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To assess the utility of an acronym, place of residence, race/ethnicity/culture/language, occupation, gender/sex, religion, education, socioeconomic status, and social capital (“PROGRESS”), in identifying factors that stratify health opportunities and outcomes. We explored the value of PROGRESS as an equity lens to assess effects of interventions on health equity. We assessed the utility of PROGRESS by using it in 11 systematic reviews and methodological studies published between 2008 and 2013. To develop the justification for each of the PROGRESS elements, we consulted experts to identify examples of unfair differences in disease burden and an intervention that can effectively address these health inequities. Each PROGRESS factor can be justified on the basis of unfair differences in disease burden and the potential for interventions to reduce these differential effects. We have not provided a rationale for why the difference exists but have attempted to explain why these differences may contribute to disadvantage and argue for their consideration in new evaluations, systematic reviews, and intervention implementation. The acronym PROGRESS is a framework and aide-memoire that is useful in ensuring that an equity lens is applied in the conduct, reporting, and use of research.

Lack of standardization of outcome measures limits the usefulness of clinical trial evidence to inform health care decisions. This can be addressed by agreeing on a minimum core set of outcome measures per health condition, containing measures relevant to patients and decision makers. Since 1992, the Outcome Measures in Rheumatology (OMERACT) consensus initiative has successfully developed core sets for many rheumatologic conditions, actively involving patients since 2002. Its expanding scope required an explicit formulation of its underlying conceptual framework and process. Literature searches and iterative consensus process (surveys and group meetings) of stakeholders including patients, health professionals, and methodologists within and outside rheumatology. To comprehensively sample patient-centered and intervention-specific outcomes, a framework emerged that comprises three core “Areas,” namely Death, Life Impact, and Pathophysiological Manifestations; and one strongly recommended Resource Use. Through literature review and consensus process, core set development for any specific health condition starts by identifying at least one core “Domain” within each of the Areas to formulate the “Core Domain Set.” Next, at least one applicable measurement instrument for each core Domain is identified to formulate a “Core Outcome Measurement Set.” Each instrument must prove to be truthful (valid), discriminative, and feasible. In 2012, 96% of the voting participants (n=125) at the OMERACT 11 consensus conference endorsed this model and process. The OMERACT Filter 2.0 explicitly describes a comprehensive conceptual framework and a recommended process to develop core outcome measurement sets for rheumatology likely to be useful as a template in other areas of health care.

Procedures for controlling the false positive rate when performing many hypothesis tests are commonplace in health and medical studies. Such procedures, most notably the Bonferroni adjustment, suffer from the problem that error rate control cannot be localized to individual tests, and that these procedures do not distinguish between exploratory and/or data-driven testing vs. hypothesis-driven testing. Instead, procedures derived from limiting false discovery rates may be a more appealing method to control error rates in multiple tests. Controlling the false positive rate can lead to philosophical inconsistencies that can negatively impact the practice of reporting statistically significant findings. We demonstrate that the false discovery rate approach can overcome these inconsistencies and illustrate its benefit through an application to two recent health studies. The false discovery rate approach is more powerful than methods like the Bonferroni procedure that control false positive rates. Controlling the false discovery rate in a study that arguably consisted of scientifically driven hypotheses found nearly as many significant results as without any adjustment, whereas the Bonferroni procedure found no significant results. Although still unfamiliar to many health researchers, the use of false discovery rate control in the context of multiple testing can provide a solid basis for drawing conclusions about statistical significance.

This study aims to (1) elucidate whether the Hawthorne effect exists, (2) explore under what conditions, and (3) estimate the size of any such effect. This systematic review summarizes and evaluates the strength of available evidence on the Hawthorne effect. An inclusive definition of any form of research artifact on behavior using this label, and without cointerventions, was adopted. Nineteen purposively designed studies were included, providing quantitative data on the size of the effect in eight randomized controlled trials, five quasiexperimental studies, and six observational evaluations of reporting on one's behavior by answering questions or being directly observed and being aware of being studied. Although all but one study was undertaken within health sciences, study methods, contexts, and findings were highly heterogeneous. Most studies reported some evidence of an effect, although significant biases are judged likely because of the complexity of the evaluation object. Consequences of research participation for behaviors being investigated do exist, although little can be securely known about the conditions under which they operate, their mechanisms of effects, or their magnitudes. New concepts are needed to guide empirical studies.

The objective of this study was to determine if the authors mention overlapping reviews in overviews (reviews of reviews). In addition, we aimed to calculate the actual overlap in published overviews using newly introduced, validated measures. We systematically searched for overviews from 2009 to 2011. Reviews included in the overviews were obtained. Tables (review×primary publication) were generated for each overview. The first occurrence of a primary publication is defined as the index publication. We calculated the “corrected covered area” (CCA) as a measure of overlap by dividing the frequency of repeated occurrences of the index publication in other reviews by the product of index publications and reviews, reduced by the number of index publications. Subgroup analyses were performed to investigate further differences in the overviews. Only 32 of 60 overviews mentioned overlaps. The median CCA was 4.0. Validation of the CCA and other overlap measures was in accordance with our predefined hypotheses. The degree of overlap tended to be higher in health technology assessment reports than in journal publications and was higher with increasing numbers of publications. Overlaps must be reported in well-conducted overviews, and this can comprehensively be accomplished using the CCA method.

This article describes the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) approach to classifying the direction and strength of recommendations. The strength of a recommendation, separated into strong and weak, is defined as the extent to which one can be confident that the desirable effects of an intervention outweigh its undesirable effects. Alternative terms for a weak recommendation include conditional, discretionary, or qualified. The strength of a recommendation has specific implications for patients, the public, clinicians, and policy makers. Occasionally, guideline developers may choose to make “only-in-research” recommendations. Although panels may choose not to make recommendations, this choice leaves those looking for answers from guidelines without the guidance they are seeking. GRADE therefore encourages panels to, wherever possible, offer recommendations.

To document the development and psychometric evaluation of the Patient-Reported Outcomes Measurement Information System (PROMIS) Physical Function (PF) item bank and static instruments. The items were evaluated using qualitative and quantitative methods. A total of 16,065 adults answered item subsets (n>2,200/item) on the Internet, with oversampling of the chronically ill. Classical test and item response theory methods were used to evaluate 149 PROMIS PF items plus 10 Short Form-36 and 20 Health Assessment Questionnaire-Disability Index items. A graded response model was used to estimate item parameters, which were normed to a mean of 50 (standard deviation [SD]=10) in a US general population sample. The final bank consists of 124 PROMIS items covering upper, central, and lower extremity functions and instrumental activities of daily living. In simulations, a 10-item computerized adaptive test (CAT) eliminated floor and decreased ceiling effects, achieving higher measurement precision than any comparable length static tool across four SDs of the measurement range. Improved psychometric properties were transferred to the CAT's superior ability to identify differences between age and disease groups. The item bank provides a common metric and can improve the measurement of PF by facilitating the standardization of patient-reported outcome measures and implementation of CATs for more efficient PF assessments over a larger range.

To assess the utility of funnel plots in assessing publication bias (PB) in meta-analyses of proportion studies. Meta-analysis simulation study and meta-analysis of published literature reporting peri-operative mortality after abdominal aortic aneurysm (AAA) repair. Data for the simulation study were stochastically generated. A literature search of Medline and Embase was performed to identify studies for inclusion in the published literature meta-analyses. The simulation study demonstrated that conventionally constructed funnel plots (log odds vs. 1/standard error [1/SE]) for extreme proportional outcomes were asymmetric despite no PB. Alternative funnel plots constructed using study size rather than 1/SE showed no asymmetry for extreme proportional outcomes. When used in meta-analyses of the mortality of AAA repair, these alternative funnel plots highlighted the possibility for conventional funnel plots to demonstrate asymmetry when there was no evidence of PB. Conventional funnel plots used to assess for potential PB in meta-analyses are inaccurate for meta-analyses of proportion studies with low proportion outcomes. Funnel plots of study size against log odds may be a more accurate way of assessing for PB in these studies.

The aim of this review was to identify studies on patterns of associative multimorbidity, defined as the nonrandom association between diseases, focusing on the main methodological features of the studies and the similarities among the detected patterns. Studies were identified through MEDLINE and EMBASE electronic database searches from their inception to June 2012 and bibliographies. The final 14 articles exhibited methodological heterogeneity in terms of the sample size, age and recruitment of study participants, the data source, the number of baseline diseases considered, and the statistical procedure used. A total of 97 patterns composed of two or more diseases were identified. Among these, 63 patterns were composed of three or more diseases. Despite the methodological variability among studies, this review demonstrated relevant similarities for three groups of patterns. The first one comprised a combination of cardiovascular and metabolic diseases, the second one was related with mental health problems, and the third one with musculoskeletal disorders. The existence of associations beyond chance among the different diseases that comprise these patterns should be considered with the aim of directing future lines of research that measure their intensity, clarify their nature, and highlight the possible causal underlying mechanisms.

In the GRADE approach, the strength of a recommendation reflects the extent to which we can be confident that the composite desirable effects of a management strategy outweigh the composite undesirable effects. This article addresses GRADE's approach to determining the direction and strength of a recommendation. The GRADE describes the balance of desirable and undesirable outcomes of interest among alternative management strategies depending on four domains, namely estimates of effect for desirable and undesirable outcomes of interest, confidence in the estimates of effect, estimates of values and preferences, and resource use. Ultimately, guideline panels must use judgment in integrating these factors to make a strong or weak recommendation for or against an intervention.