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Optimal Estimator for Logistic Model with Distribution-free Random Intercept.

ABSTRACT: Logistic models with a random intercept are prevalent in medical and social research where clustered and longitudinal data are often collected. Traditionally, the random intercept in these models is assumed to follow some parametric distribution such as the normal distribution. However, such an assumption inevitably raises concerns about model misspecification and misleading inference conclusions, especially when there is dependence between the random intercept and model covariates. To protect against such issues, we use a semiparametric approach to develop a computationally simple and consistent estimator where the random intercept is distribution-free. The estimator is revealed to be optimal and achieve the efficiency bound without the need to postulate or estimate any latent variable distributions. We further characterize other general mixed models where such an optimal estimator exists.

SUBMITTER: Garcia TP

PROVIDER: S-EPMC9521814 | biostudies-literature | 2016 Mar

REPOSITORIES: biostudies-literature

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Optimal Estimator for Logistic Model with Distribution-free Random Intercept.

Garcia Tanya P TP Ma Yanyuan Y

Scandinavian journal of statistics, theory and applications 20150806 1

Logistic models with a random intercept are prevalent in medical and social research where clustered and longitudinal data are often collected. Traditionally, the random intercept in these models is assumed to follow some parametric distribution such as the normal distribution. However, such an assumption inevitably raises concerns about model misspecification and misleading inference conclusions, especially when there is dependence between the random intercept and model covariates. To protect a ...[more]

PMID: 36187208

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Project description:IntroductionThe Child Health and Nutrition Research Initiative (CHNRI) method for health research prioritisation relies on stakeholders weighting criteria used to assess research options. These weights in turn impact on the final scores and ranks assigned to research options. Three quarters of CHNRI studies published to date have not involved stakeholders in criteria weighting. Of those that have, few incorporated members of the public into stakeholder groups. Those that have compared different stakeholder groups, such as donors, researchers, or policy makers, showed that different groups place different values upon CHNRI criteria. When choosing the composition of a stakeholder group, it may be important to understand factors that may influence weighting. Drawing upon a group of international public stakeholders, this study explores some of the effects of individual and demographic characteristics has on the weights assigned to the most commonly used CHNRI criteria, with the aim of informing future researchers on avoiding future biases.MethodsIndividual and demographic information and 5-point Likert scale responses to questions about the importance of 15 CHNRI criteria were collected from 1031 "Turkers" (Amazon Mechanical Turk workers) via Amazon Mechanical Turk (AMT), which is an online crowdsourcing platform. Thirteen of the fifteen criteria were analysed using random-intercept models and the remaining two were analysed through logistic regression.ResultsSelf-reported health status explained most of the variability in participants' responses across criteria (11/15 criteria), followed by being female (10/15), ethnicity (9/15), employment (8/15), and religion (7/15). Differences across criteria indicate that when choosing stakeholder groups, researchers need to consider these factors to minimise bias.ConclusionResearchers should collect and report more detailed information from stakeholders, including individual and demographic characteristics, and ensure participation from both genders, multiple ethnicities, religious beliefs, and people with differing health statuses to be transparent regarding possible biases in health research prioritisation. Our analyses indicate that these factors do influence the relative importance of these values, even when the data appears fairly homogeneous.

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Dataset Information

Optimal Estimator for Logistic Model with Distribution-free Random Intercept.

Publications

Optimal Estimator for Logistic Model with Distribution-free Random Intercept.

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