COSMOS-E: Guidance on conducting systematic reviews and meta-analyses of observational studies of etiology

PLoS Medicine

Volumn 16, Issue 2, 2019, Pages

  Dekkers, Olaf M ^a,b   Vandenbroucke, Jan P ^a,b,c   Cevallos, Myriam ^d   Renehan, Andrew G ^e   Altman, Douglas G ^f   Egger, Matthias ^d,g  

^a LEIDEN UNIVERSITY MEDICAL CENTER   (Netherlands)

^b AARHUS UNIVERSITY HOSPITAL   (Denmark)

^c LONDON SCHOOL OF HYGIENE AND TROPICAL MEDICINE   (United Kingdom)

^d UNIVERSITY OF BERN   (Switzerland)

^e University of Manchester   (United Kingdom)

^f UNIVERSITY OF OXFORD   (United Kingdom)

^g UNIVERSITY OF CAPE TOWN   (South Africa)

Author keywords

[No Author keywords available]

Indexed keywords

ARTICLE; COSMOS; HUMAN; HUMAN EXPERIMENT; META ANALYSIS; OBSERVATIONAL STUDY; OUTCOME ASSESSMENT; PEER REVIEW; PUBLICATION; RANDOMIZED CONTROLLED TRIAL (TOPIC); RISK ASSESSMENT; SCIENTIST; SELECTION BIAS; STATISTICAL ANALYSIS; SYSTEMATIC REVIEW; VALIDITY; WRITING; META ANALYSIS (TOPIC); PROCEDURES;

HUMANS; META-ANALYSIS AS TOPIC; OBSERVATIONAL STUDIES AS TOPIC; SELECTION BIAS; SYSTEMATIC REVIEWS AS TOPIC;

EID: 85061987100     PISSN: 15491277     EISSN: 15491676     Source Type: Journal    
DOI: 10.1371/journal.pmed.1002742     Document Type: Article

References (104)

1. Page MJ, Shamseer L, Altman DG, Tetzlaff J, Sampson M, Tricco AC, et al. Epidemiology and Reporting Characteristics of Systematic Reviews of Biomedical Research: A Cross-Sectional Study. PLoS Med. 2016; 13(5):e1002028. https://doi.org/10.1371/journal.pmed.1002028 PMID: 27218655
2. Mansournia MA, Higgins JP, Sterne JA, Hernan MA. Biases in Randomized Trials: A Conversation between Trialists and Epidemiologists. Epidemiology. 2017; 28(1):54–9. https://doi.org/10.1097/EDE. 0000000000000564 PMID: 27748683
3. Vandenbroucke JP, von Elm E, Altman DG, Gotzsche PC, Mulrow CD, Pocock SJ, et al. Strengthening the Reporting of Observational Studies in Epidemiology (STROBE): explanation and elaboration. PLoS Med. 2007; 4(10):e297. https://doi.org/10.1371/journal.pmed.0040297 PMID: 17941715
4. Dekkers OM, Horvath-Puho E, Jorgensen JO, Cannegieter SC, Ehrenstein V, Vandenbroucke JP, et al. Multisystem morbidity and mortality in Cushing’s syndrome: a cohort study. J Clin Endocrinol Metab. 2013; 98(6):2277–84. https://doi.org/10.1210/jc.2012-3582 PMID: 23533241
5. Hernan MA, Robins JM. Instruments for causal inference: an epidemiologist’s dream? Epidemiology. 2006; 17(4):360–72. https://doi.org/10.1097/01.ede.0000222409.00878.37 PMID: 16755261
6. Rassen JA, Brookhart MA, Glynn RJ, Mittleman MA, Schneeweiss S. Instrumental variables I: instrumental variables exploit natural variation in nonexperimental data to estimate causal relationships. J Clin Epidemiol. 2009; 62(12):1226–32. https://doi.org/10.1016/j.jclinepi.2008.12.005 PMID: 19356901
7. Mountjoy E, Davies NM, Plotnikov D, Smith GD, Rodriguez S, Williams CE, et al. Education and myopia: assessing the direction of causality by mendelian randomisation. BMJ. 2018; 361:k2022. https://doi.org/10.1136/bmj.k2022 PMID: 29875094
8. Petersen I, Douglas I, Whitaker H. Self controlled case series methods: an alternative to standard epidemiological study designs. BMJ. 2016; 354:i4515. https://doi.org/10.1136/bmj.i4515 PMID: 27618829
9. Ponjoan A, Blanch J, Alves-Cabratosa L, Marti-Lluch R, Comas-Cufi M, Parramon D, et al. Effects of extreme temperatures on cardiovascular emergency hospitalizations in a Mediterranean region: a self-controlled case series study. Environ Health. 2017; 16(1):32. https://doi.org/10.1186/s12940-017-0238-0 PMID: 28376798
10. Coureau G, Bouvier G, Lebailly P, Fabbro-Peray P, Gruber A, Leffondre K, et al. Mobile phone use and brain tumours in the CERENAT case-control study. Occup Environ Med. 2014; 71(7):514–22. https://doi.org/10.1136/oemed-2013-101754 PMID: 24816517
11. Mason KE, Pearce N, Cummins S. Associations between fast food and physical activity environments and adiposity in mid-life: cross-sectional, observational evidence from UK Biobank. Lancet Public Health. 2018; 3(1):e24–e33. https://doi.org/10.1016/S2468-2667(17)30212-8 PMID: 29307385
12. Moses S, Bradley JE, Nagelkerke NJ, Ronald AR, Ndinya-Achola JO, Plummer FA. Geographical patterns of male circumcision practices in Africa: association with HIV seroprevalence. Int J Epidemiol. 1990; 19(3):693–7. PMID: 2262266
13. Siegfried N, Muller M, Deeks JJ, Volmink J. Male circumcision for prevention of heterosexual acquisition of HIV in men. Cochrane Database Syst Rev. 2009;(2):CD003362. https://doi.org/10.1002/14651858.CD003362.pub2 PMID: 19370585
14. Higgins JPT, Green S (Editors). Cochrane Handbook for Systematic Reviews of Interventions: online version (5.1.0, March 2011) The Cochrane Collaboration, 2011. 2011;(Available from www. handbook-5-1.cochrane.org).
15. Liberati A, Altman DG, Tetzlaff J, Mulrow C, Gotzsche PC, Ioannidis JP, et al. The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate health care interventions: explanation and elaboration. PLoS Med. 2009; 6(7):e1000100. https://doi.org/10.1371/journal.pmed. 1000100 PMID: 19621070
16. Stroup DF, Berlin JA, Morton SC, Olkin I, Williamson GD, Rennie D, et al. Meta-analysis of observational studies in epidemiology: a proposal for reporting. Meta-analysis Of Observational Studies in Epidemiology (MOOSE) group. JAMA. 2000; 283(15):2008–12. PMID: 10789670
17. Hoffmann S, de Vries RBM, Stephens ML, Beck NB, Dirven H, Fowle JR 3rd, et al. A primer on systematic reviews in toxicology. Arch Toxicol. 2017; 91(7):2551–75. https://doi.org/10.1007/s00204-017-1980-3 PMID: 28501917
18. Mueller M, D’Addario M, Egger M, Cevallos M, Dekkers O, Mugglin C, et al. Methods to systematically review and meta-analyse observational studies: a systematic scoping review of recommendations. BMC Med Res Methodol. 2018; 18(1):44. https://doi.org/10.1186/s12874-018-0495-9 PMID: 29783954
19. Morgan RL, Whaley P, Thayer KA, Schunemann HJ. Identifying the PECO: A framework for formulating good questions to explore the association of environmental and other exposures with health outcomes. Environ Int. 2018. 121(Pt 1):1027–1031 https://doi.org/10.1016/j.envint.2018.07.015 PMID: 30166065
20. Dekkers OM, von Elm E, Algra A, Romijn JA, Vandenbroucke JP. How to assess the external validity of therapeutic trials: a conceptual approach. Int J Epidemiol. 2010; 39(1):89–94. https://doi.org/10. 1093/ije/dyp174 PMID: 19376882
21. Burgers AM, Biermasz NR, Schoones JW, Pereira AM, Renehan AG, Zwahlen M, et al. Meta-analysis and dose-response metaregression: circulating insulin-like growth factor I (IGF-I) and mortality. J Clin Endocrinol Metab. 2011; 96(9):2912–20. https://doi.org/10.1210/jc.2011-1377 PMID: 21795450
22. Amitay EL, Keinan-Boker L. Breastfeeding and Childhood Leukemia Incidence: A Meta-analysis and Systematic Review. JAMA Pediatr. 2015; 169(6):e151025. https://doi.org/10.1001/jamapediatrics. 2015.1025 PMID: 26030516
23. Cooper GS, Lunn RM, Agerstrand M, Glenn BS, Kraft AD, Luke AM, et al. Study sensitivity: Evaluating the ability to detect effects in systematic reviews of chemical exposures. Environ Int. 2016; 92– 93:605–10. https://doi.org/10.1016/j.envint.2016.03.017PMID: 27156196
24. Gast KB, Tjeerdema N, Stijnen T, Smit JW, Dekkers OM. Insulin resistance and risk of incident cardiovascular events in adults without diabetes: meta-analysis. PLoS ONE. 2012; 7(12):e52036. https://doi. org/10.1371/journal.pone.0052036 PMID: 23300589
25. Vandenbroucke JP. When are observational studies as credible as randomised trials? Lancet. 2004; 363(9422):1728–31. https://doi.org/10.1016/S0140-6736(04)16261-2 PMID: 15158638
26. Blair A, Stewart P, Lubin JH, Forastiere F. Methodological issues regarding confounding and exposure misclassification in epidemiological studies of occupational exposures. Am J Ind Med. 2007; 50 (3):199–207. https://doi.org/10.1002/ajim.20281 PMID: 17096363
27. Booth A, Clarke M, Dooley G, Ghersi D, Moher D, Petticrew M, et al. The nuts and bolts of PROS-PERO: an international prospective register of systematic reviews. Syst Rev. 2012; 1:2. https://doi. org/10.1186/2046-4053-1-2 PMID: 22587842
28. McGowan J, Sampson M. Systematic reviews need systematic searchers. J Med Libr Assoc. 2005; 93 (1):74–80. PMID: 15685278
29. Greenhalgh T, Peacock R. Effectiveness and efficiency of search methods in systematic reviews of complex evidence: audit of primary sources. BMJ. 2005; 331(7524):1064–5. https://doi.org/10.1136/bmj.38636.593461.68 PMID: 16230312
30. Kuper H, Nicholson A, Hemingway H. Searching for observational studies: what does citation tracking add to PubMed? A case study in depression and coronary heart disease. BMC Med Res Methodol. 2006; 6:4. https://doi.org/10.1186/1471-2288-6-4 PMID: 16483366
31. Lemeshow AR, Blum RE, Berlin JA, Stoto MA, Colditz GA. Searching one or two databases was insufficient for meta-analysis of observational studies. J Clin Epidemiol. 2005; 58(9):867–73. https://doi. org/10.1016/j.jclinepi.2005.03.004 PMID: 16085190
32. Waffenschmidt S, Hermanns T, Gerber-Grote A, Mostardt S. No suitable precise or optimized epidemiologic search filters were available for bibliographic databases. J Clin Epidemiol. 2017; 82:112–8. https://doi.org/10.1016/j.jclinepi.2016.08.008 PMID: 27570049
33. von Elm E, Poglia G, Walder B, Tramer MR. Different patterns of duplicate publication: an analysis of articles used in systematic reviews. JAMA. 2004; 291(8):974–80. https://doi.org/10.1001/jama.291.8. 974 PMID: 14982913
34. Berlin JA. Invited commentary: benefits of heterogeneity in meta-analysis of data from epidemiologic studies. Am J Epidemiol. 1995; 142(4):383–7. PMID: 7625402
35. Tendal B, Higgins JP, Juni P, Hrobjartsson A, Trelle S, Nuesch E, et al. Disagreements in meta-analyses using outcomes measured on continuous or rating scales: observer agreement study. BMJ. 2009; 339:b3128. https://doi.org/10.1136/bmj.b3128 PMID: 19679616
36. Gotzsche PC, Hrobjartsson A, Maric K, Tendal B. Data extraction errors in meta-analyses that use standardized mean differences. JAMA. 2007; 298(4):430–7. https://doi.org/10.1001/jama.298.4.430 PMID: 17652297
37. Rohner E Bolius J, da Costa RR, Trelle S. Managing people and data in systematic reviews. In: Egger M, Davey Smith G., editor. Systematic Reviews in Health Care: Meta-analysis in Context Chichester, England. John Wiley & Sons; 2019 (in press).
38. Grimes DA. "Case-control" confusion: mislabeled reports in obstetrics and gynecology journals. Obstet Gynecol. 2009; 114(6):1284–6. https://doi.org/10.1097/AOG.0b013e3181c03421 PMID: 19935031
39. Nesvick CL, Thompson CJ, Boop FA, Klimo P Jr. Case-control studies in neurosurgery. J Neurosurg. 2014; 121(2):285–96. https://doi.org/10.3171/2014.5.JNS132329 PMID: 24949675
40. Renehan AG, Zwahlen M, Minder C, O’Dwyer ST, Shalet SM, Egger M. Insulin-like growth factor (IGF)-I, IGF binding protein-3, and cancer risk: systematic review and meta-regression analysis. Lancet. 2004; 363(9418):1346–53. https://doi.org/10.1016/S0140-6736(04)16044-3 PMID: 15110491
41. Egger M, Schneider M, Davey Smith G. Spurious precision? Meta-analysis of observational studies. BMJ. 1998; 316(7125):140–4. PMID: 9462324
42. Friedenreich CM, Speidel TP, Neilson HK, Langley AR, Courneya KS, Magliocco AM, et al. Case-control study of lifetime alcohol consumption and endometrial cancer risk. Cancer Causes Control. 2013; 24(11):1995–2003. https://doi.org/10.1007/s10552-013-0275-0 PMID: 23929278
43. Tourangeau R, Yan T. Sensitive questions in surveys. Psychol Bull. 2007; 133(5):859–83. https://doi. org/10.1037/0033-2909.133.5.859 PMID: 17723033
44. Hernan MA, Hernandez-Diaz S, Robins JM. A structural approach to selection bias. Epidemiology. 2004; 15(5):615–25. PMID: 15308962
45. Sanderson S, Tatt ID, Higgins JP. Tools for assessing quality and susceptibility to bias in observational studies in epidemiology: a systematic review and annotated bibliography. Int J Epidemiol. 2007; 36 (3):666–76. https://doi.org/10.1093/ije/dym018 PMID: 17470488
46. Austin PC. An Introduction to Propensity Score Methods for Reducing the Effects of Confounding in Observational Studies. Multivariate Behav Res. 2011; 46(3):399–424. https://doi.org/10.1080/00273171.2011.568786 PMID: 21818162
47. Costanza MC. Matching. Prev Med. 1995; 24(5):425–33. https://doi.org/10.1006/pmed.1995.1069 PMID: 8524715
48. Mansournia MA, Jewell NP, Greenland S. Case-control matching: effects, misconceptions, and recommendations. Eur J Epidemiol. 2018; 33(1):5–14. https://doi.org/10.1007/s10654-017-0325-0 PMID: 29101596
49. Greenland S. An introduction to instrumental variables for epidemiologists. Int J Epidemiol. 2000; 29 (4):722–9. PMID: 10922351
50. Holmes MV, Dale CE, Zuccolo L, Silverwood RJ, Guo Y, Ye Z, et al. Association between alcohol and cardiovascular disease: Mendelian randomisation analysis based on individual participant data. BMJ. 2014; 349:g4164. https://doi.org/10.1136/bmj.g4164 PMID: 25011450
51. Lipsitch M, Tchetgen Tchetgen E, Cohen T. Negative controls: a tool for detecting confounding and bias in observational studies. Epidemiology. 2010; 21(3):383–8. https://doi.org/10.1097/EDE. 0b013e3181d61eeb PMID: 20335814
52. Fowler JS, Volkow ND, Wang GJ, Pappas N, Logan J, Shea C, et al. Brain monoamine oxidase A inhibition in cigarette smokers. Proc Natl Acad Sci U S A. 1996; 93(24):14065–9. PMID: 8943061
53. Smith GD, Phillips AN, Neaton JD. Smoking as "independent" risk factor for suicide: illustration of an artifact from observational epidemiology? Lancet. 1992; 340(8821):709–12. PMID: 1355809
54. Zaadstra BM, Chorus AM, van Buuren S, Kalsbeek H, van Noort JM. Selective association of multiple sclerosis with infectious mononucleosis. Mult Scler. 2008; 14(3):307–13. https://doi.org/10.1177/1352458507084265 PMID: 18208871
55. Higgins JP, Altman DG, Gotzsche PC, Juni P, Moher D, Oxman AD, et al. The Cochrane Collabora-tion’s tool for assessing risk of bias in randomised trials. BMJ. 2011; 343:d5928. https://doi.org/10. 1136/bmj.d5928 PMID: 22008217
56. Sterne JA, Hernan MA, Reeves BC, Savovic J, Berkman ND, Viswanathan M, et al. ROBINS-I: a tool for assessing risk of bias in non-randomised studies of interventions. BMJ. 2016; 355:i4919. https://doi.org/10.1136/bmj.i4919 PMID: 27733354
57. Whiting PF, Rutjes AW, Westwood ME, Mallett S, Deeks JJ, Reitsma JB, et al. QUADAS-2: a revised tool for the quality assessment of diagnostic accuracy studies. Ann Intern Med. 2011; 155(8):529–36. https://doi.org/10.7326/0003-4819-155-8-201110180-00009 PMID: 22007046
58. Morgan RL, Thayer KA, Santesso N, Holloway AC, Blain R, Eftim SE, et al. Evaluation of the risk of bias in non-randomized studies of interventions (ROBINS-I) and the ’target experiment’ concept in studies of exposures: Rationale and preliminary instrument development. Environ Int. 2018; 120:382– 7. https://doi.org/10.1016/j.envint.2018.08.018 PMID: 30125855
59. da Costa BR, Hilfiker R, Egger M. PEDro’s bias: summary quality scores should not be used in meta-analysis. J Clin Epidemiol. 2013; 66(1):75–7. https://doi.org/10.1016/j.jclinepi.2012.08.003 PMID: 23177896
60. Juni P, Witschi A, Bloch R, Egger M. The hazards of scoring the quality of clinical trials for meta-analysis. JAMA. 1999; 282(11):1054–60. PMID: 10493204
61. Stang A. Critical evaluation of the Newcastle-Ottawa scale for the assessment of the quality of nonran-domized studies in meta-analyses. Eur J Epidemiol. 2010; 25(9):603–5. https://doi.org/10.1007/s10654-010-9491-z PMID: 20652370
62. Turner EH, Matthews AM, Linardatos E, Tell RA, Rosenthal R. Selective publication of antidepressant trials and its influence on apparent efficacy. N Engl J Med. 2008; 358(3):252–60. https://doi.org/10. 1056/NEJMsa065779 PMID: 18199864
63. Roest AM, de Jonge P, Williams CD, de Vries YA, Schoevers RA, Turner EH. Reporting Bias in Clinical Trials Investigating the Efficacy of Second-Generation Antidepressants in the Treatment of Anxiety Disorders: A Report of 2 Meta-analyses. JAMA Psychiatry. 2015; 72(5):500–10. https://doi.org/10. 1001/jamapsychiatry.2015.15 PMID: 25806940
64. Head ML, Holman L, Lanfear R, Kahn AT, Jennions MD. The extent and consequences of p-hacking in science. PLoS Biol. 2015; 13(3):e1002106. https://doi.org/10.1371/journal.pbio.1002106 PMID: 25768323
65. Althuis MD, Weed DL, Frankenfeld CL. Evidence-based mapping of design heterogeneity prior to meta-analysis: a systematic review and evidence synthesis. Syst Rev. 2014; 3:80. https://doi.org/10. 1186/2046-4053-3-80 PMID: 25055879
66. Davey Smith G, Egger M, Phillips AN. Meta-analysis. Beyond the grand mean? BMJ. 1997; 315 (7122):1610–4. PMID: 9437284
67. Feller M, Huwiler K, Stephan R, Altpeter E, Shang A, Furrer H, et al. Mycobacterium avium subspecies paratuberculosis and Crohn’s disease: a systematic review and meta-analysis. Lancet Infect Dis. 2007; 7(9):607–13. https://doi.org/10.1016/S1473-3099(07)70211-6 PMID: 17714674
68. Bartholomew LL, Grimes DA. The alleged association between induced abortion and risk of breast cancer: biology or bias? Obstet Gynecol Surv. 1998; 53(11):708–14. PMID: 9812330
69. Ioannidis JP, Patsopoulos NA, Rothstein HR. Reasons or excuses for avoiding meta-analysis in forest plots. BMJ. 2008; 336(7658):1413–5. https://doi.org/10.1136/bmj.a117 PMID: 18566080
70. Borenstein M, Hedges LV, Higgins JP, Rothstein HR. A basic introduction to fixed-effect and random-effects models for meta-analysis. Res Synth Methods. 2010; 1(2):97–111. https://doi.org/10.1002/jrsm.12 PMID: 26061376
71. Greenland S. Invited commentary: a critical look at some popular meta-analytic methods. Am J Epidemiol. 1994; 140(3):290–6. PMID: 8030632
72. Higgins JP, Thompson SG, Deeks JJ, Altman DG. Measuring inconsistency in meta-analyses. BMJ. 2003; 327(7414):557–60. https://doi.org/10.1136/bmj.327.7414.557 PMID: 12958120
73. Ioannidis JP, Patsopoulos NA, Evangelou E. Uncertainty in heterogeneity estimates in meta-analyses. BMJ. 2007; 335(7626):914–6. https://doi.org/10.1136/bmj.39343.408449.80 PMID: 17974687
74. Takkouche B, Cadarso-Suarez C, Spiegelman D. Evaluation of old and new tests of heterogeneity in epidemiologic meta-analysis. Am J Epidemiol. 1999; 150(2):206–15. PMID: 10412966
75. Sterne JA, Egger M. Funnel plots for detecting bias in meta-analysis: guidelines on choice of axis. J Clin Epidemiol. 2001; 54(10):1046–55. PMID: 11576817
76. Sterne JA, Gavaghan D, Egger M. Publication and related bias in meta-analysis: power of statistical tests and prevalence in the literature. J Clin Epidemiol. 2000; 53(11):1119–29. PMID: 11106885
77. Sterne JA, Sutton AJ, Ioannidis JP, Terrin N, Jones DR, Lau J, et al. Recommendations for examining and interpreting funnel plot asymmetry in meta-analyses of randomised controlled trials. BMJ. 2011; 343:d4002. https://doi.org/10.1136/bmj.d4002 PMID: 21784880
78. Egger M, Davey Smith G, Schneider M, Minder C. Bias in meta-analysis detected by a simple, graphical test. BMJ. 1997; 315(7109):629–34. PMID: 9310563
79. Higgins JP, Thompson SG. Controlling the risk of spurious findings from meta-regression. Stat Med. 2004; 23(11):1663–82. https://doi.org/10.1002/sim.1752 PMID: 15160401
80. Thompson SG, Higgins JP. How should meta-regression analyses be undertaken and interpreted? Stat Med. 2002; 21(11):1559–73. https://doi.org/10.1002/sim.1187 PMID: 12111920
81. Riley RD, Lambert PC, Abo-Zaid G. Meta-analysis of individual participant data: rationale, conduct, and reporting. BMJ. 2010; 340:c221. https://doi.org/10.1136/bmj.c221 PMID: 20139215
82. Anzures-Cabrera J, Higgins JP. Graphical displays for meta-analysis: An overview with suggestions for practice. Res Synth Methods. 2010; 1(1):66–80. https://doi.org/10.1002/jrsm.6 PMID: 26056093
83. Ford N, Mills EJ, Egger M. Editorial commentary: immunodeficiency at start of antiretroviral therapy: the persistent problem of late presentation to care. Clin Infect Dis. 2015; 60(7):1128–30. https://doi. org/10.1093/cid/ciu1138 PMID: 25516184
84. Vandenbroucke JP, Pearce N. Case-control studies: basic concepts. Int J Epidemiol. 2012; 41 (5):1480–9. https://doi.org/10.1093/ije/dys147 PMID: 23045208
85. Knol MJ, Vandenbroucke JP, Scott P, Egger M. What do case-control studies estimate? Survey of methods and assumptions in published case-control research. Am J Epidemiol. 2008; 168(9):1073– 81. https://doi.org/10.1093/aje/kwn217 PMID: 18794220
86. Chinn S. A simple method for converting an odds ratio to effect size for use in meta-analysis. Stat Med. 2000; 19(22):3127–31. PMID: 11113947
87. Cleophas TJ, Zwinderman AH. Transforming Odds Ratios into Correlation Coefficients. In: Modern Meta-Analysis Springer, Cham. 2017.
88. da Costa BR, Rutjes AW, Johnston BC, Reichenbach S, Nuesch E, Tonia T, et al. Methods to convert continuous outcomes into odds ratios of treatment response and numbers needed to treat: meta-epi-demiological study. Int J Epidemiol. 2012; 41(5):1445–59. https://doi.org/10.1093/ije/dys124 PMID: 23045205
89. Gielen C, Dekkers O, Stijnen T, Schoones J, Brand A, Klautz R, et al. The effects of pre-and postoperative fibrinogen levels on blood loss after cardiac surgery: a systematic review and meta-analysis. Interact Cardiovasc Thorac Surg. 2014; 18(3):292–8. https://doi.org/10.1093/icvts/ivt506 PMID: 24316606
90. Hartemink N, Boshuizen HC, Nagelkerke NJ, Jacobs MA, van Houwelingen HC. Combining risk estimates from observational studies with different exposure cutpoints: a meta-analysis on body mass index and diabetes type 2. Am J Epidemiol. 2006; 163(11):1042–52. https://doi.org/10.1093/aje/kwj141 PMID: 16611666
91. Greenland S, Longnecker MP. Methods for trend estimation from summarized dose-response data, with applications to meta-analysis. Am J Epidemiol. 1992; 135(11):1301–9. PMID: 1626547
92. Balshem H, Helfand M, Schunemann HJ, Oxman AD, Kunz R, Brozek J, et al. GRADE guidelines: 3. Rating the quality of evidence. J Clin Epidemiol. 2011; 64(4):401–6. https://doi.org/10.1016/j.jclinepi. 2010.07.015 PMID: 21208779
93. Morgan RL, Thayer KA, Bero L, Bruce N, Falck-Ytter Y, Ghersi D, et al. GRADE: Assessing the quality of evidence in environmental and occupational health. Environ Int. 2016; 92–93:611–6. https://doi.org/10.1016/j.envint.2016.01.004 PMID: 26827182
94. Wensley F, Gao P, Burgess S, Kaptoge S, Di Angelantonio E, Shah T, et al. Association between C reactive protein and coronary heart disease: mendelian randomisation analysis based on individual participant data. BMJ. 2011; 342:d548. https://doi.org/10.1136/bmj.d548 PMID: 21325005
95. Smith GD, Ebrahim S. Mendelian randomization: prospects, potentials, and limitations. Int J Epidemiol. 2004; 33(1):30–42. https://doi.org/10.1093/ije/dyh132 PMID: 15075143
96. Boef AG, le Cessie S, Dekkers OM. Mendelian randomization studies in the elderly. Epidemiology. 2015; 26(2):e15–6. https://doi.org/10.1097/EDE.0000000000000243 PMID: 25643110
97. Davies NM, Holmes MV, Davey Smith G. Reading Mendelian randomisation studies: a guide, glossary, and checklist for clinicians. BMJ. 2018; 362:k601. https://doi.org/10.1136/bmj.k601 PMID: 30002074
98. Lawlor DA, Tilling K, Davey Smith G. Triangulation in aetiological epidemiology. Int J Epidemiol. 2016; 45(6):1866–86. https://doi.org/10.1093/ije/dyw314 PMID: 28108528
99. Vandenbroucke JP. Commentary: ’Smoking and lung cancer’—the embryogenesis of modern epidemiology. Int J Epidemiol. 2009; 38(5):1193–6. https://doi.org/10.1093/ije/dyp292 PMID: 19773412
100. Maclure M. Demonstration of deductive meta-analysis: ethanol intake and risk of myocardial infarction. Epidemiol Rev. 1993; 15(2):328–51. PMID: 8174661
101. Renehan AG, Zwahlen M, Egger M. Adiposity and cancer risk: new mechanistic insights from epidemiology. Nature Rev Cancer. 2015; 15(8):484–98.
102. Greenland S. Epidemiologic measures and policy formulation: lessons from potential outcomes. Emerging Themes Epidemiol. 2005; 2:5.
103. Renehan AG, Tyson M, Egger M, Heller RF, Zwahlen M. Body-mass index and incidence of cancer: a systematic review and meta-analysis of prospective observational studies. Lancet. 2008; 371 (9612):569–78. https://doi.org/10.1016/S0140-6736(08)60269-X PMID: 18280327
104. Hernan MA, Taubman SL. Does obesity shorten life? The importance of well-defined interventions to answer causal questions. Int J Obes. 2008; 32 Suppl 3:S8–14.