scholarly journals Can’t see the wood for the trees: Using common likelihoods to share information regarding selection of functional form in survival analysis

2020 ◽  
Author(s):  
Neil Hawkins ◽  
Chris Parker ◽  
David A. Scott
Keyword(s):  
Survival Analysis ◽  
Cost Effectiveness ◽  
Goodness Of Fit ◽  
Functional Form ◽  
Information Criterion ◽  
Functional Forms ◽  
Parametric Survival ◽  
Parametric Survival Analysis ◽  
Share Information ◽  
Selection Of

Abstract Background The estimation of mean survival is a key consideration when estimating the cost-effectiveness of interventions. Estimation typically requires extrapolation using parametric survival analysis and may be sensitive to choice of functional form. We demonstrate a simple method that allows goodness-of-fit information to be assessed jointly across studies, where such information is believed likely to be exchangeable, to help inform the selection of functional form for parametric survival analysis. Methods Individual patient data for survival was estimated from digitised Kaplan-Meier curves for four trials in advanced soft tissue sarcoma. A range of parametric survival models were fitted. Two approaches were explored for identifying the preferred parametric model: (i) selecting functional forms independently for each study; and (ii) selecting a common functional form across studies. Akaike’s Information Criterion (AIC) or Bayesian Information Criterion (BIC) were used to select optimal functional form. For approach (ii) joint BIC or AIC statistics were calculated for each model by combining the components of the AIC or BIC, respectively, across studies. A bootstrap analysis was conducted to estimate the uncertainty in selection of functional form. Results Estimates of mean survival varied markedly according to choice of functional form. Independent selection led to different functional forms being selected for each study with considerable uncertainty regarding the optimum functional form according to AIC or BIC. In this case study, selecting an optimum functional form based on joint AIC or BIC across studies reduced uncertainty in model selection and variance in mean survival compared to selection of functional form independently. Conclusion Extrapolation of estimates of survival may be sensitive to choice of functional form. If it is believed, a priori, reasonable to share information regarding the choice of functional form for survival analysis across studies, estimating joint goodness-of-fit information across studies could reduce uncertainty and lead to more reliable estimates of mean survival and cost-effectiveness.

scholarly journals Pitfalls and perils of survival analysis under incorrect assumptions: the case of Covid-19 data

Biomédica ◽  
2021 ◽  
Vol 41 (Sp. 2) ◽  
Author(s):  
Daniele Piovani ◽  
Georgios K. Nikolopoulos ◽  
Stefanos Bonovas
Keyword(s):  
Decision Making ◽  
Survival Analysis ◽  
Preventive Measures ◽  
Clinical Course ◽  
Virus Disease ◽  
Current Medical Research ◽  
Basic Assumptions ◽  
Parametric Survival ◽  
Parametric Survival Analysis ◽  
Non Parametric

Non-parametric survival analysis has become a very popular statistical method in current medical research. Employing, however, survival methodology when its fundamental assumptions are not fulfilled can severely bias the results. Currently, hundreds of clinical studies are using survival methods to investigate factors potentially associated with the prognosis of Corona Virus Disease 2019 (Covid-19), and test new preventive and therapeutic strategies. In the pandemic era, it is more critical than ever that decision-making is evidence-based and relies on solid statistical methods. However, this is not always the case. Serious methodologic errors have been identified in recent seminal studies about Covid-19: one reporting outcomes of patients treated with remdesivir, and another one on the epidemiology, clinical course and outcomes of critically-ill patients. High-quality evidence is essential to inform clinicians about optimal Covid-19 therapies, and policymakers about the true effect of preventive measures aiming to tackle the pandemic. Though timely evidence is needed, we should encourage the appropriate application of survival analysis methods and careful peer-review to avoid publishing flawed results, which could affect decision-making. In this paper, we recapitulate the basic assumptions underlying non-parametric survival analysis and frequent errors in its application, and discuss how to handle data of Covid-19.

scholarly journals ANALISIS SINTASAN PARAMETRIK PADA PASIEN STROKE DENGAN PENDEKATAN DISTRIBUSI WEIBULL

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.

Selection of a Barley Yield Model Using Information–Theoretic Criteria

Weed Science ◽  
2008 ◽  
Vol 56 (4) ◽  
pp. 628-636 ◽  
Author(s):  
Marie Jasieniuk ◽  
Mark L. Taper ◽  
Nicole C. Wagner ◽  
Robert N. Stougaard ◽  
Monica Brelsford ◽  
...  
Keyword(s):  
Functional Form ◽  
Information Criterion ◽  
Small Sample ◽  
Theoretic Approach ◽  
Wild Oat ◽  
Information Theoretic ◽  
Information Theoretic Criteria ◽  
The Impact ◽  
Selection Of ◽  
Bioeconomic Models

Empirical models of crop–weed competition are integral components of bioeconomic models, which depend on predictions of the impact of weeds on crop yields to make cost-effective weed management recommendations. Selection of the best empirical model for a specific crop–weed system is not straightforward, however. We used information–theoretic criteria to identify the model that best describes barley yield based on data from barley–wild oat competition experiments conducted at three locations in Montana over 2 yr. Each experiment consisted of a complete addition series arranged as a randomized complete block design with three replications. Barley was planted at 0, 0.5, 1, and 2 times the locally recommended seeding rate. Wild oat was planted at target infestation densities of 0, 10, 40, 160, and 400 plants m−2. Twenty-five candidate yield models were used to describe the data from each location and year using maximum likelihood estimation. Based on Akaike's Information Criterion (AIC), a second-order small-sample version ofAIC(AICc), and the Bayesian Information Criterion (BIC), most data sets supported yield models with crop density (Dc), weed density (Dw), and the relative time of emergence of the two species (T) as variables, indicating that all variables affected barley yield in most locations.AIC,AICc, andBICselected identical best models for all but one data set. In contrast, the Information Complexity criterion,ICOMP, generally selected simpler best models with fewer parameters. For data pooled over years and locations,AIC,AICc, andBICstrongly supported a single best model with variablesDc,Dw,T, and a functional form specifying both intraspecific and interspecific competition.ICOMPselected a simpler model withDcandDwonly, and a functional form specifying interspecific, but no intraspecific, competition. The information–theoretic approach offers a rigorous, objective method for choosing crop yield and yield loss equations for bioeconomic models.

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