There is a continued mandate for practicing evidence-based medicine and the prerequisite rigorous analysis of the comparative effectiveness of alternative treatments. There is also an increasing emphasis on delivering value-based health care. Both these high priorities and their related endeavors require correct information about the outcomes of care. Accurately measuring and confirming health care outcomes are thus likely now of even greater importance. The present basic statistical tutorial focuses on the germane topic of psychometrics. In its narrower sense, psychometrics is the science of evaluating the attributes of such psychological tests. However, in its broader sense, psychometrics is concerned with the objective measurement of the skills, knowledge, and abilities, as well as the subjective measurement of the interests, values, and attitudes of individuals—both patients and their clinicians. While psychometrics is principally the domain and content expertise of psychiatry, psychology, and social work, it is also very pertinent to patient care, education, and research in anesthesiology, perioperative medicine, critical care, and pain medicine. A key step in selecting an existing or creating a new health-related assessment tool, scale, or survey is confirming or establishing the usefulness of the existing or new measure; this process conventionally involves assessing its reliability and its validity. Assessing reliability involves demonstrating that the measurement instrument generates consistent and hence reproducible results—in other words, whether the instrument produces the same results each time it is used in the same setting, with the same type of subjects. This includes interrater reliability, intrarater reliability, test–retest reliability, and internal reliability. Assessing validity is answering whether the instrument is actually measuring what it is intended to measure. This includes content validity, criterion validity, and construct validity. In evaluating a reported set of research data and its analyses, in a similar manner, it is important to assess the overall internal validity of the attendant study design and the external validity (generalizability) of its findings. Accepted for publication September 10, 2018. Funding: None. The authors declare no conflicts of interest. Reprints will not be available from the authors. Address correspondence to Thomas R. Vetter, MD, MPH, Department of Surgery and Perioperative Care, Dell Medical School at the University of Texas at Austin, Health Discovery Bldg, Room 6.812, 1701 Trinity St, Austin, TX 78712. Address e-mail to thomas.vetter@austin.utexas.edu. © 2018 International Anesthesia Research Society
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Objective The aim of this study was to determine the impact of the SLCO1B1, apolipoprotein E (ApoE), and CYP2C9 genotypes on the lipid-lowering efficacy of fluvastatin. Methods We performed electronic searches on the PubMed, Embase, and Cochrane Library databases to identify studies published through October 2017. Studies that reported the effect estimates with 95% confidence intervals (CIs) of total cholesterol (TC), triglyceride, low-density lipoprotein (LDL), and high-density lipoprotein were included so that the different genotype categories could be compared. Weighted mean difference (WMD) was used to summarize the effect estimates. Results Six studies, involving a total of 1171 individuals, were included in the final analysis. We noted that the patient carrier SLCO1B1 521TT was associated with greater change in TC (WMD: −2.98; 95% CI: −5.12 to −0.84; P=0.006) and LDL (WMD: −5.58; 95% CI: −10.64 to −0.52; P=0.031) compared with 521TC or CC. Furthermore, the patient carrier ApoE*2/*3 showed more change in high-density lipoprotein compared with ApoE*3/*3 (WMD: 18.76; 95% CI: 8.97–28.55; P