User Tools

Site Tools


observational_studies

Observational studies

Austin PC. The use of propensity score methods with survival or time-to-event outcomes: reporting measures of effect similar to those used in randomized experiments. Stat Med 2014;33:1242–1258.

Bender R, Lange S. Adjusting for multiple testing: when and how? J Clin Epidemiol 2001; 54: 343–9.

Christenfeld NJS, Sloan RP, Carrol D, Greenland S. Risk factors, confounding, and the illusion of statistical control. Psychosom Med 2004;66:868-875.

Cole SR, Platt RW, Schisterman EF, Haitao C, Westreich D, Richardson D, Poole C. Illustrating bias due to conditioning on a collider. Int J Epidemiol 2010;39:417-420.

Freemantle N, Marston L, Walters K, Wood J, Reynolds MR, Petersen I. Making inferences on treatment effects from real world data: propensity scores, confounding by indication, and other perils for the unwary in observational research. BMJ 2013;347:f6409

Glymour MM, Weuve J, Berkman LF, et al. When is baseline adjustment useful in analyses of change? An example with education and cognitive change. Am J Epidemiol 2005;162:267-78 (see also Cologne B. RE: When is baseline adjustment useful in analyses of change? An example with education and cognitive change. Am J Epidemiol 2006;164:1138-1140).

Greenland S. Quantifying biases in causal models: classical confounding vs collider-stratification bias. Epidemiology 2003; 14:300–306.

Greenland S, Pearce N. Statistical foundations for model-based adjustments. Annu Rev Public Health 2015;36:89-108.

Hanley JA, Foster BJ. Avoiding blunders involving ‘immortal time’. Int J Epidemiol 2014;43:949-961.

Hernán MA, Hernández-Díaz S, Werler MM, Mitchell AA. Causal knowledge as a prerequisite for confounding evaluation: An application to birth defects epidemiology. Am J Epidemiol 2002;155:176-184.

Joseph KS, Mehrabadi A, Lisonkova S. Confounding by Indication and Related Concepts. Current Epidemiology Reports 2014;1:1-8.

Kahan BC. Accounting for centre-effects in multicentre trials with a binary outcome – when, why, and how? BMC Medical Research Methodology 2014,14:20.

King G, Nielsen R. Why Propensity Scores Should Not Be Used for Matching. Political Analysis 2019;27:1-20. DOI: 10.1017/pan.2019.11

Malek MM, Berger DE, Coburn JW. On the inappropriateness of stepwise regression for model building and testing. Eur J Appl Physiol 2007;101:263-264.

Pan W, Bai H. Propensity score methods for causal inference: an overview. Behaviormetrika 2018;45:317–334. Pearl J. Remarks on the method of propensity score. Stat Med 2009;28:1415–1424.

Ranstam J, Cook JA. Causal relationship and confounding in statistical models. Br J Surg. 2016 Oct;103(11):1445-6.

Ripollone JE, Huybrechts KF, Rothman KJ, Ferguson RE, Franklin JM. Implications of the Propensity Score Matching Paradox in Pharmacoepidemiology. Am J Epidemiol 2018 May 10. doi: 10.1093/aje/kwy078.

Sauer B, Brookhart MA, Roy JA, VanderWeele TJ. Developing a Protocol for Observational Comparative Effectiveness Research: A User’s Guide. Rockville (MD), Agency for Healthcare Research and Quality (US); 2013 Jan. Publication No.: 12(13)-EHC099. Chapter 7. Covariate Selection.

Schisterman EF, Cole SR, Platt RW. Overadjustment bias and unnecessary adjustment in epidemiological studies. Epidemiol 2009;20:488-495.

Shrier I, Platt RW. Reducing bias through directed acyclic graphs. BMC Med Res Methodol 2008;8:70.

Shmueli G. To Explain or to Predict? Statistical Science 2010;25:289–310.

Signorello LB, McLaughlin JK, Lipworth L, Friis S, Sørensen HT, Blot WJ. Confounding by indication in epidemiologic studies of commonly used analgesics. Am J Ther 2002;9:199-205.

Schuster T, Lowea WK, Platt RW. Propensity score model overfitting led to inflated variance of estimated odds ratios. J Clin Epidemiol 2016 September, Doi:10.1016/j.jclinepi.2016.05.017.

Sjölander A. Propensity scores and M-structures. Stat Med 2009;28:1416–1420.

Sjölander A, Greenland S. Ignoring the matching variables in cohort studies – when is it valid and why? Stat Med 2013;32:4696-4708.

Thompson SG. Modelling in matched case-control studies in epidemiology. The Statistician 1986;35:237-244. Tu Y-K, Gunnell D, Gilthorpe MS. Simpson’s Paradox, Lord’s Paradox, and Suppression Effects are the same phenomenon – the reversal paradox. Emerging Themes in Epidemiology 2008, 5:2.

Westreich D, Greenland S. The table 2 fallacy: presenting and interpreting confounder and modifier coefficients. Am J Epidemiol 2013;177:292-298.

observational_studies.txt · Last modified: 2020/05/24 06:59 by ranstam