Alternatives to randomisation in the evaluation of public-health interventions: Statistical analysis and causal inference

Journal of Epidemiology and Community Health

Volumn 65, Issue 7, 2011, Pages 576-581

  Cousens, S ^a   Hargreaves, J ^a   Bonell, C ^a   Armstrong, B ^a   Thomas, J ^b   Kirkwood, B R ^a   Hayes, R ^a  

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

^b UNIVERSITY OF LONDON   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

ACCURACY ASSESSMENT; DATA SET; DISEASE CONTROL; ESTIMATION METHOD; LITERATURE REVIEW; MATHEMATICAL ANALYSIS; MEASUREMENT METHOD; NUMERICAL MODEL; PUBLIC HEALTH; REGRESSION ANALYSIS; STATISTICAL ANALYSIS;

ARTICLE; EPIDEMIOLOGY; HEALTH SERVICES RESEARCH; HUMAN; METHODOLOGY; PUBLIC HEALTH;

BIAS (EPIDEMIOLOGY); CONFOUNDING FACTORS (EPIDEMIOLOGY); EPIDEMIOLOGIC METHODS; HEALTH SERVICES RESEARCH; HUMANS; PUBLIC HEALTH;

EID: 79960207894     PISSN: 0143005X     EISSN: 14702738     Source Type: Journal    
DOI: 10.1136/jech.2008.082610     Document Type: Article

References (40)

1. Bonell CP, Hargreaves JR, Cousens SN, et al. Evaluating the effects of public-health interventions: barriers to randomized trials and alternative options. J Epidemiol Community Health. In press.
2. Des Jarlais DC, Lyles C, Crepaz N. Improving the reporting quality of nonrandomized evaluations of behavioral and public health interventions: the TREND statement. Am J Public Health 2004;94:361-6.
3. von Elm E, Altman DG, Egger M, et al. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies. PLoS Med 2007;4:e296.
4. Altman DG. Better reporting of randomised controlled trials: the CONSORT statement. BMJ 1996;313:570-1.
5. Hernan MA. A definition of causal effect for epidemiological research. J Epidemiol Community Health 2004;58:265-71.
6. Halloran ME, Haber M, Longini IM Jr, et al. Direct and indirect effects in vaccine efficacy and effectiveness. Am J Epidemiol 1991;133:323-31.
7. Bradford Hill A. The Environment and Disease: Association or Causation? Proc R Soc Med 1965;58:295-300.
8. Rothman KJ, Greenland S, Lash TL. Modern epidemiology. New York: Lippincott, Williams & Wilkins, 2008.
9. Hutchinson P, Wheeler J. Advanced methods for evaluating the impact of family planning communication programs: evidence from Tanzania and Nepal. Stud Fam Plann 2006;3:169-86.
10. Greenland S, Pearl J, Robins JM. Causal diagrams for epidemiologic research. Epidemiology 1999;10:37-48.
11. Kirkwood B, Sterne J. Essential medical statistics 2nd edn. Oxford: Blackwell Publishing, 2003.
12. Fewell Z, Davey Smith G, Sterne JAC. The impact of residual and unmeasured confounding in epidemiologic studies: a simulation study. Am J Epidemiol 2007;166:646-55.
13. Kupper LL. Effects of the use of unreliable surrogate variables on the validity of epidemiologic research studies. Am J Epidemiol 1984;120:643-8.
14. D'Agostino RB Jr. Propensity score methods for bias reduction in the comparison of a treatment to a non-randomized control group. Stat Med 1998;1:2265-81.
15. Stürmer T, Joshi M, Glynn RJ, et al. A review of the application of propensity score methods yielded increasing use, advantages in specific settings, but not substantially different estimates compared with conventional multivariable methods. J Clin Epidemiol 2006;59:437-47.
16. Peduzzi P, Concato J, Kemper E, et al. A simulation study of the number of events per variable in logistic regression analysis. J Clin Epidemiol 1996;49:1373-9.
17. Rosenbaum PR, Rubin DB. Constructing a control group using multivariate matched sampling methods that incorporate the propensity score. Am Stat 1985;39:33-8.
18. Cepeda MS, Boston R, Farrar JT, et al. Comparison of logistic regression versus propensity score when the number of events is low and there are multiple confounders. Am J Epidemiol 2003;158:280-7.
19. Shah BR, Laupacis A, Hux JE, et al. Propensity score methods gave similar results to traditional regression modeling in observational studies: a systematic review. J Clin Epidemiol 2005;58:550-9.
20. Stukel TA, Fisher ES, Wennberg DE, et al. Analysis of observational studies in the presence of treatment selection bias: effects of invasive cardiac management on AMI survival using propensity score and instrumental variable methods. JAMA 2007;29:278-85.
21. Hernan MA, Hernandez-Diaz S, Werler MM, et al. Causal knowledge as a prerequisite for confounding evaluation: an application to birth defects epidemiology. Am J Epidemiol 2002;155:176-84.
22. Winkelmayer WC, Kurth T. Propensity scores: help or hype? Nephrol Dial Transplant 2004;19:1671-3.
23. Hernan MA, Robins JM. Instruments for causal inference: an epidemiologist's dream? Epidemiology 2006;1:360-72.
24. Angrist JD, Imbens GW, Rubin DB. Identification of causal effects using instrumental variables. J Am Stat Assoc 1996;91:444-55.
25. Martens EP, Pestman WR, de Boer A, et al. Instrumental variables: application and limitations. Epidemiology 2006;1:260-7.
26. Newhouse JP, McClellan M. Econometrics in outcomes research: the use of instrumental variables. Annu Rev Public Health 1998;19:17-34.
27. Senn S. Change from baseline and analysis of covariance revisited. Stat Med 2006;25:4334-344.
28. Lord FM. A paradox in the interpretation of group comparisons. Psychol Bull 1967;68:304-5.
29. Wainer H, Brown LM. Two statistical paradoxes in the interpretation of group differences: illustrated with medical school admission and licensing data. Am Stat 2004;58:117-23.
30. Frost C, Thompson SG. Correcting for regression dilution bias: comparison of methods for a single predictor variable. J R Stat Soc Ser A 2000;163:173-89.
31. Pettifor AE, Kleinschmidt I, Levin J, et al. A community-based study to examine the effect of a youth HIV prevention intervention on young people aged 15-24 in South Africa: results of the baseline survey. Trop Med Int Health 2005;10:971-80.
32. Cornfield J. Randomization by group: a formal analysis. Am J Epidemiol 1978;108:100-2.
33. Victora CG, Habicht JP, Bryce J. Evidence-based public health: moving beyond randomized trials. Am J Public Health 2004;94:400-5.
34. Shadish WR, Cook TD, Campbell DT. Quasi-experiments: interrupted time-series designs. Experimental and quasi-experimental designs for generalized causal inference Boston: Houghton Mifflin, 2001.
35. Biglan A, Ary D, Wagenaar AC. The value of interrupted time-series experiments for community intervention research. Prev Sci 2000;1:31-49.
36. Grijalva CG, Nuorti JP, Arbogast PG, et al. Decline in pneumonia admissions after routine childhood immunisation with pneumococcal conjugate vaccine in the USA: a time-series analysis. Lancet 2007;369:1179-86.
37. Clancy L, Goodman P, Sinclair H, et al. Effect of air-pollution control on death rates in Dublin, Ireland: an intervention study. Lancet 2002;360:1210-14.
38. Perez A, Dennis RJ, Rodriguez B, et al. An interrupted time series analysis of parenteral antibiotic use in Colombia. J Clin Epidemiol 2003;56:1013-20.
39. Brumback B, Ryan L, Schwartz J, et al. Transitional regression models, with application to environmental time series. J Am Stat Assoc 2000;95:16-27.
40. Cole SR, Hernan MA, Robins JM, et al. Effect of highly active antiretroviral therapy on time to acquired immunodeficiency syndrome or death using marginal structural models. Am J Epidemiol 2003;158:687-94.