Integrated likelihoods in parametric survival models for highly clustered censored data

Introduction: Despite medical and surgical advances over the past few decades, mortality for infants with single ventricle congenital heart disease remains as high as 8-12% during the interstage period, the time between discharge after the Norwood procedure and before the stage II palliation. The objective of our study was to determine the effect of digoxin use on interstage mortality in infants with single ventricle congenital heart disease. Hypothesis: We hypothesized that digoxin would be associated with lower interstage mortality. Methods: We conducted a retrospective cohort study using the Pediatric Heart Network Single Ventricle Reconstruction Trial public use dataset, which includes data on infants with single right ventricle congenital heart disease randomized to receive either a Blalock-Taussig shunt or right ventricle-to-pulmonary artery shunt during the Norwood procedure at 15 institutions in North America from 2005-2008. Parametric survival models were used to compare the risk of interstage mortality between those discharged to home on digoxin vs. those discharged to home not on digoxin, adjusting for center volume, ascending aorta diameter, shunt type, and socioeconomic status. Further comparisons were made to compare the number of other adverse events in the two groups. Results: Of the 330 infants eligible for this study, 102 (31%) were discharged home on digoxin. Interstage mortality for those not on digoxin was 12.3%, compared to 2.9% among those on digoxin (Figure), with a number needed to treat of 11 patients to prevent one death. The adjusted hazard ratio was 3.5 (95%CI 1.1-11.7, p=0.04). There were no differences in complications between the two groups during the interstage period. Conclusions: Digoxin use in infants with single ventricle congenital heart disease is associated with significantly reduced interstage mortality and should be considered for all such infants unless otherwise contraindicated.

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Discrimination Between Parametric Survival Models for Removal Times of Bird Carcasses in Scavenger Removal Trials at Wind Turbines Sites

Advances in Regression, Survival Analysis, Extreme Values, Markov Processes and Other Statistical Applications ◽

10.1007/978-3-642-34904-1_6 ◽

2013 ◽

pp. 65-72 ◽

Cited By ~ 4

Author(s):

Regina Bispo ◽

Joana Bernardino ◽

Tiago A. Marques ◽

Dinis Pestana

Keyword(s):

Wind Turbines ◽

Survival Models ◽

Parametric Survival

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Full-Fat Dairy Food Intake is Associated with a Lower Risk of Incident Diabetes Among American Indians with Low Total Dairy Food Intake

Journal of Nutrition ◽

10.1093/jn/nxz058 ◽

2019 ◽

Vol 149 (7) ◽

pp. 1238-1244

Author(s):

Kim Kummer ◽

Paul N Jensen ◽

Mario Kratz ◽

Rozenn N Lemaitre ◽

Barbara V Howard ◽

...

Keyword(s):

Food Intake ◽

American Indians ◽

Lower Risk ◽

Incident Diabetes ◽

Survival Models ◽

Low Fat ◽

Dairy Food ◽

Family Based ◽

Parametric Survival ◽

Serving Sizes

ABSTRACT Background Diet plays a key role in development of diabetes, and there has been recent interest in better understanding the association of dairy food intake with diabetes. Objective This study examined the associations of full-fat and low-fat dairy food intake with incident diabetes among American Indians—a population with a high burden of diabetes. Methods The study included participants from the Strong Heart Family Study (SHFS), a family-based study of cardiovascular disease in American Indians, free of diabetes at baseline (2001–2003) (n = 1623). Participants were 14–86-y-old at baseline and 60.8% were female. Dairy food intake was assessed using a Block food frequency questionnaire. Incident diabetes was defined using American Diabetes Association criteria. Parametric survival models with a Weibull distribution were used to evaluate the associations of full-fat and low-fat dairy food intake with incident diabetes. Serving sizes were defined as 250 mL for milk and 42.5 g for cheese. Results We identified 277 cases of diabetes during a mean follow-up of 11 y. Reported intake of dairy foods was low [median full-fat dairy food intake: 0.11 serving/1000 kcal; median low-fat dairy food intake: 0.03 serving/1000 kcal]. Participants who reported the highest full-fat dairy food intake had a lower risk of diabetes compared to those who reported the lowest full-fat food dairy intake [HR (95% CI): 0.79 (0.59, 1.06); P-trend = 0.03, comparing extreme tertiles, after adjustment for age, sex, site, physical activity, education, smoking, diet quality, and low-fat dairy food intake]. Low-fat dairy food intake was not associated with diabetes. Conclusions American Indians who participated in the SHFS reported low dairy food intake. Participants who reported higher full-fat dairy food intake had a lower risk of diabetes than participants who reported lower intake. These findings may be of interest to populations with low dairy food intake.

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Multilevel mixed-effects parametric survival analysis: Estimation, simulation, and application

The Stata Journal Promoting communications on statistics and Stata ◽

10.1177/1536867x19893639 ◽

2019 ◽

Vol 19 (4) ◽

pp. 931-949 ◽

Cited By ~ 3

Author(s):

Michael J. Crowther

Keyword(s):

Random Effects ◽

Survival Data ◽

Relative Survival ◽

Survival Models ◽

Left Truncation ◽

Delayed Entry ◽

Working Paper ◽

Expected Mortality ◽

Parametric Survival ◽

Parametric Survival Analysis

In this article, I present the community-contributed stmixed command for fitting multilevel survival models. It serves as both an alternative to Stata’s official mestreg command and a complimentary command with substantial extensions. stmixed can fit multilevel survival models with any number of levels and random effects at each level, including flexible spline-based approaches (such as Royston–Parmar and the log-hazard equivalent) and user-defined hazard models. Simple or complex time-dependent effects can be included, as can expected mortality for a relative survival model. Left-truncation (delayed entry) is supported, and t-distributed random effects are provided as an alternative to Gaussian random effects. I illustrate the methods with a commonly used dataset of patients with kidney disease suffering recurrent infections and a simulated example illustrating a simple approach to simulating clustered survival data using survsim (Crowther and Lambert 2012, Stata Journal 12: 674–687; 2013, Statistics in Medicine 32: 4118–4134). stmixed is part of the merlin family (Crowther 2017, arXiv Working Paper No. arXiv:1710.02223; 2018, arXiv Working Paper No. arXiv:1806.01615).

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ANALISIS SINTASAN PARAMETRIK PADA PASIEN STROKE DENGAN PENDEKATAN DISTRIBUSI WEIBULL

E-Jurnal Matematika ◽

10.24843/mtk.2019.v08.i01.p235 ◽

2019 ◽

Vol 8 (1) ◽

pp. 55

Author(s):

NI MADE SRI WAHYUNI ◽

I WAYAN SUMARJAYA ◽

NI LUH PUTU SUCIPTAWATI

Keyword(s):

Body Mass Index ◽

Survival Analysis ◽

Weibull Distribution ◽

Survival Data ◽

The Body ◽

Predictor Variables ◽

Survival Models ◽

Stroke Patients ◽

Parametric Survival ◽

Parametric Survival Analysis

Parametric survival analysis is one of the survival analysis that has a distribution of survival data that follows a certain distribution. Weibull distribution is a distribution that is often used in parametric survival analysis. The purpose of this study is to determine parametric survival models using the Weibull distribution and to determine the factors that can influence the recovery of stroke patients. This study uses data on stroke patients in the Wangaya hospital, Denpasar in 2017. The best model obtained in this study is a model that consists of two predictor variables, namely the age and the body mass index (BMI).Therefore the factors that can influence the recovery of stroke patients are age and BMI.

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