BACKGROUND & AIMS: Health economic analyses are increasingly common in the digestive diseases literature and often are cited to frame practice guidelines. Although clinical trials are subjected routinely to critical appraisal, there has been no attempt to appraise the quality of health economic analyses with a validated instrument. We sought to appraise the quality of health economic analyses in digestive diseases, and to identify predictors of study quality. METHODS: We performed a systematic review to identify digestive disease health economic analyses published since 1980. We assessed these studies using the Quality of Health Economic Studies (QHES), a validated quality-scoring instrument (score range = 0-100; >75 = high quality). We conducted regression analysis to identify predictors of high quality. RESULTS: Of 186 identified analyses, 29% were high quality, 71% failed to address potential model biases, 52% failed to disclose conflicts of interest, and 74% failed to describe methods for deriving the model assumptions. Four factors predicted high quality in logistic regression: (1) one or more authors had an advanced degree in health services or a related field (odds ratio for high quality, 5.0; 95% confidence interval, 2.6-9.3); (2) the study used decision-analysis software package (odds ratio, 2.4; 95% confidence interval, 1.2-4.7); (3) the study was federally funded (odds ratio, 2.2; 95% confidence interval, 1.2-4.1); and (4) the study cited the National Panel on Cost Effectiveness guidelines (odds ratio, 2.1; 95% confidence interval, 1.1-4.2). CONCLUSIONS: Less than one third of health economic analyses in digestive diseases meet criteria for high quality. Study quality is limited by factors that potentially can be remedied. These data may be used to focus the attention of journal editors and peer reviewers to ensure the future high quality of health economic analyses in digestive diseases.
BACKGROUND & AIMS: Health economic analyses are increasingly common in the digestive diseases literature and often are cited to frame practice guidelines. Although clinical trials are subjected routinely to critical appraisal, there has been no attempt to appraise the quality of health economic analyses with a validated instrument. We sought to appraise the quality of health economic analyses in digestive diseases, and to identify predictors of study quality. METHODS: We performed a systematic review to identify digestive disease health economic analyses published since 1980. We assessed these studies using the Quality of Health Economic Studies (QHES), a validated quality-scoring instrument (score range = 0-100; >75 = high quality). We conducted regression analysis to identify predictors of high quality. RESULTS: Of 186 identified analyses, 29% were high quality, 71% failed to address potential model biases, 52% failed to disclose conflicts of interest, and 74% failed to describe methods for deriving the model assumptions. Four factors predicted high quality in logistic regression: (1) one or more authors had an advanced degree in health services or a related field (odds ratio for high quality, 5.0; 95% confidence interval, 2.6-9.3); (2) the study used decision-analysis software package (odds ratio, 2.4; 95% confidence interval, 1.2-4.7); (3) the study was federally funded (odds ratio, 2.2; 95% confidence interval, 1.2-4.1); and (4) the study cited the National Panel on Cost Effectiveness guidelines (odds ratio, 2.1; 95% confidence interval, 1.1-4.2). CONCLUSIONS: Less than one third of health economic analyses in digestive diseases meet criteria for high quality. Study quality is limited by factors that potentially can be remedied. These data may be used to focus the attention of journal editors and peer reviewers to ensure the future high quality of health economic analyses in digestive diseases.
Authors: Benedict U Nwachukwu; Kevin J Bozic; William W Schairer; Jaime L Bernstein; David S Jevsevar; Robert G Marx; Douglas E Padgett Journal: Clin Orthop Relat Res Date: 2014-09-30 Impact factor: 4.176
Authors: M Jason Highsmith; Jason T Kahle; Amanda Lewandowski; Tyler D Klenow; John J Orriola; Rebecca M Miro; Owen T Hill; Sylvia Ursula Raschke; Michael S Orendurff; James T Highsmith; Bryce S Sutton Journal: Technol Innov Date: 2016-09-01