scholarly journals Factors Determining Birth Intervals: A Multilevel Mixed Effect Parametric Survival Approach

2021 ◽  
Vol 28 (2) ◽  
pp. 29-35
Author(s):  
O.I. Adeniyi ◽  
I.R. Olonijolu ◽  
A.A. Akinrefon
Keyword(s):  
Economic Status ◽  
Random Effect ◽  
Survival Models ◽  
Age At First Birth ◽  
Birth Intervals ◽  
Mixed Effect ◽  
Flexible Parametric Model ◽  
Parametric Survival Model ◽  
Effect Parameter ◽  
Parametric Survival

Interval between births plays an important role in maternal health as well as child health. This study applies the methodology of Flexible parametric survival models to data on successive births among Nigeria women using the dataset from 2018 National Demographic Health survey. The flexible parametric survival model with Weibull baseline distribution was found to be the best among other fitted baseline distributions. The factors, zone of residence, educational qualification, religion, economic status and age at first birth were found to be significant in predicting the birth intervals. It was found that random effect parameter indicates that the interval between successive births is similar from the same woman. Keywords: Birth intervals, Baseline hazard, Mixed effect, Flexible parametric model, AIC. 

A comparison of cause-specific and competing risk models to assess risk factors for dementia

2020 ◽  
Vol 9 (1) ◽  
Author(s):  
Michael Waller ◽  
Gita D. Mishra ◽  
Annette J. Dobson
Keyword(s):  
Risk Factors ◽  
Competing Risks ◽  
Hazard Model ◽  
Specific Model ◽  
Competing Risk ◽  
Survival Models ◽  
Parametric Survival Model ◽  
Competing Risk Models ◽  
Using Data ◽  
Parametric Survival

AbstractThe study of dementia risk factors is complicated by the competing risk of dying. The standard approaches are the cause-specific Cox proportional hazard model with deaths treated as censoring events (and removed from the risk set) and the Fine and Gray sub-distribution hazard model in which those who die remain in the risk set. An alternative approach is to modify the risk set between these extremes. We propose a novel method of doing this based on estimating the time at which the person might have been diagnosed if they had not died using a parametric survival model, and then applying the cause-specific and Fine and Gray models to the modified dataset. We compare these methods using data on dementia from the Australian Longitudinal Study on Women’s Health and discuss the assumptions and limitations of each model. The results from survival models to assess risk factors for dementia varied considerably between the cause-specific model and the models designed to account for competing risks. Therefore, when assessing risk factors in the presence of competing risks it is important to examine results from: the cause-specific model, different models which account for competing risks, and the model which assesses risk factors associated with the competing risk.

scholarly journals Performance evaluation of survival regression models in analysing Swedish dental implant complication data with frailty

PLoS ONE ◽  
2021 ◽  
Vol 16 (1) ◽  
pp. e0245111
Author(s):  
Adeniyi Francis Fagbamigbe ◽  
Karolina Karlsson ◽  
Jan Derks ◽  
Max Petzold
Keyword(s):  
Survival Data ◽  
Regression Models ◽  
Dental Restorations ◽  
Implant Complications ◽  
Flexible Parametric Model ◽  
Parametric Survival Model ◽  
Parametric Survival ◽  
Single Crowns ◽  
Swedish Cohort ◽  
Survival Regression

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.

scholarly journals comparison of analytic models for the costs of the hospitalized diabetic patients

2005 ◽  
Vol 2 (1) ◽  
Author(s):  
Giulia Zigon ◽  
Rosalba Rosato ◽  
Simona Bò ◽  
Dario Gregori
Keyword(s):  
Regression Model ◽  
Least Square ◽  
Survival Models ◽  
Diabetic Patients ◽  
Hospitalization Costs ◽  
Parametric Survival Model ◽  
Additive Regression Model ◽  
Ordinary Least Square Regression ◽  
Additive Regression ◽  
Parametric Survival

Objective: To compare some survival models as applied to the estimation of the costs of hospitalization as a function of several covariates in diabetic patients. The application of the Aalen regression model (Aalen, 1989) to medical costs is stressed. Design: Retrospective observational study analyzing hospitalizations in a cohort of diabetic patients with a follow up of 4.5 years. Study population: A total of 2550 patients have been included in the cohort, according to clinical-based enrollment standards. The patients with at least one hospitalization have been considered in this analysis. Methods: Costs have been modelled using five different regression model: the ordinary least square regression model, the logistic regression model using the median and the third quartile of the costs distribution as cut-off points, the parametric survival model assuming Weibull distribution, the Cox proportional hazard model and the Aalen additive regression model. Conclusions: The Aalen additive regression model applied to the costs has the best performances in estimating the mean hospitalization costs for specific clinical profiles.

Estimating long-term survival of previously untreated patients with EGFR mutation-positive (EGFRm) advanced non-small cell lung cancer (NSCLC) who received osimertinib in the FLAURA study.

2019 ◽  
Vol 37 (15_suppl) ◽  
pp. e20560-e20560
Author(s):  
Matthew Dyer ◽  
Matthew Green ◽  
simon jones ◽  
Rachel Hodge
Keyword(s):  
Advanced Nsclc ◽  
Survival Rates ◽  
Survival Model ◽  
Survival Models ◽  
Term Survival ◽  
Parametric Survival Model ◽  
Best Fitting ◽  
Parametric Survival

e20560 Background: In the Phase III FLAURA trial (NCT02296125), osimertinib, a third-generation EGFR-TKI, provided clinically and statistically significantly longer progression-free survival versus gefitinib/erlotinib as first-line treatment for patients with EGFRm advanced NSCLC. At the time of analysis, data on overall survival (OS) were immature (25% maturity). To better understand the long-term survival potential of osimertinib beyond the observed trial follow-up period, mathematical parametric survival models were used to estimate clinically plausible survival rates up to 5 years from FLAURA. Methods: Following published best-practice guidelines, candidate parametric survival models were evaluated based on both statistical and visual goodness-of-fit to the observed FLAURA OS data. Two modeling approaches were considered: single models with treatment included as a covariate; and separate models fitted to the osimertinib and gefitinib/erlotinib arms. Point estimates of 5-year survival rates with 95% confidence intervals (CIs) are reported for the best fitting model. Results: The best fitting parametric survival model to the FLAURA OS data was the Weibull model with treatment included as a covariate. Based on this model, estimated median OS was longer with osimertinib than with gefitinib/erlotinib (41.4 months vs 30.6 months). The estimated 3- and 5-year survival rates with osimertinib were 57.3% (95% CI 46.6%, 69.2%) and 31.1% (95% CI 23.7%, 41.8%), respectively. In comparison, the estimated 3- and 5-year survival rates with gefitinib/erlotinib were 41.1% (95% CI 31.9%, 52.9%) and 15.5% (95% CI 11.6%, 22.1%), respectively. Conclusions: Based on the best fitting parametric survival model to FLAURA OS data, the estimated 5-year survival rate with osimertinib was double that with gefitinib/erlotinib (31.1% vs 15.5%) in patients with EGFRm advanced NSCLC. Long-term follow-up data from FLAURA (60% OS maturity) will further validate this finding. Clinical trial information: NCT02296125.

scholarly journals Diagnostic Value of Clinical Features to Distinguish Enteric Fever From Other Febrile Illnesses in Bangladesh, Nepal, and Pakistan

2020 ◽  
Vol 71 (Supplement_3) ◽  
pp. S257-S265 ◽  
Author(s):  
Kristen Aiemjoy ◽  
Dipesh Tamrakar ◽  
Shampa Saha ◽  
Shiva R Naga ◽  
Alexander T Yu ◽  
...  
Keyword(s):  
Blood Culture ◽  
Clinical Features ◽  
Diagnostic Performance ◽  
Enteric Fever ◽  
Random Effect ◽  
Clinical Signs ◽  
Febrile Illness ◽  
Diagnostic Value ◽  
Case Definition ◽  
Mixed Effect

Abstract Background Enteric fever, a bacterial infection caused by Salmonella enterica serotypes Typhi and Paratyphi A, frequently presents as a nonlocalizing febrile illness that is difficult to distinguish from other infectious causes of fever. Blood culture is not widely available in endemic settings and, even when available, results can take up to 5 days. We evaluated the diagnostic performance of clinical features, including both reported symptoms and clinical signs, of enteric fever among patients participating in the Surveillance for Enteric Fever in Asia Project (SEAP), a 3-year surveillance study in Bangladesh, Nepal, and Pakistan. Methods Outpatients presenting with ≥3 consecutive days of reported fever and inpatients with clinically suspected enteric fever from all 6 SEAP study hospitals were eligible to participate. We evaluated the diagnostic performance of select clinical features against blood culture results among outpatients using mixed-effect regression models with a random effect for study site hospital. We also compared the clinical features of S. Typhi to S. Paratyphi A among both outpatients and inpatients. Results We enrolled 20 899 outpatients, of whom 2116 (10.1%) had positive blood cultures for S. Typhi and 297 (1.4%) had positive cultures for S. Paratyphi A. The sensitivity of absence of cough was the highest among all evaluated features, at 65.5% (95% confidence interval [CI], 55.0–74.7), followed by measured fever at presentation at 59.0% (95% CI, 51.6–65.9) and being unable to complete normal activities for 3 or more days at 51.0% (95% CI, 23.8–77.6). A combined case definition of 3 or more consecutive days of reported fever and 1 or more of the following (a) either the absence of cough, (b) fever at presentation, or (c) 3 or more consecutive days of being unable to conduct usual activity--yielded a sensitivity of 94.6% (95% CI, 93.4–95.5) and specificity of 13.6% (95% CI, 9.8–17.5). Conclusions Clinical features do not accurately distinguish blood culture–confirmed enteric fever from other febrile syndromes. Rapid, affordable, and accurate diagnostics are urgently needed, particularly in settings with limited or no blood culture capacity.

scholarly journals A Repeated Time-to-Positive Symptoms Improvement among Malaysian Patients with Schizophrenia Spectrum Disorders Treated with Clozapine

Pharmaceutics ◽  
2021 ◽  
Vol 13 (8) ◽  
pp. 1121
Author(s):  
Orwa Albitar ◽  
Sabariah Noor Harun ◽  
Siti Nor Aizah Ahmad ◽  
Siti Maisharah Sheikh Ghadzi
Keyword(s):  
Visual Predictive Check ◽  
Clinical Importance ◽  
Antipsychotic Agent ◽  
Survival Models ◽  
Psychiatric Clinic ◽  
Positive Symptoms ◽  
Schizophrenia Spectrum Disorders ◽  
Mixed Effect ◽  
Essential Information ◽  
Schizophrenia Spectrum

Clozapine remains the drug of choice for resistant schizophrenia. However, its dose-response relationship is still controversial. The current investigation aimed to develop a repeated time-to-positive symptoms improvement following the onset of clozapine treatment in Malaysian schizophrenia spectrum disorder patients. Data from patients’ medical records in the Psychiatric Clinic, Penang General Hospital, were retrospectively analyzed. Several parametric survival models were evaluated using nonlinear mixed-effect modeling software (NONMEM 7.3.0). Kaplan–Meier-visual predictive check (KM-VPC) and sampling-importance resampling (SIR) methods were used to validate the final model. A total of 116 patients were included in the study, with a mean follow-up of 306 weeks. Weibull hazard function best fitted the data. The hazard of positive symptoms improvement decreased 4% for every one-year increase in age over the median of 41 years (adjusted hazard ratio (aHR), 0.96; 95% confidence intervals (95% CI), (0.93–0.98)). However, patients receiving a second atypical antipsychotic agent had four-folds higher hazard (aHR, 4.01; 95% CI, (1.97–7.17)). The hazard increased 2% (aHR, 1.02; 95% CI, (1.01–1.03)) for every 1 g increase in the clozapine six months cumulative dose over the median of 34 g. The developed model provides essential information on the hazard of positive symptoms improvement after the first clozapine dose administration, including modifiable predictors of high clinical importance.

scholarly journals Different approaches to quantify years of life lost from COVID-19

2021 ◽  
Author(s):  
Tamás Ferenci
Keyword(s):  
Years Of Life Lost ◽  
Expected Number ◽  
Long Term Conditions ◽  
Developed Country ◽  
Individual Patient Level ◽  
Level Data ◽  
Parametric Survival Model ◽  
Epidemiologic Risk ◽  
Parametric Survival

AbstractThe burden of an epidemic is often characterized by death counts, but this can be misleading as it fails to acknowledge the age of the deceased patients. Years of life lost is therefore widely used as a more relevant metric, however, such calculations in the context of COVID-19 are all biased upwards: patients dying from COVID-19 are typically multimorbid, having far worse life expectation than the general population. These questions are quantitatively investigated using a unique Hungarian dataset that contains individual patient level data on comorbidities for all COVID-19 deaths in the country. To account for the comorbidities of the patients, a parametric survival model using 11 important long-term conditions was used to estimate a more realistic years of life lost. As of 12 May, 2021, Hungary reported a total of 27,837 deaths from COVID-19 in patients above 50 years of age. The usual calculation indicates 10.5 years of life lost for each death, which decreases to 9.2 years per death after adjusting for 11 comorbidities. The expected number of years lost implied by the life table, reflecting the mortality of a developed country just before the pandemic is 11.1 years. The years of life lost due to COVID-19 in Hungary is therefore 12% or 1.3 years per death lower when accounting for the comorbidities and is below its expected value, but how this should be interpreted is still a matter of debate. Further research is warranted on how to optimally integrate this information into epidemiologic risk assessments during a pandemic.

Integrated likelihoods in parametric survival models for highly clustered censored data

2015 ◽  
Vol 22 (3) ◽  
pp. 382-404
Author(s):  
Giuliana Cortese ◽  
Nicola Sartori
Keyword(s):  
Censored Data ◽  
Survival Models ◽  
Parametric Survival
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