Review of guidance papers on regression modeling in statistical series of medical journals.

Although regression models play a central role in the analysis of medical research projects, there still exist many misconceptions on various aspects of modeling leading to faulty analyses. Indeed, the rapidly developing statistical methodology and its recent advances in regression modeling do not seem to be adequately reflected in many medical publications. This problem of knowledge transfer from statistical research to application was identified by some medical journals, which have published series of statistical tutorials and (shorter) papers mainly addressing medical researchers. The aim of this review was to assess the current level of knowledge with regard to regression modeling contained in such statistical papers. We searched for target series by a request to international statistical experts. We identified 23 series including 57 topic-relevant articles. Within each article, two independent raters analyzed the content by investigating 44 predefined aspects on regression modeling. We assessed to what extent the aspects were explained and if examples, software advices, and recommendations for or against specific methods were given. Most series (21/23) included at least one article on multivariable regression. Logistic regression was the most frequently described regression type (19/23), followed by linear regression (18/23), Cox regression and survival models (12/23) and Poisson regression (3/23). Most general aspects on regression modeling, e.g. model assumptions, reporting and interpretation of regression results, were covered. We did not find many misconceptions or misleading recommendations, but we identified relevant gaps, in particular with respect to addressing nonlinear effects of continuous predictors, model specification and variable selection. Specific recommendations on software were rarely given. Statistical guidance should be developed for nonlinear effects, model specification and variable selection to better support medical researchers who perform or interpret regression analyses.

Influence of lipid profile and statin administration on arterial stiffness in renal transplant recipients.

BACKGROUND: Hyperlipidemia is one of the major risk factors for developing a cardiovascular disease (CVD) and it is a frequent post-transplant complication, occurring in up to 60% of the renal transplant recipients (RTRs). Lipid lowering therapy with HMG-CoA reductase inhibitors (statins) is generally recommended and may reduce the overall cardiovascular risk. The aim of this study was to evaluate the lipid profile, statin administration and their relationship with arterial stiffness parameters in RTRs. METHODS: Three hundred and forty-four stable RTRs (62.5% male) transplanted between 1994 and 2018 were randomly enrolled to the study. The following parameters of arterial stiffness was measured in each patient: ankle brachial index, carotid femoral pulse wave velocity (baPWV left and right, cfPWV) and pulse pressure (PP right and left). The study group was divided based on the use statins: 143 (41.6%) and 201 (58.4%). RTRs were qualified to the statin (+) and the statin (-) group, respectively. RESULTS: In the statin (+) as compared to statin (-) group there were more patients with a CVD (32.9% vs. 14.9%) and diabetes (25.2% vs. 14.4%). In the whole study group, CVD was associated with a significant increase of both baPWV and cfPWV as well as PP (8.5 mmHg). There were significant differences in arterial stiffness parameters (baPWV, cfPWV, PP) between the statin (+) and the statin (-) group. CONCLUSIONS: Arterial stiffness was increased in RTRs with CVD and hyperlipidemia. The control of hyperlipidemia was poor in RTRs.

Diagnosing Pathologic Complete Response in the Breast After Neoadjuvant Systemic Treatment of Breast Cancer Patients by Minimal Invasive Biopsy: Oral Presentation at the San Antonio Breast Cancer Symposium on Friday, December 13, 2019, Program Number GS5-03.

OBJECTIVE: We evaluated the ability of minimally invasive, image-guided vacuum-assisted biopsy (VAB) to reliably diagnose a pathologic complete response in the breast (pCR-B). SUMMARY BACKGROUND DATA: Neoadjuvant systemic treatment (NST) elicits a pathologic complete response in up to 80% of women with breast cancer. In such cases, breast surgery, the gold standard for confirming pCR-B, may be considered overtreatment. METHODS: This multicenter, prospective trial enrolled 452 women presenting with initial stage 1-3 breast cancer of all biological subtypes. Fifty-four women dropped out; 398 were included in the full analysis. All participants had an imaging-confirmed partial or complete response to NST and underwent study-specific image-guided VAB before guideline-adherent breast surgery. The primary endpoint was the false-negative rate (FNR) of VAB-confirmed pCR-B. RESULTS: Image-guided VAB alone did not detect surgically confirmed residual tumor in 37 of 208 women [FNR, 17.8%; 95% confidence interval (CI), 12.8-23.7%]. Of these 37 women, 12 (32.4%) had residual DCIS only, 20 (54.1%) had minimal residual tumor (<5 mm), and 19 of 25 (76.0%) exhibited invasive cancer cellularity of ≤10%. In 19 of the 37 cases (51.4%), the false-negative result was potentially avoidable. Exploratory analysis showed that performing VAB with the largest needle by volume (7-gauge) resulted in no false-negative results and that combining imaging and image-guided VAB into a single diagnostic test lowered the FNR to 6.2% (95% CI, 3.4%-10.5%). CONCLUSIONS: Image-guided VAB missed residual disease more often than expected. Refinements in procedure and patient selection seem possible and necessary before omitting breast surgery.

Vacuum-Assisted Breast Biopsy After Neoadjuvant Systemic Treatment for Reliable Exclusion of Residual Cancer in Breast Cancer Patients.

BACKGROUND: About 40 % of women with breast cancer achieve a pathologic complete response in the breast after neoadjuvant systemic treatment (NST). To identify these women, vacuum-assisted biopsy (VAB) was evaluated to facilitate risk-adaptive surgery. In confirmatory trials, the rates of missed residual cancer [false-negative rates (FNRs)] were unacceptably high (> 10%). This analysis aimed to improve the ability of VAB to exclude residual cancer in the breast reliably by identifying key characteristics of false-negative cases. METHODS: Uni- and multivariable logistic regressions were performed using data of a prospective multicenter trial (n = 398) to identify patient and VAB characteristics associated with false-negative cases (no residual cancer in the VAB but in the surgical specimen). Based on these findings FNR was exploratively re-calculated. RESULTS: In the multivariable analysis, a false-negative VAB result was significantly associated with accompanying ductal carcinoma in situ (DCIS) in the initial diagnostic biopsy [odds ratio (OR), 3.94; p < 0.001], multicentric disease on imaging before NST (OR, 2.74; p = 0.066), and age (OR, 1.03; p = 0.034). Exclusion of women with DCIS or multicentric disease (n = 114) and classication of VABs that did not remove the clip marker as uncertain representative VABs decreased the FNR to 2.9% (3/104). CONCLUSION: For patients without accompanying DCIS or multicentric disease, performing a distinct representative VAB (i.e., removing a well-placed clip marker) after NST suggests that VAB might reliably exclude residual cancer in the breast without surgery. This evidence will inform the design of future trials evaluating risk-adaptive surgery for exceptional responders to NST.

The renin-angiotensin-aldosterone system blockade and arterial stiffness in renal transplant recipients - a cross-sectional prospective observational clinical study.

INTRODUCTION: Arterial stiffness parameters can be used as a predictor of cardiovascular events in the general population and renal transplant recipients (RTRs). Additionally, the renin-angiotensin-aldosterone-system (RAAS) blockade mitigates arterial stiffness in the general population. There are no sufficient data concerning the role of the RAAS blockade in reducing arterial stiffness among patients after kidney transplantation. The aim of this study is to assess the influence of the above blockade on arterial stiffness in RTRs. METHODS: 344 stable RTRs were enrolled in the study. 204 (59.3%) of them received RAAS blockers (angiotensin convertase inhibitors - ACEIs or angiotensin receptor blockers - ARBs): group RAAS (+), and 140 (40.7%) were not treated with such agents: group RAAS (-). RESULTS: In the RAAS (+) group, 55.9% of the patients used ARBs and 44.1% ACEIs. Cardiovascular disease (coronary artery disease and/or peripheral obliterans artery disease) (27.9% vs 14.3%, p<0.05), and heart failure (27.4% vs 24.3%, p<0.05) were significantly more often diagnosed in the RAAS (+) group when compared to the RAAS (-) group. Systolic blood pressure, diastolic blood pressure and all arterial stiffness parameters (baPWV, cfPWV, pulse pressure) did not differ significantly between the RAAS (+) and RAAS (-) groups. The results revealed that cardiovascular disease in patients was associated with a significant increase in both, the PWV and pulse pressure. No difference between the arterial stiffness parameters was observed in patients with a cardiovascular disease, diabetes and heart failure in the RAAS (+) and RAAS (-) groups. Moreover, beta-blockers and diuretics ameliorated the arterial stiffness parameters. CONCLUSIONS: This study showed the indication bias of the RAAS prescription, and no conclusion on the influence of RAAS on arterial stiffness can be drawn. The results indicated diuretics and beta-blockers as agents lowering the arterial stiffness in RTRs.

Systematic review of education and practical guidance on regression modeling for medical researchers who lack a strong statistical background: Study protocol.

In the last decades, statistical methodology has developed rapidly, in particular in the field of regression modeling. Multivariable regression models are applied in almost all medical research projects. Therefore, the potential impact of statistical misconceptions within this field can be enormous Indeed, the current theoretical statistical knowledge is not always adequately transferred to the current practice in medical statistics. Some medical journals have identified this problem and published isolated statistical articles and even whole series thereof. In this systematic review, we aim to assess the current level of education on regression modeling that is provided to medical researchers via series of statistical articles published in medical journals. The present manuscript is a protocol for a systematic review that aims to assess which aspects of regression modeling are covered by statistical series published in medical journals that intend to train and guide applied medical researchers with limited statistical knowledge. Statistical paper series cannot easily be summarized and identified by common keywords in an electronic search engine like Scopus. We therefore identified series by a systematic request to statistical experts who are part or related to the STRATOS Initiative (STRengthening Analytical Thinking for Observational Studies). Within each identified article, two raters will independently check the content of the articles with respect to a predefined list of key aspects related to regression modeling. The content analysis of the topic-relevant articles will be performed using a predefined report form to assess the content as objectively as possible. Any disputes will be resolved by a third reviewer. Summary analyses will identify potential methodological gaps and misconceptions that may have an important impact on the quality of analyses in medical research. This review will thus provide a basis for future guidance papers and tutorials in the field of regression modeling which will enable medical researchers 1) to interpret publications in a correct way, 2) to perform basic statistical analyses in a correct way and 3) to identify situations when the help of a statistical expert is required.