Intention-to-Treat Analysis but for Treatment Intention: How should Consumer Product Randomized Controlled Trials be Analyzed?

Authors

  • Rolf Weitkunat Philip Morris Products SA, Quai Jeanrenaud 5, 2000 Neuchâtel, Switzerland
  • Gizelle Baker Philip Morris Products SA, Quai Jeanrenaud 5, 2000 Neuchâtel, Switzerland
  • Frank Lüdicke Philip Morris Products SA, Quai Jeanrenaud 5, 2000 Neuchâtel, Switzerland

DOI:

https://doi.org/10.6000/1929-6029.2016.05.02.3

Keywords:

Randomization, self-selection, intention-to-treat, actual use, consumer products

Abstract

Background: Experimental study design, randomization, blinding, control, and the analysis of such data according to the intention-to-treat (ITT) principle are de-facto “gold standards” in pharmacotherapy research. While external treatment allocation under conditions of medical practice is conceptually reflected by in-study randomization in randomized controlled trials (RCTs) of therapeutic drugs, actual product use is based on self-selection in a consumer product setting.

Discussion: With in-market product allocation being consumer-internal, there is no standard against which protocol adherence can be attuned, and the question arises, as to whether compliance-based analysis concepts reflect the real-world effects of consumer products.

Summary: The lack of correspondence between RCTs and consumer market conditions becomes evident by the fact that even if, theoretically, all data would be available from all members of the real-world target population, it would be impossible to calculate either an ITT or a per-protocol effect. This renders the calculation of such estimates meaningless in consumer product research contexts.

References

Campbell DT, Stanley JC. Experimental and quasi-experimental designs for research. Chicago: Rand-McNally 1963.

Rubin DB. Estimating causal effects of treatments in randomized and nonrandomized treatments. J Educ Psychol 1974; 66: 688-701. http://dx.doi.org/10.1037/h0037350 DOI: https://doi.org/10.1037/h0037350

Rosenbaum P, Rubin DB. The central role of the propensity score in observational studies for causal effects. Biometrika 1983; 70: 41-55. http://dx.doi.org/10.1093/biomet/70.1.41 DOI: https://doi.org/10.1093/biomet/70.1.41

Fitelson B, Hitchcock C. Probabilistic measures of causal strength. In: Illari PM, Russo, R, Willamson J, editors. Causality in the sciences. Oxford: Oxford University Press 2011; pp. 600-27. http://dx.doi.org/10.1093/acprof:oso/9780199574131.003.0029 DOI: https://doi.org/10.1093/acprof:oso/9780199574131.003.0029

Cartwright N. Knowing what we are talking about: why evidence doesn’t always travel. Evid Policy 2013; 9: 97-112. http://dx.doi.org/10.1332/174426413X662581 DOI: https://doi.org/10.1332/174426413X662581

Goetghebeur E, Loeys T. Beyond intention to treat. Epidemiol Rev 2002; 24: 85-90. http://dx.doi.org/10.1093/epirev/24.1.85 DOI: https://doi.org/10.1093/epirev/24.1.85

Ten Have TR, Normand SLT, Marcus SM, Brown CH, Lavori P, Duan N. Intent-to-treat vs. non-intent-to-treat analyses under treatment non-adherence in mental health randomized trials. Psychiatr Ann 2008; 38: 772-83. http://dx.doi.org/10.3928/00485713-20081201-10 DOI: https://doi.org/10.3928/00485713-20081201-10

Cartwright N. A philosopher’s view of the long road from RCTs to effectiveness. Lancet 2011; 377: 1400-1. http://dx.doi.org/10.1016/S0140-6736(11)60563-1 DOI: https://doi.org/10.1016/S0140-6736(11)60563-1

West SG, Duan N, Pequegnat W, Gaist P, Des Jarlais DC, Holtgrave D, Szapocznik J, Fishbein M, Rapkin B, Clatts M, Mullen PD. Alternatives to the randomized controlled trial. Am J Public Health 2008; 98: 1359-66. http://dx.doi.org/10.2105/AJPH.2007.124446 DOI: https://doi.org/10.2105/AJPH.2007.124446

Hudgens G, Gilbert PB, Self SG. Endpoints in vaccine trials. Stat Methods Med Res 2004; 13: 1-26. http://dx.doi.org/10.1191/0962280204sm356ra DOI: https://doi.org/10.1191/0962280204sm356ra

Dawid AP. Conditional independence in statistical theory. J Roy Statist Soc B 1979; 41: 1-31. DOI: https://doi.org/10.1111/j.2517-6161.1979.tb01052.x

Dawid AP. The decision-theoretic approach to causal inference. In: Berzuini C, Dawid P, Bernardinelli L, editors. Causality. Statistical perspectives and applications. Chichester: Wiley 2012; pp. 25-42. http://dx.doi.org/10.1002/9781119945710.ch4 DOI: https://doi.org/10.1002/9781119945710.ch4

Begg C. Ruminations on the intent-to-treat principle. Control Clin Trials 2000; 21: 241-3. http://dx.doi.org/10.1016/S0197-2456(00)00050-7 DOI: https://doi.org/10.1016/S0197-2456(00)00050-7

Armijo-Olivo S, Warren S, Magee, D. Intention to treat analysis, compliance, drop-outs and how to deal with missing data in clinical research: a review. Phys Ther Rev 2009; 14: 36-49. http://dx.doi.org/10.1179/174328809X405928 DOI: https://doi.org/10.1179/174328809X405928

Robins JM, Greenland S. Adjusting for differential rates of PCP prophylaxis in high- versus low-dose AZT treatment arms in and AIDS randomized trial. J Am Stat Assoc 1994; 89: 737-49. http://dx.doi.org/10.1080/01621459.1994.10476807 DOI: https://doi.org/10.1080/01621459.1994.10476807

Altman D. Missing outcomes in randomized trials: addressing the dilemma. Open Med 2009; 3: 2.

National Research Council. The prevention and treatment of missing data in clinical trials. Washington: National Academies Press 2010.

Wright CC, Sim J. Intention-to-treat approach to data from randomized controlled trials: A sensitivity analysis. J Clin Epidemiol 2003; 56: 833-42. http://dx.doi.org/10.1016/S0895-4356(03)00155-0 DOI: https://doi.org/10.1016/S0895-4356(03)00155-0

Hernán MA, Alonso A, Logan R, Grodstein F, Michels KB, Stampfer MJ, Willett WC, Manson JE, Robins JM. Observational studies analyzed like randomized experiments: an application to postmenopausal hormone therapy and coronary heart disease. Epidemiology 2008; 19: 766-79. http://dx.doi.org/10.1097/EDE.0b013e3181875e61 DOI: https://doi.org/10.1097/EDE.0b013e3181875e61

Hernán MA, Robins JM. Causal inference. Boca Raton: Chapman & Hall/CRC 2013.

Weitkunat R, Lee PN, Baker G, Sponsiello-Wang Z, González-Zuloeta Ladd AM, Lüdicke F. A novel approach to assess the population health impact of introducing a Modified Risk Tobacco Product. Regul Toxicol Pharmacol 2015; 72: 87-93. http://dx.doi.org/10.1016/j.yrtph.2015.03.011 DOI: https://doi.org/10.1016/j.yrtph.2015.03.011

Rubin DB. Causal inference using potential outcomes: Design, modeling, decisions. J Am Stat Assoc 2005; 100: 322-31. http://dx.doi.org/10.1198/016214504000001880 DOI: https://doi.org/10.1198/016214504000001880

Hoxby CM, Murarka S. Methods of assessing the achievement of students in charter schools. In: Berends M, Springer MG, Walberg HJ, editors. Charter school outcomes. New York: Lawrence Erlbaum 2008; pp. 7-38. DOI: https://doi.org/10.4324/9781315095806-2

Feinman RD. Intention-to-treat. What is the question? Nutr Metab 2009; 6: 1. http://dx.doi.org/10.1186/1743-7075-6-1 DOI: https://doi.org/10.1186/1743-7075-6-1

Sackett DL, Wennberg JE. Choosing the best research design for each question. BMJ 1997; 315: 1636. http://dx.doi.org/10.1136/bmj.315.7123.1636 DOI: https://doi.org/10.1136/bmj.315.7123.1636

Welsh AW. Randomised controlled trials and clinical maternity care: moving on from intention-to-treat and other simplistic analyses of efficacy. BMC Pregnancy Childbirth 2013; 13: 15. http://dx.doi.org/10.1186/1471-2393-13-15 DOI: https://doi.org/10.1186/1471-2393-13-15

Sackett DL. Rules of evidence and clinical recommendations on the use of antithrombotic agents. Chest 1989; 95(Suppl 2): 2-4. http://dx.doi.org/10.1378/chest.95.2_Supplement.2S DOI: https://doi.org/10.1378/chest.95.2_Supplement.2S

Shrier I, Steele RJ, Verhagen E, Herbert R, Riddell CA, Kaufman JS. Beyond intention to treat: What is the right question? Clin Trials 2014; 11: 28-37. http://dx.doi.org/10.1177/1740774513504151 DOI: https://doi.org/10.1177/1740774513504151

Pocock SJ, Abdalla M. The hope and the hazards of using compliance data in randomized controlled trials. Stat Med 1998; 17: 303-17. http://dx.doi.org/10.1002/(SICI)1097-0258(19980215)17:3<303::AID-SIM764>3.0.CO;2-0 DOI: https://doi.org/10.1002/(SICI)1097-0258(19980215)17:3<303::AID-SIM764>3.0.CO;2-0

Thabane L, Mbuagbaw L, Zhang S, Samaan Z, Marcucci M, Ye C, Thabane M, Giangregorio L, Dennis B, Kosa D, Debono VB, Dillenburg R, Fruci V, Bawor M, Lee J, Wells G, Goldsmith CH. A tutorial on sensitivity analyses in clinical trials: the what, why, when and how. BMC Med Res Methodol 2013; 13: 92. http://dx.doi.org/10.1186/1471-2288-13-92 DOI: https://doi.org/10.1186/1471-2288-13-92

Downloads

Published

2016-06-02

How to Cite

Weitkunat, R., Baker, G., & Lüdicke, F. (2016). Intention-to-Treat Analysis but for Treatment Intention: How should Consumer Product Randomized Controlled Trials be Analyzed?. International Journal of Statistics in Medical Research, 5(2), 90–98. https://doi.org/10.6000/1929-6029.2016.05.02.3

Issue

Section

General Articles