Users of clinical practice guidelines and other recommendations need to know how much confidence they can place in the recommendations Systematic and explicit methods of making judgments can reduce errors and improve communication We have developed a system for grading the quality of evidence and the strength of recommendations that can be applied across a wide range of interventions and contexts In this article we present a summary of our approach from the perspective of a guideline user Judgments about the strength of a recommendation require consideration of the balance between benefits and harms, the quality of the evidence, translation of the evidence into specific circumstances, and the certainty of the baseline risk It is also important to consider costs (resource utilisation) before making a recommendation Inconsistencies among systems for grading the quality of evidence and the strength of recommendations reduce their potential to facilitate critical appraisal and improve communication of these judgments Our system for guiding these complex judgments balances the need for simplicity with the need for full and transparent consideration of all important issues

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Grading quality of evidence and strength of recommendations.

OBJECTIVES: To consider methods and related evidence for evaluating bias in non-randomised intervention studies. DATA SOURCES: Systematic reviews and methodological papers were identified from a search of electronic databases; handsearches of key medical journals and contact with experts working in the field. New empirical studies were conducted using data from two large randomised clinical trials. METHODS: Three systematic reviews and new empirical investigations were conducted. The reviews considered, in regard to non-randomised studies, (1) the existing evidence of bias, (2) the content of quality assessment tools, (3) the ways that study quality has been assessed and addressed. (4) The empirical investigations were conducted generating non-randomised studies from two large, multicentre randomised controlled trials (RCTs) and selectively resampling trial participants according to allocated treatment, centre and period. RESULTS: In the systematic reviews, eight studies compared results of randomised and non-randomised studies across multiple interventions using meta-epidemiological techniques. A total of 194 tools were identified that could be or had been used to assess non-randomised studies. Sixty tools covered at least five of six pre-specified internal validity domains. Fourteen tools covered three of four core items of particular importance for non-randomised studies. Six tools were thought suitable for use in systematic reviews. Of 511 systematic reviews that included non-randomised studies, only 169 (33%) assessed study quality. Sixty-nine reviews investigated the impact of quality on study results in a quantitative manner. The new empirical studies estimated the bias associated with non-random allocation and found that the bias could lead to consistent over- or underestimations of treatment effects, also the bias increased variation in results for both historical and concurrent controls, owing to haphazard differences in case-mix between groups. The biases were large enough to lead studies falsely to conclude significant findings of benefit or harm. Four strategies for case-mix adjustment were evaluated: none adequately adjusted for bias in historically and concurrently controlled studies. Logistic regression on average increased bias. Propensity score methods performed better, but were not satisfactory in most situations. Detailed investigation revealed that adequate adjustment can only be achieved in the unrealistic situation when selection depends on a single factor. CONCLUSIONS: Results of non-randomised studies sometimes, but not always, differ from results of randomised studies of the same intervention. Non-randomised studies may still give seriously misleading results when treated and control groups appear similar in key prognostic factors. Standard methods of case-mix adjustment do not guarantee removal of bias. Residual confounding may be high even when good prognostic data are available, and in some situations adjusted results may appear more biased than unadjusted results. Although many quality assessment tools exist and have been used for appraising non-randomised studies, most omit key quality domains. Healthcare policies based upon non-randomised studies or systematic reviews of non-randomised studies may need re-evaluation if the uncertainty in the true evidence base was not fully appreciated when policies were made. The inability of case-mix adjustment methods to compensate for selection bias and our inability to identify non-randomised studies that are free of selection bias indicate that non-randomised studies should only be undertaken when RCTs are infeasible or unethical. Recommendations for further research include: applying the resampling methodology in other clinical areas to ascertain whether the biases described are typical; developing or refining existing quality assessment tools for non-randomised studies; investigating how quality assessments of non-randomised studies can be incorporated into reviews and the implications of individual quality features for interpretation of a review's results; examination of the reasons for the apparent failure of case-mix adjustment methods; and further evaluation of the role of the propensity score.

/pdf/evaluating-non-randomised-intervention-studies-2k25cz7x6r.pdf

Evaluating non-randomised intervention studies.

https://www.just.edu.jo/DIC/ClinicGuidlines/Management%20of%20hip%20and%20knee%20Osteoarthritis.pdf

OARSI recommendations for the management of hip and knee osteoarthritis, Part II: OARSI evidence-based, expert consensus guidelines

To comprehend the results of a randomized controlled trial (RCT), readers must understand its design, conduct, analysis and interpretation. That goal can only be achieved through complete transparency from authors. Despite several decades of educational efforts, the reporting of RCTs needs improvement. Investigators and editors developed the original CONSORT (Consolidated Standards of Reporting Trials) statement to help authors improve reporting by using a checklist and flow diagram. The revised CONSORT statement presented in this paper incorporates new evidence and addresses some criticisms of the original statement.

http://www.people.vcu.edu/~albest/Guidance/CONSORTjama.pdf

The CONSORT statement: revised recommendations for improving the quality of reports of parallel group randomized trials

Recently, Ware and Sherbourne published a new short-form health survey, the MOS 36-Item Short-Form Health Survey (SF-36), consisting of 36 items included in long-form measures developed for the Medical Outcomes Study. The SF-36 taps eight health concepts: physical functioning, bodily pain, role limitations due to physical health problems, role limitations due to personal or emotional problems, general mental health, social functioning, energy/fatigue, and general health perceptions. It also includes a single item that provides an indication of perceived change in health. The SF-36 items and scoring rules are distributed by MOS Trust, Inc. Strict adherence to item wording and scoring recommendations is required in order to use the SF-36 trademark. The RAND 36-Item Health Survey 1.0 (distributed by RAND) includes the same items as those in the SF-36, but the recommended scoring algorithm is somewhat different from that of the SF-36. Scoring differences are discussed here and new T-scores are presented for the 8 multi-item scales and two factor analytically-derived physical and mental health composite scores.

The rand 36-item health survey 1.0

The Oregon Health Services Commission recently completed work on its principal charge: creation of a prioritized list of health care services, ranging from the most important to the least important Oregon's draft priority list was criticized because it seemed to favor minor treatments over lifesaving ones This reaction reflects a fundamental and irreconcilable conflict between cost-effectiveness analysis and the powerful human proclivity to rescue endangered life: the "Rule of Rescue" Oregon's final priority list was generated without reference to costs and is, therefore, more intuitively sensible than the initial list However, the utility of the final list is limited by its lack of specificity with regard to conditions and treatments An alternative approach for setting health care priorities would circumvent the Rule of Rescue by carefully defining necessary indications for treatment Such an approach might be applied to Oregon's final list in order to achieve better specificity ( JAMA  1991;265:2218-2225)

Setting health care priorities in Oregon. Cost-effectiveness meets the rule of rescue.

Identifying and Valuing Outcomes

Rating the quality of evidence for clinical practice guidelines

The role of public values in setting health care priorities

Interest in health-related quality of life (HRQOL) is burgeoning and there has been a proliferation of self-report measures of HRQOL. However, only two instruments available for measuring HRQOL have been calibrated using empirically derived preferences; both are long and complex. This study tested a

https://www.jstor.org/stable/info/3765852

David C. Hadorn

Papers

Setting health care priorities in Oregon. Cost-effectiveness meets the rule of rescue.

Identifying and Valuing Outcomes

Rating the quality of evidence for clinical practice guidelines

The role of public values in setting health care priorities

Multitrait-multimethod analysis of health-related quality-of-life measures.