scholarly journals GIVE Statistic for Goodness of Fit in Instrumental Variables Models with Application to COVID Data

2021 ◽  
Author(s):  
Subhra Sankar Dhar ◽  
Shalabh Shalabh
Keyword(s):  
Immune Responses ◽  
Instrumental Variables ◽  
Instrumental Variable ◽  
Goodness Of Fit ◽  
Large Population ◽  
Latent Tuberculosis ◽  
Simulation Studies ◽  
Instrumental Variables Estimation ◽  
The Relationship ◽  
Choice Of Instruments

Abstract Since COVID-19 outbreak, scientists have been interested to know whether there is any impact of the Bacillus Calmette-Guerin (BCG) vaccine against COVID-19 mortality or not. It becomes more relevant as a large population in the world may have latent tuberculosis infection (LTBI), for which a person may not have active tuberculosis but persistent immune responses stimulated by Mycobacterium tuberculosis antigens, and that means, both LTBI and BCG generate immunity against COVID-19. In order to understand the relationship between LTBI and COVID-19 mortality, this article proposes a measure of goodness of fit, viz., Goodness of Instrumental Variable Estimates (GIVE) statistic, of a model obtained by Instrumental Variables estimation. The GIVE helps in finding the appropriate choice of instruments, which provides a better fitted model. In the course of study, the large sample properties of the GIVE statistic are investigated. As indicated before, the COVID-19 data is analysed using the GIVE statistic, and moreover, simulation studies are also conducted to show the usefulness of the GIVE statistic.

scholarly journals GIVE Statistic for Goodness of Fit in Instrumental Variables Models with Application to COVID Data

2021 ◽  
Author(s):  
Subhra Sankar Dhar ◽  
Shalabh
Keyword(s):  
Immune Responses ◽  
Instrumental Variables ◽  
Goodness Of Fit ◽  
Latent Tuberculosis Infection ◽  
Large Population ◽  
Latent Tuberculosis ◽  
Simulation Studies ◽  
Instrumental Variables Estimation ◽  
The Relationship ◽  
Choice Of Instruments

AbstractSince COVID-19 outbreak, scientists have been interested to know whether there is any impact of the Bacillus Calmette-Guerin (BCG) vaccine against COVID-19 mortality or not. It becomes more relevant as a large population in the world may have latent tuberculosis infection (LTBI), for which a person may not have active tuberculosis but persistent immune responses stimulated by Mycobacterium tuberculosis antigens, and that means, both LTBI and BCG generate immunity against COVID-19. In order to understand the relationship between LTBI and COVID-19 mortality, this article proposes a measure of goodness of fit, viz., Goodness of Instrumental Variable Estimates (GIVE) statistic, of a model obtained by Instrumental Variables estimation. The GIVE helps in finding the appropriate choice of instruments, which provides a better fitted model. In the course of study, the large sample properties of the GIVE statistic are investigated. As indicated before, the COVID-19 data is analysed using the GIVE statistic, and moreover, simulation studies are also conducted to show the usefulness of the GIVE statistic.

Estimation across Data Sets: Two-Stage Auxiliary Instrumental Variables Estimation (2SAIV)

1989 ◽  
Vol 1 ◽  
pp. 1-23 ◽  
Author(s):  
Charles H. Franklin
Keyword(s):  
Instrumental Variables ◽  
Cross Sections ◽  
Data Sets ◽  
Important Research ◽  
Sample Design ◽  
The Past ◽  
Instrumental Variables Estimation ◽  
Research Questions ◽  
Single Data ◽  
The Relationship

Theories demand much of data, often more than a single data collection can provide. For example, many important research questions are set in the past and must rely on data collected at that time and for other purposes. As a result, we often find that the data lack crucial variables. Another common problem arises when we wish to estimate the relationship between variables that are measured in different data sets. A variation of this occurs with a split half sample design in which one or more important variables appear on the “wrong” half. Finally, we may need panel data but have only cross sections available. In each of these cases our ability to estimate the theoretically determined equation is limited by the data that are available.

scholarly journals Instrumental variables estimation with competing risk data

Biostatistics ◽  
2018 ◽  
Vol 21 (1) ◽  
pp. 158-171
Author(s):  
Torben Martinussen ◽  
Stijn Vansteelandt
Keyword(s):  
Instrumental Variables ◽  
Data Distribution ◽  
Parametric Model ◽  
Competing Risk ◽  
Simulation Studies ◽  
Time To Event ◽  
Unmeasured Confounding ◽  
Hazard Functions ◽  
Instrumental Variables Estimation ◽  
Effect Estimation

Summary Time-to-event analyses are often plagued by both—possibly unmeasured—confounding and competing risks. To deal with the former, the use of instrumental variables (IVs) for effect estimation is rapidly gaining ground. We show how to make use of such variables in competing risk analyses. In particular, we show how to infer the effect of an arbitrary exposure on cause-specific hazard functions under a semi-parametric model that imposes relatively weak restrictions on the observed data distribution. The proposed approach is flexible accommodating exposures and IVs of arbitrary type, and enabling covariate adjustment. It makes use of closed-form estimators that can be recursively calculated, and is shown to perform well in simulation studies. We also demonstrate its use in an application on the effect of mammography screening on the risk of dying from breast cancer.

scholarly journals Remarriage after Divorce and Health in Later Life: Findings from CHARLS in China

2019 ◽  
Author(s):  
xiaomin li ◽  
Quanbao Jiang ◽  
Tingshuai Ge ◽  
Xinfeng Cheng
Keyword(s):  
Mental Health ◽  
Instrumental Variables ◽  
Instrumental Variable ◽  
Health Benefits ◽  
Later Life ◽  
Descriptive Statistics ◽  
Self Rated Health ◽  
History Of ◽  
The Relationship ◽  
Positive Effect

Abstract Background : Marriage has a positive effect on health. After the dissolution of a marriage, the health of divorcees worsens. The focus of this paper is on whether remarriage can help a person regain the health benefits that comes with marriage. Methods: This study used the national baseline data from the China Health and Retirement Longitudinal Study (CHARLS), a survey conducted from 2011 to 2012, this article applies instrumental variables to deal with the endogenous problems and to investigate the association between remarriage after divorce and late-life health. Result: According to descriptive statistics, among the 235 middle-aged and elderly respondents with a history of divorce, 46% remarried, 75% of them thought their SRH was fair or bad, 15% of them suffered from ADL impairment and the scale score for depression averaged 9.33 ( SD =7.1). According to the regression results, compared with divorcees who have not remarried, those who remarry suffer less from depression and have better self-rated health. There are gender differences reflected in the relationship between remarriage after divorce and mental health. Remarriage can promote the mental health of men, but there is no significant correlation between remarriage and the mental health of women. Conclusion: Marriage has a protective effect on health, and remarriage can help people regain the health benefits. Key words: ADL ; Depression; Self-rated health ; Instrumental variable; Remarriage after divorce

MHC and malaria: the relationship between HLA class II alleles and immune responses to Plasmodium falciprum

1992 ◽  
Vol 4 (9) ◽  
pp. 1055-1063 ◽  
Author(s):  
E. M. Riley ◽  
O. Olerup ◽  
S. Bennett ◽  
P. Rowe ◽  
S. J. Allen ◽  
...  
Keyword(s):  
Immune Responses ◽  
Class Ii ◽  
Hla Class Ii ◽  
The Relationship ◽  
Hla Class Ii Alleles

scholarly journals Pituitary MRI characteristics in 297 acromegaly patients based on T2-weighted sequences

2015 ◽  
Vol 22 (2) ◽  
pp. 169-177 ◽  
Author(s):  
Iulia Potorac ◽  
Patrick Petrossians ◽  
Adrian F Daly ◽  
Franck Schillo ◽  
Claude Ben Slama ◽  
...  
Keyword(s):  
Large Population ◽  
Optic Chiasm ◽  
Research Priority ◽  
Maximal Diameter ◽  
Magnetic Resonance Imaging Mri ◽  
Multimodal Management ◽  
Mri Characteristics ◽  
Weighted Sequences ◽  
The Relationship

Responses of GH-secreting adenomas to multimodal management of acromegaly vary widely between patients. Understanding the behavioral patterns of GH-secreting adenomas by identifying factors predictive of their evolution is a research priority. The aim of this study was to clarify the relationship between the T2-weighted adenoma signal on diagnostic magnetic resonance imaging (MRI) in acromegaly and clinical and biological features at diagnosis. An international, multicenter, retrospective analysis was performed using a large population of 297 acromegalic patients recently diagnosed with available diagnostic MRI evaluations. The study was conducted at ten endocrine tertiary referral centers. Clinical and biochemical characteristics, and MRI signal findings were evaluated. T2-hypointense adenomas represented 52.9% of the series, were smaller than their T2-hyperintense and isointense counterparts (P<0.0001), were associated with higher IGF1 levels (P=0.0001), invaded the cavernous sinus less frequently (P=0.0002), and rarely caused optic chiasm compression (P<0.0001). Acromegalic men tended to be younger at diagnosis than women (P=0.067) and presented higher IGF1 values (P=0.01). Although in total, adenomas had a predominantly inferior extension in 45.8% of cases, in men this was more frequent (P<0.0001), whereas in women optic chiasm compression of macroadenomas occurred more often (P=0.0067). Most adenomas (45.1%) measured between 11 and 20 mm in maximal diameter and bigger adenomas were diagnosed at younger ages (P=0.0001). The T2-weighted signal differentiates GH-secreting adenomas into subgroups with particular behaviors. This raises the question of whether the T2-weighted signal could represent a factor in the classification of acromegalic patients in future studies.

scholarly journals Early Education Care from Its Practitioners to Achieve Sustainability

2021 ◽  
Vol 13 (6) ◽  
pp. 3396
Author(s):  
Óscar Gavín-Chocano ◽  
David Molero ◽  
Inmaculada García-Martínez
Keyword(s):  
Early Intervention ◽  
Structural Equation ◽  
Goodness Of Fit ◽  
Satisfaction With Life ◽  
Well Being ◽  
Satisfaction With Life Scale ◽  
Life Scale ◽  
Fit Index ◽  
The Relationship ◽  
Early Intervention Professionals

(1) Background: Early intervention professionals are involved in the reconceptualisation of their service due to the exceptional situation caused by the COVID-19 epidemic, within the family context and aware of the children’s needs, with an impact on their emotional well-being to ensure sustainability. An analysis of their socio–emotional profile and training is increasingly needed to face their professional development effectively; (2) Methods: In this study, 209 early intervention professionals participated (n = 209), with an average age of 37.62 (±9.02). The following instruments were used: Satisfaction with Life Scale (SWLS), Wong Law Emotional Intelligence Scale (WLEIS-S) and the Utrecht Work Engagement Scale (UWES-9). The purpose of the study was to examine the relationship between early intervention (EI) and engagement as predictors of greater life satisfaction using Structural Equation Modelling (SEM). (3) Results: There exists a relationship between some dimensions of the instruments used (p < 0.01). The model obtained good structural validity (χ² = 3.264; Root Mean Square Error of Approximation (RMSEA) =.021; Goodness-of-Fit Index (GFI) = 0.991; Comparative Goodness of Fit Index (CFI) = 0.999; Incremental Fit Index (IFI) = 0.999). Subsequently, the results described above were verified through Bayesian statistics, thereby reinforcing the evidence provided; (4) Conclusions: Findings highlight the importance of providing professionals with emotional tools and strategies, from the educational context, in order to carry out their activity effectively and ensure the sustainability within the current situation, while remaining fully engaged.

scholarly journals A comparison of zero-inflated and hurdle models for modeling zero-inflated count data

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Cindy Xin Feng
Keyword(s):  
Health Services ◽  
Count Data ◽  
Goodness Of Fit ◽  
Negative Binomial ◽  
Simulation Studies ◽  
Final Choice ◽  
Hurdle Models ◽  
Count Distribution ◽  
Careful Assessment

AbstractCounts data with excessive zeros are frequently encountered in practice. For example, the number of health services visits often includes many zeros representing the patients with no utilization during a follow-up time. A common feature of this type of data is that the count measure tends to have excessive zero beyond a common count distribution can accommodate, such as Poisson or negative binomial. Zero-inflated or hurdle models are often used to fit such data. Despite the increasing popularity of ZI and hurdle models, there is still a lack of investigation of the fundamental differences between these two types of models. In this article, we reviewed the zero-inflated and hurdle models and highlighted their differences in terms of their data generating processes. We also conducted simulation studies to evaluate the performances of both types of models. The final choice of regression model should be made after a careful assessment of goodness of fit and should be tailored to a particular data in question.

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