Читать книгу Evidence in Medicine - Iain K. Crombie - Страница 39

REFERENCES

Оглавление

1 1. Riechelmann, R.P., Peron, J., Seruga, B. et al. (2018). Meta‐research on oncology trials: a toolkit for researchers with limited resources. Oncologist 23: 1467–1473.

2 2. Page, M.J., Higgins, J.P., Clayton, G. et al. (2016). Empirical evidence of study design biases in randomized trials: systematic review of meta‐epidemiological studies. PLoS One https://doi.org/10.1371/journal.pone.0159267.

3 3. Adie, S., Harris, I.A., Naylor, J.M. et al. (2017). The quality of surgical versus non‐surgical randomized controlled trials. Contemp. Clin. Trials Commun. 5: 63–66.

4 4. Savovic, J., Jones, H., Altman, D. et al. (2012). Influence of reported study design characteristics on intervention effect estimates from randomised controlled trials: combined analysis of meta‐epidemiological studies. Health Technol. Assess. 16: 1–82.

5 5. Dechartres, A., Trinquart, L., Atal, I. et al. (2017). Evolution of poor reporting and inadequate methods over time in 20 920 randomised controlled trials included in Cochrane reviews: research on research study. BMJ https://doi.org/10.1136/bmj.j2490.

6 6. Wuytack, F., Regan, M., Biesty, L. et al. (2019). Risk of bias assessment of sequence generation: a study of 100 systematic reviews of trials. Syst. Rev. https://doi.org/10.1186/s13643‐018‐0924‐1.

7 7. Savovic, J., Turner, R.M., Mawdsley, D. et al. (2018). Association between risk‐of‐bias assessments and results of randomized trials in Cochrane reviews: the ROBES meta‐epidemiologic study. Am. J. Epidemiol. 187: 1113–1122.

8 8. Zhai, X., Cui, J., Wang, Y. et al. (2017). Quality of reporting randomized controlled trials in five leading neurology journals in 2008 and 2013 using the modified “risk of bias” tool. World Neurosurg. 99: 687–694.

9 9. Rikos, D., Dardiotis, E., Tsivgoulis, G. et al. (2016). Reporting quality of randomized‐controlled trials in multiple sclerosis from 2000 to 2015, based on CONSORT statement. Mult. Scler. Relat. Disord. 9: 135–139.

10 10. Saltaji, H., Armijo‐Olivo, S., Cummings, G.G. et al. (2018). Impact of selection bias on treatment effect size estimates in randomized trials of Oral health interventions: a meta‐epidemiological study. J. Dent. Res. 97: 5–13.

11 11. Schulz, K.F. and Grimes, D.A. (2002). Unequal group sizes in randomised trials: guarding against guessing. Lancet 359: 966–970.

12 12. Clark, L., Fairhurst, C., Hewitt, C.E. et al. (2014). A methodological review of recent meta‐analyses has found significant heterogeneity in age between randomized groups. J. Clin. Epidemiol. 67: 1016–1024.

13 13. Clark, L., Fairhurst, C., Cook, E. et al. (2015). Important outcome predictors showed greater baseline heterogeneity than age in two systematic reviews. J. Clin. Epidemiol. 68: 175–181.

14 14. Schulz, KF. (1995). Subverting randomization in controlled trials. JAMA 274: 1456–1458.

15 15. Paludan‐Muller, A., Laursen, D.R.T., and Hrobjartsson, A. (2016). Mechanisms and direction of allocation bias in randomised clinical trials. BMC Med. Res. Methodol. https://doi.org/10.1186/s12874‐016‐0235‐y.

16 16. Clark, L., Fairhurst, C., and Torgerson, D.J. (2016). Allocation concealment in randomised controlled trials: are we getting better? BMJ https://doi.org/10.1136/bmj.i5663.

17 17. Schulz, K.F., Chalmers, I., Hayes, R.J. et al. (1995). Empirical evidence of bias. Dimensions of methodological quality associated with estimates of treatment effects in controlled trials. JAMA 273: 408–412.

18 18. Dechartres, A., Trinquart, L., Faber, T. et al. (2016). Empirical evaluation of which trial characteristics are associated with treatment effect estimates. J. Clin. Epidemiol. 77: 24–37.

19 19. Pansieri, C., Pandolfini, C., and Bonati, M. (2015). The evolution in registration of clinical trials: a chronicle of the historical calls and current initiatives promoting transparency. Eur. J. Clin. Pharmacol. 71: 1159–1164.

20 20. Zarin, D.A., Tse, T., Williams, R.J. et al. (2017). Update on trial registration 11 years after the ICMJE policy was established. N. Engl. J. Med. 376: 383–391.

21 21. Howard, B., Scott, J.T., Blubaugh, M. et al. (2017). Systematic review: outcome reporting bias is a problem in high impact factor neurology journals. PLoS One https://doi.org/10.1371/journal.pone.0180986.

22 22. Wayant, C., Scheckel, C., Hicks, C. et al. (2017). Evidence of selective reporting bias in hematology journals: a systematic review. PLoS One https://doi.org/10.1371/journal.pone.0178379.

23 23. Hannink, G., Gooszen, H.G., and Rovers, M.M. (2013). Comparison of registered and published primary outcomes in randomized clinical trials of surgical interventions. Ann. Surg. 257: 818–823.

24 24. Raghav, K.P., Mahajan, S., Yao, J.C. et al. (2015). From protocols to publications: a study in selective reporting of outcomes in randomized trials in oncology. J. Clin. Oncol. 33: 3583–3590.

25 25. Dwan, K., Gamble, C., Williamson, P.R. et al. (2013). Systematic review of the empirical evidence of study publication bias and outcome reporting bias – an updated review. PLoS One https://doi.org/10.1371/journal.pone.0066844.

26 26. Li, G., Abbade, L.P.F., Nwosu, I. et al. (2018). A systematic review of comparisons between protocols or registrations and full reports in primary biomedical research. BMC Med. Res. Methodol. https://doi.org/10.1186/s12874‐017‐0465‐7.

27 27. Chen, T., Li, C., Qin, R. et al. (2019). Comparison of clinical trial changes in primary outcome and reported intervention effect size between trial registration and publication. JAMA Netw. Open https://doi.org/10.1001/jamanetworkopen.2019.7242.

28 28. Smyth, R.M., Kirkham, J.J., Jacoby, A. et al. (2011). Frequency and reasons for outcome reporting bias in clinical trials: interviews with trialists. BMJ https://doi.org/10.1136/bmj.c7153.

29 29. van der Steen, J.T., van den Bogert, C.A., van Soest‐Poortvliet, M.C. et al. (2018). Determinants of selective reporting: a taxonomy based on content analysis of a random selection of the literature. PLoS One https://doi.org/10.1371/journal.pone.0188247.

30 30. Bello, S., Moustgaard, H., and Hrobjartsson, A. (2017). Unreported formal assessment of unblinding occurred in 4 of 10 randomized clinical trials, unreported loss of blinding in 1 of 10 trials. J. Clin. Epidemiol. 81: 42–50.

31 31. Bello, S., Moustgaard, H., and Hrobjartsson, A. (2014). The risk of unblinding was infrequently and incompletely reported in 300 randomized clinical trial publications. J. Clin. Epidemiol. 67: 1059–1069.

32 32. Yi, J., Haibo, H.L., Li, Y. et al. (2020). Risk of bias and its impact on intervention effect estimates of randomized controlled trials in endodontics. J. Endodontics 46: 12–18.

33 33. Moustgaard, H., Clayton, G.L., Jones, H.E. et al. (2020). Impact of blinding on estimated treatment effects in randomised clinical trials: meta‐epidemiological study. BMJ https://doi.org/10.1136/bmj.l6802.

34 34. Huupponen, R. and Viikari, J. (2013). Statins and the risk of developing diabetes. BMJ https://doi.org/10.1136/bmj.f3156.

35 35. Tang, E., Ravaud, P., Riveros, C. et al. (2015). Comparison of serious adverse events posted at http://ClinicalTrials.gov and published in corresponding journal articles. BMC Med. https://doi.org/10.1186/s12916‐015‐0430‐4.

36 36. Favier, R. and Crepin, S. (2018). The reporting of harms in publications on randomized controlled trials funded by the “Programme Hospitalier de Recherche Clinique,” a French academic funding scheme. Clin. Trials 15: 257–267.

37 37. Hughes, S., Cohen, D., and Jaggi, R. (2014). Differences in reporting serious adverse events in industry sponsored clinical trial registries and journal articles on antidepressant and antipsychotic drugs: a cross‐sectional study. BMJ Open https://doi.org/10.1136/bmjopen‐2014‐005535.

38 38. Golder, S., Loke, Y.K., Wright, K. et al. (2016). Reporting of adverse events in published and unpublished studies of health care interventions: a systematic review. PLoS Med. https://doi.org/10.1371/journal.pmed.1002127.

39 39. Hodkinson, A., Kirkham, J.J., Tudur‐Smith, C. et al. (2013). Reporting of harms data in RCTs: a systematic review of empirical assessments against the CONSORT harms extension. BMJ Open https://doi.org/10.1136/bmjopen‐2013‐003436.

40 40. Fewtrell, M.S., Kennedy, K., Singhal, A. et al. (2008). How much loss to follow‐up is acceptable in long‐term randomised trials and prospective studies? Arch. Dis. Child. 93: 458–461.

41 41. Schulz, K.F. and Grimes, D.A. (2002). Sample size slippages in randomised trials: exclusions and the lost and wayward. Lancet 359: 781–785.

42 42. Akl, E.A., Briel, M., You, J.J. et al. (2012). Potential impact on estimated treatment effects of information lost to follow‐up in randomised controlled trials (LOST‐IT): systematic review. BMJ https://doi.org/10.1136/bmj.e2809.

43 43. Zhang, Y., Florez, I.D., Colunga Lozano, L.E. et al. (2017). A systematic survey on reporting and methods for handling missing participant data for continuous outcomes in randomized controlled trials. J. Clin. Epidemiol. 88: 57–66.

44 44. Nuesch, E., Trelle, S., Reichenbach, S. et al. (2009). The effects of excluding patients from the analysis in randomised controlled trials: meta‐epidemiological study. BMJ https://doi.org/10.1136/bmj.b3244.

45 45. Bell, M.L., Fiero, M., Horton, N.J. et al. (2014). Handling missing data in RCTs; a review of the top medical journals. BMC Med. Res. Methodol. https://doi.org/10.1186/1471‐2288‐14‐118.

46 46. Walters, S.J., Bonacho Dos Anjos Henriques‐Cadby, I., Bortolami, O. et al. (2017). Recruitment and retention of participants in randomised controlled trials: a review of trials funded and published by the United Kingdom Health Technology Assessment Programme. BMJ Open https://doi.org/10.1136/bmjopen‐2016‐015276.

47 47. Hussain, J.A., White, I.R., Langan, D. et al. (2016). Missing data in randomized controlled trials testing palliative interventions pose a significant risk of bias and loss of power: a systematic review and meta‐analyses. J. Clin. Epidemiol. 74: 57–65.

48 48. Ibrahim, F., Tom, B.D., Scott, D.L. et al. (2016). A systematic review of randomised controlled trials in rheumatoid arthritis: the reporting and handling of missing data in composite outcomes. Trials https://doi.org/10.1186/s13063‐016‐1402‐5.

49 49. Miller, B.M. and Brennan, L. (2015). Measuring and reporting attrition from obesity treatment programs: a call to action! Obes. Res. Clin. Pract. 9: 187–202.

50 50. Marciniak, T.A., Cherepanov, V., Golukhova, E. et al. (2016). Drug discontinuation and follow‐up rates in oral antithrombotic trials. JAMA Intern. Med. 176: 257–259.

51 51. Hussain, J.A., Bland, M., Langan, D. et al. (2017). Quality of missing data reporting and handling in palliative care trials demonstrates that further development of the CONSORT statement is required: a systematic review. J. Clin. Epidemiol. 88: 81–91.

52 52. White, I.R., Horton, N.J., Carpenter, J. et al. (2011). Strategy for intention to treat analysis in randomised trials with missing outcome data. BMJ https://doi.org/10.1136/bmj.d40.

53 53. Joseph, R., Sim, J., Ogollah, R. et al. (2015). A systematic review finds variable use of the intention‐to‐treat principle in musculoskeletal randomized controlled trials with missing data. J. Clin. Epidemiol. 68: 15–24.

54 54. Kahale, L.A., Diab, B., Khamis, A.M. et al. (2019). Potentially missing data are considerably more frequent than definitely missing data: a methodological survey of 638 randomized controlled trials. J. Clin. Epidemiol. 106: 18–31.

55 55. Altman, D.G. (2009). Missing outcomes in randomized trials: addressing the dilemma. Open Med. 3: 51–53.

56 56. Molnar, F.J., Hutton, B., and Fergusson, D. (2008). Does analysis using “last observation carried forward” introduce bias in dementia research? CMAJ 179: 751–753.

57 57. Lachin, J.M. (2016). Fallacies of last observation carried forward analyses. Clin. Trials 13: 161–168.

58 58. Lee, K.J. and Simpson, J.A. (2014). Introduction to multiple imputation for dealing with missing data. Respirology 19: 162–167.

59 59. Jakobsen, J.C., Gluud, C., Wetterslev, J. et al. (2017). When and how should multiple imputation be used for handling missing data in randomised clinical trials – a practical guide with flowcharts. BMC Med. Res. Methodol. https://doi.org/10.1186/s12874‐017‐0442‐1.

60 60. Donders, A.R., van der Heijden, G.J., Stijnen, T. et al. (2006). Review: a gentle introduction to imputation of missing values. J. Clin. Epidemiol. 59: 1087–1091.

61 61. Montedori, A., Bonacini, M.I., Casazza, G. et al. (2011). Modified versus standard intention‐to‐treat reporting: are there differences in methodological quality, sponsorship, and findings in randomized trials? A cross‐sectional study. Trials https://doi.org/10.1186/1745‐6215‐12‐58.

62 62. Abraha, I., Cozzolino, F., Orso, M. et al. (2017). A systematic review found that deviations from intention‐to‐treat are common in randomized trials and systematic reviews. J. Clin. Epidemiol. 84: 37–46.

63 63. Abraha, I. and Montedori, A. (2010). Modified intention to treat reporting in randomised controlled trials: systematic review. BMJ https://doi.org/10.1136/bmj.c2697.

64 64. Abraha, I., Cherubini, A., Cozzolino, F. et al. (2015). Deviation from intention to treat analysis in randomised trials and treatment effect estimates: meta‐epidemiological study. BMJ https://doi.org/10.1136/bmj.h2445.

65 65. Dossing, A., Tarp, S., Furst, D.E. et al. (2016). Modified intention‐to‐treat analysis did not bias trial results. J. Clin. Epidemiol. 72: 66–74.

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.gov and 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.

Evidence in Medicine

Подняться наверх