is consistent with chance, usually expressed as the “p value.” Outcome studies increasingly report the confidence interval of the absolute difference in outcomes related to two interventions. This method provides a graphic measure of the uncertainty in a conclusion. If the confidence interval of the difference includes 0 or a value that is very close to 0, the difference is not statistically significant or is of borderline statistical significance, respectively. If the lower limit of the confidence interval of the difference is far from 0, one can be sure that the difference itself is unlikely to be the product of a chance variation in the samples drawn from the same universe.

Confidence intervals enter into the interpretation of predictive models designed to identify clinical predictors of a response to a treatment, such as an element of a package of CAM interventions for a clinical problem. The coefficient of an interaction term has a confidence interval. If it includes 0, the interaction term is not a statistically significant predictor of the dependent variable (e.g., Drug A, salt intake, and end-of-study blood pressure, as in the earlier example).

Measurement error adds uncertainty. The inclusion of a measurement error widens the 95 percent confidence interval. Failure to take into account measurement error will lead one to overestimate precision and draw incorrect conclusions about differences in outcomes.

Efficacy studies mean, by common agreement, that the comparison of two technologies has taken place under strictly controlled conditions designed to show a difference if a difference is truly present. Typically, an efficacy study will exclude patients who are likely to die of diseases other than the target disease for the technology under study to maximize the information value of each death in characterizing the two technologies. The study population of an efficacy study is typically narrowly defined (and therefore relatively small), which means that the patients are very similar to one another and, therefore, that the results may not apply to a wider population. All measurements take place under optimum conditions, and the doctors interpreting the test results undergo special training so that they give the same interpretation, for example, to the same computed tomography scan, eliminating one source of measurement error. Typically, efficacy studies precede effectiveness studies, and the results are used as a “proof of

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)