scholarly journals Quantification of rhBMP2 in bioactive bone materials

2019 ◽  
Vol 7 (1) ◽  
pp. 71-75
Author(s):  
Huan Lian ◽  
Han Wang ◽  
Qianqian Han ◽  
Chunren Wang
Keyword(s):  
Logistic Regression ◽  
Regression Models ◽  
Logistic Model ◽  
Transforming Growth Factor ◽  
Logistic Models ◽  
Transforming Growth Factor Β ◽  
C2c12 Cells ◽  
Logistic Regression Models ◽  
Alp Activity

Abstract Bone morphogenetic protein (BMP), belongs to transforming growth factor-β (TGF-β) superfamily except BMP-1. Implanting BMP into muscular tissues induces ectopic bone formation at the site of implantation, which provides opportunity for the treatment of bone defects. Recombinant human BMP-2 (rhBMP-2) has been used clinically, but the lack of standard methods for quantifying rhBMP-2 biological activity greatly hindered the progress of commercialization. In this article, we describe an in vitro rhBMP-2 quantification method, as well as the data analyzation pipeline through logistic regression in RStudio. Previous studies indicated that alkaline phosphatase (ALP) activity of C2C12 cells was significantly increased when exposed to rhBMP-2, and showed dose-dependent effects in a certain concentration range of rhBMP-2. Thus, we chose to quantify ALP activity as an indicator of rhBMP-2 bioactivity in vitro. A sigmoid relationship between the ALP activity and concentration of rhBMP-2 was discovered. However, there are tons of regression models for such a non-linear relationship. It has always been a major concern for researchers to choose a proper model that not only fit data accurately, but also have parameters representing practical meanings. Therefore, to fit our rhBMP-2 quantification data, we applied two logistic regression models, three-parameter log-logistic model and four-parameter log-logistic model. The four-parameter log-logistic model (adj-R2 > 0.98) fits better than three-parameter log-logistic model (adj-R2 > 0.75) for the sigmoid curves. Overall, our results indicate rhBMP-2 quantification in vitro can be accomplished by detecting ALP activity and fitting four-parameter log-logistic model. Furthermore, we also provide a highly adaptable R script for any additional logistic models.

On Graphically Checking Goodness-of-fit of Binary Logistic Regression Models

2009 ◽  
Vol 48 (03) ◽  
pp. 306-310 ◽  
Author(s):  
C. E. Minder ◽  
G. Gillmann
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Regression Models ◽  
Goodness Of Fit ◽  
Graphical Method ◽  
Logistic Models ◽  
Binary Logistic Regression ◽  
Logistic Regression Models ◽  
Logistic Analysis ◽  
Viable Solution

Summary Objectives: This paper is concerned with checking goodness-of-fit of binary logistic regression models. For the practitioners of data analysis, the broad classes of procedures for checking goodness-of-fit available in the literature are described. The challenges of model checking in the context of binary logistic regression are reviewed. As a viable solution, a simple graphical procedure for checking goodness-of-fit is proposed. Methods: The graphical procedure proposed relies on pieces of information available from any logistic analysis; the focus is on combining and presenting these in an informative way. Results: The information gained using this approach is presented with three examples. In the discussion, the proposed method is put into context and compared with other graphical procedures for checking goodness-of-fit of binary logistic models available in the literature. Conclusion: A simple graphical method can significantly improve the understanding of any logistic regression analysis and help to prevent faulty conclusions.

scholarly journals Defensible inferences from a nested sequence of logistic regressions: a guide for the perplexed

2021 ◽  
Vol 9 (1) ◽  
Author(s):  
Gulsah Gurkan ◽  
Yoav Benjamini ◽  
Henry Braun
Keyword(s):  
Logistic Regression ◽  
Cognitive Skills ◽  
Regression Models ◽  
Large Scale ◽  
Family Background ◽  
National Income ◽  
Logistic Models ◽  
Logistic Regression Models ◽  
Logistic Regressions ◽  
The Impact

AbstractEmploying nested sequences of models is a common practice when exploring the extent to which one set of variables mediates the impact of another set. Such an analysis in the context of logistic regression models confronts two challenges: (i) direct comparisons of coefficients across models are generally biased due to the changes in scale that accompany the changes in the set of explanatory variables, (ii) conducting a large number of tests induces a problem of multiplicity that can lead to spurious findings of significance if not heeded. This article aims to illustrate a practical strategy for conducting analyses in the face of these challenges. The challenges—and how to address them—are illustrated using a subset of the findings reported by Braun (Large-scale Assess Educ 6(4):1–52, 2018. 10.1186/s40536-018-0058-x), drawn from the Programme for the International Assessment of Adult Competencies (PIAAC), an international, large-scale assessment of adults. For each country in the dataset, a nested pair of logistic regression models was fit in order to investigate the role of Educational Attainment and Cognitive Skills in mediating the impact of family background and demographic characteristics on the location of an individual’s annual income in the national income distribution. A modified version of the Karlson–Holm–Breen (KHB) method was employed to obtain an unbiased estimate of the true differences in the coefficients between nested logistic models. In order to address the issue of multiplicity, a recent generalization of the Benjamini–Hochberg (BH) False Discovery Rate (FDR)-controlling procedure to hierarchically structured hypotheses was employed and compared to two conventional methods. The differences between the changes in coefficients calculated conventionally and with the KHB adjustment varied from negligible to very substantial. When combined with the actual magnitudes of the coefficients, we concluded that the more proximal factors indeed act as strong mediators for the background factors, but less so for Age, and hardly at all for Gender. With respect to multiplicity, applying the FDR-controlling procedure yielded results very similar to those obtained by applying a standard per-comparison procedure, but quite a few more discoveries in comparison to the Bonferroni procedure. The KHB methodology illustrated here can be applied wherever there is interest in comparing nested logistic regressions. Modifications to account for probability sampling are practicable. The categorization of variables and the order of entry should be determined by substantive considerations. On the other hand, the BH procedure is perfectly general and can be implemented to address multiplicity issues in a broad range of settings.

scholarly journals Predictors of Healthcare Associated Infections and Their Role on Mortality in an Intensive Care Unit

2020 ◽  
Vol 30 (Supplement_5) ◽  
Author(s):  
G Migliara ◽  
V Baccolini ◽  
L M Salvatori ◽  
A Angelozzi ◽  
C Isonne ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Intensive Care ◽  
Surveillance System ◽  
Regression Models ◽  
Propensity Scores ◽  
Logistic Models ◽  
Mortality Data ◽  
Healthcare Associated Infections ◽  
Logistic Regression Models ◽  
Healthcare Associated

Abstract Background Healthcare associated Infections (HAIs) represent a significant burden in terms of mortality, morbidity, length of stay and costs for patients in intensive care units (ICUs). In this study, we analyzed the predictors of HAIs development and assessed the HAIs association with mortality. Data were retrieved from a general ICU active surveillance system of a large teaching hospital in Rome. Methods Logistic regression models were built to quantify the association between demographic and clinical factors and the development of HAIs, device-related HAIs and Multi Drug Resistant (MDR)-associated HAIs. The HAIs independent predictors were used to create propensity scores (PS) specific for each model, that was subsequently used to adjust the association between these conditions and mortality in logistic regression models. Results From May 2016 to September 2019, 864 patients were included in the surveillance system, 236 (27.3%) of which had at least one HAI during their hospitalization. Specifically, 162 (18.8%) patients had at least a device-related HAI and the overall mortality rate was 34.3%. Factors associated with the HAIs and the device-related HAIs were mechanical ventilation and admission for trauma. The PS-adjusted logistic models showed an association between HAI and device-related HAI and mortality (OR 1.82, 95%CI 1.30-2.54; OR 2.03, 95%CI 1.40-2.95, respectively). MDR-associated HAIs had a significant association with diabetes mellitus; however, these infections weren't associated with mortality (OR 1.42, 95%CI 0.98-2.08), even in the subgroup of infected patients (OR 0.99, 95%CI 0.56-1.73). Conclusions The study confirms the association between HAIs and device-related HAIs with mortality in ICUs. Apparently, MDR-associated infection subset appears not having a specific association with mortality. However, given the extra effort that these infections require to be managed, they should be adequately surveilled and contrasted. Key messages Healthcare associated infections are strongly associated with mortality in ICU. MDR-associated infections do not seem to give a specific drawback in our setting.

scholarly journals Modeling the Decision to Add New Products by Channel Intermediaries

1989 ◽  
Vol 53 (1) ◽  
pp. 80-88 ◽  
Author(s):  
Vithala R. Rao ◽  
Edward W. McLaughlin
Keyword(s):  
Logistic Regression ◽  
Regression Models ◽  
Logistic Model ◽  
New Products ◽  
Marketing Strategies ◽  
Marketing System ◽  
Logistic Regression Models ◽  
Using Data

Using data collected on new products presented to a major channel intermediary, the authors estimate logistic regression models to describe the intermediary's accept/reject decisions for those products. Results indicate how different variables influence those decisions. The logistic model is shown to fit extremely well with excellent validation performance. Implications of these results for marketing strategies and for improving performance of the marketing system are discussed.

scholarly journals Evaluating predictive biomarkers for a binary outcome with linear versus logistic regression – Practical recommendations for the choice of the model

2018 ◽  
Author(s):  
Damian Gola ◽  
Nicole Heßler ◽  
Markus Schwaninger ◽  
Andreas Ziegler ◽  
Inke R. König
Keyword(s):  
Logistic Regression ◽  
Regression Models ◽  
Logistic Model ◽  
Absolute Risk ◽  
Specific Treatment ◽  
Predictive Biomarkers ◽  
Predictive Biomarker ◽  
Binary Outcome ◽  
Logistic Regression Models ◽  
Study Designs

AbstractA predictive biomarker can forecast whether a patient benefits from a specific treatment under study. To establish predictiveness of a biomarker, a statistical interaction between the biomarker status and the treatment group concerning the clinical outcome needs to be shown. In clinical trials looking at a binary outcome, linear or logistic regression models may be used to evaluate the interaction, but the effects in the two models are different and differently interpreted. Specifically, the effects are estimated as absolute risk reductions (ARRs) and odds ratios (ORs) in the linear and logistic model, thus measuring the effect on an additive and multiplicative scale, respectively.We derived the relationship between the effects of the linear and the logistic regression model allowing for translations between the effect estimates between both models. In addition, we performed a comprehensive simulation study to compare the power of the two models under a variety of scenarios in different study designs. In general, the differences in power to detect interaction were minor, and visible differences were detected in rather unrealistic scenarios of effect size combinations and were usually in favor of the logistic model.Based on our results and theoretical considerations, we recommend to 1) estimate logistic regression models because of their statistical properties, 2) test for interaction effects and 3) calculate and report both ARRs and ORs from these using the formulae provided.

scholarly journals Logistic regression models of factors influencing the location of bioenergy and biofuels plants

BioResources ◽  
2010 ◽  
Vol 6 (1) ◽  
pp. 329-343
Author(s):  
Timothy M. Young ◽  
Russell L. Zaretzki ◽  
James H. Perdue ◽  
Frank M. Guess ◽  
Xu Liu
Keyword(s):  
Logistic Regression ◽  
Regression Models ◽  
Logistic Models ◽  
Basal Area ◽  
Woody Biomass ◽  
Study Region ◽  
Logistic Regression Models ◽  
Logging Residues ◽  
Mill Residues ◽  
Harvesting Costs

Logistic regression models were developed to identify significant factors that influence the location of existing wood-using bioenergy/biofuels plants and traditional wood-using facilities. Logistic models provided quantitative insight for variables influencing the location of woody biomass-using facilities. Availability of “thinnings to a basal area of 31.7m2/ha,” “availability of unused mill residues,” and “high density of railroad availability” had positive significant influences on the location of all wood-using faciities. “Median family income,” “population,” “low density of railroad availability,” and “harvesting costs for logging residues” had negative significant influences on the location of all wood-using faciities. For larger woody biomass-using mills (e.g., biopower) availability of “thinnings to a basal area of 79.2m2/ha,” “number of primary and secondary wood-using mills within an 128.8km haul distance,” and “amount of total mill residues,” had positive significant influences on the location of larger wood-using faciities. “Population” and “harvesting costs for logging residues” have negative significant influences on the location of larger wood-using faciities. Based on the logistic models, 25 locations were predicted for bioenergy or biofuels plants for a 13-state study region in the Southern United States.

scholarly journals On the Complexity of Logistic Regression Models

2019 ◽  
Vol 31 (8) ◽  
pp. 1592-1623
Author(s):  
Nicola Bulso ◽  
Matteo Marsili ◽  
Yasser Roudi
Keyword(s):  
Logistic Regression ◽  
Model Selection ◽  
Regression Models ◽  
Selection Criterion ◽  
Logistic Models ◽  
Upper And Lower Bounds ◽  
Model Parameters ◽  
Data Set ◽  
Entire Axis ◽  
Logistic Regression Models

We investigate the complexity of logistic regression models, which is defined by counting the number of indistinguishable distributions that the model can represent (Balasubramanian, 1997 ). We find that the complexity of logistic models with binary inputs depends not only on the number of parameters but also on the distribution of inputs in a nontrivial way that standard treatments of complexity do not address. In particular, we observe that correlations among inputs induce effective dependencies among parameters, thus constraining the model and, consequently, reducing its complexity. We derive simple relations for the upper and lower bounds of the complexity. Furthermore, we show analytically that defining the model parameters on a finite support rather than the entire axis decreases the complexity in a manner that critically depends on the size of the domain. Based on our findings, we propose a novel model selection criterion that takes into account the entropy of the input distribution. We test our proposal on the problem of selecting the input variables of a logistic regression model in a Bayesian model selection framework. In our numerical tests, we find that while the reconstruction errors of standard model selection approaches (AIC, BIC, [Formula: see text] regularization) strongly depend on the sparsity of the ground truth, the reconstruction error of our method is always close to the minimum in all conditions of sparsity, data size, and strength of input correlations. Finally, we observe that when considering categorical instead of binary inputs, in a simple and mathematically tractable case, the contribution of the alphabet size to the complexity is very small compared to that of parameter space dimension. We further explore the issue by analyzing the data set of the “13 keys to the White House,” a method for forecasting the outcomes of US presidential elections.

The impact of SPY angiography on intraoperative decision making and outcomes for post-mastectomy reconstruction

2019 ◽  
pp. 1-5
Keyword(s):  
Decision Making ◽  
Logistic Regression ◽  
Skin Necrosis ◽  
Regression Models ◽  
Multivariable Analysis ◽  
P Value ◽  
Flap Necrosis ◽  
Single Institution ◽  
Logistic Regression Models ◽  
The Impact

Objective: While the use of intraoperative laser angiography (SPY) is increasing in mastectomy patients, its impact in the operating room to change the type of reconstruction performed has not been well described. The purpose of this study is to investigate whether SPY angiography influences post-mastectomy reconstruction decisions and outcomes. Methods and materials: A retrospective analysis of mastectomy patients with reconstruction at a single institution was performed from 2015-2017.All patients underwent intraoperative SPY after mastectomy but prior to reconstruction. SPY results were defined as ‘good’, ‘questionable’, ‘bad’, or ‘had skin excised’. Complications within 60 days of surgery were compared between those whose SPY results did not change the type of reconstruction done versus those who did. Preoperative and intraoperative variables were entered into multivariable logistic regression models if significant at the univariate level. A p-value <0.05 was considered significant. Results: 267 mastectomies were identified, 42 underwent a change in the type of planned reconstruction due to intraoperative SPY results. Of the 42 breasts that underwent a change in reconstruction, 6 had a ‘good’ SPY result, 10 ‘questionable’, 25 ‘bad’, and 2 ‘had areas excised’ (p<0.01). After multivariable analysis, predictors of skin necrosis included patients with ‘questionable’ SPY results (p<0.01, OR: 8.1, 95%CI: 2.06 – 32.2) and smokers (p<0.01, OR:5.7, 95%CI: 1.5 – 21.2). Predictors of any complication included a change in reconstruction (p<0.05, OR:4.5, 95%CI: 1.4-14.9) and ‘questionable’ SPY result (p<0.01, OR: 4.4, 95%CI: 1.6-14.9). Conclusion: SPY angiography results strongly influence intraoperative surgical decisions regarding the type of reconstruction performed. Patients most at risk for flap necrosis and complication post-mastectomy are those with questionable SPY results.

scholarly journals Effect of prostatic apex shape (Lee types) and urethral sphincter length in preoperative MRI on very early continence rates after radical prostatectomy

2021 ◽  
Author(s):  
Mike Wenzel ◽  
Felix Preisser ◽  
Matthias Mueller ◽  
Lena H. Theissen ◽  
Maria N. Welte ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Radical Prostatectomy ◽  
Regression Models ◽  
Urethral Sphincter ◽  
Anatomical Characteristics ◽  
Logistic Regression Models ◽  
Pad Test ◽  
Type D ◽  
Multivariable Logistic Regression ◽  
Type C

Abstract Purpose To test the effect of anatomic variants of the prostatic apex overlapping the membranous urethra (Lee type classification), as well as median urethral sphincter length (USL) in preoperative multiparametric magnetic resonance imaging (mpMRI) on the very early continence in open (ORP) and robotic-assisted radical prostatectomy (RARP) patients. Methods In 128 consecutive patients (01/2018–12/2019), USL and the prostatic apex classified according to Lee types A–D in mpMRI prior to ORP or RARP were retrospectively analyzed. Uni- and multivariable logistic regression models were used to identify anatomic characteristics for very early continence rates, defined as urine loss of ≤ 1 g in the PAD-test. Results Of 128 patients with mpMRI prior to surgery, 76 (59.4%) underwent RARP vs. 52 (40.6%) ORP. In total, median USL was 15, 15 and 10 mm in the sagittal, coronal and axial dimensions. After stratification according to very early continence in the PAD-test (≤ 1 g vs. > 1 g), continent patients had significantly more frequently Lee type D (71.4 vs. 54.4%) and C (14.3 vs. 7.6%, p = 0.03). In multivariable logistic regression models, the sagittal median USL (odds ratio [OR] 1.03) and Lee type C (OR: 7.0) and D (OR: 4.9) were independent predictors for achieving very early continence in the PAD-test. Conclusion Patients’ individual anatomical characteristics in mpMRI prior to radical prostatectomy can be used to predict very early continence. Lee type C and D suggest being the most favorable anatomical characteristics. Moreover, longer sagittal median USL in mpMRI seems to improve very early continence rates.

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