service delivery. Queueing theory offers insights that initially may be hidden. For example, a queueing model allows one to incorporate randomness, which is inherent in the actual system, into the mathematical analysis. As a result of this randomness, these systems often perform much worse than one might have guessed based on deterministic conditions. Poor performance is reflected in longer lines, longer waits, and lower levels of server utilization.As an illustration, we specify a queueing model of a representative drug treatment facility. The analysis of this model provides mathematical expressions for some of the key performance measures, such as average waiting time for admission. RESULTS: We calculate average occupancy in the facility and its relationship to system characteristics. For example, when the facility has 28 beds, the average wait for admission is 4 days. We also explore the relationship between arrival rate at the facility, the capacity of the facility, and waiting times. CONCLUSIONS: One key aspect of the healthcare system is its complexity, and policy makers want to design and reform the system in a way that affects competing goals. OR methodologies, particularly queueing theory, can be very useful in gaining deeper understanding of this complexity and exploring the potential effects of proposed changes on the system without making any actual changes." [Accessed October 25, 2010]. Available at: http://www.biomedcentral.com/1471-2288/10/60.
89. Academe: The Conflicted University
88. David Kent, Georgios Kitsios. Against pragmatism: on efficacy, effectiveness and the real world. Trials. 2009;10(1):48. Abstract: "Explanatory and pragmatic trials represent ends of a continuum of attitudes about clinical trial design. Recent literature argues that pragmatic trials are more informative about clinical care in the real world. Although there is place for more pragmatic studies to inform clinical practice and health policy decision-making, we are concerned that it is generally under-appreciated that extrapolating the results of broadly inclusive pragmatic trials to the care of real patients may often be as problematic as extrapolating the results of narrowly focused explanatory or efficacy trials. Simplistic interpretation of pragmatic trials runs the risk of driving harmful policies." [Accessed December 4, 2010]. Available at: http://www.trialsjournal.com/content/10/1/48.
87. Am�lie Perron, Carol Fluet, Dave Holmes. Agents of care and agents of the state: bio-power and nursing practice. J Adv Nurs. 2005;50(5):536-544. Abstract: "AIM: This paper presents a conceptual analysis of the concept of bio-power in the context of nursing, including a critique of the widespread rhetoric that nursing is deprived of power and consequently is an apolitical agency. BACKGROUND: Traditionally, power tends to be defined in terms of repression, interdiction and punishment. On the contrary, work by Michel Foucault with regard to bio-power brings into evidence the productive and positive nature of power at the heart of society. Despite being often used by various academic and professional disciplines, the concept of bio-power is rarely cited in nursing. FINDINGS: Nursing as a profession is at the heart of bio-power in that nurses lie at the crossroads between the anatomo-political and bio-political ranges of power over life. They therefore contribute to social regulation through a vast array of diverse political technologies. Nurses are at the flexing point of the state's requirements and of individual and collective aspirations. They occupy a strategic position that allows them to act as instruments of governmentality. Consequently, nurses constitute a fully-fledged political entity making use of disciplinary technologies and responding to state ideologies. CONCLUSION: The concept of bio-power offers a rich theoretical perspective for nursing, as it questions the definition of nursing care as neutral and mainly provided according to patients' best interests." [Accessed October 26, 2010]. Available at: http://onlinelibrary.wiley.com/doi/10.1111/j.1365-2648.2005.03432.x/full.
86. Donald G. Mcneil, Jr. An AIDS Advance, Hiding in the Open. The New York Times. 2010. Excerpt: "In the war against AIDS, a new weapon has emerged. It wasn�t a secret weapon. It was a well-established treatment pill that has only now been shown to be effective as a prevention pill too. Which raises a question: What took so long? " [Accessed December 27, 2010]. Available at: http://www.nytimes.com/2010/11/28/weekinreview/28mcneil.html.
85. Solange Whegang, Leonardo Basco, Henri Gwet, Jean-Christophe Thalabard. Analysis of an ordinal outcome in a multicentric randomized controlled trial: application to a 3- arm anti- malarial drug trial in Cameroon. BMC Medical Research Methodology. 2010;10(1):58. Abstract: "BACKGROUND: Malaria remains a burden in Sub-Saharan Countries. The strategy proposed by the World Health Organization (WHO) is to systematically compare the therapeutic efficacy of antimalarial drugs using as primary outcome for efficacy, a four-category ordered criterion. The objective of the present work was to analyze the treatment effects on this primary outcome taking into account both a center-effect and individual covariates. A three-arm, three-centre trial of Amodiaquine (AQ), sulfadoxine-pyrimethamine (SP) and their combination (AQ + SP), conducted by OCEAC-IRD in 2003, in 538 children with uncomplicated Plasmodium falciparum malaria, is used as an illustration. METHODS: Analyses were based on ordinal regression methods, assuming an underlying continuous latent variable, using either the proportional odds (PO) or the proportional hazards (PH) models. Different algorithms, corresponding to both frequentist- and bayesian-approaches, were implemented using the freely available softwares R and Winbugs, respectively. The performances of the different methods were evaluated on a simulated data set, and then they were applied on the trial data set. RESULTS: Good coverage probability and type-1 error for the treatment effect were achieved. When the methods were applied on the trial data set, results highlighted a significance decrease of SP efficacy when compared to AQ (PO, odds ratio [OR] 0.14, 95% confidence interval [CI] 0.04-0.57; hazard ratio [HR] 0.605, 95% CI 0.42-0.82), and an equal effectiveness between AQ + SP and AQ (PO, odds ratio [OR] 1.70, 95% confidence interval [CI] 0.25-11.44; hazard ratio [HR] 1.40, 95% CI 0.88-2.18). The body temperature was significantly related to the responses. The patient weights were marginally associated to the clinical response. CONCLUSION: The proposed analyses, based on usual statistical packages, appeared adapted to take into account the full information contained in the four categorical outcome in malaria trials, as defined by WHO, with the possibility of adjusting on individual and global covariates." [Accessed October 25, 2010]. Available at: http://www.biomedcentral.com/1471-2288/10/58.
83. Applied Bayesian Hierarchical Methods
82. Are Unadjusted Analyses of Clinical Trials Inappropriately Biased Toward the Null?
81. Assessing and reporting heterogeneity in treatment effects in clinical trials: a proposal
79. [[Duplicate]]
77. Baseline imbalances; an issue in longitudinal clinical trials
76. Bayesian Statistical Modelling
75. Competing risk and heterogeneity of treatment effect in clinical trials
74. H T Stelfox, G Chua, K O'Rourke, A S Detsky. Conflict of interest in the debate over calcium-channel antagonists. N. Engl. J. Med. 1998;338(2):101-106. Abstract: "BACKGROUND: Physicians' financial relationships with the pharmaceutical industry are controversial because such relationships may pose a conflict of interest. It is unknown to what extent industry support of medical education and research influences the opinions and behavior of clinicians and researchers. The recent debate over the safety of calcium-channel antagonists provided an opportunity to examine the effect of financial conflicts of interest. METHODS: We searched the English-language medical literature published from March 1995 through September 1996 for articles examining the controversy about the safety of calcium-channel antagonists. Articles were reviewed and classified as being supportive, neutral, or critical with respect to the use of calcium-channel antagonists. The authors of the articles were asked about their financial relationships with both manufacturers of calcium-channel antagonists and manufacturers of competing products (i.e., beta-blockers, angiotensin-converting-enzyme inhibitors, diuretics, and nitrates). We examined the authors' published positions on the safety of calcium-channel antagonists according to their financial relationships with pharmaceutical companies. RESULTS: Authors who supported the use of calcium-channel antagonists were significantly more likely than neutral or critical authors to have financial relationships with manufacturers of calcium-channel antagonists (96 percent, vs. 60 percent and 37 percent, respectively; P<0.001). Supportive authors were also more likely than neutral or critical authors to have financial relationships with any pharmaceutical manufacturer, irrespective of the product (100 percent, vs. 67 percent and 43 percent, respectively; P< 0.001). CONCLUSIONS: Our results demonstrate a strong association between authors' published positions on the safety of calcium-channel antagonists and their financial relationships with pharmaceutical manufacturers. The medical profession needs to develop a more effective policy on conflict of interest. We support complete disclosure of relationships with pharmaceutical manufacturers for clinicians and researchers who write articles examining pharmaceutical products." [Accessed October 26, 2010]. Available at: http://www.ncbi.nlm.nih.gov/pubmed/9420342.
73. Marya Zilberberg. Could our application of EBM be unethical?. Healthcare, etc. blog posting on 2010-10-09. Exceprt: "As you may have noticed, I have been thinking a lot about the nature of our research enterprise and its output as it relates to practical decisions that need to be made in the real world by physicians and policy makers. I have come to the conclusion that it is woefully inadequate in so many ways, and in fact it may even be borderline unethical. My thinking on this has been influenced at least in part by some of my reading of the work by the Tufts group led by David M. Kent, who has written a lot about the impact of heterogeneous treatment response on the central measures we report in trials -- I urge you to look their work up on Medline. (My potential COI here is that I did all of my Internal Medicine and Pulmonary and Critical Care training at Tufts in the 1990s, but did not know or work with Kent or his group). But admittedly I have been thinking about a lot of this stuff on my own as well, as I have advocated risk stratification for quite some time now. So, here is what I have been thinking." [Accessed December 20, 2010]. Available at: http://evimedgroup.blogspot.com/2010/11/could-our-application-of-ebm-be.html#comment-form.
72. Mark Elwood. Critical Appraisal of Epidemiological Studies and Clinical Trials. 3rd ed. Oxford University Press, USA; 2007.
71. John Horgan. Cross-check: Cybertherapy, placebos and the dodo effect: Why psychotherapies never get better. Excerpt: "When the media report on a new diet that supposedly helps people lose weight once and for all, I wonder, "Does anyone still believe these claims, given the dismal track record of diets?" I have the same reaction to new treatments for psychological disorders, such as 'cybertherapy.'" [Accessed December 8, 2010]. Available at: http://www.scientificamerican.com/blog/post.cfm?id=cybertherapy-placebos-and-the-dodo-2010-11-29.
70. Joel Best. Damned Lies and Statistics: Untangling Numbers from the Media, Politicians, and Activists. 1st ed. University of California Press; 2001. Description from the publisher's website: "Does the number of children gunned down double each year? Does anorexia kill 150,000 young women annually? Do white males account for only a sixth of new workers? Startling statistics shape our thinking about social issues. But all too often, these numbers are wrong. This book is a lively guide to spotting bad statistics and learning to think critically about these influential numbers. Damned Lies and Statistics is essential reading for everyone who reads or listens to the news, for students, and for anyone who relies on statistical information to understand social problems." Available at: http://www.ucpress.edu/book.php?isbn=9780520219786.
69. besteconometrician@gmail.com. Econometrics_Help: First step in model building - Data Reading.. Description: Some simple guidelines for basic quality checks prior to data analysis. [Accessed October 25, 2010]. Available at: http://costaleconomist.blogspot.com/2008/11/first-step-in-model-building-data.html.
68. besteconometrician@gmail.com. Econometrics_Help: Not to forget to do data cleansing before modeling. Description: Some simple guidelines for basic quality checks prior to data analysis. [Accessed October 25, 2010]. Available at: http://costaleconomist.blogspot.com/2008/11/not-to-forget-to-do-data-cleansing.html.
67. Evidence-based medicine: a commentary on common criticisms
65. Evidence-Based To Value-based Medicine
64. Exploratory factor analysis of self-reported symptoms in a large, population-based military cohort
63. Following Trail of Lost AIDS Patients in Africa
62. Foundations and Applications of Statistics: An Introduction Using Re
61. Governing nursing conduct: the rise of evidence-based practice
59. 200 Countries, 200 Years, 4 Minutes - The Joy of Stats
58. Homeopathy, non-specific effects and good medicine
56. Integrated Development Environoments / Script Editors for R
55. Integrative medicine and the point of credulity
54. Interpretation of evidence in data by untrained medical students: a scenario-based study
53. Interpreting the results of observational research: chance is not such a fine thing
52. Introduction to Statistical Thought
51. Invited commentary: propensity scores
50. Emmanuelle Deslandes, Sylvie Chevret. Joint modeling of multivariate longitudinal data and the dropout process in a competing risk setting: application to ICU data. BMC Medical Research Methodology. 2010;10(1):69. Abstract: "BACKGROUND: Joint modeling of longitudinal and survival data has been increasingly considered in clinical trials, notably in cancer and AIDS. In critically ill patients admitted to an intensive care unit (ICU), such models also appear to be of interest in the investigation of the effect of treatment on severity scores due to the likely association between the longitudinal score and the dropout process, either caused by deaths or live discharges from the ICU. However, in this competing risk setting, only cause-specific hazard sub-models for the multiple failure types data have been used. METHODS: We propose a joint model that consists of a linear mixed effects submodel for the longitudinal outcome, and a proportional subdistribution hazards submodel for the competing risks survival data, linked together by latent random effects. We use Markov chain Monte Carlo technique of Gibbs sampling to estimate the joint posterior distribution of the unknown parameters of the model. The proposed method is studied and compared to joint model with cause-specific hazards submodel in simulations and applied to a data set that consisted of repeated measurements of severity score and time of discharge and death for 1,401 ICU patients. "RESULTS: Time by treatment interaction was observed on the evolution of the mean SOFA score when ignoring potentially informative dropouts due to ICU deaths and live discharges from the ICU. In contrast, this was no longer significant when modeling the cause-specific hazards of informative dropouts. Such a time by treatment interaction persisted together with an evidence of treatment effect on the hazard of death when modeling dropout processes through the use of the Fine and Gray model for sub-distribution hazards.CONCLUSIONS:In the joint modeling of competing risks with longitudinal response, differences in the handling of competing risk outcomes appear to translate into the estimated difference in treatment effect on the longitudinal outcome. Such a modeling strategy should be carefully defined prior to analysis. [Accessed October 25, 2010]. Available at: http://www.biomedcentral.com/1471-2288/10/69.
49. Jim Albert. Latex in my blog?. Intorduction to Bayesian Thinking blog posting on 2007-11-25. Excerpt: "I didn't know if it was possible to add latex to my postings. I asked John Shonder who is currently working on solutions on my book and has some latex in his WordPress blog. John referred me to a page that describes a simple procedure for typing in latex in one's postings." [Accessed December 20, 2010]. Available at: http://learnbayes.blogspot.com/2007/11/latex-in-my-blog.html.
48. David H. Freedman. Lies, Damned Lies, and Medical Science. The Atlantic. 2010. Excerpt: "Much of what medical researchers conclude in their studies is misleading, exaggerated, or flat-out wrong. So why are doctors�to a striking extent�still drawing upon misinformation in their everyday practice? Dr. John Ioannidis has spent his career challenging his peers by exposing their bad science." [Accessed October 19, 2010]. Available at: http://www.theatlantic.com/magazine/archive/2010/11/lies-damned-lies-and-medical-science/8269/.
47. Making Sense of Non-Financial Competing Interests
46. Miscellaneous material on R and StatWeave
45. Jos� Pinheiro, Douglas Bates. Mixed-Effects Models in S and S-PLUS. 1st ed. Springer; 2009.
44. Todd D. Jick. Mixing Qualitative and Quantitative Methods: Triangulation in Action.. Administrative Science Quarterly. 1979;24(4):602-11. Excerpt: "There is a distinct tradition in the literature on social science research methods that advocates the use of multiple methods. This form of research strategy is usually described as one of convergent methodology, multimethod/multitrait (Campbell and Fiske, 1959), convergent validation or, what has been called "triangulation" (Webb et al., 1 966). These various notions share the conception that qualitative and quantitative methods should be viewed as complementary rather than as rival camps. In fact, most textbooks under- score the desirability of mixing methods given the strengths and weaknesses found in single method designs." Available at: http://www.jstor.org/stable/pdfplus/2392366.pdf.
43. W.N. Venables, B.D. Ripley. Modern Applied Statistics with S. 4th ed. Springer; 2010.
42. Noemie Soullier, Elise de La Rochebrochard, Jean Bouyer. Multiple imputation for estimation of an occurrence rate in cohorts with attrition and discrete follow-up time points: a simulation study. BMC Medical Research Methodology. 2010;10(1):79. Abstract: "BACKGROUND: In longitudinal cohort studies, subjects may be lost to follow-up at any time during the study. This leads to attrition and thus to a risk of inaccurate and biased estimations. The purpose of this paper is to show how multiple imputation can take advantage of all the information collected during follow-up in order to estimate the cumulative probability P(E) of an event E, when the first occurrence of this event is observed at t successive time points of a longitudinal study with attrition. METHODS: We compared the performance of multiple imputation with that of Kaplan-Meier estimation in several simulated attrition scenarios. RESULTS:I n missing-completely-at-random scenarios, the multiple imputation and Kaplan-Meier methods performed well in terms of bias (less than 1%) and coverage rate (range = [94.4%; 95.8%]). In missing-at-random scenarios, the Kaplan-Meier method was associated with a bias ranging from -5.1% to 7.0% and with a very poor coverage rate (as low as 0.2%). Multiple imputation performed much better in this situation (bias <2%, coverage rate >83.4%). CONCLUSIONS: Multiple imputation shows promise for estimation of an occurrence rate in cohorts with attrition. This study is a first step towards defining appropriate use of multiple imputation in longitudinal studies." [Accessed October 25, 2010]. Available at: http://www.biomedcentral.com/1471-2288/10/79.
40. Naturopathy, Pseudoscience, and Medicine: Myths and Fallacies vs Truth
39. Nursing in corrections: lessons from France
38. Objectionable 'objectives'
36. Patient Adherence to Tuberculosis Treatment: A Systematic Review of Qualitative Research
35. Patient Safety Is Not Improving in Hospitals, Study Finds
34. Paying for access to medical journals
31. Quality of Cochrane reviews: assessment of sample from 1998
30. Quick-R
29. Re-calculating the sample size in internal pilot study designs with control of the type I error rate
27. Reductionist inference-based medicine, i.e. EBM
26. Reporting Clinical Trial Results To Inform Providers, Payers, And Consumers
23. RIPL - Statistical Mediation
22. Sample size recalculation in internal pilot study designs: a review
21. Science-Based Medicine Blog
20. Seven alternatives to evidence based medicine
19. Survey of claims of no effect in abstracts of Cochrane reviews
18. Systematic review of dexketoprofen in acute and chronic pain
17. The Design and Analysis of Sequential Clinical Trials
16. The Immortal Life of Henrietta Lacks
15. The Ongoing Problem with the National Center for Complementary and Alternative Medicine
14. The role of internal pilot studies in increasing the efficiency of clinical trials
13. The search for stable prognostic models in multiple imputed data sets
12. The Team Handbook Third Edition
11. Using Facebook Updates to Chronicle Breakups
9. What's in Placebos: Who Knows? Analysis of Randomized, Controlled Trials
8. Why we need observational studies to evaluate the effectiveness of health care
7. Essential Evidence-based Medicine
5. Evidence-Based Medicine: How to Practice and Teach EBM
4. Interpreting the Medical Literature: Practical Epidemiology for Clinicians
3. How To Appraise Research: A Guide For Chiropractic Students and Practitioners
2. Studying a Study and Testing a Test: How to Read the Medical Evidence
1. The interpretation of systematic reviews with meta-analyses: an objective or subjective process?
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