scholarly journals Parameter Interpretation in Skewed Logistic Regression with Random Intercept

2013 ◽  
Vol 8 (2) ◽  
pp. 381-410 ◽  
Author(s):  
Cristiano C. Santos ◽  
Rosangela H. Loschi ◽  
Reinaldo B. Arellano-Valle
Keyword(s):  
Logistic Regression ◽  
Random Intercept ◽  
Parameter Interpretation

scholarly journals Longitudinal Joint Modelling of Ordinal and Overdispersed Count Outcomes: A Bridge Distribution for the Ordinal Random Intercept

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Payam Amini ◽  
Abbas Moghimbeigi ◽  
Farid Zayeri ◽  
Leili Tapak ◽  
Saman Maroufizadeh ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Normal Distribution ◽  
Ordinal Logistic Regression ◽  
Accurate Estimation ◽  
Joint Modelling ◽  
Random Intercept ◽  
Bridge Distribution ◽  
Response Variables ◽  
Normally Distributed ◽  
Ordinal Outcome

Associated longitudinal response variables are faced with variations caused by repeated measurements over time along with the association between the responses. To model a longitudinal ordinal outcome using generalized linear mixed models, integrating over a normally distributed random intercept in the proportional odds ordinal logistic regression does not yield a closed form. In this paper, we combined a longitudinal count and an ordinal response variable with Bridge distribution for the random intercept in the ordinal logistic regression submodel. We compared the results to that of a normal distribution. The two associated response variables are combined using correlated random intercepts. The random intercept in the count outcome submodel follows a normal distribution. The random intercept in the ordinal outcome submodel follows Bridge distribution. The estimations were carried out using a likelihood-based approach in direct and conditional joint modelling approaches. To illustrate the performance of the model, a simulation study was conducted. Based on the simulation results, assuming a Bridge distribution for the random intercept of ordinal logistic regression results in accurate estimation even if the random intercept is normally distributed. Moreover, considering the association between longitudinal count and ordinal responses resulted in estimation with lower standard error in comparison to univariate analysis. In addition to the same interpretation for the parameter in marginal and conditional estimates thanks to the assumption of a Bridge distribution for the random intercept of ordinal logistic regression, more efficient estimates were found compared to that of normal distribution.

scholarly journals Biomarker combinations for diagnosis and prognosis in multicenter studies: Principles and methods

2017 ◽  
Vol 28 (4) ◽  
pp. 969-985 ◽  
Author(s):  
Allison Meisner ◽  
Chirag R Parikh ◽  
Kathleen F Kerr
Keyword(s):  
Logistic Regression ◽  
Characteristic Curve ◽  
Kidney Injury ◽  
Underlying Disease ◽  
Multicenter Studies ◽  
Logistic Regression Models ◽  
Diagnosis And Prognosis ◽  
Random Intercept ◽  
The Relationship

Many investigators are interested in combining biomarkers to predict a binary outcome or detect underlying disease. This endeavor is complicated by the fact that many biomarker studies involve data from multiple centers. Depending upon the relationship between center, the biomarkers, and the target of prediction, care must be taken when constructing and evaluating combinations of biomarkers. We introduce a taxonomy to describe the role of center and consider how a biomarker combination should be constructed and evaluated. We show that ignoring center, which is frequently done by clinical researchers, is often not appropriate. The limited statistical literature proposes using random intercept logistic regression models, an approach that we demonstrate is generally inadequate and may be misleading. We instead propose using fixed intercept logistic regression, which appropriately accounts for center without relying on untenable assumptions. After constructing the biomarker combination, we recommend using performance measures that account for the multicenter nature of the data, namely the center-adjusted area under the receiver operating characteristic curve. We apply these methods to data from a multicenter study of acute kidney injury after cardiac surgery. Appropriately accounting for center, both in construction and evaluation, may increase the likelihood of identifying clinically useful biomarker combinations.

scholarly journals Social inequalities in low birthweight outcomes in Sri Lanka: evidence from the Demographic and Health Survey 2016

BMJ Open ◽  
2020 ◽  
Vol 10 (5) ◽  
pp. e037223
Author(s):  
Gayathri Abeywickrama ◽  
Sabu S Padmadas ◽  
Andrew Hinde
Keyword(s):  
Logistic Regression ◽  
Sri Lanka ◽  
Health Survey ◽  
Low Birthweight ◽  
Social Inequalities ◽  
Demographic And Health Survey ◽  
Outcome Variable ◽  
Biological Variables ◽  
Random Intercept ◽  
Concentration Indices

ObjectiveTo investigate social inequalities underlying low birthweight (LBW) outcomes in Sri Lanka.DesignCross-sectional study.SettingThis study used the Sri Lanka Demographic and Health Survey 2016, the first such survey to cover the entire country since the Civil War ended in 2001.ParticipantsBirthweight data extracted from the child health development records available for 7713 babies born between January 2011 and the date of interview in 2016.Outcome measuresThe main outcome variable was birth weight, classified as LBW (≤2500 g) and normal.MethodsWe applied random intercept three-level logistic regression to examine the association between LBW and maternal, socioeconomic and geographic variables. Concentration indices were estimated for different population subgroups.ResultsThe population-level prevalence of LBW was 16.9% but was significantly higher in the estate sector (28.4%) compared with rural (16.6%) and urban (13.6%) areas. Negative concentration indices suggest a relatively higher concentration of LBW in poor households in rural areas and the estate sector. Results from fixed effects logistic regression models confirmed our hypothesis of significantly higher risk of LBW outcomes across poorer households and Indian Tamil communities (AOR 1.70, 95% CI 1.02 to 2.83, p<0.05). Results from random intercept models confirmed there was substantial unobserved variation in LBW outcomes at the mother level. The effect of maternal biological variables was larger than that of socioeconomic factors.ConclusionLBW rates are significantly higher among babies born in poorer households and Indian Tamil communities. The findings highlight the need for nutrition interventions targeting pregnant women of Indian Tamil ethnicity and those living in economically deprived households.

From Bi-Dimensionality to Uni-Dimensionality in Self-Report Questionnaires

2020 ◽  
pp. 1-14 ◽  
Author(s):  
Bjarne Schmalbach ◽  
Markus Zenger ◽  
Michalis P. Michaelides ◽  
Karin Schermelleh-Engel ◽  
Andreas Hinz ◽  
...  
Keyword(s):  
Factor Model ◽  
Common Factor ◽  
Self Regulation ◽  
Model Fit ◽  
Self Report ◽  
External Criterion ◽  
Common Factor Model ◽  
Random Intercept ◽  
Using Data ◽  
Core Self

Abstract. The common factor model – by far the most widely used model for factor analysis – assumes equal item intercepts across respondents. Due to idiosyncratic ways of understanding and answering items of a questionnaire, this assumption is often violated, leading to an underestimation of model fit. Maydeu-Olivares and Coffman (2006) suggested the introduction of a random intercept into the model to address this concern. The present study applies this method to six established instruments (measuring depression, procrastination, optimism, self-esteem, core self-evaluations, and self-regulation) with ambiguous factor structures, using data from representative general population samples. In testing and comparing three alternative factor models (one-factor model, two-factor model, and one-factor model with a random intercept) and analyzing differential correlational patterns with an external criterion, we empirically demonstrate the random intercept model’s merit, and clarify the factor structure for the above-mentioned questionnaires. In sum, we recommend the random intercept model for cases in which acquiescence is suspected to affect response behavior.

How Useful is the Power Law of Practice for Recognizing Practice in Concentration Tests?

2007 ◽  
Vol 23 (3) ◽  
pp. 157-165 ◽  
Author(s):  
Carmen Hagemeister
Keyword(s):  
Logistic Regression ◽  
Reaction Time ◽  
Power Law ◽  
Error Rate ◽  
Reaction Times ◽  
Practice Effect ◽  
Practice Effects ◽  
Rate Decrease ◽  
Small Practice

Abstract. When concentration tests are completed repeatedly, reaction time and error rate decrease considerably, but the underlying ability does not improve. In order to overcome this validity problem this study aimed to test if the practice effect between tests and within tests can be useful in determining whether persons have already completed this test. The power law of practice postulates that practice effects are greater in unpracticed than in practiced persons. Two experiments were carried out in which the participants completed the same tests at the beginning and at the end of two test sessions set about 3 days apart. In both experiments, the logistic regression could indeed classify persons according to previous practice through the practice effect between the tests at the beginning and at the end of the session, and, less well but still significantly, through the practice effect within the first test of the session. Further analyses showed that the practice effects correlated more highly with the initial performance than was to be expected for mathematical reasons; typically persons with long reaction times have larger practice effects. Thus, small practice effects alone do not allow one to conclude that a person has worked on the test before.

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