The Impact of Predictor Variable(s) with Skewed Cell Probabilities on Wald Tests in Binary Logistic Regression

Alkhalaf, Arwa; Zumbo, Bruno D.

doi:10.22237/jmasm/1509494640

Cited by 6 publications

(4 citation statements)

References 30 publications

(28 reference statements)

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“…Alongside different screening protocols and different regional prevalence of risk factors for GDM, this affected regional contribution to inclusion in the study and may have impacted on maternal characteristic profiles. However, we controlled for inclusion bias by adjusting for the regional annual year‐specific GDM prevalence which captures both known and unknown determinants of GDM diagnosis, and the large sample size can further be expected to sufficiently overcome issues with skewed predictor covariates in logistic regression analyses, thereby compensating for these limitations 37 . The evidence for an impact on clinical outcome by the implementation of lower diagnostic thresholds is further inconclusive 38 .…”

Section: Discussionmentioning

confidence: 99%

“…However, we controlled for inclusion bias by adjusting for the regional annual year‐specific GDM prevalence which captures both known and unknown determinants of GDM diagnosis, and the large sample size can further be expected to sufficiently overcome issues with skewed predictor covariates in logistic regression analyses, thereby compensating for these limitations. 37 The evidence for an impact on clinical outcome by the implementation of lower diagnostic thresholds is further inconclusive. 38 Moreover, the exposure was not GDM diagnosis but the mode of treatment at the time of delivery, and the proportion managed on the reference treatment nutrition therapy in regions applying IADPSG/WHO 2013 criteria was not significantly different from regions applying higher diagnostic thresholds (data not shown).…”

Section: Discussionmentioning

confidence: 99%

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Neonatal outcome following metformin‐treated gestational diabetes mellitus: A population‐based cohort study

Molin,

Domellöf,

Häggström

et al. 2024

Acta Obstet Gynecol Scand

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IntroductionNeonatal hypoglycemia is a common complication associated with gestational diabetes and therefore relevant to consider in evaluations of maternal treatment. We aimed to investigate the risk of neonatal hypoglycemia in offspring exposed to metformin treatment alone (MT) or combined with insulin (MIT) in comparison with nutrition therapy alone (NT), and insulin treatment alone (IT). In addition, we investigated MT in comparison with MIT. Secondary outcomes included neonatal anthropometrics, respiratory morbidity, hyperbilirubinemia, 5‐min Apgar score, and preterm birth.Material and methodsThis Swedish population‐based cohort included 16 181 women diagnosed with gestational diabetes, and their singleton offspring born in 2019–2021. We estimated risk as adjusted odds ratio (aOR) with 95% confidence interval (CI), using individual‐level, linkage register‐data in multivariable logistic regression models.ResultsIn the main analysis, MT was associated with a lower risk of neonatal hypoglycemia versus NT (aOR 0.85, 95% CI: 0.74–0.96), versus MIT (0.74 [0.64–0.87]), and versus IT (0.47 [0.40–0.55]), whereas MIT was associated with a similar risk of neonatal hypoglycemia versus NT (1.14 [0.99–1.30]) and with lower risk versus IT (0.63 [0.53–0.75]). However, supplemental feeding rates were lower for NT versus pharmacological treatments (p < 0.001). In post hoc subgroup analyses including only exclusively breastfed offspring, the risk of neonatal hypoglycemia was modified and similar among MT and NT, and higher in MIT versus NT. Insulin exposure, alone or combined with metformin, was associated with increased risk of being large for gestational age. Compared with NT, exposure to any pharmacological treatment was associated with significantly lower risk of 5‐min Apgar score < 4. All other secondary outcomes were comparable among the treatment categories.ConclusionsThe risk of neonatal hypoglycemia appears to be comparable among offspring exposed to single metformin treatment and nutrition therapy alone, and the lower risk that we observed in favor of metformin is probably explained by a difference in supplemental feeding practices rather than metformin per se. By contrast, the lower risk favoring metformin exposure over insulin exposure was not explained by supplemental feeding. However, further investigations are required to determine whether the difference is an effect of metformin per se or mediated by other external factors.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Neonatal outcome following metformin‐treated gestational diabetes mellitus: A population‐based cohort study

Molin,

Domellöf,

Häggström

et al. 2024

Acta Obstet Gynecol Scand

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“…Ref. [16] analyzed logistic regression when some of the explanatory variables have skewed cell probabilities and lastly [17] considered the logistic model proposed by [1] to examine correlated infant morbidity data. More recently, Ref.…”

Section: Introductionmentioning

confidence: 99%

Asymmetric versus Symmetric Binary Regresion: A New Proposal with Applications

2022

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The classical logit and probit models allow to explain a dichotomous dependent variable as a function of factors or covariates which can influence the response variable. This paper introduces a new skew-logit link for item response theory by considering the arctan transformation over the scobit logit model, yielding a very flexible link function from a new class of generalized distribution. This approach assumes an asymmetric model, which reduces to the standard logit model for a special case of the parameters that control the distribution’s symmetry. The model proposed is simple and allows us to estimate the parameters without using Bayesian methods, which requires implementing Markov Chain Monte Carlo methods. Furthermore, no special function appears in the formulation of the model. We compared the proposed model with the classical logit specification using three datasets. The first one deals with the well-known data collection widely studied in the statistical literature, concerning with mortality of adult beetle after exposure to gaseous carbon disulphide, the second one considers an automobile insurance portfolio. Finally, the third dataset examines touristic data related to tourist expenditure. For these examples, the results illustrate that the new model changes the significance level of some explanatory variables and the marginal effects. For the latter example, we have also modified the definition of the intercept in the linear predictor to prevent confounding.

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“…Pérez-Sánchez et al (2014) studied the risk variables underlying automobile insurance claims taking into account the asymmetry of the database. Alkhalaf and Zumbo (2017) studied logistic regression when some of the predictors have skewed cell probabilities and finally Mwenda et al (2021) uses the logistic model proposed by Prentice (1976) to study correlated infant morbidity data.…”

Section: Introductionmentioning

confidence: 99%

Skewed Binary Regression to Study Rental Cars by Tourists in the Canary Islands

Cárdenes

Sánchez

Gómez–Déniz

et al. 2021

JRFM

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Tourism is one of the economic sectors that contributes the most to the gross domestic product in many countries, moving, in turn, other economic sectors such as transport. In particular, the automotive industry constitutes an economic subsector that moves vast amounts of money. Concerning tourism and transport sectors, car rental is a crucial element contributing considerably to gross domestic product and job creation. Due to the effects that vehicle rental seems to have on various economic sectors, it seems interesting to know why a tourist chooses to rent a car during their vacation in a specific destination. This work aims to study those factors that can be considered relevant and affect the probability of renting a vehicle. The document addressed the following research topics: (a) identifying significant variables; and (b) can information on these factors help car rental firms? Empirically, it is shown that more tourists do not rent a car and this fact has to be considered. Thus, the classical logistic and Bayesian regression models do not seem adequate in this case, so that the authors will consider an asymmetric logistic regression model. This work analyzes 28,235 tourists who visited the Canary Islands during 2017. From a Bayesian point of view, asymmetric logistics regression is chosen as the best model because it detects relevant development factors not seen by standard logistic regressions. In light of the document’s findings, various practice recommendations improve decision-making in this field. The asymmetric logit link is a helpful device that can help rental companies make decisions about their clients.

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The Impact of Predictor Variable(s) with Skewed Cell Probabilities on Wald Tests in Binary Logistic Regression

Cited by 6 publications

References 30 publications

Neonatal outcome following metformin‐treated gestational diabetes mellitus: A population‐based cohort study

Neonatal outcome following metformin‐treated gestational diabetes mellitus: A population‐based cohort study

Asymmetric versus Symmetric Binary Regresion: A New Proposal with Applications

Skewed Binary Regression to Study Rental Cars by Tourists in the Canary Islands

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