scholarly journals Stata Tip 94: Manipulation of Prediction Parameters for Parametric Survival Regression Models

Author(s):  
Theresa Boswell ◽  
Roberto G. Gutierrez
PLoS ONE ◽  
2021 ◽  
Vol 16 (1) ◽  
pp. e0245111
Author(s):  
Adeniyi Francis Fagbamigbe ◽  
Karolina Karlsson ◽  
Jan Derks ◽  
Max Petzold

The use of inappropriate methods for estimating the effects of covariates in survival data with frailty leads to erroneous conclusions in medical research. This study evaluated the performance of 13 survival regression models in assessing the factors associated with the timing of complications in implant-supported dental restorations in a Swedish cohort. Data were obtained from randomly selected cohort (n = 596) of Swedish patients provided with dental restorations supported in 2003. Patients were evaluated over 9 years of implant loss, peri-implantitis or technical complications. Best Model was identified using goodness, AIC and BIC. The loglikelihood, the AIC and BIC were consistently lower in flexible parametric model with frailty (df = 2) than other models. Adjusted hazard of implant complications was 45% (adjusted Hazard Ratio (aHR) = 1.449; 95% Confidence Interval (CI): 1.153–1.821, p = 0.001) higher among patients with periodontitis. While controlling for other variables, the hazard of implant complications was about 5 times (aHR = 4.641; 95% CI: 2.911–7.401, p<0.001) and 2 times (aHR = 2.338; 95% CI: 1.553–3.519, p<0.001) higher among patients with full- and partial-jaw restorations than those with single crowns. Flexible parametric survival model with frailty are the most suitable for modelling implant complications among the studied patients.


2021 ◽  
pp. 097215092098865
Author(s):  
Amare Wubishet Ayele ◽  
Abebaw Bizuayehu Derseh

The contributions of small and medium-sized enterprises (SMEs) to socio-economic development are generally recognized, but they have faced several obstacles that impede their sustainability. This manuscript seeks to identify factors for the survival of SMEs in the East Gojjam Zone, Ethiopia. The prospective study design was employed. Both descriptive and inferential statistics, particularly families of parametric survival regression models, have been used. Of the 650 enterprises included in this study, 330 (50.8%) were censored (sustained enterprises) and the remaining 320 (49.2%) were events or withdrawn enterprises. The findings of this study revealed that the incidence of termination or withdrawal of SMEs in the study area is relatively common. The results from multivariable Weibull regression model revealed that woreda, sector, manger profile (gender, age, educational status, experience (in year) and source of experience), working place, marketing channel and profitability district status of enterprise were found to be statistically significant factors for the sustainability of enterprises in the study area. The bodies concerned, in particular the enterprise administrative offices at various levels, should work with collaborative organizations to develop a strong marketing platform (network), should be able to make workplaces accessible with the required infrastructure at minimal rental costs, and should prioritize the type of sector that has the highest customer needs at the onset, for instance, agriculture and service sectors.


2014 ◽  
Vol 13s7 ◽  
pp. CIN.S16351
Author(s):  
Christina Ruggeri ◽  
Kevin H. Eng

Modeling signal transduction in cancer cells has implications for targeting new therapies and inferring the mechanisms that improve or threaten a patient's treatment response. For transcriptome-wide studies, it has been proposed that simple correlation between a ligand and receptor pair implies a relationship to the disease process. Statistically, a differential correlation (DC) analysis across groups stratified by prognosis can link the pair to clinical outcomes. While the prognostic effect and the apparent change in correlation are both biological consequences of activation of the signaling mechanism, a correlation-driven analysis does not clearly capture this assumption and makes inefficient use of continuous survival phenotypes. To augment the correlation hypothesis, we propose that a regression framework assuming a patient-specific, latent level of signaling activation exists and generates both prognosis and correlation. Data from these systems can be inferred via interaction terms in survival regression models allowing signal transduction models beyond one pair at a time and adjusting for other factors. We illustrate the use of this model on ovarian cancer data from the Cancer Genome Atlas (TCGA) and discuss how the finding may be used to develop markers to guide targeted molecular therapies.


2008 ◽  
Vol 14 (3) ◽  
pp. 316-332 ◽  
Author(s):  
Michelli Barros ◽  
Gilberto A. Paula ◽  
Víctor Leiva

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