scholarly journals On the Use of Aggregate Survey Data for Estimating Regional Major Depressive Disorder Prevalence

Psychometrika ◽  
2021 ◽  
Author(s):  
Domingo Morales ◽  
Joscha Krause ◽  
Jan Pablo Burgard
Keyword(s):  
Major Depression ◽  
Measurement Errors ◽  
Mixed Model ◽  
Mean Squared Error ◽  
Regional Analysis ◽  
Age Groups ◽  
Parametric Bootstrap ◽  
Important Indicator ◽  
Auxiliary Data ◽  
Public Health Reporting

AbstractMajor depression is a severe mental disorder that is associated with strongly increased mortality. The quantification of its prevalence on regional levels represents an important indicator for public health reporting. In addition to that, it marks a crucial basis for further explorative studies regarding environmental determinants of the condition. However, assessing the distribution of major depression in the population is challenging. The topic is highly sensitive, and national statistical institutions rarely have administrative records on this matter. Published prevalence figures as well as available auxiliary data are typically derived from survey estimates. These are often subject to high uncertainty due to large sampling variances and do not allow for sound regional analysis. We propose a new area-level Poisson mixed model that accounts for measurement errors in auxiliary data to close this gap. We derive the empirical best predictor under the model and present a parametric bootstrap estimator for the mean squared error. A method of moments algorithm for consistent model parameter estimation is developed. Simulation experiments are conducted to show the effectiveness of the approach. The methodology is applied to estimate the major depression prevalence in Germany on regional levels crossed by sex and age groups.

scholarly journals $$\ell _2$$-penalized approximate likelihood inference in logit mixed models for regional prevalence estimation under covariate rank-deficiency

Metrika ◽  
2021 ◽  
Author(s):  
Joscha Krause ◽  
Jan Pablo Burgard ◽  
Domingo Morales
Keyword(s):  
Mixed Model ◽  
Mean Squared Error ◽  
Routine Data ◽  
Parametric Bootstrap ◽  
Monte Carlo Simulation Study ◽  
Rank Deficiency ◽  
Prevalence Estimation ◽  
Fitting Algorithm ◽  
Individual Contributions ◽  
The Individual

AbstractRegional prevalence estimation requires the use of suitable statistical methods on epidemiologic data with substantial local detail. Small area estimation with medical treatment records as covariates marks a promising combination for this purpose. However, medical routine data often has strong internal correlation due to diagnosis-related grouping in the records. Depending on the strength of the correlation, the space spanned by the covariates can become rank-deficient. In this case, prevalence estimates suffer from unacceptable uncertainty as the individual contributions of the covariates to the model cannot be identified properly. We propose an area-level logit mixed model for regional prevalence estimation with a new fitting algorithm to solve this problem. We extend the Laplace approximation to the log-likelihood by an $$\ell _2$$ ℓ 2 -penalty in order to stabilize the estimation process in the presence of covariate rank-deficiency. Empirical best predictors under the model and a parametric bootstrap for mean squared error estimation are presented. A Monte Carlo simulation study is conducted to evaluate the properties of our methodology in a controlled environment. We further provide an empirical application where the district-level prevalence of multiple sclerosis in Germany is estimated using health insurance records.

Use of simulation–extrapolation estimation in catch–effort analyses

1999 ◽  
Vol 56 (7) ◽  
pp. 1234-1240
Author(s):  
W R Gould ◽  
L A Stefanski ◽  
K H Pollock
Keyword(s):  
Measurement Error ◽  
Measurement Errors ◽  
Mean Squared Error ◽  
Estimation Procedure ◽  
Bias Reduction ◽  
Estimation Methods ◽  
Effort Estimation ◽  
Stat Assoc ◽  
General Terms ◽  
Naive Estimator

All catch-effort estimation methods implicitly assume catch and effort are known quantities, whereas in many cases, they have been estimated and are subject to error. We evaluate the application of a simulation-based estimation procedure for measurement error models (J.R. Cook and L.A. Stefanski. 1994. J. Am. Stat. Assoc. 89: 1314-1328) in catch-effort studies. The technique involves a simulation component and an extrapolation step, hence the name SIMEX estimation. We describe SIMEX estimation in general terms and illustrate its use with applications to real and simulated catch and effort data. Correcting for measurement error with SIMEX estimation resulted in population size and catchability coefficient estimates that were substantially less than naive estimates, which ignored measurement errors in some cases. In a simulation of the procedure, we compared estimators from SIMEX with "naive" estimators that ignore measurement errors in catch and effort to determine the ability of SIMEX to produce bias-corrected estimates. The SIMEX estimators were less biased than the naive estimators but in some cases were also more variable. Despite the bias reduction, the SIMEX estimator had a larger mean squared error than the naive estimator for one of two artificial populations studied. However, our results suggest the SIMEX estimator may outperform the naive estimator in terms of bias and precision for larger populations.

Association of age with risk of major depression among patients with chronic kidney disease over midlife: a nationwide cohort study in Taiwan

2018 ◽  
Vol 31 (08) ◽  
pp. 1171-1179 ◽  
Author(s):  
Shih-Feng Chen ◽  
Yu-Huei Chien ◽  
Pau-Chung Chen ◽  
I-Jen Wang
Keyword(s):  
Chronic Kidney Disease ◽  
Cohort Study ◽  
Major Depression ◽  
Kidney Disease ◽  
Comparison Group ◽  
Age Groups ◽  
International Classification Of Diseases ◽  
Classification Of Diseases ◽  
The Impact

ABSTRACTBackground:The impact of age on the development of depression among patients with chronic kidney disease (CKD) at stages before dialysis is not well known. We aimed to explore the incidence of major depression among predialysis CKD patients of successively older ages through midlife.Methods:We conducted a retrospective cohort study using the longitudinal health insurance database 2005 in Taiwan. This study investigated 17,889 predialysis CKD patients who were further categorized into study (i.e. middle and old-aged) groups and comparison group aged 18–44. The International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) was applied for coding diseases.Results:The group aged 75 and over had the lowest (hazard ratio [HR] 0.47; 95% confidence interval [CI] 0.32–0.69) risk of developing major depression, followed by the group aged 65–74 (HR 0.67; 95% CI 0.49–0.92), using the comparison group as reference. The adjusted survival curves showed significant differences in cumulative major depression-free survival between different age groups. We observed that the risk of major depression development decreases with higher age. Females were at a higher risk of major depression than males among predialyasis CKD patients.Conclusions:The incidence of major depression declines with higher age in predialysis CKD patients over midlife. Among all age groups, patients aged 75 and over have the lowest risk of developing major depression. A female preponderance in major depression development is present. We suggest that depression prevention and therapy should be integrated into the standard care for predialysis CKD patients, especially for those young and female.

scholarly journals Time-Varying Associations Between an Exposure History and a Subsequent Health Outcome: A landmark Approach to Identify Critical Windows.

2021 ◽  
Author(s):  
Maude Wagner ◽  
Francine Grodstein ◽  
Karen Leffondre ◽  
Cécilia Samieri ◽  
Cécile Proust-Lima
Keyword(s):  
Risk Factors ◽  
Health Outcome ◽  
Measurement Errors ◽  
Mixed Model ◽  
Time Window ◽  
Time Windows ◽  
Critical Time ◽  
Cumulative Exposure ◽  
Exposure Measurement ◽  
Exposure History

Abstract Background: Long-term behavioral and health risk factors constitute a primary focus of research on the etiology of chronic diseases. Yet, identifying critical time-windows during which risk factors have the strongest impact on disease risk is challenging. To assess the trajectory of association of an exposure history with an outcome, the weighted cumulative exposure index (WCIE) has been proposed, with weights reflecting the relative importance of exposures at different times. However, WCIE is restricted to a complete observed error-free exposure whereas exposures are often measured with intermittent missingness and error. Moreover, it rarely explores exposure history that is very distant from the outcome as usually sought in life-course epidemiology.Methods: We extend the WCIE methodology to (i) exposures that are intermittently measured with error, and (ii) contexts where the exposure time-window precedes the outcome time-window using a landmark approach. First, the individual exposure history up to the landmark time is estimated using a mixed model that handles missing data and error in exposure measurement, and the predicted complete error-free exposure history is derived. Then the WCIE methodology is applied to assess the trajectory of association between the predicted exposure history and the health outcome collected after the landmark time. In our context, the health outcome is a longitudinal marker analyzed using a mixed model.Results: A simulation study first demonstrates the correct inference obtained with this approach. Then, applied to the Nurses’ Health Study (19,415 women) to investigate the association between body mass index history (collected from midlife) and subsequent cognitive decline (evaluated after age 70), the method identified two major critical windows of association: long before the first cognitive evaluation (roughly 24 to 12 years), higher levels of BMI were associated with poorer cognition. In contrast, adjusted for the whole history, higher levels of BMI became associated with better cognition in the last years prior to the first cognitive interview, thus reflecting reverse causation (changes in exposure due to underlying disease).Conclusions: This approach, easy to implement, provides a flexible tool for studying complex dynamic relationships and identifying critical time windows while accounting for exposure measurement errors.

Wideband Absorbance in Young Infants (0–6 months): A Cross-Sectional Study

2014 ◽  
Vol 25 (05) ◽  
pp. 471-481 ◽  
Author(s):  
Sreedevi Aithal ◽  
Joseph Kei ◽  
Carlie Driscoll
Keyword(s):  
Mixed Model ◽  
Age Groups ◽  
Cross Sectional Study ◽  
Distortion Product Otoacoustic Emission ◽  
Sectional Study ◽  
Cross Sectional ◽  
Distortion Product ◽  
Young Infants ◽  
Developmental Effects ◽  
Data Points

Background: Wideband acoustic immittance (WAI) studies on infants have shown changes in WAI measures with age. These changes are attributed, at least in part, to developmental effects. However, developmental effects in young infants (0–6 mo) on WAI have not been systematically investigated. Purpose: The objective of this study was to compare wideband absorbance (WBA) in healthy neonates and infants aged 1, 2, 4, and 6 mo. Research Design: This was a prospective cross-sectional study. All participants were assessed by using 1-kHz tympanometry, distortion product otoacoustic emission (DPOAE) tests, and WBA tests. Study Sample: Participants included 35 newborns (35 ears), 16 infants aged 1 mo (29 ears), 16 infants aged 2 mo (29 ears), 15 infants aged 4 mo (28 ears), and 14 infants aged 6 mo (27 ears). For each participant, the ears that passed both high-frequency (1-kHz) tympanometry and DPOAE tests were included for analysis. Data Collection and Analysis: WBA was recorded at ambient pressure conditions, and the response consisted of 16 data points at 1/3-octave frequencies from 0.25 to 8 kHz. A mixed-model analysis of variance (ANOVA) was applied to the data in each age group to evaluate the effects of sex, ear, and frequency on WBA. WBA was compared between various age groups. In addition, a separate mixed-model ANOVA was applied to WBA data, and post hoc analyses with the Bonferroni correction were performed at each of the 16 data points at 1/3-octave frequencies across age groups to examine the effect of age on WBA. Results: For all age groups, WBA was highest between 1.5 and 5 kHz and lowest at frequencies of less than 1.5 kHz and greater than 5 kHz. A developmental trend was evident, with both the 0- and 6-mo-old infants being significantly different from other age groups at most frequencies. The WBA results exhibited a multipeaked pattern for infants aged 0 to 2 mo, whereas a single broad peaked pattern for 4- and 6-mo-old infants was observed. The difference in WBA between 0- and 6-mo-old infants was statistically significant across most frequencies. In contrast, the WBA results for 1- and 2-mo-old infants were comparable. There were no significant sex or ear effects on WBA for all age groups. Conclusions: Developmental effects of WBA were evident for infants during the first 6 mo of life. The WBA data can be used as a reference for detecting disorders in the sound-conductive pathways (outer and middle ear) in young infants. Further development of age-specific normative WBA data in young infants is warranted.

Electrophysiological Correlates of Major Depression Disorder with Anxious Distress in Patients of Different Age Groups

2018 ◽  
Vol 44 (1) ◽  
pp. 1-6
Author(s):  
T. F. Shamaeva ◽  
M. V. Pronina ◽  
G. Yu. Polyakova ◽  
Y. I. Polyakov ◽  
V. M. Klimenko
Keyword(s):  
Major Depression ◽  
Age Groups ◽  
Major Depression Disorder ◽  
Depression Disorder

Poisson mixed models for predicting number of fires

2019 ◽  
Vol 28 (3) ◽  
pp. 237
Author(s):  
Miguel Boubeta ◽  
María José Lombardía ◽  
Manuel Marey-Pérez ◽  
Domingo Morales
Keyword(s):  
Forest Fires ◽  
Mixed Models ◽  
Mixed Model ◽  
Parametric Bootstrap ◽  
Temporal Correlation ◽  
Autoregressive Process ◽  
Burned Area ◽  
Time Effects ◽  
North West ◽  
Temporal Models

Wildfires are considered one of the main causes of forest destruction. In recent years, the number of forest fires and burned area in Mediterranean regions have increased. This problem particularly affects Galicia (north-west of Spain). Conventional modelling of the number of forest fires in small areas may have a high error. For this reason, four area-level Poisson mixed models with time effects are proposed. The first two models contain independent time effects, whereas the random effects of the other models are distributed according to an autoregressive process AR(1). A parametric bootstrap algorithm is given to measure the accuracy of the plug-in predictor of fire number under the temporal models. A significant prediction improvement is observed when using Poisson regression models with random time effects. Analysis of historical data finds significant meteorological and socioeconomic variables explaining the number of forest fires by area and reveals the presence of a temporal correlation structure captured by the area-level Poisson mixed model with AR(1) time effects.

Generalized linear mixed models for multi-reader multi-case studies of diagnostic tests

2015 ◽  
Vol 26 (3) ◽  
pp. 1373-1388 ◽  
Author(s):  
Wei Liu ◽  
Norberto Pantoja-Galicia ◽  
Bo Zhang ◽  
Richard M Kotz ◽  
Gene Pennello ◽  
...  
Keyword(s):  
Mixed Models ◽  
Diagnostic Tests ◽  
Fixed Effects ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Generalized Linear Mixed Models ◽  
Characteristic Curve ◽  
Linear Mixed Models ◽  
Parametric Bootstrap ◽  
Pseudo Likelihood

Diagnostic tests are often compared in multi-reader multi-case (MRMC) studies in which a number of cases (subjects with or without the disease in question) are examined by several readers using all tests to be compared. One of the commonly used methods for analyzing MRMC data is the Obuchowski–Rockette (OR) method, which assumes that the true area under the receiver operating characteristic curve (AUC) for each combination of reader and test follows a linear mixed model with fixed effects for test and random effects for reader and the reader–test interaction. This article proposes generalized linear mixed models which generalize the OR model by incorporating a range-appropriate link function that constrains the true AUCs to the unit interval. The proposed models can be estimated by maximizing a pseudo-likelihood based on the approximate normality of AUC estimates. A Monte Carlo expectation-maximization algorithm can be used to maximize the pseudo-likelihood, and a non-parametric bootstrap procedure can be used for inference. The proposed method is evaluated in a simulation study and applied to an MRMC study of breast cancer detection.

scholarly journals A COMPARISON OF NONLINEAR MODELS FOR DESCRIBING WEIGHT-AGE DATA IN ANGLO-NUBIAN DOES

2019 ◽  
Vol 32 (1) ◽  
pp. 251-258 ◽  
Author(s):  
Francisco Arthur Arré ◽  
José Elivalto Guimarães Campelo ◽  
José Lindenberg Rocha Sarmento ◽  
Luiz Antônio Silva Figueiredo Filho ◽  
Diego Helcias Cavalcante
Keyword(s):  
Goodness Of Fit ◽  
Mean Squared Error ◽  
Nonlinear Models ◽  
Age Groups ◽  
Coefficient Of Determination ◽  
Group Model ◽  
Age Group ◽  
Von Bertalanffy ◽  
Absolute Deviation ◽  
Model Interaction

ABSTRACT The objective of this study was to determine the optimum age at last weighing and compare the goodness of fit of nonlinear models used to fit longitudinal weight-age data to describe the growth pattern of Anglo-Nubian does. Weights of 104 animals from birth to 60 months of age were grouped into 10 age groups at six-month intervals. In each age group, parameters A (asymptotic weight), B (integration constant), and K (maturity index) were estimated using the Brody, Gompertz, logistic, and von Bertalanffy models. Data were analyzed using analysis of variance in a factorial design (10 age groups × 4 nonlinear models). The age group × model interaction was not significant. Mean estimates of A, B, and K were significantly different between age groups up to 30 months (p < 0.05), indicating that the estimated curve is affected by weights taken before this age independent of the model. The values of mean squared error (MSE), mean absolute deviation (MAD), coefficient of determination (R2) and Rate of convergence (RC) at each age group up to 30 months were compared to determine the goodness of fit of nonlinear models. The ranking of fit was logistic, Brody, von Bertalanffy, and Gompertz. The logistic and Brody models respectively estimated the smallest and largest asymptotic weight. Longitudinal weight records taken until 30 months of age are most appropriate for estimating the growth of Anglo-Nubian does using nonlinear models.

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