Abstract

BackgroundObservational studies dominate the surgical literature. Statistical adjustment is an important strategy to account for confounders in observational studies. Research has shown that published articles are often poor in statistical quality, which may jeopardize their conclusions. The Statistical Analyses and Methods in the Published Literature (SAMPL) guidelines have been published to help establish standards for statistical reporting.This study will seek to determine whether the quality of statistical adjustment and the reporting of these methods are adequate in surgical observational studies. We hypothesize that incomplete reporting will be found in all surgical observational studies, and that the quality and reporting of these methods will be of lower quality in surgical journals when compared with medical journals. Finally, this work will seek to identify predictors of high-quality reporting.Methods/DesignThis work will examine the top five general surgical and medical journals, based on a 5-year impact factor (2007–2012). All observational studies investigating an intervention related to an essential component area of general surgery (defined by the American Board of Surgery), with an exposure, outcome, and comparator, will be included in this systematic review. Essential elements related to statistical reporting and quality were extracted from the SAMPL guidelines and include domains such as intent of analysis, primary analysis, multiple comparisons, numbers and descriptive statistics, association and correlation analyses, linear regression, logistic regression, Cox proportional hazard analysis, analysis of variance, survival analysis, propensity analysis, and independent and correlated analyses. Each article will be scored as a proportion based on fulfilling criteria in relevant analyses used in the study. A logistic regression model will be built to identify variables associated with high-quality reporting. A comparison will be made between the scores of surgical observational studies published in medical versus surgical journals. Secondary outcomes will pertain to individual domains of analysis. Sensitivity analyses will be conducted.DiscussionThis study will explore the reporting and quality of statistical analyses in surgical observational studies published in the most referenced surgical and medical journals in 2013 and examine whether variables (including the type of journal) can predict high-quality reporting.

Highlights

  • The framework for this study will be that of a systematic review of all observational studies pertaining to general surgical topics published in leading medical and surgical journals, where such studies are compared and analyzed for statistical quality and reporting

  • It is expected that significant problems with statistical methodology will be identified, and that this problem will be more pronounced within studies published in general surgical journals

  • The main limitation of the study is the abstraction tool derived from the SAMPL guideline, which was not constructed for scoring statistical quality

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Summary

Discussion

This study will examine the quality and reporting of statistical methodology in surgical observational studies. It is expected that significant problems with statistical methodology will be identified, and that this problem will be more pronounced within studies published in general surgical journals. This work is important, as it will shed a critical light onto the most common type of surgical research performed to date. While the instrument that is proposed in this work is not validated, it is important to emphasize that no validated instrument currently exists (including SAMPL), and as such it can be argued that this is an appropriate first step in examining this topic. The findings of this review may provide an opportunity for surgical researchers and journal editors to improve the quality of statistical analyses being performed, as well as to call for improved and more transparent reporting of statistical methodology

Background
Methods
Elstein A
Cook JA
10. Altman DG
12. Yancey JM
Findings
22. Austin PC
Full Text
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