Abstract
Risk of bias in translational medicine may take one of three forms: A. a systematic error of methodology as it pertains to measurement or sampling (e.g., selection bias), B. a systematic defect of design that leads to estimates of experimental and control groups, and of effect sizes that substantially deviate from true values (e.g., information bias), and C. a systematic distortion of the analytical process, which results in a misrepresentation of the data with consequential errors of inference (e.g., inferential bias). Risk of bias can seriously adulterate the internal and the external validity of a clinical study, and, unless it is identified and systematically evaluated, can seriously hamper the process of comparative effectiveness and efficacy research and analysis for practice. The Cochrane Group and the Agency for Healthcare Research and Quality have independently developed instruments for assessing the meta-construct of risk of bias. The present article begins to discuss this dialectic.
Highlights
As recently discussed in this journal [1], translational medicine is a rapidly evolving field
Depending upon the tool of measurement, the validity of an instrument in a study is obtained by means of criterion validity through correlation coefficients
Criterion validity refers to the extent to which one measures or predicts the value of another measure or quality based on a previously well-established criterion
Summary
As recently discussed in this journal [1], translational medicine is a rapidly evolving field. In an effort to aid clinicians and patients in making effective health care related decisions, AHRQ developed an alternative Risk of Bias instrument for enabling systematical evaluation of evidence reporting [22]. The AHRQ Risk of Bias instrument is designed to yield an overall grade of the estimated risk of bias in quality reporting: Strength of Evidence Grades (scored as high – moderate - low – insufficient). This global assessment, in addition to incorporating the assessments above, rates:. To begin the process of engaging in a systematic dialectic of the two instruments in terms of their respective construct and content validity, it is necessary to validate each for reliability and validity either by means of the classic psychometric theory or generalizability (G) theory, which allows the simultaneous estimation of multiple sources of measurement error variance (i.e., facets) while
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