In a preference RCT (Brewin and Bradley, 1989; McPherson and Britton, 1999; Pocock and Elbourne, 2000), a pool of eligible patients is first asked to indicate whether they have a preference among the treatments being compared. Those who have a preference are given that treatment. Those expressing no preference are randomized to a treatment arm as in a traditional RCT. If the pool of patients is sufficiently large, the design allows three sets of comparisons to be made among the treatments: (1) the effectiveness of different treatments among the randomized patients (which is the same as that in a traditional RCT); (2) the effectiveness of different treatments in those who chose those treatments; and (3) the effectiveness of a specific treatment in those randomized to it compared with the effectiveness in those who chose it. This analysis provides a stronger base from which to make inferences about the effects of treatments in routine daily practice, when patients typically receive a particular treatment on the basis of their preferences.

Wennberg and colleagues (1993) describe a pilot preference RCT in the atricle, Outcomes Research, PORTs, and Health Care Reform. The currently funded NIH Spine Patient Outcomes Research Trial (SPORT), which is in the final stages of recruiting, is another example of this design.

This type of study design may be useful for the study of many CAM modalities for which therapies are widely presumed by practitioners and the lay public to be safe and effective and patients may have existing preferences either for or against a specific therapy.

Observational and cohort studies involve the identification of patients who are eligible for study and who may receive a specified treatment but who may not choose the therapy received as part of the study. Problems with the inferences about effectiveness that can be drawn from observational studies are well known, but in some instances data from these studies may be the only or the best data available. One of the most well-known and recent examples of this comes from the Women’s Health Initiative (WHI). In response to observational data that hormone supplements may improve a woman’s health peri- and postmenopause, WHI prospectively evaluated the benefits and risks to women of taking hormones during menopause and concluded that the overall health risks exceeded the benefits (Rossouw et al., 2002).

The problems with causal inferences in studies with these designs mainly have to do with the possibility that unmeasured patient characteristics, not balanced by random assignment to treatment, may be the true cause of any effects observed (Little and Rubin, 2000). Methods that can be used to control for measured characteristics (e.g., analysis of covariance, linear

Front Matter (R1-R22)

Executive Summary (1-12)

1 Introduction (13-33)

2 Prevalence, Cost, and Patterns of CAM Use (34-73)

3 Contemporary Approaches to Evidence of Treatment Effectiveness: A Context for CAM Research (74-107)

4 Need for Innovative Designs in Research on CAM and Conventional Medicine (108-128)

5 State of Emerging Evidence on CAM (129-167)

6 An Ethical Framework for CAM Research, Practice, and Policy (168-195)

7 Integration of CAM and Conventional Medicine (196-225)

8 Educational Programs in CAM (226-252)

9 Dietary Supplements (253-277)

10 Conclusion (278-282)

Appendix A: CAM Therapies, Practices, and Systems (283-292)

Appendix B: Consortium of Academic Health Centers for Integrative Medicine (293-294)

Appendix C: Table 1-1 List of Abbreviations (295-295)

Appendix D: Liaison Panel Organizations (296-297)

Appendix E: Model Guidelines for the Use of Complementary and Alternative Therapies in Medical Practice (298-306)

Appendix F: National Center for Complementary and Alternative Medicine Research Centers (307-309)

Appendix G: Public Meetings (310-318)

Appendix H: Committee Biosketches (319-326)

Index (327-338)