Patients who are ill (and their family members) often want to hear details about other individuals with similar illnesses who have used a treatment and recovered. If the treatment appears to be safe and there is little risk of harm, evidence from these stories frequently appears to be sufficient for them to decide to use the treatment. Patients may interpret this as a sign that the treatment is effective, and to them this evidence is important and relevant for both CAM and conventional medicine. The skilled and informed clinician will need to place this type of evidence into an individual and patient-centred context with respect for all the evidence available from both qualitative and quantitative investigations.

Health care practitioners (conventional doctors, CAM practitioners, nurses, physical therapists) also want to know what the likelihood or probability is that a patient will recover or be harmed based on a series of similar patients who have received that treatment in clinical practice. Such information may come from case series or clinical outcomes studies or RCTs (Guthlin et al., 2004, Witt et al., 2005a and Witt et al., 2005b). They also want to know about the safety, complications, complexity and cost of using the therapy, and this information comes from the collection of careful safety data and health economic analyses.

Clinical scientists will value the same type of evidence as clinicians but will often look at the data differently because of their research training and skills. They often want to know how much improvement occurred in a group who received the treatment compared to another group who did not receive it or a group that received a placebo. If 80% of patients who received a treatment got better, do 60% of similar patients get better just from coming to the doctor? This type of comparative evidence can only come from RCTs, which is the major area of interest for most clinical researchers. These types of studies can include a placebo control but sometimes are pragmatic studies, which compare two treatments or have a non-treatment arm or other types of controls.

Laboratory scientists focus on discovering mechanisms of action. Basic science facilitates understanding of underlying mechanisms and allows for greater precision testing in more highly controlled, (and artificial) environments.

Those in charge of determining public policy often need aggregate ‘proof’ that a practice is safe, effective and cost-effective. This usually involves a health economic perspective within a complex process of treatment evaluation. Systematic reviews, meta-analyses and health services research including randomized trials and outcome studies provide this type of evidence. Health services research also provides data that evaluates the cost, feasibility, utility and safety of delivering treatments within existing delivery systems.

While day-to-day decision-making is more complex than the brief summaries above, the point is that different audiences have legimate evidence needs that cannot be accommodated by a ‘one size fits all’ strategy. The CAM researcher must keep in mind the need for quality research in a variety of domains and attend carefully to the audience and use of the results of their research once collected and interpreted (Callahan, 2002 and Jonas and Callahan, 2002). As stated by Ian Coulter, there is a difference between the academic creation of information in EBM and the clinical application of knowledge in EBP and investigators should keep EBP and the patient perspective in mind when designing and interpreting research (Coulter & Khorsan 2008). In addition, more social science research is needed on models, applications and dynamics of EBM to help guide that interpretation (Mykhalovskiy & Weir 2004).

What are the elements of a research strategy that matches this pluralistic reality? How can we build an evidence base that has both rigour and relevance? In the diverse areas that CAM (or indeed conventional primary care) encompasses, at least six major knowledge domains are relevant. Within these domains are variations that allow for precise exploration of differing aspects of both CAM and conventional health care practice.

In conventional medicine, knowledge domains often follow a hierarchical strategy with sophisticated evidence-based synthesis at its acme (Sackett et al. 1991) (www.cochrane.org). The hierarchical strategy can be graphically depicted by a pyramid (Figure 1.1). At the base of this hierarchy are case series and observational studies. This is then followed by cohort studies in which groups of patients or treatments are observed over time in a natural way, often without any inclusion or exclusion criteria. Randomized studies come next. Here, the decision about which treatments an individual receives is generated by a random allocation algorithm. This usually involves the comparison of two or more treatments with or without a sham or placebo treatment. If several of those studies are then pooled they produce a meta-analysis. This is a summary of the true effect size of an intervention against control.

There are several alternative strategies to the evidence hierarchy that attempt to balance the risk of the poor relevance it produces. One of these is called the ‘evidence house’ and seeks to lateralize the main knowledge domains in science in order to highlight their purposes. It does this by aligning methodologies that isolate specific effects (those with high internal validity potential) and those that seek to explore utility of practices in real clinical settings (those with high external validity potential) (Figure 1.2). For example, mechanisms, attribution and research synthesis each seeks to isolate specific effects whereas utility is investigated though methods that assess meaning, association and cost (both financial cost and safety). Each of these knowledge domains has its own goals, methodology and quality criteria. The corresponding methods for each of the six domains are: for isolating effects, laboratory techniques, RCTs, meta-analysis; and for utility testing, qualitative research methods, observational methods, health services (including economic) research. Of course, variations in these methods can often be mixed in single studies, producing ‘mixed-methods’ research (such as qualitative studies nested inside RCTs) that seek to address dual goals. This strategy can be conceptualized in what has been called an ‘evidence house’ (Jonas 2001; Jonas, 2005). In this strategy the knowledge domains are placed in relationship to each other and the primary audience they serve. The evidence house helps balance the misalignment produced by the evidence hierarchy, by linking the method to the goal to the primary audience. The six major knowledge domains of the evidence house are briefly described below.

The circular model explores the relationship of the clinical methods used in the middle two domains of the evidence house. It assumes that there is no such thing as ‘an entirely true effect size’ but that the effect sizes vary based on patient recruitment, specific therapists (Kim et al. 2006) and the environment (context) in which that therapy is provided (Hyland et al. 2007). This suggests that we may have difficulty in completely controlling for bias and confounding when we have no real understanding of the underlying mechanisms of the treatments being delivered. In these circumstances further development of a circular model may allow us to arrive at an approximate estimate of reality as it relates to complex pictures of chronic disease within the community (Walach et al. 2006) (Figure 1.3).

In a circular strategy the principle is that information from all sources is used to establish consensus around the most appropriate therapeutic approaches in a particular therapeutic environment. This allows development of best practices even when there is little RCT evidence. We have to bear in mind that clinicians are obliged to treat people even when they present with conditions for which the treatment is not supported by a substantial body of research (Flower & Lewith 2007). The circular strategy also actively involves patients in the decision-making process, which could be important since there is evidence that empowering patient decisions has an impact on outcome (Kalauokalani et al., 2001, Bausell et al., 2005 and Linde et al., 2007a). Acupuncture, as practised within a western European medical environment, is a prime example of this, as the debate about the evidence from both the German Acupuncture Trials (GERAC) and Acupuncture Research Trial (ART) studies illustrates (Linde et al., 2005, Melchart et al., 2005, Witt et al., 2005a, Witt et al., 2005c, Witt et al., 2006, Brinkhaus et al., 2006 and Scharf et al., 2006).

By and large most complementary treatments are widely available, have often been in use for a long time and in some countries even have a special legal status. Consequently we may wish (for reasons of public health and pragmatism) to evaluate the safety of the intervention and the quality of the practitioners providing that intervention, before conducting research on theoretical or specific biochemical mechanisms that may be triggered by a particular product or practice. These strategic differences between the research approaches that may need to be applied to conventional and complementary medicine are summarized by Fonnebo et al. (2007) (Figure 1.4).

For example, a large pharmaceutical company will develop a new pharmaceutical through a phased approach that will involve both in vivo and in vitro laboratory experiments before preliminary human evaluation of a new chemical agent. This contrasts dramatically with the evaluation of a complementary medical intervention such as homeopathy and acupuncture, where opinions and beliefs about its veracity, effect and safety are widely debated and diverse. Inevitably this influences the public’s expectation and opinion about a particular intervention which may have an impact on any systematic evaluation of the therapy and its equipoise within a clinical trial.

New, more sophisticated and pluralistic approaches to EBM incorporating the full spectrum of the information needed for making clinical decisions are being developed for all medical interventions; examples include the RAND ‘appropriateness’ approaches (Coulter et al. 1995), the Agency for Health Care Research and Quality’s (AHRQ) efforts on consumer or patient-centred evidence evaluations (Clancy and Cronin, 2005 and Nilsen et al., 2006), decision models and new ‘synthesis’ approaches (Haynes 2006) and ‘care pathway’ applications of EBM (Astin et al. 2006), as well as the comparative effectiveness research by the Institute of Medicine (Sox 2009). All of these approaches have their own strengths and weaknesses and call for systematic incorporation of concepts such as goals, problem formulation and values into the formulation of consistent and customized EBM decisions that account for the complexity of information needed in the clinical setting. Taken together, it might be worthwhile distinguishing some of the more academic debates around EBM from the practical applications by defining EBP (Coulter & Khorsan 2008). We believe that EBP requires, at a minimum, the establishment of quality standards for each of the information domains described in Figures 1.2 and 1.3 and may be thought of as ‘best research guidelines’.