Estimating treatment efficacy over time: a logistic regression model for binary longitudinal outcomes

2005 ◽  
Vol 24 (18) ◽  
pp. 2789-2805 ◽  
Author(s):  
Leena Choi ◽  
Francesca Dominici ◽  
Scott L. Zeger ◽  
Peter Ouyang
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Treatment Efficacy ◽  
Logistic Regression Model ◽  
Longitudinal Outcomes ◽  
Over Time

Institutions, entrepreneurial adaptation, and the legal form of the organization

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Indu Khurana ◽  
Dmitriy Krichevskiy ◽  
Gregory Dempster ◽  
Sean Stimpson
Keyword(s):  
Logistic Regression ◽  
Survival Rate ◽  
Regression Model ◽  
Economic Freedom ◽  
Logistic Regression Model ◽  
Legal Form ◽  
Content Type ◽  
Legal Structure ◽  
The Usa ◽  
Over Time

PurposeThis paper aims to examine how economic freedom impacts the initial choice of legal structure for startup firms. The authors do this by first exploring whether economic freedom is an essential determinant of the initial legal form of organization (LFO). The authors then explore the impact of economic freedom on firms' choice of changing their initial legal structure over time and how this change impacts their survival rate.Design/methodology/approachThe authors employ a multinomial logistic regression model to measure the initial determinants of LFO by utilizing an eight-year panel data set of 4,928 startups in the USA through the Kauffman firm survey and merge it with the Economic Freedom in North American index from the Fraser Institute. The authors then employ a logistic regression model to examine the determinants facilitating a change in legal structure over time.FindingsThe results show that economic freedom is a significant determinant in the choice of legal structure. The findings also report that the majority of startups do not change their legal form, but of those that do change the legal structure show a higher survival rate.Research limitations/implicationsMajor limitations are the size of the data and the nature of somewhat limited economic freedom differences with the USA. More nuanced measures of economic freedom would be highly desirable.Practical implicationsPolicymakers should take note that limited red tape, smoothly working labor markets and straightforward processes for changes of legal structures of organizations would improve survival and growth odds for entrepreneurs.Originality/valueDrawing on the theory of institutions, the authors attempt to bridge a gap in the literature by explicitly analyzing the determinants of the legal structure in startups in light of economic freedom. Institutional factors do not work in isolation; therefore, the authors also employ traditional entrepreneur-specific variables that affect the choice of legal structure in addition to the institutional framework.

scholarly journals Effectiveness of Mid-Regional Pro-Adrenomedullin (MR-proADM) as Prognostic Marker in COVID-19 Critically Ill Patients: an Observational Prospective Study

2020 ◽  
Author(s):  
Giorgia Montrucchio ◽  
Gabriele Sales ◽  
Francesca Rumbolo ◽  
Filippo Palmesino ◽  
Vito Fanelli ◽  
...  
Keyword(s):  
Cardiovascular Disease ◽  
Logistic Regression ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Predictive Ability ◽  
Rank Test ◽  
Operating Characteristics ◽  
Icu Patients ◽  
D Dimer ◽  
Over Time

Abstract Background Due to the lack of validated biomarkers to predict disease progression and mortality in COVID-19 ICU-patients, we tested the effectiveness of mid-regional pro-adrenomedullin (MR-proADM) in comparison to C-reactive protein (CRP), procalcitonin (PCT), D-dimer, lactate dehydrogenase (LDH) in predicting outcome.Methods All consecutive COVID-19 adult patients admitted between March and June 2020 to the ICU of the ‘Città della Salute e della Scienza’ hospital in Turin (Italy) were enrolled. MR-proADM, clinical and routine laboratory test were measured within 48 hours from ICU admission, on day 3, 7 and 14. Survival curves difference with MR-proADM cut-off set to 1.8 nmol/L were tested using log-rank test. Predictive ability was compared using area under the curve and 95% confidence interval of different receiver-operating characteristics curves. Potential confounding effects were tested using a logistic regression model. Results Fifty-seven patients were enrolled. ICU and overall mortality were 54.4%. Within the first 24 hours, lymphocytopenia was present in 86%; increased D-dimer and CRP levels were found in 84.2% and 87.7% respectively, while PCT values higher than 0.5 μg/L were observed in 47.4%. MR-proADM, CRP and LDH were significantly different between surviving and non-surviving patients and over time, while PCT, D-dimer and NT-pro-BNP did not show any difference between the groups and over time; lymphocytes count was different between surviving and non-surviving patients only.MR-proADM was higher in dying patients (2.65+2.33vs1.18+0.47, p=0.0001) and a higher mortality characterized patients with MR-proADM exceeding 1.8 nmol/L (p=0.0157). The logistic regression model adjusted for age, gender, cardiovascular disease, diabetes mellitus and PCT values confirmed an odds ratio equal to 10.274 (95%CI 1.970-53.578) (p=0.0057) for MR-proADM higher than 1.8 nmol/L and equal to 22.206 (95%CI 1.56-316.960) (p=0.0223) for cardiovascular disease. Overall, MR-proADM was found to have the best predictive ability (AUC=0.846 – 95%CI 0.779-0.899).Conclusions In COVID-19 ICU-patients, MR-proADM seems able to provide a more precise stratification of disease severity and mortality risk than other biomarkers. Repeated MR-proADM measurement may support a rapid and effective decision-making. Further studies are needed to better explain the mechanisms responsible of the increase in MR-proADM observed in COVID-19 patients.

Culling in Quebec Holstein herds. 2. Study of phenotypic trends in reasons for disposal

1997 ◽  
Vol 77 (4) ◽  
pp. 601-608 ◽  
Author(s):  
J. W. Dürr ◽  
H. G. Monardes ◽  
R. I. Cue ◽  
J. C. Philpot
Keyword(s):  
Logistic Regression ◽  
Statistical Analysis ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Dairy Herds ◽  
Parity Number ◽  
Increasing Trends ◽  
Agricultural Regions ◽  
Linear Contrasts ◽  
Over Time

A total of 1 558 080 lactation records from PATLQ Holstein cows were used to describe the annual trends in reasons for disposal in Quebec dairy herds from 1981 to 1994. Differences in culling trends between official and owner sampler herds, between parities, and between Quebec agricultural regions were compared. Statistical analysis was carried out by means of a logistic regression model, and the significance of trends was tested by linear contrasts. Involuntary culling had a clearly ascending trend during the period of study (from 23% in 1981 to 32% in 1994), as opposed to culling for low production (voluntary), which had a descending trend (from 16% in 1981 to 4.5% in 1994). This increase in involuntary culling was mainly due to increasing trends in culling for reproductive problems, mastitis and feet and leg problems. Official herds had a greater proportion of cows with sale codes and fewer cows culled for mastitis than owner sampler herds, and the trend for sale codes was ascending for official and stable for owner sampler herds. Culling for low production was more intensive in first parity, but all parities showed a descending trend over time. The proportion of cows with sale codes decreased with parity number. For all involuntary reasons, proportion of cows culled increased with parity number. Key words: Reasons for disposal, Holstein, Quebec, culling

Trends of changes in the specific contribution of selected risk factors for shoulder dystocia over a period of more than two decades

2020 ◽  
Vol 48 (6) ◽  
pp. 567-573
Author(s):  
Leah Grossman ◽  
Gali Pariente ◽  
Yael Baumfeld ◽  
David Yohay ◽  
Reut Rotem ◽  
...  
Keyword(s):  
Risk Factors ◽  
Logistic Regression ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Linear Increase ◽  
Shoulder Dystocia ◽  
Fetal Weight ◽  
Independent Risk Factors ◽  
Specific Contribution ◽  
Over Time

AbstractObjectivesShoulder dystocia (SD) is an obstetrical emergency with well-recognized risk factors. We aimed to identify trends of changes in the specific contribution of risk factors for SD over time.MethodsA nested case control study comparing all singleton deliveries with and without SD was undertaken. A multivariable logistic regression model was used in order to identify independent risk factors for SD and a comparison of the prevalence and the specific contribution (odds ratio (OR)) of the chosen risk factors in three consecutive eight-year intervals from 1988 to 2014 was performed.ResultsDuring the study period, there were 295,946 deliveries. Of them 514 (0.174%) were complicated with SD. Between 1988 and 2014 the incidence of SD has decreased from 0.3% in 1988 to 0.1% in 2014. Using a logistic regression model grandmultiparity, diabetes mellitus (DM), fetal weight, and large for gestational age (LGA) were found to be independent risk factors for SD (OR 1.25 95% CI 1.04–1.51, p=0.02; OR 1.53 95% CI 1.19–1.97, p=0.001; OR 1.002 95% CI 1.001–1.002, p < 0.001; OR 3.88 95% CI 3.09–4.87, p < 0.001; respectively). While the OR for grandmultiparity, fetal weight, and LGA has significantly changed during the study period with a mixed trend, the OR of DM has demonstrated a significant linear increase over time.ConclusionsThe individual contribution of selected risk factors for the occurrence of SD has significantly changed throughout the years. The contribution of DM has demonstrated a linear increase over time, emphasizing the great impact of DM on SD.

Statistical Process Control for Monitoring Standardized Mortality Ratios of a Classification Tree Model

2012 ◽  
Vol 51 (04) ◽  
pp. 353-358 ◽  
Author(s):  
S. Eslami ◽  
N. de Keizer ◽  
E. de Jonge ◽  
S. E. de Rooij ◽  
A. Abu-Hanna ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Quality Of Care ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Classification Tree ◽  
Brier Score ◽  
Tree Model ◽  
Care Assessment ◽  
Over Time

SummaryObjectives: The ratio of observed to expected mortality (standardized mortality ratio, SMR), is a key indicator of quality of care. We use PreControl Charts to investigate SMR behavior over time of an existing tree-model for predicting mortality in intensive care units (ICUs) and its implications for hospital ranking. We compare the results to those of a logistic regression model.Methods: We calculated SMRs of 30 equally-sized consecutive subsets from a total of 12,143 ICU patients aged 80 years or older and plotted them on a PreControl Chart. We calculated individual hospital SMRs in 2009, with and without repeated recalibration of the models on earlier data.Results: The overall SMR of the tree-model was stable over time, in contrast to logistic regression. Both models were stable after repeated recalibration. The overall SMR of the tree on the whole validation set was statistically significantly different (SMR 1.00 ± 0.012 vs. 0.94 ± 0.01) and worse in performance than the logistic regression model (AUC 0.76 ± 0.005 vs. 0.79 ± 0.004; Brier score 0.17 ± 0.012 vs. 0.16 ± 0.010). The individual SMRs’ range in 2009 was 0.53–1.31 for the tree and 0.64–1.27 for logistic regression. The proportion of individual hospitals with SMR >1, hinting at poor quality of care, reduced from 38% to 29% after recalibration for the tree, and increased from 15% to 35% for logistic regression.Conclusions: Although the tree-model has seemingly a longer shelf life than the logistic regression model, its SMR may be less useful for quality of care assessment as it insufficiently responds to changes in the population over time.

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