Estimating marginal and incremental effects in the analysis of medical expenditure panel data using marginalized two-part random-effects generalized Gamma models: Evidence from China healthcare cost data

Conditional two-part random-effects models have been proposed for the analysis of healthcare cost panel data that contain both zero costs from the non-users of healthcare facilities and positive costs from the users. These models have been extended to accommodate more flexible data structures when using the generalized Gamma distribution to model the positive healthcare expenditures. However, a major drawback with the extended model, which is inherited from the conditional models, is that it is fairly difficult to make direct marginal inference with respect to overall healthcare costs that includes both zeros and non-zeros, or even on positive healthcare costs. In this article, we first propose two types of marginalized two-part random-effects generalized Gamma models (m2RGGMs): Type I m2RGGMs for the inference on positive healthcare costs and Type II m2RGGMs for the inference on overall healthcare costs. Then, the concepts of marginal effect and incremental effect of a covariate on overall and positive healthcare costs are introduced, and estimation of these effects is carefully discussed. Especially, we derive the variance estimates of these effects by following the delta methods and Taylor series approximations for the purpose of making marginal inference. Parameter estimates of Type I and Type II m2RGGMs are obtained through maximum likelihood estimation. An empirical analysis of longitudinal healthcare costs collected in the China Health and Nutrition Survey is conducted using the proposed methodologies.

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A collection of marginalized two-part random-effects models for analyzing medical expenditure panel data: Impact of the New Cooperative Medical Scheme on healthcare expenditures in China

Statistical Methods in Medical Research ◽

10.1177/0962280218784725 ◽

2018 ◽

Vol 28 (8) ◽

pp. 2494-2523 ◽

Cited By ~ 1

Author(s):

Bo Zhang ◽

Wei Liu ◽

Ning Zhang ◽

Arlene S Ash ◽

Jeroan J Allison

Keyword(s):

Panel Data ◽

Random Effects ◽

Medical Expenditure ◽

Marginal Effect ◽

Healthcare Expenditures ◽

New Cooperative Medical Scheme ◽

Random Effects Models ◽

Generalized Gamma ◽

Incremental Effect ◽

Cooperative Medical Scheme

Marginalized two-part random-effects generalized Gamma models have been proposed for analyzing medical expenditure panel data with excessive zeros. While these models provide marginal inference on expected healthcare expenditures, the usual unilateral specification of heteroscedastic variance on one of the two shape parameters for the generalized Gamma distribution in these models fails to encompass important special cases within the generalized gamma modeling framework. In this article, we construct marginalized two-part random-effects models that employ the log-normal, log-skew-normal, generalized Gamma, Weibull, Gamma, and inverse Gamma distributions to delineate the spectrum of nonzero healthcare expenditures in the second part of the models. These marginalized models supply additional choices for analyzing healthcare expenditure panel data with excessive zeros. We review the concepts of marginal effect and incremental effect, and summarize how these effects are estimated. For studies whose primary goal is to make inference on marginal effect or incremental effect of an independent variable with respect to healthcare expenditures, we advocate empirical mean square error criterion and information criteria to choose among candidate models. Then, we use the proposed models in an empirical analysis to examine the impact of the New Cooperative Medical Scheme on healthcare expenditures among older adults in rural China.

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Including random effects in statistical models in ecology: fewer than five levels?

10.1101/2021.04.11.439357 ◽

2021 ◽

Author(s):

Dylan G.E. Gomes

Keyword(s):

Random Effects ◽

Fixed Effects ◽

Linear Models ◽

Random Effect ◽

Mixed Effects ◽

Parameter Estimates ◽

Type I ◽

Type I Errors ◽

Ecological Sites ◽

Generalized Linear Mixed Effects

AbstractAs generalized linear mixed-effects models (GLMMs) have become a widespread tool in ecology, the need to guide the use of such tools is increasingly important. One common guideline is that one needs at least five levels of a random effect. Having such few levels makes the estimation of the variance of random effects terms (such as ecological sites, individuals, or populations) difficult, but it need not muddy one’s ability to estimate fixed effects terms – which are often of primary interest in ecology. Here, I simulate ecological datasets and fit simple models and show that having too few random effects terms does not influence the parameter estimates or uncertainty around those estimates for fixed effects terms. Thus, it should be acceptable to use fewer levels of random effects if one is not interested in making inference about the random effects terms (i.e. they are ‘nuisance’ parameters used to group non-independent data). I also use simulations to assess the potential for pseudoreplication in (generalized) linear models (LMs), when random effects are explicitly ignored and find that LMs do not show increased type-I errors compared to their mixed-effects model counterparts. Instead, LM uncertainty (and p values) appears to be more conservative in an analysis with a real ecological dataset presented here. These results challenge the view that it is never appropriate to model random effects terms with fewer than five levels – specifically when inference is not being made for the random effects, but suggest that in simple cases LMs might be robust to ignored random effects terms. Given the widespread accessibility of GLMMs in ecology and evolution, future simulation studies and further assessments of these statistical methods are necessary to understand the consequences of both violating and blindly following simple guidelines.

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Type I and Type II Error Under Random-Effects Misspecification in Generalized Linear Mixed Models

Biometrics ◽

10.1111/j.1541-0420.2007.00782.x ◽

2007 ◽

Vol 63 (4) ◽

pp. 1038-1044 ◽

Cited By ~ 74

Author(s):

Saskia Litière ◽

Ariel Alonso ◽

Geert Molenberghs

Keyword(s):

Random Effects ◽

Mixed Models ◽

Generalized Linear Mixed Models ◽

Linear Mixed Models ◽

Type I ◽

Type Ii ◽

Type Ii Error

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The Sensitivity of Labor-Supply Parameter Estimates to Unobserved Individual Effects: Fixed- and Random-Effects Estimates in a Nonlinear Model Using Panel Data

Journal of Labor Economics ◽

10.1086/298185 ◽

1988 ◽

Vol 6 (3) ◽

pp. 302-329 ◽

Cited By ~ 36

Author(s):

George Jakubson

Keyword(s):

Panel Data ◽

Labor Supply ◽

Random Effects ◽

Nonlinear Model ◽

Parameter Estimates ◽

Fixed And Random Effects ◽

Individual Effects

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Heat-Etching with a Bullivant Type II Simple Freeze-Cleave Device

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100067522 ◽

1970 ◽

Vol 28 ◽

pp. 106-107 ◽

Cited By ~ 1

Author(s):

Ronald S. Weinstein ◽

N. Scott McNutt

Keyword(s):

New Zealand ◽

General Hospital ◽

Massachusetts General Hospital ◽

Type I ◽

Type Ii ◽

Collaborative Effort ◽

Temperature And Pressure ◽

The University

The Type I simple cold block device was described by Bullivant and Ames in 1966 and represented the product of the first successful effort to simplify the equipment required to do sophisticated freeze-cleave techniques. Bullivant, Weinstein and Someda described the Type II device which is a modification of the Type I device and was developed as a collaborative effort at the Massachusetts General Hospital and the University of Auckland, New Zealand. The modifications reduced specimen contamination and provided controlled specimen warming for heat-etching of fracture faces. We have now tested the Mass. General Hospital version of the Type II device (called the “Type II-MGH device”) on a wide variety of biological specimens and have established temperature and pressure curves for routine heat-etching with the device.

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Labelling of non-A, non-B hepatitis infected chimpanzee hepatocytes with nuclease-gold conjugates

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100111367 ◽

1984 ◽

Vol 42 ◽

pp. 250-251

Author(s):

G. D. Gagne ◽

M. F. Miller ◽

D. A. Peterson

Keyword(s):

Endoplasmic Reticulum ◽

Experimental Infection ◽

Uranyl Acetate ◽

Type I ◽

Cellular Injury ◽

Type Ii ◽

Tubular Structures ◽

Type Iii ◽

Type Iv ◽

Infected Chimpanzee

Experimental infection of chimpanzees with non-A, non-B hepatitis (NANB) or with delta agent hepatitis results in the appearance of characteristic cytoplasmic alterations in the hepatocytes. These alterations include spongelike inclusions (Type I), attached convoluted membranes (Type II), tubular structures (Type III), and microtubular aggregates (Type IV) (Fig. 1). Type I, II and III structures are, by association, believed to be derived from endoplasmic reticulum and may be morphogenetically related. Type IV structures are generally observed free in the cytoplasm but sometimes in the vicinity of type III structures. It is not known whether these structures are somehow involved in the replication and/or assembly of the putative NANB virus or whether they are simply nonspecific responses to cellular injury. When treated with uranyl acetate, type I, II and III structures stain intensely as if they might contain nucleic acids. If these structures do correspond to intermediates in the replication of a virus, one might expect them to contain DNA or RNA and the present study was undertaken to explore this possibility.

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Comparative surface and ultrastructural views of methylotrophic bacteria by TEM, STEM, SE, and freeze-etch electron microscopy.

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100128274 ◽

1987 ◽

Vol 45 ◽

pp. 792-793

Author(s):

T.A. Fassel ◽

M.J. Schaller ◽

M.E. Lidstrom ◽

C.C. Remsen

Keyword(s):

Cytoplasmic Membrane ◽

Structural Features ◽

Methylotrophic Bacteria ◽

Type I ◽

Type Ii ◽

Membrane Complex ◽

Oxidation Of Methane ◽

Methane Oxidizing Bacteria ◽

Wall Structures ◽

Methylomonas Albus

Methylotrophic bacteria play an Important role in the environment in the oxidation of methane and methanol. Extensive intracytoplasmic membranes (ICM) have been associated with the oxidation processes in methylotrophs and chemolithotrophic bacteria. Classification on the basis of ICM arrangement distinguishes 2 types of methylotrophs. Bundles or vesicular stacks of ICM located away from the cytoplasmic membrane and extending into the cytoplasm are present in Type I methylotrophs. In Type II methylotrophs, the ICM form pairs of peripheral membranes located parallel to the cytoplasmic membrane. Complex cell wall structures of tightly packed cup-shaped subunits have been described in strains of marine and freshwater phototrophic sulfur bacteria and several strains of methane oxidizing bacteria. We examined the ultrastructure of the methylotrophs with particular view of the ICM and surface structural features, between representatives of the Type I Methylomonas albus (BG8), and Type II Methylosinus trichosporium (OB-36).

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