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Article ; Online: A theoretical analysis of the putative ORF10 protein in SARS-CoV-2

Schuster, Noah A.

bioRxiv

Abstract: Found just upstream of the 3’-untranslated region in the SARS-CoV-2 genome is the putative ORF10 which has been proposed to encode for the hypothetical ORF10 protein. Even though current research suggests this protein is not likely to be produced, ... ...

Abstract	Found just upstream of the 3’-untranslated region in the SARS-CoV-2 genome is the putative ORF10 which has been proposed to encode for the hypothetical ORF10 protein. Even though current research suggests this protein is not likely to be produced, further investigations into this protein are still warranted. Herein, this study uses multiple bioinformatic programs to theoretically characterize and construct the ORF10 protein in SARS-CoV-2. Results indicate this protein is mostly ordered and hydrophobic with high protein-binding propensity, especially in the N-terminus. Although minimal, an assessment of twenty-two missense mutations for this protein suggest slight changes in protein flexibility and hydrophobicity. When compared against two other protein models, this study’s model was found to possess higher quality. As such, this model suggests the ORF10 protein contains a β-α-β motif with a β-molecular recognition feature occurring as the first β-strand. Furthermore, this protein also shares a strong phylogenetic relationship with other putative ORF10 protein’s in closely related coronaviruses. Despite not yielding evidence for the existence of this protein within SARS-CoV-2, this study does present theoretical examinations that can serve as platforms to drive additional experimental work that assess the biological relevance of this hypothetical protein in SARS-CoV-2.
Keywords	covid19
Publisher	BioRxiv; WHO
Document type	Article ; Online
DOI	10.1101/2020.10.26.355784
Database	COVID19

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Article ; Online: Modeling non-linear relationships in epidemiological data: The application and interpretation of spline models.

Schuster, Noah A / Rijnhart, Judith J M / Twisk, Jos W R / Heymans, Martijn W

Frontiers in epidemiology

2022 Volume 2, Page(s) 975380

Abstract: Objective: Traditional methods to deal with non-linearity in regression analysis often result in loss of information or compromised interpretability of the results. A recommended but underutilized method for modeling non-linear associations in ... ...

Abstract	Objective: Traditional methods to deal with non-linearity in regression analysis often result in loss of information or compromised interpretability of the results. A recommended but underutilized method for modeling non-linear associations in regression models is spline functions. We explain spline functions in a non-mathematical way and illustrate the application and interpretation to an empirical data example. Methods: Using data from the Amsterdam Growth and Health Longitudinal Study, we examined the non-linear relationship between the sum of four skinfolds and VO Results: The spline models fitted the data better than the traditional methods. Increasing the number of knots in the LSP model increased the explained variance (from Conclusion: Spline functions should be considered more often as they are flexible and can be applied in commonly used regression analysis. RCS regression is generally recommended for prediction research (i.e., to obtain the predicted outcome for a specific exposure value), whereas LSP regression is recommended if one is interested in the effects in a population.
Language	English
Publishing date	2022-08-18
Publishing country	Switzerland
Document type	Journal Article
ISSN	2674-1199
ISSN (online)	2674-1199
DOI	10.3389/fepid.2022.975380
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Misspecification of confounder-exposure and confounder-outcome associations leads to bias in effect estimates.

Schuster, Noah A / Rijnhart, Judith J M / Bosman, Lisa C / Twisk, Jos W R / Klausch, Thomas / Heymans, Martijn W

BMC medical research methodology

2023 Volume 23, Issue 1, Page(s) 11

Abstract: Background: Confounding is a common issue in epidemiological research. Commonly used confounder-adjustment methods include multivariable regression analysis and propensity score methods. Although it is common practice to assess the linearity assumption ... ...

Abstract	Background: Confounding is a common issue in epidemiological research. Commonly used confounder-adjustment methods include multivariable regression analysis and propensity score methods. Although it is common practice to assess the linearity assumption for the exposure-outcome effect, most researchers do not assess linearity of the relationship between the confounder and the exposure and between the confounder and the outcome before adjusting for the confounder in the analysis. Failing to take the true non-linear functional form of the confounder-exposure and confounder-outcome associations into account may result in an under- or overestimation of the true exposure effect. Therefore, this paper aims to demonstrate the importance of assessing the linearity assumption for confounder-exposure and confounder-outcome associations and the importance of correctly specifying these associations when the linearity assumption is violated. Methods: A Monte Carlo simulation study was used to assess and compare the performance of confounder-adjustment methods when the functional form of the confounder-exposure and confounder-outcome associations were misspecified (i.e., linearity was wrongly assumed) and correctly specified (i.e., linearity was rightly assumed) under multiple sample sizes. An empirical data example was used to illustrate that the misspecification of confounder-exposure and confounder-outcome associations leads to bias. Results: The simulation study illustrated that the exposure effect estimate will be biased when for propensity score (PS) methods the confounder-exposure association is misspecified. For methods in which the outcome is regressed on the confounder or the PS, the exposure effect estimate will be biased if the confounder-outcome association is misspecified. In the empirical data example, correct specification of the confounder-exposure and confounder-outcome associations resulted in smaller exposure effect estimates. Conclusion: When attempting to remove bias by adjusting for confounding, misspecification of the confounder-exposure and confounder-outcome associations might actually introduce bias. It is therefore important that researchers not only assess the linearity of the exposure-outcome effect, but also of the confounder-exposure or confounder-outcome associations depending on the confounder-adjustment method used.
MeSH term(s)	Humans ; Confounding Factors, Epidemiologic ; Computer Simulation ; Bias ; Regression Analysis ; Epidemiologic Studies
Language	English
Publishing date	2023-01-12
Publishing country	England
Document type	Journal Article
ZDB-ID	2041362-2
ISSN	1471-2288 ; 1471-2288
ISSN (online)	1471-2288
ISSN	1471-2288
DOI	10.1186/s12874-022-01817-0
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Noncollapsibility and its role in quantifying confounding bias in logistic regression.

Schuster, Noah A / Twisk, Jos W R / Ter Riet, Gerben / Heymans, Martijn W / Rijnhart, Judith J M

BMC medical research methodology

2021 Volume 21, Issue 1, Page(s) 136

Abstract: Background: Confounding bias is a common concern in epidemiological research. Its presence is often determined by comparing exposure effects between univariable- and multivariable regression models, using an arbitrary threshold of a 10% difference to ... ...

Abstract	Background: Confounding bias is a common concern in epidemiological research. Its presence is often determined by comparing exposure effects between univariable- and multivariable regression models, using an arbitrary threshold of a 10% difference to indicate confounding bias. However, many clinical researchers are not aware that the use of this change-in-estimate criterion may lead to wrong conclusions when applied to logistic regression coefficients. This is due to a statistical phenomenon called noncollapsibility, which manifests itself in logistic regression models. This paper aims to clarify the role of noncollapsibility in logistic regression and to provide guidance in determining the presence of confounding bias. Methods: A Monte Carlo simulation study was designed to uncover patterns of confounding bias and noncollapsibility effects in logistic regression. An empirical data example was used to illustrate the inability of the change-in-estimate criterion to distinguish confounding bias from noncollapsibility effects. Results: The simulation study showed that, depending on the sign and magnitude of the confounding bias and the noncollapsibility effect, the difference between the effect estimates from univariable- and multivariable regression models may underestimate or overestimate the magnitude of the confounding bias. Because of the noncollapsibility effect, multivariable regression analysis and inverse probability weighting provided different but valid estimates of the confounder-adjusted exposure effect. In our data example, confounding bias was underestimated by the change in estimate due to the presence of a noncollapsibility effect. Conclusion: In logistic regression, the difference between the univariable- and multivariable effect estimate might not only reflect confounding bias but also a noncollapsibility effect. Ideally, the set of confounders is determined at the study design phase and based on subject matter knowledge. To quantify confounding bias, one could compare the unadjusted exposure effect estimate and the estimate from an inverse probability weighted model.
MeSH term(s)	Bias ; Epidemiologic Studies ; Humans ; Logistic Models ; Probability ; Research Design
Language	English
Publishing date	2021-07-05
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2041362-2
ISSN	1471-2288 ; 1471-2288
ISSN (online)	1471-2288
ISSN	1471-2288
DOI	10.1186/s12874-021-01316-8
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Ignoring competing events in the analysis of survival data may lead to biased results: a nonmathematical illustration of competing risk analysis.

Schuster, Noah A / Hoogendijk, Emiel O / Kok, Almar A L / Twisk, Jos W R / Heymans, Martijn W

Journal of clinical epidemiology

2020 Volume 122, Page(s) 42–48

Abstract: Objective: Competing events are often ignored in epidemiological studies. Conventional methods for the analysis of survival data assume independent or noninformative censoring, which is violated when subjects that experience a competing event are ... ...

Abstract	Objective: Competing events are often ignored in epidemiological studies. Conventional methods for the analysis of survival data assume independent or noninformative censoring, which is violated when subjects that experience a competing event are censored. Because many survival studies do not apply competing risk analysis, we explain and illustrate in a nonmathematical way how to analyze and interpret survival data in the presence of competing events. Study design and setting: Using data from the Longitudinal Aging Study Amsterdam, both marginal analyses (Kaplan-Meier method and Cox proportional-hazards regression) and competing risk analyses (cumulative incidence function [CIF], cause-specific and subdistribution hazard regression) were performed. We analyzed the association between sex and depressive symptoms, in which death before the onset of depression was a competing event. Results: The Kaplan-Meier method overestimated the cumulative incidence of depressive symptoms. Instead, the CIF should be used. As the subdistribution hazard model has a one-to-one relation with the CIF, it is recommended for prediction research, whereas the cause-specific hazard model is recommended for etiologic research. Conclusion: When competing risks are present, the type of research question guides the choice of the analytical model to be used. In any case, results should be presented for all event types.
MeSH term(s)	Bias ; Depression/epidemiology ; Depression/mortality ; Female ; Humans ; Incidence ; Kaplan-Meier Estimate ; Male ; Observational Studies as Topic ; Proportional Hazards Models ; Research Design ; Risk Assessment ; Risk Factors ; Sex Characteristics ; Survival Analysis
Language	English
Publishing date	2020-03-09
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	639306-8
ISSN	1878-5921 ; 0895-4356
ISSN (online)	1878-5921
ISSN	0895-4356
DOI	10.1016/j.jclinepi.2020.03.004
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article: Explaining the association between frailty and mortality in older adults: The mediating role of lifestyle, social, psychological, cognitive, and physical factors.

de Breij, Sascha / Rijnhart, Judith J M / Schuster, Noah A / Rietman, M Liset / Peters, Mike J L / Hoogendijk, Emiel O

Preventive medicine reports

2021 Volume 24, Page(s) 101589

Abstract: Frailty is associated with a higher risk of mortality, but not much is known about underlying pathways of the frailty-mortality association. In this study, we explore a wide range of possible mediators of the relation between frailty and mortality. Data ... ...

Abstract	Frailty is associated with a higher risk of mortality, but not much is known about underlying pathways of the frailty-mortality association. In this study, we explore a wide range of possible mediators of the relation between frailty and mortality. Data were used from the Longitudinal Aging Study Amsterdam (LASA). We included 1477 older adults aged 65 years and over who participated in the study in 2008-2009 and linked their data to register data on mortality up to 2015. We examined a range of lifestyle, social, psychological, cognitive, and physical factors as potential mediators. All analyses were stratified by sex. We used causal mediation analyses to estimate the indirect effects in single-mediator analyses. Statistically significant mediators were then included in multiple-mediator analyses to examine their combined effect. The results showed that older men (OR = 2.79, 95% CI = 1.23;6.34) and women (OR = 2.31, 95% CI = 1.24;4.30) with frailty had higher odds of being deceased 6 years later compared to those without frailty. In men, polypharmacy (indirect effect OR = 1.21, 95% CI = 1.03;1.50) was a statistically significant mediator in this association. In women, polypharmacy, self-rated health, and multimorbidity were statistically significant mediators in the single-mediator models, but only the indirect effect of polypharmacy remained in the multiple-mediator model (OR = 1.16, 95% CI = 1.03;1.38). In conclusion, of many factors that were considered, we identified polypharmacy as explanatory factor of the association between frailty and mortality in older men and women. This finding has important clinical implications, as it suggests that targeting polypharmacy in frail older adults could reduce their risk of mortality.
Language	English
Publishing date	2021-10-07
Publishing country	United States
Document type	Journal Article
ZDB-ID	2785569-7
ISSN	2211-3355
ISSN	2211-3355
DOI	10.1016/j.pmedr.2021.101589
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Noncollapsibility and its role in quantifying confounding bias in logistic regression

Noah A. Schuster / Jos W. R. Twisk / Gerben ter Riet / Martijn W. Heymans / Judith J. M. Rijnhart

BMC Medical Research Methodology, Vol 21, Iss 1, Pp 1-

2021 Volume 9

Abstract: Abstract Background Confounding bias is a common concern in epidemiological research. Its presence is often determined by comparing exposure effects between univariable- and multivariable regression models, using an arbitrary threshold of a 10% ... ...

Abstract	Abstract Background Confounding bias is a common concern in epidemiological research. Its presence is often determined by comparing exposure effects between univariable- and multivariable regression models, using an arbitrary threshold of a 10% difference to indicate confounding bias. However, many clinical researchers are not aware that the use of this change-in-estimate criterion may lead to wrong conclusions when applied to logistic regression coefficients. This is due to a statistical phenomenon called noncollapsibility, which manifests itself in logistic regression models. This paper aims to clarify the role of noncollapsibility in logistic regression and to provide guidance in determining the presence of confounding bias. Methods A Monte Carlo simulation study was designed to uncover patterns of confounding bias and noncollapsibility effects in logistic regression. An empirical data example was used to illustrate the inability of the change-in-estimate criterion to distinguish confounding bias from noncollapsibility effects. Results The simulation study showed that, depending on the sign and magnitude of the confounding bias and the noncollapsibility effect, the difference between the effect estimates from univariable- and multivariable regression models may underestimate or overestimate the magnitude of the confounding bias. Because of the noncollapsibility effect, multivariable regression analysis and inverse probability weighting provided different but valid estimates of the confounder-adjusted exposure effect. In our data example, confounding bias was underestimated by the change in estimate due to the presence of a noncollapsibility effect. Conclusion In logistic regression, the difference between the univariable- and multivariable effect estimate might not only reflect confounding bias but also a noncollapsibility effect. Ideally, the set of confounders is determined at the study design phase and based on subject matter knowledge. To quantify confounding bias, one could compare the unadjusted ...
Keywords	Logistic regression ; Confounding ; Noncollapsibility ; Confounder-adjustment ; Univariable regression analysis ; Multivariable regression analysis ; Medicine (General) ; R5-920
Subject code	310
Language	English
Publishing date	2021-07-01T00:00:00Z
Publisher	BMC
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Explaining the association between frailty and mortality in older adults

Sascha de Breij / Judith J.M. Rijnhart / Noah A. Schuster / M. Liset Rietman / Mike J.L. Peters / Emiel O. Hoogendijk

Preventive Medicine Reports, Vol 24, Iss , Pp 101589- (2021)

The mediating role of lifestyle, social, psychological, cognitive, and physical factors

2021

Abstract	Frailty is associated with a higher risk of mortality, but not much is known about underlying pathways of the frailty-mortality association. In this study, we explore a wide range of possible mediators of the relation between frailty and mortality. Data were used from the Longitudinal Aging Study Amsterdam (LASA). We included 1477 older adults aged 65 years and over who participated in the study in 2008–2009 and linked their data to register data on mortality up to 2015. We examined a range of lifestyle, social, psychological, cognitive, and physical factors as potential mediators. All analyses were stratified by sex. We used causal mediation analyses to estimate the indirect effects in single-mediator analyses. Statistically significant mediators were then included in multiple-mediator analyses to examine their combined effect. The results showed that older men (OR = 2.79, 95% CI = 1.23;6.34) and women (OR = 2.31, 95% CI = 1.24;4.30) with frailty had higher odds of being deceased 6 years later compared to those without frailty. In men, polypharmacy (indirect effect OR = 1.21, 95% CI = 1.03;1.50) was a statistically significant mediator in this association. In women, polypharmacy, self-rated health, and multimorbidity were statistically significant mediators in the single-mediator models, but only the indirect effect of polypharmacy remained in the multiple-mediator model (OR = 1.16, 95% CI = 1.03;1.38). In conclusion, of many factors that were considered, we identified polypharmacy as explanatory factor of the association between frailty and mortality in older men and women. This finding has important clinical implications, as it suggests that targeting polypharmacy in frail older adults could reduce their risk of mortality.
Keywords	Mediation analysis ; Frailty phenotype ; Survival ; Epidemiology ; Medicine ; R
Subject code	310
Language	English
Publishing date	2021-12-01T00:00:00Z
Publisher	Elsevier
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Longitudinal Aging Study Amsterdam COVID-19 exposure index: a cross-sectional analysis of the impact of the pandemic on daily functioning of older adults.

Hoogendijk, Emiel O / Schuster, Noah A / van Tilburg, Theo G / Schaap, Laura A / Suanet, Bianca / De Breij, Sascha / Kok, Almar Al / Van Schoor, Natasja M / Timmermans, Erik J / de Jongh, Renate T / Visser, Marjolein / Huisman, Martijn

BMJ open

2022 Volume 12, Issue 11, Page(s) e061745

Abstract: Objectives: The aim of this study was to develop an index to measure older adults' exposure to the COVID-19 pandemic and to study its association with various domains of functioning.: Design: Cross-sectional study.: Setting: The Longitudinal Aging ...

Abstract	Objectives: The aim of this study was to develop an index to measure older adults' exposure to the COVID-19 pandemic and to study its association with various domains of functioning. Design: Cross-sectional study. Setting: The Longitudinal Aging Study Amsterdam (LASA), a cohort study in the Netherlands. Participants: Community-dwelling older adults aged 62-102 years (n=1089) who participated in the LASA COVID-19 study (June-September 2020), just after the first wave of the pandemic. Primary outcome measures: A 35-item COVID-19 exposure index with a score ranging between 0 and 1 was developed, including items that assess the extent to which the COVID-19 situation affected daily lives of older adults. Descriptive characteristics of the index were studied, stratified by several sociodemographic factors. Logistic regression analyses were performed to study associations between the exposure index and several indicators of functioning (functional limitations, anxiety, depression and loneliness). Results: The mean COVID-19 exposure index score was 0.20 (SD 0.10). Scores were relatively high among women and in the southern region of the Netherlands. In models adjusted for sociodemographic factors and prepandemic functioning (2018-2019), those with scores in the highest tertile of the exposure index were more likely to report functional limitations (OR: 2.24; 95% CI: 1.48 to 3.38), anxiety symptoms (OR: 3.14; 95% CI: 1.82 to 5.44), depressive symptoms (OR: 2.49; 95% CI: 1.55 to 4.00) and loneliness (OR: 2.97; 95% CI: 2.08 to 4.26) than those in the lowest tertile. Conclusions: Among older adults in the Netherlands, higher exposure to the COVID-19 pandemic was associated with worse functioning in the physical, mental and social domain. The newly developed exposure index may be used to identify persons for whom targeted interventions are needed to maintain or improve functioning during the pandemic or postpandemic.
MeSH term(s)	Female ; Humans ; Aged ; Pandemics ; COVID-19/epidemiology ; Cross-Sectional Studies ; Cohort Studies ; Aging ; Depression/diagnosis
Chemical Substances	lipid-associated sialic acid
Language	English
Publishing date	2022-11-02
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2599832-8
ISSN	2044-6055 ; 2044-6055
ISSN (online)	2044-6055
ISSN	2044-6055
DOI	10.1136/bmjopen-2022-061745
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Intention-to-treat analysis when only a baseline value is available.

Twisk, Jos Wr / Rijnhart, Judith Jm / Hoekstra, Trynke / Schuster, Noah A / Ter Wee, Marieke M / Heymans, Martijn W

Contemporary clinical trials communications

2020 Volume 20, Page(s) 100684

Abstract: Objectives: How to perform an intention to treat (ITT) analysis when a patient has a baseline value but no follow-up measurements is problematic. The purpose of this study was to compare different methods that deal with this problem, i.e. no imputation ( ...

Abstract	Objectives: How to perform an intention to treat (ITT) analysis when a patient has a baseline value but no follow-up measurements is problematic. The purpose of this study was to compare different methods that deal with this problem, i.e. no imputation (standard and alternative mixed model analysis), single imputation (i.e. baseline value carried forward), and multiple imputation (selective and non-selective). Study design and setting: We used a simulation study with different scenarios regarding 1) the association between missingness and the baseline value, 2) whether the patients did or did not receive the treatment, and 3) the percentage of missing data, and two real life data sets. Results: Bias and coverage were comparable between the two mixed model analyses and multiple imputation in most situations including the real life data examples. Only in the situation when the patients in the treatment group were simulated not to have received the treatment, selective imputation using this information outperformed all other methods. Conclusions: In most situations a standard mixed model analysis without imputation is appropriate as ITT analysis. However, when patients with missing follow-up data allocated to the treatment group did not received treatment, it is advised to use selective imputation, using this information, although the results should be interpreted with caution.
Language	English
Publishing date	2020-11-26
Publishing country	Netherlands
Document type	Journal Article
ISSN	2451-8654
ISSN (online)	2451-8654
DOI	10.1016/j.conctc.2020.100684
Database	MEDical Literature Analysis and Retrieval System OnLINE

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