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Research Article

The importance of having a conceptual stage when reporting non-randomized studies

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Pages 9-18 | Received 12 Jan 2020, Accepted 04 Apr 2021, Published online: 30 Apr 2021

References

  • Cochran WG. The planning of observational studies of human populations (with discussion). J Royal Statis SocSeries Gen. 1965;128:234.
  • Tarr A, Imai K. Estimating average treatment effects with support vector machines. arXiv:2102.11926. 2021.
  • US Department of Health and Human Services, Food and Drug Administration, Center for Drug Evaluation and Research, Center for Biologics Evaluation and Research. Guidance for industry E9 statistical principles for clinical trials (section 5.1). 1998.
  • Gautret P, Lagier JC, Parola P, et al. Hydroxychloroquine and azithromycin as a treatment of COVID-19: results of an open-label non-randomized clinical trial. Int J Antimicrob Agents. 2021;57(1):106243. doi:10.1016/j.ijantimicag.2020.106243.
  • Bind MA, Rubin DB. Bridging observational studies and randomized experiments by embedding the former in the latter. Stat Methods Med Res. 2019;28(7):1958–1978. doi:10.1177/0962280217740609.
  • Rubin DB. For objective causal inference, design trumps analysis. Ann Appl Stat. 2008;2:808.
  • Rubin DB. The design versus the analysis of observational studies for causal effects: parallels with the design of randomized trials. Stat Med. 2007;26:20–36.
  • Freedman DA. Statistical models for causation: what inferential leverage do they provide? Eval Rev. 2006;30:691–713.
  • Freedman DA. Linear statistical models for causation: a critical review. Encyclopedia of statistics in behavioral science. 2005.
  • Sommer A, Leray E, Lee Y, et al. Assessing environmental epidemiology questions in practice with a causal inference pipeline: an investigation of the air pollution-multiple sclerosis relapses relationship. Stat Med. 2021a;40(6):1321–1335.
  • Sommer A, Peters A, Cyrys J, et al. A randomization-based causal inference framework for uncovering environmental exposure effects on human gut microbiota. bioRxiv. 2021b. doi:10.1101/2021.02.24.432662
  • Sommer A, Lee M, Bind MA. Comparing apples to apples: an environmental criminology analysis of the effects of heat and rain on violent crimes in Boston. Palgrave Commun. 2018;4:138. doi:10.1057/s41599-018-0188-3.
  • Zubizarreta J, Cerdá M, Rosenbaum P. Effect of the 2010 Chilean earthquake on posttraumatic stress: reducing sensitivity to unmeasured bias through study design. Epidemiology. 2013;24(1):79–87.
  • Frangakis CE, Brookmeyer RS, Varadhan R, et al. Methodology for evaluating a partially controlled longitudinal treatment using principal stratification, with application to a needle exchange program. J Am Stat Assoc. 2004;99:239–249.
  • Rojas-Rueda D, de Nazelle A, Teixido O, et al. Health impact assessment of increasing public transport and cycling use in Barcelona: a morbidity and burden of disease approach. Prev Med. 2013;57:573–579.
  • Rojas-Rueda D, de Nazelle A, Teixido O, et al. Replacing car trips by increasing bike and public transport in the greater Barcelona metropolitan area: a health impact assessment study. Environ Int. 2012;49:100–109.
  • Rojas-Rueda D, de Nazelle A, Tainio M, et al. The health risks and benefits of cycling in urban environments compared with car use: health impact assessment study. Br Med J. 2011;343:d4521.
  • Rubin DB. Randomization analysis of experimental data: the fisher randomization test comment. J Am Stat Assoc. 1980;75:591–593.
  • Holland PW. Causal inference, path analysis, and recursive structural equations models. Sociol Methodol. 1988;18:449–484.
  • Rubin DB. Bayesian analysis of a two-group randomized encouragement design. In: Dorans N, editor. Looking back. lecture notes in statistics. New York, NY: Springer; 2011. p. 55–65.
  • Zell E, Wang X, Yin L, et al. Bayesian causal inference: approaches to estimating the effect of treating hospital type on cancer survival in Sweden using principal stratification. 2013.
  • Angrist JD, Imbens GW, Rubin DB. Identification of causal effects using instrumental variables (with discussion). J Am Stat Assoc. 1996;91:444–472.
  • Rubin DB. Formal mode of statistical inference for causal effects. J Stat Plan Inf. 1990;25:279–292.
  • Schwartz J, Fong K, Zanobetti A. A national multicity analysis of the causal effect of local pollution, NO2, and PM2.5 on mortality. Environ Health Perspect. 2018;126:87004–87004.
  • Groulx N, Urch B, Duchaine C, et al. The pollution particulate concentrator (PoPCon): a platform to investigate the effects of particulate air pollutants on viral infectivity. Sci Total Environ. 2018;628-629:1101–1107.
  • Wang S, Zhang X, Chu H, et al. Impact of secondary organic aerosol exposure on the pathogenesis of human influenza virus (H1N1). AGU Fall Meeting Abstracts 2018:GH13B-0943. 2018.
  • Andric N. Exploring objective causal inference in case-noncase studies under the rubin causal model. 2015.
  • Rosenbaum PR. Design of observational studies. New York (NY): Springer New York; 2010.
  • Bind MA, Rubin DB. When possible, report a fisher-exact p-value and display its underlying null randomization distribution. Proc Natl Acad Sci USA. 2020;117(32):19151–19158. doi:10.1073/pnas.1915454117.
  • Harrington D, D’Agostino RB S, Gatsonis C, et al. New guidelines for statistical reporting in the journal. N Engl J Med. 2019;381:285–286.
  • Vandenbroucke JP, Erik VE, Altman DG, et al. Strengthening the reporting of observational studies in epidemiology (STROBE): explanation and elaboration. Epidemiology. 2007;18:805–835.