Citations

AbstractMean, median, and mode are among the most basic and consistently used measures of central tendency in statistical analysis and are crucial for simplifying data sets to a single value. However, there is a lack of understanding of when to use each metric and how various factors can impact these values. The aim of this article is to clarify some of the confusion related to each measure and explain how to select the appropriate metric for a given data set. The authors present this work as an educational resource, ensuring that these common statistical concepts are better understood throughout the Orthopedic research community.

Abstract Background Treatment wearing‐off has been reported for calcitonin gene‐related peptide‐pathway monoclonal antibodies, including erenumab, specifically in the last week of the monthly dosing cycle. Objective We sought to determine the consistency of erenumab effect throughout the monthly treatment cycle. Methods In this post hoc analysis of four pivotal double‐blind, randomized controlled studies of erenumab in episodic and chronic migraine, we assessed wearing‐off based on change in weekly migraine days at week 4 versus average over weeks 1–3 in each monthly dosing cycle. Analyses were conducted at each monthly dosing cycle in all patients, in responders (≥50% reduction in weekly migraine days), and in consistent responders (response in ≥2monthly cycles). Results There was no evidence of wearing‐off in the full study populations of two global studies (N = 946 and N = 656) and two Japan studies (N = 475 and N = 261). In the full study population, mean change in weekly migraine days at week 4 compared with the average over week 1–3 ranged from 0.15 days improvement to 0.19 days worsening in the placebo group and 0.08 days improvement to 0.20 days worsening in the erenumab groups. A subgroup of responders experienced wearing‐off, but the extent of wearing‐off did not differ between erenumab and placebo groups. The mean change in weekly migraine days at week 4 compared with the average over weeks 1–3 ranged from 0.34 to 0.61 days worsening in the placebo group and 0.27 to 0.78 days worsening in the erenumab groups. Few patients had persistent wearing‐off in ≥2 consecutive monthly treatment cycles. For erenumab‐treated responders, serum erenumab concentrations were similar among patients experiencing wearing‐off and those maintaining response. Conclusion No systematic wearing‐off with erenumab was identified. Further research is needed to determine if wearing‐off reported for some patients in clinical practice reflects a true treatment response pattern or normal fluctuations in migraine frequency.

AbstractSupervised learning is the most common form of machine learning utilized in medical research. It is used to predict outcomes of interest or classify positive and/or negative cases with a known ground truth. Supervised learning describes a spectrum of techniques, ranging from traditional regression modeling to more complex tree boosting, which are becoming increasingly prevalent as the focus on “big data” develops. While these tools are becoming increasingly popular and powerful, there is a paucity of literature available that describe the strengths and limitations of these different modeling techniques. Typically, there is no formal training for health care professionals in the use of machine learning models. As machine learning applications throughout medicine increase, it is important that physicians and other health care professionals better understand the processes underlying application of these techniques. The purpose of this study is to provide an overview of commonly used supervised learning techniques with recent case examples within the orthopedic literature. An additional goal is to address disparities in the understanding of these methods to improve communication within and between research teams.

AbstractBackgroundKnown risk alleles for epithelial ovarian cancer (EOC) account for approximately 40% of the heritability for EOC. Copy number variants (CNVs) have not been investigated as EOC risk alleles in a large population cohort.MethodsSingle nucleotide polymorphism array data from 13 071 EOC cases and 17 306 controls of White European ancestry were used to identify CNVs associated with EOC risk using a rare admixture maximum likelihood test for gene burden and a by-probe ratio test. We performed enrichment analysis of CNVs at known EOC risk loci and functional biofeatures in ovarian cancer–related cell types.ResultsWe identified statistically significant risk associations with CNVs at known EOC risk genes; BRCA1 (PEOC = 1.60E-21; OREOC = 8.24), RAD51C (Phigh-grade serous ovarian cancer [HGSOC] = 5.5E-4; odds ratio [OR]HGSOC = 5.74 del), and BRCA2 (PHGSOC = 7.0E-4; ORHGSOC = 3.31 deletion). Four suggestive associations (P < .001) were identified for rare CNVs. Risk-associated CNVs were enriched (P < .05) at known EOC risk loci identified by genome-wide association study. Noncoding CNVs were enriched in active promoters and insulators in EOC-related cell types.ConclusionsCNVs in BRCA1 have been previously reported in smaller studies, but their observed frequency in this large population-based cohort, along with the CNVs observed at BRCA2 and RAD51C gene loci in EOC cases, suggests that these CNVs are potentially pathogenic and may contribute to the spectrum of disease-causing mutations in these genes. CNVs are likely to occur in a wider set of susceptibility regions, with potential implications for clinical genetic testing and disease prevention.

68 Background: Accrual of racial and ethnic minority patients (pts) to clinical trials has been an ongoing challenge in research. Barriers exist at many levels of engagement including trial availability, screening biases, strict enrollment criteria, and structural racism. Lack of trial diversity has led to less generalizable medical evidence, suboptimal data for toxicity and efficacy of cancer treatments, poorer outcomes and continued mistrust by communities of color in research processes that exclude or marginalize them. Site factors and study design strategies associated with successful recruitment of diverse study populations are understudied. Methods: Alliance A191901 is an RCT testing combinations of text and telephone support to promote endocrine therapy adherence among breast cancer survivors. It oversamples Black participants and those < 50 years at thresholds of 30% each. Administrative enrollment data was used to track and describe monthly accrual trajectory by race among the initial 50% of participants (n = 590) and to examine associations between % Black participants accrued and accruing site characteristics, including site type, geographic region, % Black population in the site’s zip code and volume of pts by race accrued to recent (2018-2019) Alliance trials, using χ2 tests. We also examined patterns of Black participant accrual before and after closure of the A191901 study to non-Black participants. Results: At 50% accrual, 124 sites had enrolled at least 1 participant. Among 590 participants, 9.7% were Black and 22.5% were < 50 years. Neither site type nor volume was associated with % Black participants recruited. Sites in the South recruited higher proportions of Blacks (19.0% vs 5.6% for Midwest, the lowest region, p < 0.001). Neighborhood racial composition was positively associated with accrual of Black participants (17.2% at sites from highest Black composition vs. 2.3% in lowest, p < 0.001). Recruitment trajectories of Black participants consistently lagged those of non-Black participants in the first half of the study; however, Black participants recruited per month numerically increased after the study reached target accrual of non-Blacks and closed to non-Black participants. Conclusions: Sites in neighborhoods and geographic regions with larger Black populations recruited higher proportions of Black participants, but site type and volume were not associated with recruitment of Black participants. Closure of non-Black study strata based on predetermined accrual targets may positively impact recruitment trajectories of Black participants, but further study is needed. Site selection based on neighborhood composition and geography may be an appropriate tool for increasing trial diversity. Support: UG1CA189823. Clinical trial information: NCT04379570.

What is the agreement of commercial SARS-CoV-2 immunoglobulin G (IgG) assays during a time of low coronavirus disease 2019 (COVID-19) prevalence and no vaccine availability? Serological tests produced concordant results in a time of low SARS-CoV-2 prevalence and no vaccine availability, driven largely by the proportion of samples that were negative by two immunoassays. The CDC recommends two sequential tests for positivity for future pandemic preparedness.

Abstract Background: To describe the process of developing an evolving database for Mayo Clinic primary care patients who screen positive for depression. The databases will support a broad use of these data for current and future quality improvement and research efforts. The description of their development will serve as an example for other institutions attempting to create similar data resources. Methods: The adult database (patients aged ³ 18) has data from 2007 and the adolescent database (patients aged 12 to < 18) from 2011. To be included, a patient had to be eligible for collaborative care model as a primary care patient with at least moderate symptoms of unipolar depression. The patient did not have to be enrolled in collaborative care model. The main domains of measurement are demographics, social determinants of health, mental health and substance abuse conditions, other comorbidities, interaction with collaborative care model, and questionnaire results. The data is being continuously updated as new data becomes available. Results: The adult database currently includes 117,619 unique patients with a mean age of 44.7 years, and is 66.4% female, and 88.1% non-Hispanic white. The adolescent database includes 14,684 unique patients with a mean age of 14.8 years, is 66.7% female, and 79.7% non-Hispanic white. Conclusions: These large, longitudinal, and evolving databases are a key step towards developing a learning environment to improve quality of care and conduct population-based research for patients with depression in primary care settings.