On quadratic logistic regression models when predictor variables are subject to measurement error

2016 ◽  
Vol 95 ◽  
pp. 109-121 ◽  
Author(s):  
Jakub Stoklosa ◽  
Yih-Huei Huang ◽  
Elise Furlan ◽  
Wen-Han Hwang
Keyword(s):  
Logistic Regression ◽  
Measurement Error ◽  
Regression Models ◽  
Predictor Variables ◽  
Logistic Regression Models

scholarly journals Risk Assessment Models for Wheat Fusarium Head Blight Epidemics Based on Within-Season Weather Data

2003 ◽  
Vol 93 (4) ◽  
pp. 428-435 ◽  
Author(s):  
E. D. De Wolf ◽  
L. V. Madden ◽  
P. E. Lipps
Keyword(s):  
Logistic Regression ◽  
Relative Humidity ◽  
Fusarium Head Blight ◽  
Prediction Accuracy ◽  
Regression Models ◽  
Weather Conditions ◽  
Predictor Variables ◽  
Stepwise Logistic Regression ◽  
Head Blight ◽  
Logistic Regression Models

Logistic regression models for wheat Fusarium head blight were developed using information collected at 50 location-years, including four states, representing three different U.S. wheat-production regions. Non-parametric correlation analysis and stepwise logistic regression analysis identified combinations of temperature, relative humidity, and rainfall or durations of specified weather conditions, for 7 days prior to anthesis, and 10 days beginning at crop anthesis, as potential predictor variables. Prediction accuracy of developed logistic regression models ranged from 62 to 85%. Models suitable for application as a disease warning system were identified based on model prediction accuracy, sensitivity, specificity, and availability of weather variables at crop anthesis. Four of the identified models correctly classified 84% of the 50 location-years. A fifth model that used only pre-anthesis weather conditions correctly classified 70% of the location-years. The most useful predictor variables were the duration (h) of precipitation 7 days prior to anthesis, duration (h) that temperature was between 15 and 30°C 7 days prior to anthesis, and the duration (h) that temperature was between 15 and 30°C and relative humidity was greater than or equal to 90%. When model performance was evaluated with an independent validation set (n = 9), prediction accuracy was only 6% lower than the accuracy for the original data sets. These results indicate that narrow time periods around crop anthesis can be used to predict Fusarium head blight epidemics.

scholarly journals Predicting Fusarium Head Blight Epidemics With Weather-Driven Pre- and Post-Anthesis Logistic Regression Models

2013 ◽  
Vol 103 (9) ◽  
pp. 906-919 ◽  
Author(s):  
D. A. Shah ◽  
J. E. Molineros ◽  
P. A. Paul ◽  
K. T. Willyerd ◽  
L. V. Madden ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Relative Humidity ◽  
Fusarium Head Blight ◽  
Regression Models ◽  
The United States ◽  
Misclassification Rate ◽  
Predictor Variables ◽  
Head Blight ◽  
Data Set ◽  
Logistic Regression Models

Our objective was to identify weather-based variables in pre- and post-anthesis time windows for predicting major Fusarium head blight (FHB) epidemics (defined as FHB severity ≥ 10%) in the United States. A binary indicator of major epidemics for 527 unique observations (31% of which were major epidemics) was linked to 380 predictor variables summarizing temperature, relative humidity, and rainfall in 5-, 7-, 10-, 14-, or 15-day-long windows either pre- or post-anthesis. Logistic regression models were built with a training data set (70% of the 527 observations) using the leaps-and-bounds algorithm, coupled with bootstrap variable and model selection methods. Misclassification rates were estimated on the training and remaining (test) data. The predictive performance of models with indicator variables for cultivar resistance, wheat type (spring or winter), and corn residue presence was improved by adding up to four weather-based predictors. Because weather variables were intercorrelated, no single model or subset of predictor variables was best based on accuracy, model fit, and complexity. Weather-based predictors in the 15 final empirical models selected were all derivatives of relative humidity or temperature, except for one rainfall-based predictor, suggesting that relative humidity was better at characterizing moisture effects on FHB than other variables. The average test misclassification rate of the final models was 19% lower than that of models currently used in a national FHB prediction system.

Relationships between Lower Extremity Musculoskeletal Disorders Symptoms and Tibial Acceleration Measures in Distribution Center Workers

2016 ◽  
Vol 60 (1) ◽  
pp. 980-981 ◽  
Author(s):  
Mohini Dutt ◽  
Steven A. Lavender ◽  
Carolyn M. Sommerich ◽  
Ajit M.W. Chaudhari
Keyword(s):  
Logistic Regression ◽  
Lower Extremity ◽  
Regression Models ◽  
Distribution Center ◽  
Musculoskeletal Symptoms ◽  
Predictor Variables ◽  
Logistic Regression Models ◽  
Outcome Score ◽  
Symptom Frequency ◽  
Tibial Acceleration

In a survey of 341 workers, we have found lower extremity musculoskeletal symptoms to be prevalent in distribution center employees working in material handling jobs. This study was a cross-sectional field study aimed at developing risk models showing associations between tibial acceleration and lower extremity musculoskeletal disorder symptoms. One hundred thirty two participants volunteered to wear uni-axial accelerometers that quantified their bilateral tibial acceleration exposures during two hours of normal work activities and also completed a questionnaire assessing individual factors and their experience with lower extremity musculoskeletal symptoms. The questionnaire and accelerometer data were used to develop logistic regression models exploring the relationships between the likelihood of self-reported lower extremity symptoms in the hip/thigh, knee, and ankles/feet and relevant biomechanical and individual exposure variables. An outcome score was created by multiplying the symptom frequency score by the symptom severity score by the therapy score for both the knees and the ankles/feet regions. Only the symptom frequency and severity scores were multiplied to create the hip/thigh outcome score. Multiple logistic regression models were used to predict the probability of being symptomatic based on the accelerometer, work exposure, and individual characteristics predictor variables. Table 1 shows the sensitivity of the models predicting symptoms in the hip/thighs, knees, and ankles/feet and the contributing predictor variables.

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.

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