scholarly journals Detecting and Visualizing Outliers in Provider Profiling Using Funnel Plots and Mixed Effects Models—An Example from Prescription Claims Data

2018 ◽  
Vol 15 (9) ◽  
pp. 2015
Author(s):  
Oliver Hirsch ◽  
Norbert Donner-Banzhoff ◽  
Maike Schulz ◽  
Michael Erhart
Keyword(s):  
Health Insurance ◽  
Claims Data ◽  
Mixed Effects ◽  
Mixed Effects Model ◽  
Federal State ◽  
Mixed Effect ◽  
Provider Profiling ◽  
Mixed Effect Models ◽  
First Case ◽  
Funnel Plots

When prescribing a drug for a patient, a physician also has to consider economic aspects. We were interested in the feasibility and validity of profiling based on funnel plots and mixed effect models for the surveillance of German ambulatory care physicians’ prescribing. We analyzed prescriptions issued to patients with a health insurance card attending neurologists’ and psychiatrists’ ambulatory practices in the German federal state of Saarland. The German National Association of Statutory Health Insurance Physicians developed a prescribing assessment scheme (PAS) which contains a systematic appraisal of the benefit of drugs for so far 12 different indications. The drugs have been classified on the basis of their clinical evidence as “standard”, “reserve” or “third level” medication. We had 152.583 prescriptions in 56 practices available for analysis. A total of 38.796 patients received these prescriptions. The funnel plot approach with additive correction for overdispersion was almost equivalent to a mixed effects model which directly took the multilevel structure of the data into account. In the first case three practices were labeled as outliers, the mixed effects model resulted in two outliers. We suggest that both techniques should be routinely applied within a surveillance system of prescription claims data.

A semiparametric mixed-effects model for censored longitudinal data

2021 ◽  
pp. 096228022110463
Author(s):  
Thalita B Mattos ◽  
Larissa Avila Matos ◽  
Victor H Lachos
Keyword(s):  
Longitudinal Data ◽  
Em Algorithm ◽  
Longitudinal Studies ◽  
Mixed Effects ◽  
Mixed Effects Model ◽  
Modeling Tools ◽  
Mixed Effect ◽  
Response Variable ◽  
Linear Mixed Effects ◽  
Linear Mixed Effect

In longitudinal studies involving laboratory-based outcomes, repeated measurements can be censored due to assay detection limits. Linear mixed-effects (LMEs) models are a powerful tool to model the relationship between a response variable and covariates in longitudinal studies. However, the linear parametric form of linear mixed-effect models is often too restrictive to characterize the complex relationship between a response variable and covariates. More general and robust modeling tools, such as nonparametric and semiparametric regression models, have become increasingly popular in the last decade. In this article, we use semiparametric mixed models to analyze censored longitudinal data with irregularly observed repeated measures. The proposed model extends the censored linear mixed-effect model and provides more flexible modeling schemes by allowing the time effect to vary nonparametrically over time. We develop an Expectation-Maximization (EM) algorithm for maximum penalized likelihood estimation of model parameters and the nonparametric component. Further, as a byproduct of the EM algorithm, the smoothing parameter is estimated using a modified linear mixed-effects model, which is faster than alternative methods such as the restricted maximum likelihood approach. Finally, the performance of the proposed approaches is evaluated through extensive simulation studies as well as applications to data sets from acquired immune deficiency syndrome studies.

scholarly journals Fixed or random? On the reliability of mixed-effect models for a small number of levels in grouping variables

2021 ◽  
Author(s):  
Johannes Oberpriller ◽  
Melina de Souza Leite ◽  
Maximilian Pichler
Keyword(s):  
Type I Error ◽  
Random Effect ◽  
Fixed Effect Model ◽  
Mixed Effects ◽  
Fixed Effect ◽  
Statistical Properties ◽  
Type I ◽  
Mixed Effect ◽  
Mixed Effect Models ◽  
Effect Model

Biological data are often intrinsically hierarchical. Due to their ability to account for such dependencies, mixed-effect models have become a common analysis technique in ecology and evolution. While many questions around their theoretical foundations and practical applications are solved, one fundamental question is still highly debated: When having a low number of levels should we model a grouping variable as a random or fixed effect? In such situation, the variance of the random effect is presumably underestimated, but whether this affects the statistical properties of the fixed effects is unclear. Here, we analyze the consequences of including a grouping variable as fixed or random effect and possible other modeling options (over and underspecified models) for data with small number of levels in the grouping variable (2 - 8). For all models, we calculated type I error rates, power and coverage. Moreover, we show the influence of possible study designs on these statistical properties. We found that mixed-effect models already for two groups correctly estimate variance for two groups. Moreover, model choice does not influence the statistical properties when there is no random slope in the data-generating process. However, if an ecological effect differs among groups, using a random slope and intercept model, and switching to a fixed-effect model only in case of a singular fit avoids overconfidence in the results. Additionally, power and type I error are strongly influenced by the number of and the difference between groups. We conclude that inferring the correct random effect structure is of high importance to get correct statistical properties. When in doubt, we recommend starting with the simpler model and using model diagnostics to identify missing components. When having identified the correct structure, we encourage to start with a mixed-effects model independent of the number of groups and only in case of a singular fit switch to a fixed-effect model. With these recommendations, we allow for more informative choices about study design and data analysis and thus make ecological inference with mixed-effects models more robust for low number of groups.

scholarly journals A flexible nonlinear mixed effects model for HIV viral load rebound after treatment interruption

2020 ◽  
Vol 39 (15) ◽  
pp. 2051-2066 ◽  
Author(s):  
Rui Wang ◽  
Ante Bing ◽  
Cathy Wang ◽  
Yuchen Hu ◽  
Ronald J. Bosch ◽  
...  
Keyword(s):  
Viral Load ◽  
Treatment Interruption ◽  
Mixed Effects ◽  
Mixed Effects Model ◽  
Nonlinear Mixed Effects ◽  
Hiv Viral Load ◽  
Nonlinear Mixed Effects Model ◽  
Viral Load Rebound

scholarly journals OP0073 RISK OF DEVELOPING RHEUMATIC DISEASES IN PATIENTS WITH PALINDROMIC RHEUMATISM IN SOUTH KOREA: A NATION-WIDE POPULATION-BASED STUDY

2020 ◽  
Vol 79 (Suppl 1) ◽  
pp. 49.2-49
Author(s):  
J. K. Ahn ◽  
J. Hwang ◽  
J. Lee ◽  
H. Kim ◽  
G. H. Seo
Keyword(s):  
Health Insurance ◽  
South Korea ◽  
Rheumatic Diseases ◽  
Index Date ◽  
Claims Data ◽  
Population Based ◽  
Palindromic Rheumatism ◽  
Psoriatic Arthropathy ◽  
Increased Risk ◽  
The Mean

Background:Palindromic rheumatism (PR) has known to be three patterns of disease course: clinical remission of attacks, persistent attacks, and evolution to chronic arthritis or systemic disease. The spectrum in progression to chronic diseases of PR, however, is quite variable; rheumatoid arthritis (RA), systemic lupus erythematosus (SLE), systemic sclerosis (SSc), Sjögren’s syndrome (SjS), ankylosing spondylitis (AS), relapsing polychondritis (RP), Behçet’s disease (BD), sarcoidosis, and psoriatic spondylitis and arthropathy. Because of the small numbers in case-control studies and too aged investigations, now we needs to shed new light on the fate of PR.Objectives:The aim was to investigate the epidemiology of PR and the risk of developing various rheumatic diseases compared with non-PR individuals, employing the National Health Insurance Service (NHIS) medical claims data, which covers all medical institutions of South Korea.Methods:The study used 2007-2018 claims data from the Korean Health Insurance Review and Assessment Service (HIRA). The identified 19,724 PR patients from 2010 to 2016 were assessed for the incidence rate (IR) compared with the population in the given year by 100,000 person-year (py). The date of diagnosis was the index date. After matching with non-PR individuals (1:10) for age, sex and the year of index date, we calculated the hazard ratios (HRs) with 95% confidence intervals (CIs). The risk of developing the various rheumatic diseases and adult immunodeficiency syndrome (AIDS) as the outcome diseases in PR cohort was estimated. This risk was compared with that of matched non-PR cohort.Results:Of 19,724 PR patients (8,665 males and 11,059 females), the mean age was 50.2 ± 14.9 years (47.7 ± 14.4 years in males and 52.6 ± 14.9 years in females,p< 0.001). The ratio of male to female patients with PR was approximately 1:1.28. The annual IR of PR was 7.02 (6.92-7.12) per 100,000 py (6.22 (6.09-6.35) and 7.80 (7.66-7.95) per 100,000 py in males and females, respectively). The mean duration to develop the outcome diseases was significantly shorter in PR cohort compared that of non-PR cohort (19.4 vs. 35.8 months,p< 0.001). The most common outcome disease was RA (7.34% of PR patients; 80.0% of total outcome diseases), followed by AS, SLE, BD, SjS, MCTD, DM/PM, SSc, RP, psoriatic arthropathy, and AIDS in PR cohort. The patients with PR had an increased risk of RA (HR 46.6, 95% CI [41.1-52.7]), psoriatic arthropathy (44.79 [15.2-132.4]), SLE (24.5 [16.2-37.2]), MCTD (22.0 [7.7-63.3]), BD (21.0 [13.8-32.1]), SjS (12.4 [8.5-17.9]), AS (9.0 [6.7-12.2]), DM/PM (6.1 [2.6-14.8]), and SSc (3.8 [1.5-9.6]) but not of AIDS. The risk of developing RA was greater in male patients (HR 58.9, 95% CI [45.6-76.2] vs. 43.2 [37.4-49.8],pfor interaction = 0.037) while female patients encountered a higher risk of developing AS (15.8 [8.9-28.1] vs. 7.2 [5.0-10.3],pfor interaction = 0.023). The risk of developing RA, SLE, SjS, and BD were significantly more highly affected in younger age (pfor interaction < 0.001, = 0.003, 0.002, and 0.017, at each).Conclusion:This nationwide, population-based cohort study demonstrated that patients with PR had an increased risk of developing various rheumatic diseases, not only RA but also psoriatic arthropathy. Therefore, patients with PR needs to be cautiously followed up for their potential of diverse outcome other than RA: RA, SLE, SjS, and BD in younger patients, RA in males, and AS in females, in particular.Disclosure of Interests:None declared

scholarly journals The state of Spanish /s/ variation in Concepción, Chile: Linguistic and social trends

2020 ◽  
Vol 6 (1) ◽  
pp. 132-153
Author(s):  
Brandon M. A. Rogers
Keyword(s):  
Social Factors ◽  
Central City ◽  
Mixed Effects ◽  
The State ◽  
Mixed Effects Model ◽  
Proportional Odds ◽  
Random Factor ◽  
Social Trends ◽  
Southern Central ◽  
Social Demographics

AbstractThe current study examines /s/ variation in the southern-central city of Concepción, Chile and its relation to a variety of linguistic and social factors. A proportional-odds mixed effects model, with the random factor of “speaker”, was used to treat the categorically coded data on a continuum of acoustical variation ([s] > [h] > ∅). The results presented show that contrary to the previous assertions, heavy sibilant reduction, especially elision, in Concepción, Chile is the rule, rather than the exception, to the extent that it is no longer a marker of certain social demographics as has been reported previously. Furthermore, based on the trends reported, it is likely that this has been the case for several decades. Finally, the overall observed trends are indicative that the rates of /s/ elision will continue to increase across social demographics and different phonetic and phonological contexts in Concepción, Chile.

scholarly journals A hierarchical bivariate meta-analysis of diagnostic test accuracy to provide direct comparisons of immunoassays vs. indirect immunofluorescence for initial screening of connective tissue diseases

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Michelle Elaine Orme ◽  
Carmen Andalucia ◽  
Sigrid Sjölander ◽  
Xavier Bossuyt
Keyword(s):  
Connective Tissue ◽  
Indirect Immunofluorescence ◽  
Meta Analysis ◽  
Connective Tissue Diseases ◽  
Test Accuracy ◽  
Initial Screening ◽  
Case Control Studies ◽  
Cross Sectional ◽  
Mixed Effect ◽  
Mixed Effect Models

AbstractObjectivesTo compare indirect immunofluorescence (IIF) for antinuclear antibodies (ANA) against immunoassays (IAs) as an initial screening test for connective tissue diseases (CTDs).MethodsA systematic literature review identified cross-sectional or case-control studies reporting test accuracy data for IIF and enzyme-linked immunosorbent assays (ELISA), fluorescence enzyme immunoassay (FEIA), chemiluminescent immunoassay (CLIA) or multiplex immunoassay (MIA). The meta-analysis used hierarchical, bivariate, mixed-effect models with random-effects by test.ResultsDirect comparisons of IIF with ELISA showed that both tests had good sensitivity (five studies, 2321 patients: ELISA: 90.3% [95% confidence interval (CI): 80.5%, 95.5%] vs. IIF at a cut-off of 1:80: 86.8% [95% CI: 81.8%, 90.6%]; p = 0.4) but low specificity, with considerable variance across assays (ELISA: 56.9% [95% CI: 40.9%, 71.5%] vs. IIF 1:80: 68.0% [95% CI: 39.5%, 87.4%]; p = 0.5). FEIA sensitivity was lower than IIF sensitivity (1:80: p = 0.005; 1:160: p = 0.051); however, FEIA specificity was higher (seven studies, n = 12,311, FEIA 93.6% [95% CI: 89.9%, 96.0%] vs. IIF 1:80 72.4% [95% CI: 62.2%, 80.7%]; p < 0.001; seven studies, n = 3251, FEIA 93.5% [95% CI: 91.1%, 95.3%] vs. IIF 1:160 81.1% [95% CI: 73.4%, 86.9%]; p < 0.0001). CLIA sensitivity was similar to IIF (1:80) with higher specificity (four studies, n = 1981: sensitivity 85.9% [95% CI: 64.7%, 95.3%]; p = 0.86; specificity 86.1% [95% CI: 78.3%, 91.4%]). More data are needed to make firm inferences for CLIA vs. IIF given the wide prediction region. There were too few studies for the meta-analysis of MIA vs. IIF (MIA sensitivity range 73.7%–86%; specificity 53%–91%).ConclusionsFEIA and CLIA have good specificity compared to IIF. A positive FEIA or CLIA test is useful to support the diagnosis of a CTD. A negative IIF test is useful to exclude a CTD.

Estimating local arterial stiffness using mixed-effects model-based residuals: a novel approach

2021 ◽  
Author(s):  
Avinash Chandran ◽  
Derek W. Brown ◽  
Gabriel H. Zieff ◽  
Zachary Y. Kerr ◽  
Daniel Credeur ◽  
...  
Keyword(s):  
Arterial Stiffness ◽  
Mixed Effects ◽  
Mixed Effects Model ◽  
Model Based ◽  
Novel Approach
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