Clinical practice guidelines (hereafter referred to as guidelines) are one of the foundations of efforts to improve healthcare. The modern age of guidelines began with a 1992 Institute of Medicine (IOM) report, which defined guidelines as ‘systematically developed statements to assist practitioner and patient decisions about appropriate healthcare for specific clinical circumstances’ [1]. In 1999, we authored a paper about methods to develop guidelines [2]. It covered: identifying and refining the subject area of the guideline; running guideline development groups; identifying and assessing the evidence; translating evidence into a clinical practice guideline; and reviewing and updating guidelines. Since it was published, the methods of guideline development have progressed both in terms of methods and necessary procedures and the broad context for clinical practice guidelines has changed.

To help users identify and choose guidelines there has been the emergence of guideline clearing houses (such as the US Agency for Healthcare Research and Quality (AHRQ) Guideline Clearing House, http://www.guideline.gov) that identify and systematically characterize guidelines on a number of domains and the development of robust guideline appraisal instruments such as the AGREE tool [3, 4]. There has been the appearance of large-scale guideline production organisations both at a national level (such as the UK National Institute for Health and Clinical Excellence or Scottish Intercollegiate Guidelines Network) and a condition level (such as the Ontario Cancer Guideline Program). There have also been relevant reports (that some of us have participated in) for the World Health Organisation [5] and professional societies (Schünemann HJ, Woodhead M, Anzueto A, Buist AS, MacNee W, Rabe KF, Heffner J. A guide for guidelines for professional societies and other developers of recommendations: an official American Thoracic Society (ATS) / European Respiratory Society (ERS) Workshop Report; in preparation). Such organizations and those interested in producing and using guidelines now have a high profile society in the Guidelines International Network (http://www.g-i-n.net/). Against this background it seems timely to, in a series of three articles, update and extend our earlier paper on the methods of developing clinical practice guidelines. This series is based on a background paper [6] we prepared for the IOM report ‘Clinical Practice Guidelines We Can Trust’ [7].

In the first paper, we discussed target audience(s) for guidelines, identifying topics for guidelines, guideline group composition, and the processes by which guideline groups function and the important procedural issue of conflicts of interest. In this second paper, we move on to discuss issues of identifying and synthesizing evidence: deciding what type of evidence and outcomes to include in guidelines; integrating values into a guideline; incorporating economic considerations; synthesis, grading, and presentation of evidence; and moving from evidence to recommendations. In the third paper, we will discuss the issues of: reviewing, reporting, and publishing guidelines; updating guidelines; and the two emerging issues of enhancing guideline implementability and how guidelines approach dealing with patients with co-morbid conditions.

Guidelines typically consider different clinical questions including: the identification of risk factors for conditions; diagnostic criteria for conditions; prognostic factors with and without treatment; the benefits and harms of different treatment options; the resources associated with different diagnostic or treatment options; and patients’ experiences of healthcare interventions. Different study designs provide the most reliable types of evidence for these different questions. Addressing this has implications for the conduct (searching, appraising, and synthesizing stages) of knowledge syntheses being undertaken to inform guideline recommendations. Important principles at this stage of guideline development include the need for guideline developers to make explicit decisions at the outset of the analytic process regarding the specific questions to be answered and the outcomes to be assessed, to have a clear understanding of the analytic logic of the recommendations, to use this model for keeping the analytic work of the group ‘on track,’ to be explicit about the types of evidence or opinion that support each component of the analytic logic, and to transmit this information clearly to the reader in the rationale statement of the guideline. Any model that achieves these organizational principles serves the purpose of an analytic framework [8–13].

The analytic framework of a guideline is a key element in guideline development. It is in this critical stage that a group defines which questions must be answered to arrive at a recommendation, which types of evidence and information are relevant to the analysis, and by what criteria that evidence will be evaluated. The analytic work encompasses the examination of scientific evidence, expert opinion, clinical experience, and other relevant information and the use of decision rules to translate that information into recommendations. The end product of the process is captured in the analytic logic of the guideline, the rationale for the recommendations.

The first step is to define the key questions. What information is required by the group to arrive at a recommendation? It begins with defining the criteria that must be met to convince the group that a clinical behavior should be advocated. The potential options depend on the viewpoint of the group and the nature of the topic. Some groups base the decision on current practice patterns or on opinions drawn from consensus or clinical experience. Many groups base the decision on scientific evidence, but they often differ in how they define effectiveness. Benefits can be defined by various measures of morbidity and mortality. Some groups consider benefits alone, and others consider adverse effects, costs, and other outcomes. It is therefore important for guideline developers to be as explicit as possible in defining outcomes of interest. It is not enough to state that the practice should be ‘clinically effective.’ What specific outcomes need to be affected to arrive at a recommendation? The group should decide which health, intermediate, and surrogate outcomes will be considered.

A health outcome refers to direct measures of health status, including measures of physical morbidity (e.g., dyspnea, blindness, weakness), emotional well-being, and mortality (e.g., survival, life expectancy). Eddy defines these as ‘outcomes that people can experience (feel physically or mentally) and care about’ [8]. An intermediate outcome is an effect that leads to a health outcome, and a surrogate outcome is an effect that is equivalent to a health outcome or can act as its proxy. Intermediate and surrogate outcomes are often physiologic variables, test results, or other measures that do not qualify as health outcomes by themselves but that have established pathophysiologic relationships with these outcomes. For coronary angioplasty, the establishment of arterial patency is an intermediate outcome leading to the desired health outcome of preventing subsequent ischemia. Surrogate outcomes could include electrocardiographic changes as a surrogate for cardiac ischemia, serum creatinine concentration for renal insufficiency, and pulmonary function tests for obstructive pulmonary disease. Although intermediate and surrogate outcomes are clearly less persuasive indices than actual health outcomes, they are often used in the analytic process because they are frequently the only validated outcome measures available in existing research. Guideline developers should determine which of these outcomes must be affected to convince the group that the maneuver should be recommended.

The potentially complex interrelationships between these outcomes are best visualized in a graphic or tabular format. A recent example of an analytic framework is shown in Figure 1, developed by the U.S. Preventive Services Task Force when considering a guideline about screening for osteoporosis [14]. This diagrammatic approach, first described in the late 1980s, emerged from earlier work on causal pathways [10], causal models [11], influence diagrams [12], and evidence models [13]. The construction of the diagram begins with listing the outcomes that the group has identified as important. This list of benefits, harms, and other outcomes reflects the key criteria that the group must address in its analytic process to assess appropriateness and arrive at a recommendation. Intermediate or surrogate outcomes that the group considers valid markers of effectiveness are next added to the diagram. The interconnecting lines, or linkages, that appear in Figure 1 represent the critical premises in the analytic logic that must be confirmed by the review process to support the recommendation. KQ1 is the overarching question—does risk factor assessment or bone measurement testing lead to reduced fracture-related morbidity and mortality? KQ2, KQ3, KQ4, KQ5, and KQ6 are questions about intermediate steps along the path, concerning the accuracy of risk factor assessment and bone measurement testing, potential harms of testing, and treatment of persons identified as abnormal.

The specification of the presumed relationship between intermediate, surrogate, and health outcomes in a visual analytic framework serves a number of useful purposes. It forces the analysts to make explicit, a priori decisions about the outcomes of interest to arrive at a recommendation. It allows others to judge whether important outcomes were overlooked. It makes explicit the group's opinions about the appropriateness of intermediate and surrogate outcomes as valid markers of health outcomes. The proposed interrelationships depicted in the diagram revealed the analysts’ assumptions about pathophysiologic relationships. They allow others to judge whether the correct questions were asked at the outset.

This type of analytic framework bears a visual resemblance to flowcharts, algorithms, and other graphics, but it differs importantly in content and purpose. The purpose of the visual analytic framework is prospective: the group defines at the outset the criteria it wishes to consider to arrive at a recommendation. The frameworks are conceptually different from algorithms. The linkages define the types of evidence to be reviewed and the outcomes represent measures of effectiveness, whereas the ‘arrows’ and outcomes in algorithms depict clinical choices, test results, or pathophysiologic events in the workup and treatment of patients [15, 16].

The linkages in the visual analytic framework provide a ‘road map’ to guide the process of reviewing the evidence. They provide a specific list of questions that need to be answered by the group to arrive at a recommendation, though they do not define which types of evidence should be searched to provide the information. Once these questions have been answered, the literature review can proceed through an orderly process of searching admissible evidence to find support for the specific linkages in the analytic framework. The evidence supporting the linkages is often heterogeneous, with some linkages supported by randomized controlled trials and others supported by other classes of evidence.

Given the increasing availability of systematic reviews of different types of studies addressing different questions, guideline developers should initially search for relevant systematic reviews for each question as the availability of an up-to-date, high-quality, relevant systematic review could mitigate the need to undertake a systematic review de novo. Whitlock et al. provide guidance about the methodological and practical issues that developers need to consider when using existing systematic reviews in guideline development [17].

Often, the information displayed in the analytic framework is only the starting point for more detailed analysis of the data. The completed diagram indicates only the class of evidence that supports a linkage and says little about the results of the studies, the consistency of the findings, or the quality of the data. Approaches for examining the evidence in more detail include the full range of analytic methods, such as simple narrative summaries, evidence tables, meta-analyses, and modeling. As a graphics device, the visual analytic framework is not meant to capture these details. Its role is to identify where the evidence sits in the analytic logic, not to describe what the evidence shows.

Writing a clear rationale statement is facilitated by the information in the analytic framework. The rationale statement can thereby summarize the benefits, harms, and other outcomes that were considered; why the outcomes were considered important (including consideration of patient preferences); the group's assumptions about the relationship between intermediate and surrogate outcomes and health outcomes; and the types of evidence that the group found in support of the linkages. If the review found linkages that lack supporting evidence, the rationale statement can speak honestly about the role that opinion, theory, or clinical experience played in arriving at a recommendation. This ‘truth in advertising’ helps ensure that the rationale statement provides clinicians, policymakers, and other guideline users with credible information about underlying assumptions. It also helps avoid misleading generalizations about the science, such as claiming that a maneuver is supported by ‘randomized controlled trials’ when such evidence supports only one linkage in the rationale. By sharing the blueprint for the recommendations, the linkages in the analytic logic allow groups to identify the pivotal assumptions about which they disagree.

Finally, by drawing attention to linkages that lack scientific support, the analytic framework highlights the most important outcomes to be examined by researchers to establish the effectiveness of a clinical practice. This information is essential, in an era of limited research funds, to set priorities and direct outcomes research toward the fundamental questions to be answered. The outcomes identified in the framework also provide a template for testing the efficacy of the guidelines themselves in research evaluating the effect of guidelines on quality of care.

Recommendations do not emerge directly from the empirical data reviewed by a guideline group. When the science clearly indicates substantial net benefit (benefit minus harms) or that an intervention is clearly ineffective or harmful, the need to consider values and preferences is less important. However, two major circumstances occur commonly in guideline development that require sensitivity to personal preferences and subjective judgments.

First, when the evidence is unclear, judgments about the occurrence and effect magnitude of an intervention often depend on subjective judgments about the quality of the studies. For example, a number of randomized controlled trials have evaluated the effectiveness of mammography screening for breast cancer, and a large body of empirical data about the effect size is available [18]. However, for two decades, experts with different opinions about the methods used in the trials have reached different conclusions about the quality of the evidence and the likely mortality reduction from mammography at different ages [19]. In the presence of scientific uncertainty, judgments based on other considerations often, and sometimes legitimately, take on greater importance. Guideline developers often consider clinical experience, expert opinion, the health condition in question and its severity, the potential harms of the intervention, and the potential harms of inaction. These judgments inevitably color how groups characterize the evidence and frame recommendations in the face of uncertainty [20]. In some instances, groups opt for neutrality, stating that there is insufficient evidence to make a recommendation [21]. In other circumstances, as when the condition poses great risk or there is little potential harm, the group may recommend the intervention despite inadequate evidence. In the opposite circumstance, when concerns about potential harms are heightened, a group may recommend against an intervention pending more convincing evidence [22]. Whatever choice is made, it is best for guideline developers to be transparent about value judgments [23]. The rationale for concluding that the evidence is strong, weak, or equivocal should be explained, preferably in detail. Concerns about the methods used for performing studies or evaluating outcomes should be outlined, both to explain the group’s rationale but also to guide future research to address limitations in the evidence. For example, knowing that guideline groups have recurrently cited contamination of the control group as a weakness in studies of an intervention will encourage future studies to devise innovative methods to address this concern.

Second, even when the occurrence or effect size is sufficiently clear from the data, the judgment of whether benefits outweigh harms can often be inherently subjective [24]. In such ‘close calls,’ people faced with the same data about the probabilities of benefits and harms can reach different conclusions about net benefit because of the different values, or utilities, they assign to the outcomes [25, 26]. For example, the risk of developing urinary incontinence from radiation therapy for prostate cancer may be less disconcerting to an oncologist or patient who is focused on the hazard of the cancer than to a clinician or patient who is more concerned about quality of life than life expectancy. These subjective judgments are neither right nor wrong, but they do influence conclusions about net benefit and a group’s leanings on whether or not to recommend an intervention.

Groups have two options for dealing with close calls that involve difficult tradeoffs. First, they can make the decision themselves and conclude whether benefits outweigh harms. The group, with its in-depth knowledge of the clinical topic and the underlying science, can infer how most patients would react if faced with the same information. Such groups act as a proxy for patients, and the advantage of this approach is that the group has mastery of details that are often beyond the ability of most patients to digest or most busy clinicians to explain. The disadvantage of this approach is its inherent paternalism and the risk of misjudgments by the group [27]. The second option for dealing with close calls is for the group to eschew a blanket recommendation but to instead encourage shared or informed decision-making, in which the patient is encouraged to review the tradeoffs with their clinician and make an individual decision based on personal preferences [28, 29]. When this is done, the group expressly avoids taking a policy stance. Its policy is to advise clinicians to engage the patient in the decision; such groups recognize that the determination of whether benefits outweigh harms is sensitive to utilities, a determination that can only be made individually by the patient and clinician, not by a guideline group [30]. The advantage of this approach is its respect for autonomy and individual choice, in which guidelines become a tool for patient empowerment, engagement, and activation [31, 32]. If the group eschews a recommendation and instead advocates shared decision-making, it is helpful if the guideline includes details about the content areas the patient-clinician conversation should cover. The guideline group is likely to have a clear sense of the preference-sensitive issues that influence the benefit-harm tradeoff and can therefore outline the items the patient and clinician should review, the relevant evidence, the role of decision aids, and other suggestions for incorporating personal preferences into the decision-making process.

There has been no widely accepted successful way of incorporating economic considerations into guidelines. However, the reasons for considering costs are clearly stated: ‘Health interventions are not free, people are not infinitely rich, and the budgets of [healthcare] programs are limited. For every dollar’s worth of healthcare that is consumed, a dollar will be paid. While these payments can be laundered, disguised or hidden, they will not go away’ [8]. Such opportunity costs are a universal phenomenon. It is also the case that while considerations of effectiveness may be applicable across different healthcare systems, considerations of cost and values are more likely to be healthcare system-specific. Therefore, a cost-effectiveness guideline may be less transferable than one based solely on clinical effectiveness.

In the USA, the 1992 IOM report [33] offered the aspirational recommendation that every set of clinical guidelines should include information on the cost implications of the alternative preventive, diagnostic, and management strategies for each clinical situation. The stated rationale was that this information would help potential users to better evaluate the potential consequences of different practices. However, they then acknowledged that ‘the reality is that this recommendation poses major methodological and practical challenges.’ Although there is emerging practical experience, this position has not really changed. In addition, it has also become recognized that issues of cost are much more likely to be health system-specific (as compared to the clinical evidence areas of guideline development) and so, unless explicitly mandated—like the UK National Institute for Health and Clinical Excellence (NICE)—many guideline developers do not do this.

Some guideline development organizations (e.g., NICE) advocate the review of appropriate cost-effectiveness studies alongside the review of the clinical evidence, though, in their guideline development manual, they note that ‘only rarely will the health economic literature be comprehensive enough and conclusive enough that no further analysis is required. Additional economic analyses will usually be needed.’ The available ‘economic evidence’ may be limited in terms of general applicability to the specific context of the clinical guideline, but can be useful in framing the general bounds of cost-effectiveness of management options for a clinical condition and providing an explicit source for some of the assumptions that may have to be made.

The methods of incorporating economic considerations are shaped by the methods of guideline development [34]. Early on in the development of each of the guidelines, there is a fundamental decision to be made about how to summarize the data and whether or not there are common outcomes across studies. If common outcomes are available, then it may be possible to use quantitative techniques (meta-analysis or meta-regression) leading to summary relative and absolute estimates of benefit, and it may then be possible to formally combine the elements of effectiveness and cost into a summary cost-effectiveness statistic. With relatively broad clinical areas (e.g., the management of type 2 diabetes), it is more difficult to do this, whereas for narrower areas (e.g., choosing a drug to treat depression) it is may be more feasible.

If the evidence summary is to be qualitative (a narrative review of studies) the data can be set out in ways that facilitate easy comparison between studies by using common descriptors (e.g., study design, study population, intervention, duration of intervention) using evidence tables. However, under these circumstances it may not be possible to make estimates of cost-effectiveness unless the evidence summary is dominated by one study with appropriate outcomes. For guidelines that use qualitative evidence summary methods (not amenable to meta-analysis), it is usually only possible to present cost data alongside the evidence of clinical effectiveness allowing a reader to make their own judgments about the relative weight to be ascribed to these two dimensions of evidence. It is possible to make cost minimization statements such as: ‘as the treatments appear equivalent clinicians should offer the cheapest preparation that patients can tolerate and comply with.’

For guidelines focused on a single decision, it may be possible to incorporate economic data into a formal decision analysis framework. Traditionally, it is the province of health economics to model (combine, adjust, extrapolate, represent) intermediate clinical outcome data and data from other sources to explore the overall costs and consequences of treatment alternatives. In principle, it is possible to map clinical data onto generic quality of life scores, model the advancement of disease and produce cost per quality-adjusted life year (QALY) estimates for each treatment decision. However, such a process contrasts with the above methods in a number of ways. First, although they may have a role in informing the questions, values, and assumptions that go into a model, there is no clear role for a multi-disciplinary guideline development group in deriving recommendations around the clinical decision—the ‘right decision’ is produced by the model. Second, the data are aggregated into a single metric, the constituent elements of which (and their associated uncertainty) are not transparent. Third, the complexity of modeling a single decision is often such that the viability of the method to deal with more complex clinical decisions, which have multiple interdependencies, has to be questioned. Therefore, the appropriate application of a decision analysis-driven guideline is currently unclear and a question for further research.

In this paper, we have discussed the issues of identifying and synthesizing evidence: deciding what type of evidence and outcomes to include in guidelines; integrating values into a guideline; incorporating economic considerations; synthesis, grading, and presentation of evidence; and moving from evidence to recommendations. In the third and final paper in the series, we will discuss the issues of: reviewing, reporting, and publishing guidelines; updating guidelines; and the two emerging issues of enhancing guideline implementability and how guidelines approach dealing with patients with co-morbid conditions.