Iain K. Crombie - Evidence in Medicine

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Evidence in Medicine: краткое содержание, описание и аннотация

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High-quality evidence is the foundation for effective treatment in medicine. As the vast amount of published medical evidence continues to grow, concerns about the quality of many studies are increasing. 
 is a much-needed resource that addresses the ‘medical misinformation mess’ by assessing the flaws in the research environment. This authoritative text identifies and summarises the many factors that have produced the current problems in medical research, including bias in randomised controlled trials, questionable research practices, falsified data, manipulated findings, and more. 
This volume brings together the findings from meta-research studies and systematic reviews to explore the quality of clinical trials and other medical research, explaining the character and consequences of poor-quality medical evidence using clear language and a wealth of supporting references. The text suggests planning strategies to transform the research process and provides an extensive list of the actions that could be taken by researchers, regulators, and other key stakeholders to address defects in medical evidence. This timely volume: 
Enables readers to select reliable studies and recognise misleading research Highlights the main types of biased and wasted studies Discusses how incentives in the research environment influence the quality of evidence Identifies the problems researchers need to guard against in their work Describes the scale of poor-quality research and explores why the problems are widespread Includes a summary of key findings on poor-quality research and a listing of proposed initiatives to improve research evidence Contains extensive citations to references, reviews, commentaries, and landmark studies 
 is required reading for all researchers who create evidence, funders and publishers of medical research, students who conduct their own research studies, and healthcare practitioners wanting to deliver high-quality, evidence-based care.

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66 66. Berger, V.W. (2017). Subjecting known facts to flawed empirical testing. J. Clin. Epidemiol. 84: 188.

67 67. Rainville, T., Laskine, M., and Durand, M. (2019). Use of modified intention‐to‐treat analysis in studies of direct oral anticoagulants and risk of selection bias: a systematic review. BMJ Evid. Based Med. 24: 63–69.

68 68. Farquhar, C.M., Showell, M.G., Showell, E.A.E. et al. (2017). Clinical trial registration was not an indicator for low risk of bias. J. Clin. Epidemiol. 84: 47–53.

69 69. Trinquart, L., Dunn, A.G., and Bourgeois, F.T. (2018). Registration of published randomized trials: a systematic review and meta‐analysis. BMC Med. https://doi.org/10.1186/s12916‐018‐1168‐6.

70 70. Odutayo, A., Emdin, C.A., Hsiao, A.J. et al. (2017). Association between trial registration and positive study findings: cross sectional study (epidemiological study of randomized trials‐ESORT). BMJ https://doi.org/10.1136/bmj.j917.

71 71. Dechartres, A., Ravaud, P., Atal, I. et al. (2016). Association between trial registration and treatment effect estimates: a meta‐epidemiological study. BMC Med. https://doi.org/10.1186/s12916‐016‐0639‐x.

72 72. Nuesch, E., Trelle, S., Reichenbach, S. et al. (2010). Small study effects in meta‐analyses of osteoarthritis trials: meta‐epidemiological study. BMJ https://doi.org/10.1136/bmj.c3515.

73 73. Dechartres, A., Trinquart, L., Boutron, I. et al. (2013). Influence of trial sample size on treatment effect estimates: meta‐epidemiological study. BMJ https://doi.org/10.1136/bmj.f2304.

74 74. Papageorgiou, S.N., Antonoglou, G.N., Tsiranidou, E. et al. (2014). Bias and small‐study effects influence treatment effect estimates: a meta‐epidemiological study in oral medicine. J. Clin. Epidemiol. 67: 984–992.

75 75. Pereira, T.V., Horwitz, R.I., and Ioannidis, J.P. (2012). Empirical evaluation of very large treatment effects of medical interventions. JAMA 308: 1676–1684.

76 76. Wang, Z., Alahdab, F., Almasri, J. et al. (2016). Early studies reported extreme findings with large variability: a meta‐epidemiologic study in the field of endocrinology. J. Clin. Epidemiol. 72: 27–32.

77 77. Gartlehner, G., Dobrescu, A., Evans, T.S. et al. (2016). Average effect estimates remain similar as evidence evolves from single trials to high‐quality bodies of evidence: a meta‐epidemiologic study. J. Clin. Epidemiol. 69: 16–22.

78 78. Ioannidis, J.P. (2005). Contradicted and initially stronger effects in highly cited clinical research. JAMA 294: 218–228.

79 79. Ingre, M. (2013). Why small low‐powered studies are worse than large high‐powered studies and how to protect against “trivial” findings in research: comment on Friston (2012). NeuroImage 81: 496–498.

80 80. Walsh, M., Srinathan, S.K., McAuley, D.F. et al. (2014). The statistical significance of randomized controlled trial results is frequently fragile: a case for a fragility index. J. Clin. Epidemiol. 67: 622–628.

81 81. Ridgeon, E.E., Young, P.J., Bellomo, R. et al. (2016). The fragility index in multicenter randomized controlled critical care trials. Crit. Care Med. 44: 1278–1284.

82 82. Noel, C.W., McMullen, C., Yao, C. et al. (2018). The fragility of statistically significant findings from randomized trials in head and neck surgery. Laryngoscope 128: 2094–2100.

83 83. Evaniew, N., Files, C., Smith, C. et al. (2015). The fragility of statistically significant findings from randomized trials in spine surgery: a systematic survey. Spine J. 15: 2188–2197.

84 84. Mazzinari, G., Ball, L., Serpa Neto, A. et al. (2018). The fragility of statistically significant findings in randomised controlled anaesthesiology trials: systematic review of the medical literature. Br. J. Anaesth. 120: 935–941.

85 85. Edwards, E., Wayant, C., Besas, J. et al. (2018). How fragile are clinical trial outcomes that support the CHEST clinical practice guidelines for VTE? Chest 154: 512–520.

86 86. Lamberink, H.J., Otte, W.M., Sinke, M.R.T. et al. (2018). Statistical power of clinical trials increased while effect size remained stable: an empirical analysis of 136,212 clinical trials between 1975 and 2014. J. Clin. Epidemiol. 102: 123–128.

87 87. Colquhoun, D. (2014). An investigation of the false discovery rate and the misinterpretation of p‐values. R. Soc. Open Sci. https://doi.org/10.1098/rsos.140216.

88 88. IntHout, J., Ioannidis, J.P., Borm, G.F. et al. (2015). Small studies are more heterogeneous than large ones: a meta‐meta‐analysis. J. Clin. Epidemiol. 68: 860–869.

89 89. Froud, R., Rajendran, D., Patel, S. et al. (2017). The power of low Back pain trials: a systematic review of power, sample size, and reporting of sample size calculations over time, in trials published between 1980 and 2012. Spine 42: E680–E686.

90 90. Azad, T.D., Veeravagu, A., Mittal, V. et al. (2018). Neurosurgical randomized controlled trials‐distance travelled. Neurosurgery 82: 604–612.

91 91. Gan, H.K., You, B., Pond, G.R. et al. (2012). Assumptions of expected benefits in randomized phase III trials evaluating systemic treatments for cancer. J. Natl. Cancer Inst. 104: 590–598.

92 92. Matheson, A. (2017). Marketing trials, marketing tricks – how to spot them and how to stop them. Trials https://doi.org/10.1186/s13063‐017‐1827‐5.

93 93. Lundh, A., Lexchin, J., Mintzes, B. et al. (2018). Industry sponsorship and research outcome: systematic review with meta‐analysis. Intensive Care Med. 44: 1603–1612.

94 94. Riaz, H., Raza, S., Khan, M.S. et al. (2015). Impact of funding source on clinical trial results including cardiovascular outcome trials. Am. J. Cardiol. 116: 1944–1947.

95 95. Sismondo, S. (2008). Pharmaceutical company funding and its consequences: a qualitative systematic review. Contemp. Clin. Trials 29: 109–113.

96 96. Sturmberg, J.P. (2019). From probability to believability. J. Eval. Clin. Pract. 26: 1081–1086.

97 97. Smith, R. (2005). Medical journals are an extension of the marketing arm of pharmaceutical companies. PLoS Med. https://doi.org/10.1371/journal.pmed.0020138.

98 98. Pyke, S., Julious, S.A., Day, S. et al. (2011). The potential for bias in reporting of industry‐sponsored clinical trials. Pharm. Stat. 10: 74–79.

99 99. Zwierzyna, M., Davies, M., Hingorani, A.D. et al. (2018). Clinical trial design and dissemination: comprehensive analysis of http://clinicaltrials.govand PubMed data since 2005. BMJ https://doi.org/10.1136/bmj.k2130.

100 100. Rasmussen, K., Bero, L., Redberg, R. et al. (2018). Collaboration between academics and industry in clinical trials: cross sectional study of publications and survey of lead academic authors. BMJ https://doi.org/10.1136/bmj.k3654.

101 101. Lexchin, J. (2012). Those who have the gold make the evidence: how the pharmaceutical industry biases the outcomes of clinical trials of medications. Sci. Eng. Ethics 18: 247–261.

102 102. Dunn, A.G., Bourgeois, F.T., and Coiera, E. (2013). Industry influence in evidence production. J. Epidemiol. Community Health 67: 537–538.

103 103. Every‐Palmer, S. and Howick, J. (2014). How evidence‐based medicine is failing due to biased trials and selective publication. J. Eval. Clin. Pract. 20: 908–914.

104 104. Flacco, M.E., Manzoli, L., Boccia, S. et al. (2015). Head‐to‐head randomized trials are mostly industry sponsored and almost always favor the industry sponsor. J. Clin. Epidemiol. 68: 811–820.

105 105. Spielmans, G.I. and Parry, P.I. (2010). From evidence‐based medicine to marketing‐based medicine: evidence from internal industry documents. J. Bioethic Inquiry 7: 13–29.

106 106. Ioannidis, J.P.A. (2018). Randomized controlled trials: often flawed, mostly useless, clearly indispensable: a commentary on Deaton and cartwright. Soc. Sci. Med. 210: 53–56.

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