Conventional statistical methods assume that the outcomes for individual patients are independent of one another, so that each patient contributes new additional information about the relationship of an outcome to the two interventions. When the care given by different providers (either institutions or doctors) results in different outcomes, outcomes are said to be “clustered.” When a study takes place in several different institutions, which is common practice, it is possible that care provided at each of the institutions differs, so that knowledge of what institution is providing the care allows one to predict the outcomes for the patients. Under these circumstances, the outcome for each study patient is not independent of those for other patients at that institution, and the assumptions of conventional statistical methods do not hold. The assumption of independence when outcomes are related means that measures of variability, such as the 95 percent confidence interval, appear to be more precise than they really are. The true 95 percent confidence interval is wider than it appears to be from the findings of the study, which means that an apparent true difference may be consistent with random variation between the study patients who receive the intervention and those who do not. The use of an appropriate statistical design can account for the effects of clustering, so that the statistical power of the study and the widths of resulting confidence intervals are accurately known. Widening of the 95 percent confidence interval after this statistical adjustment is made means that clustering of outcomes is present. Clustering of outcomes makes it more difficult to conclude that a difference between two interventions is due to the interventions rather than to chance variation.

Clustering of outcomes is especially important in studies in which the deployment of an intervention may vary from practitioner to practitioner or from study site to study site. CAM experts commonly cite the special role of the practitioner as a characteristic of CAM interventions, so it is important to know when outcomes vary in this way. If adjustment for clustering widens the confidence interval, the clustering of outcomes by provider or by site may be occurring. If some providers or sites are doing better (or worse), researchers have an opportunity to discover what makes certain providers or sites more effective.

The U.S. Preventive Services Task Force (1996) and groups organized to develop treatment guidelines have adopted a concept of “levels of evidence” or a “hierarchy of evidence” that they use to rate the strength of the

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)