Gencrm: A New Command for Generalized Continuation-Ratio Models

2018 ◽  
Vol 18 (4) ◽  
pp. 924-936 ◽  
Author(s):  
Shawn Bauldry ◽  
Jun Xu ◽  
Andrew S. Fullerton
Keyword(s):  
Maximum Likelihood ◽  
Multiple Imputation ◽  
Common Factors ◽  
Modeling Processes ◽  
Ratio Model ◽  
Complete Coverage ◽  
Link Functions ◽  
Continuation Ratio ◽  
Job Promotion ◽  
Parameter Constraints

A continuation-ratio model represents a variant of an ordered regression model that is suited to modeling processes that unfold in stages, particularly those in which a return to a previous stage is not possible (for example, educational attainment, job promotion, or disease progression). The parameters for covariates in continuation-ratio models may be constrained to be equal, vary by a set of common factors (that is, proportionality constraints), or freely vary across stages. Currently, there are three community-contributed commands that fit continuation-ratio models. Each of these commands fits some subset of continuation-ratio models involving parameter constraints, but none of them offer complete coverage of the range of possibilities. The new gencrm command expands the options for continuation-ratio models to include the possibility for some of or all the covariates to be constrained to be equal, to freely vary, or to vary by a set of common factors across stages. gencrm relies on Stata's maximum likelihood routines for estimation and avoids reshaping the data. gencrm includes options for three link functions (logit, probit, and cloglog) and supports Stata's multiple-imputation suites of commands.

scholarly journals The efficiency of multiple imputation and maximum likelihood methods for estimating missing values

2018 ◽  
Vol 11 (16) ◽  
pp. 1-11
Author(s):  
Tlhalitshi Volition Montshiwa ◽  
Ntebo Moroke ◽  
Elias Munapo ◽  
◽  
◽  
...  
Keyword(s):  
Maximum Likelihood ◽  
Multiple Imputation ◽  
Missing Values ◽  
Likelihood Methods ◽  
Maximum Likelihood Methods

scholarly journals Best (but oft-forgotten) practices: missing data methods in randomized controlled nutrition trials

2019 ◽  
Vol 109 (3) ◽  
pp. 504-508 ◽  
Author(s):  
Peng Li ◽  
Elizabeth A Stuart
Keyword(s):  
Missing Data ◽  
Maximum Likelihood ◽  
Causal Inference ◽  
Randomized Controlled Trials ◽  
Multiple Imputation ◽  
Controlled Trials ◽  
Full Information ◽  
Complete Case ◽  
Full Information Maximum Likelihood ◽  
Randomized Controlled

ABSTRACT Missing data ubiquitously occur in randomized controlled trials and may compromise the causal inference if inappropriately handled. Some problematic missing data methods such as complete case (CC) analysis and last-observation-carried-forward (LOCF) are unfortunately still common in nutrition trials. This situation is partially caused by investigator confusion on missing data assumptions for different methods. In this statistical guidance, we provide a brief introduction of missing data mechanisms and the unreasonable assumptions that underlie CC and LOCF and recommend 2 appropriate missing data methods: multiple imputation and full information maximum likelihood.

Radiomics features of ground glass nodules tailored different pathological grades of lung adenocarcinoma.

2017 ◽  
Vol 35 (15_suppl) ◽  
pp. e23127-e23127
Author(s):  
C Zhang ◽  
Haoran Zhai ◽  
Lan He ◽  
Zai-Yi Liu ◽  
Yi-Long Wu ◽  
...  
Keyword(s):  
Smoking Status ◽  
Ground Glass ◽  
Low Grade ◽  
High Grade ◽  
Ratio Model ◽  
Intermediate Grade ◽  
Potential Predictor ◽  
Continuation Ratio ◽  
Radiomics Signature ◽  
Ground Glass Nodules

e23127 Background: Different pathological subtypes as well as different grades of adenocarcinoma based on the IASLC/ATS/ERS classification had been proven to be stage-independent predictor of survival. Radiomics features, as a novel analytic method, has been increasingly applied in variety cancer research and may be a potential predictor for preoperatively differentiating pathological grades of adenocarcinoma. Methods: Patients (pts) with radiological proved as solitary ground glass nodule were eligible in this study. Radiomics features derived from computed tomography (CT) images were extracted by Chinese Academy of Science. All pts will be categorized into three groups with lepidic predominance as low-grade, acinar and papillary predominance as intermediate-grade, micropapillary and solid predominance as high-grade. We used L1 penalized constrained continuation ratio model to select relevant radiomics features, and corresponding radiomics signature was constructed. Association between the radiomics signature and pathological grades of adenocarcinoma was explored using the Kruskal-Wallis test and C-index was performed to test the efficacy of differentiating. Results: 82 pts were included in this study. Low-grade, intermediate-grade and high-grade contained 15 (18.3%), 53 (64.6%), 14 (17.1%) pts respectively. 475 radiomics features were extracted from thin section CT image and 10 of them selected through L1 penalized constrained continuation ratio model composed radiomics signature which significantly associated with pathological grades (P < 0.0001). C-index for radiomics signature were 0.813 (95%CI 0.793-0.833). Since clinical characters including gender, age, smoking status, NSE, CEA and CYFRA21-1 were not associated with different grades of adenocarcinoma, we could not establish nomogram based on the radiomics signature and correlated clinical characters. Conclusions: Radiomics features only can be a potential predictor for preoperatively differentiating pathological grades of adenocarcinoma, which may be a more applicable clinical predictor for patients’ survival. Yet large sample sizes are warranted to confirm the results.

scholarly journals Data Missing Not at Random in Mobile Health Research: Assessment of the Problem and a Case for Sensitivity Analyses

10.2196/26749 ◽  
2021 ◽  
Vol 23 (6) ◽  
pp. e26749
Author(s):  
Simon B Goldberg ◽  
Daniel M Bolt ◽  
Richard J Davidson
Keyword(s):  
Sensitivity Analysis ◽  
Missing Data ◽  
Maximum Likelihood ◽  
Multiple Imputation ◽  
Mixture Model ◽  
Mobile Health ◽  
Sensitivity Analyses ◽  
Research Assessment ◽  
Missing Not At Random ◽  
Pattern Mixture Model

Background Missing data are common in mobile health (mHealth) research. There has been little systematic investigation of how missingness is handled statistically in mHealth randomized controlled trials (RCTs). Although some missing data patterns (ie, missing at random [MAR]) may be adequately addressed using modern missing data methods such as multiple imputation and maximum likelihood techniques, these methods do not address bias when data are missing not at random (MNAR). It is typically not possible to determine whether the missing data are MAR. However, higher attrition in active (ie, intervention) versus passive (ie, waitlist or no treatment) conditions in mHealth RCTs raise a strong likelihood of MNAR, such as if active participants who benefit less from the intervention are more likely to drop out. Objective This study aims to systematically evaluate differential attrition and methods used for handling missingness in a sample of mHealth RCTs comparing active and passive control conditions. We also aim to illustrate a modern model-based sensitivity analysis and a simpler fixed-value replacement approach that can be used to evaluate the influence of MNAR. Methods We reanalyzed attrition rates and predictors of differential attrition in a sample of 36 mHealth RCTs drawn from a recent meta-analysis of smartphone-based mental health interventions. We systematically evaluated the design features related to missingness and its handling. Data from a recent mHealth RCT were used to illustrate 2 sensitivity analysis approaches (pattern-mixture model and fixed-value replacement approach). Results Attrition in active conditions was, on average, roughly twice that of passive controls. Differential attrition was higher in larger studies and was associated with the use of MAR-based multiple imputation or maximum likelihood methods. Half of the studies (18/36, 50%) used these modern missing data techniques. None of the 36 mHealth RCTs reviewed conducted a sensitivity analysis to evaluate the possible consequences of data MNAR. A pattern-mixture model and fixed-value replacement sensitivity analysis approaches were introduced. Results from a recent mHealth RCT were shown to be robust to missing data, reflecting worse outcomes in missing versus nonmissing scores in some but not all scenarios. A review of such scenarios helps to qualify the observations of significant treatment effects. Conclusions MNAR data because of differential attrition are likely in mHealth RCTs using passive controls. Sensitivity analyses are recommended to allow researchers to assess the potential impact of MNAR on trial results.

scholarly journals Estimation of the Average Kappa Coefficient of a Binary Diagnostic Test in the Presence of Partial Verification

Mathematics ◽  
2021 ◽  
Vol 9 (14) ◽  
pp. 1694
Author(s):  
José Antonio Roldán-Nofuentes ◽  
Saad Bouh Regad
Keyword(s):  
Maximum Likelihood ◽  
Multiple Imputation ◽  
Diagnostic Test ◽  
Gold Standard ◽  
Maximum Likelihood Method ◽  
Disease Prevalence ◽  
Kappa Coefficient ◽  
Likelihood Method ◽  
Simulation Experiments ◽  
Asymptotic Behaviors

The average kappa coefficient of a binary diagnostic test is a measure of the beyond-chance average agreement between the binary diagnostic test and the gold standard, and it depends on the sensitivity and specificity of the diagnostic test and on disease prevalence. In this manuscript the estimation of the average kappa coefficient of a diagnostic test in the presence of verification bias is studied. Confidence intervals for the average kappa coefficient are studied applying the methods of maximum likelihood and multiple imputation by chained equations. Simulation experiments have been carried out to study the asymptotic behaviors of the proposed intervals, given some application rules. The results obtained in our simulation experiments have shown that the multiple imputation by chained equations method provides better results than the maximum likelihood method. A function has been written in R to estimate the average kappa coefficient by applying multiple imputation. The results have been applied to the diagnosis of liver disease.

scholarly journals Modelling Tabular Data with an Ordered Outcome

1996 ◽  
Vol 1 (3) ◽  
pp. 1-10
Author(s):  
Vernon Gayle
Keyword(s):  
Model Building ◽  
Linear Models ◽  
Statistical Modelling ◽  
Categorical Variables ◽  
Sociological Research ◽  
Ratio Model ◽  
Ordered Categories ◽  
Continuation Ratio ◽  
Recent Developments ◽  
Log Linear

A large amount of data that is considered within sociological studies consists of categorical variables that lend themselves to tabular analysis. In the sociological analysis of data regarding social class and educational attainment, for example, the variables of interest can often plausibly be considered as having a substantively interesting order. Standard log-linear models do not take ordinality into account, thereby potentially they may disregard useful information. Analyzing tables where the response variable has ordered categories through model building has been problematic in software packages such as GLIM (Aitken et al., 1989). Recent developments in statistical modelling have offered new possibilities and this paper explores one option, namely the continuation ratio model which was initially reported by Fienberg and Mason (1979). The fitting of this model to data in tabular form is possible in GLIM although not especially trivial and by and large this approach has not been employed in sociological research. In this paper I outline the continuation ratio model and comment upon how it can be fitted to data by sociologists using the GLIM software. In addition I present a short description of the relative merits of such an approach. Presenting this paper in an electronic format facilitates the possibility of replicating the analysis. The data is appended to the paper in the appropriate format along with a copy of the GLIM transcript. A dumped GLIM4 file is also attached.

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