Published since December 1, 2011

Epidemiologic Methods

ISSN: 2161-962X

Editor-in-chief: Hélène Karcher

Edited by: Rino Bellocco, Li Fan, Mark J. van der Laan, Mohammad Mansournia, Kara Rudolph, Alejandro Schuler

Managing Editor: Rita Moreira da Silva

Submit manuscript

About this journal

Objective

Epidemiologic Methods aims at filling the gap for a journal to publish innovative methodology for epidemiologic research. Most social and applied science fields have one or more journals devoted exclusively to methodology. Epidemiologic Methods provides such a journal for the field of epidemiology.

In 2020-2022 we have invited less methodology-oriented epidemiology papers to support in part dissemination of knowledge on the COVID pandemic.

From January 1^st, 2023 onwards, we are re-centering the journal on methodological publications. We seek contributions on innovative methodology for epidemiologic research, real-world evidence, and evidence generation in a broader sense, and welcome submissions that provide a comprehensive evaluation or comparison of state-of-the-art methods in the context of epidemiologic research (via simulation studies or analyses of real data sets). We invite papers that may be more technical or of greater length than what has traditionally been allowed by journals in epidemiology. Applications and examples with real data to illustrate the methodology are strongly encouraged.

Submit your article

We invite you to contribute to the following special issue:

"Causal inference in comparative effectiveness research"

CALL FOR SUBMISSIONS for the special thematic issue on Causal inference in comparative effectiveness, with Dr. Hélène Karcher Novartis, Switzerland) and Prof. Fan Li (Yale University, US) and as Issue Editors. The (extended) deadline for submission is December 31, 2023.

The proposed special issue aims to inform and educate a broad community of researchers conducting prospective and/or retrospective observational (real-world) studies or related comparative effectiveness studies using a range of possible causal inference methods. We aim to support these studies with innovative methods and/or practical considerations with thoughtful case study illustrations.

Therefore, we welcome article submissions dealing with the development of novel methods or case studies in the field of causal inference, including, but not limited, to the following topics:

Review of current statistical methods for causal inference in epidemiology and comparative effectiveness

Development of innovative causal inference methods for the design or analysis of observational data

Comprehensive simulation evaluation to compare state-of-the-art causal inference methods in the context of epidemiological studies

Case studies or tutorials on the application of state-of-the-art causal inference methods, with explanations on their implementation, how to improve current practice and possible limitations

In particular, the best paper nominated by the editors will be awarded a full Open Access waiver by the publisher De Gruyter!

More information you can find HERE

Your Benefits

Useful forum for in-depth articles on epidemiologic methods
Digital repository of methodologic contributions, case studies and tutorials　
Close cooperation between epidemiologists and statisticians　
High quality peer-review led by our top-notch editors (list below)

Latest Special Issues:

2021: Covid-19: Modelling the impact of vaccination campaigns, containment measures, mutations and geographical spread on the pandemic

2020: Covid-19 dynamics and containment: findings from epidemiologic modeling experts

Abstracting and Indexing

Epidemiologic Methods is covered by the following services:

Baidu Scholar
Cabells Journalytics
CNKI Scholar (China National Knowledge Infrastructure)
CNPIEC - cnpLINKer
Dimensions
EBSCO (relevant databases)
EBSCO Discovery Service
EMBASE
Genamics JournalSeek
Google Scholar
Japan Science and Technology Agency (JST)
J-Gate
JournalTOCs
KESLI-NDSL (Korean National Discovery for Science Leaders)
MyScienceWork
Naver Academic
Naviga (Softweco)
Norwegian Register for Scientific Journals, Series and Publishers
Primo Central (ExLibris)
ProQuest (relevant databases)
Publons
QOAM (Quality Open Access Market)
ReadCube
Reaxys
Scilit
SCImago (SJR)
SCOPUS
Semantic Scholar
Sherpa/RoMEO
Summon (ProQuest)
TDNet
Text Mining
Ulrich's Periodicals Directory/ulrichsweb
WanFang Data
WorldCat (OCLC)
X-MOL
Yewno Discover

Supplementary Materials

Topics

External Links

Volume 12 Issue 1

January 2023

Research Articles

November 10, 2023

Outliers in nutrient intake data for U.S. adults: national health and nutrition examination survey 2017–2018

Sara Burcham, Yuki Liu, Ashley L. Merianos, Angelico Mendy

Article number: 20230018

Abstract

Objectives An important step in preparing data for statistical analysis is outlier detection and removal, yet no gold standard exists in current literature. The objective of this study is to identify the ideal decision test using the National Health and Nutrition Examination Survey (NHANES) 2017–2018 dietary data. Methods We conducted a secondary analysis of NHANES 24-h dietary recalls, considering the survey's multi-stage cluster design. Six outlier detection and removal strategies were assessed by evaluating the decision tests' impact on the Pearson's correlation coefficient among macronutrients. Furthermore, we assessed changes in the effect size estimates based on pre-defined sample sizes. The data were collected as part of the 2017–2018 24-h dietary recall among adult participants (N=4,893). Results Effect estimate changes for macronutrients varied from 6.5 % for protein to 39.3 % for alcohol across all decision tests. The largest proportion of outliers removed was 4.0 % in the large sample size, for the decision test, >2 standard deviations from the mean. The smallest sample size, particularly for alcohol analysis, was most affected by the six decision tests when compared to no decision test. Conclusions This study, the first to use 2017–2018 NHANES dietary data for outlier evaluation, emphasizes the importance of selecting an appropriate decision test considering factors such as statistical power, sample size, normality assumptions, the proportion of data removed, effect estimate changes, and the consistency of estimates across sample sizes. We recommend the use of non-parametric tests for non-normally distributed variables of interest.

November 13, 2023

Addressing substantial covariate imbalance with propensity score stratification and balancing weights: connections and recommendations

Laine E. Thomas, Steven M. Thomas, Fan Li, Roland A. Matsouaka

Article number: 20220131

Abstract

Objectives Propensity score (PS) weighting methods are commonly used to adjust for confounding in observational treatment comparisons. However, in the setting of substantial covariate imbalance, PS values may approach 0 and 1, yielding extreme weights and inflated variance of the estimated treatment effect. Adaptations of the standard inverse probability of treatment weights (IPTW) can reduce the influence of extremes, including trimming methods that exclude people with PS values near 0 or 1. Alternatively, overlap weighting (OW) optimizes criteria related to bias and variance, and performs well compared to other PS weighting and matching methods. However, it has not been compared to propensity score stratification (PSS). PSS has some of the same potential advantages; being insensitive extreme values. We sought to compare these methods in the setting of substantial covariate imbalance to generate practical recommendations. Methods Analytical derivations were used to establish connections between methods, and simulation studies were conducted to assess bias and variance of alternative methods. Results We find that OW is generally superior, particularly as covariate imbalance increases. In addition, a common method for implementing PSS based on Mantel–Haenszel weights (PSS-MH) is equivalent to a coarsened version of OW and can perform nearly as well. Finally, trimming methods increase bias across methods (IPTW, PSS and PSS-MH) unless the PS model is re-fit to the trimmed sample and weights or strata are re-derived. After trimming with re-fitting, all methods perform similarly to OW. Conclusions These results may guide the selection, implementation and reporting of PS methods for observational studies with substantial covariate imbalance.

Publicly Available August 9, 2023

Using repeated antibody testing to minimize bias in estimates of prevalence and incidence of SARS-CoV-2 infection

Michele Santacatterina, Brian Burke, Mihili Gunaratne, William S. Weintraub, Mark A. Espeland, Adolfo Correa, DeAnna Friedman-Klabanoff, Michael Gibbs, David Herrington, Kristen E. Miller, John W. Sanders, Austin L. Seals, Diane Uschner, Thomas F. Wierzba, Morgana Mongraw-Chaffin

Article number: 20230012

More Download PDF

Abstract

Objectives The prevalence and incidence of SARS-CoV-2, the virus which causes COVID-19, at any given time remains controversial, and is an essential piece in understanding the dynamics of the epidemic. Cross-sectional studies and single time point testing approaches continue to struggle with appropriate adjustment methods for the high false positive rates in low prevalence settings or high false negative rates in high prevalence settings, and post-hoc adjustment at the group level does not fully address this issue for incidence even at the population level. Methods In this study, we use seroprevalence as an illustrative example of the benefits of using a case definition using a combined parallel and serial testing framework to confirm antibody-positive status. In a simulation study, we show that our proposed approach reduces bias and improves positive and negative predictive value across the range of prevalence compared with cross-sectional testing even with gold standard tests and post-hoc adjustment. Using data from the North Carolina COVID-19 Community Research Partnership, we applied the proposed case definition to the estimation of SARS-CoV-2 seroprevalence and incidence early in the pandemic. Results The proposed approach is not always feasible given the cost and time required to administer repeated tests; however, it reduces bias in both low and high prevalence settings and addresses misclassification at the individual level. This approach can be applied to almost all testing contexts and platforms. Conclusions This systematic approach offers better estimation of both prevalence and incidence, which is important to improve understanding and facilitate controlling the pandemic.

Publicly Available June 29, 2023

A compartmental model of the COVID-19 pandemic course in Germany

Yıldırım Adalıoğlu, Çağan Kaplan

Article number: 20220126

More Download PDF

Abstract

Objectives In late 2019, the novel coronavirus, known as COVID-19, emerged in Wuhan, China, and rapidly spread worldwide, including in Germany. To mitigate the pandemic’s impact, various strategies, including vaccination and non-pharmaceutical interventions, have been implemented. However, the emergence of new, highly infectious SARS-CoV-2 strains has become the primary driving force behind the disease’s spread. Mathematical models, such as deterministic compartmental models, are essential for estimating contagion rates in different scenarios and predicting the pandemic’s behavior. Methods In this study, we present a novel model that incorporates vaccination dynamics, the three most prevalent virus strains (wild-type, alpha, and delta), infected individuals’ detection status, and pre-symptomatic transmission to represent the pandemic’s course in Germany from March 2, 2020, to August 17, 2021. Results By analyzing the behavior of the German population over 534 days and 25 time intervals, we estimated various parameters, including transmission, recovery, mortality, and detection. Furthermore, we conducted an alternative analysis of vaccination scenarios under the same interval conditions, emphasizing the importance of vaccination administration and awareness. Conclusions Our 534-day analysis provides policymakers with a range of circumstances and parameters that can be used to simulate future scenarios. The proposed model can also be used to make predictions and inform policy decisions related to pandemic control in Germany and beyond.

June 27, 2023

Development and application of an evidence-based directed acyclic graph to evaluate the associations between metal mixtures and cardiometabolic outcomes

Emily Riseberg, Rachel D. Melamed, Katherine A. James, Tanya L. Alderete, Laura Corlin

Article number: 20220133

Abstract

Objectives Specifying causal models to assess relationships among metal mixtures and cardiometabolic outcomes requires evidence-based models of the causal structures; however, such models have not been previously published. The objective of this study was to develop and evaluate a directed acyclic graph (DAG) diagraming metal mixture exposure and cardiometabolic outcomes. Methods We conducted a literature search to develop the DAG of metal mixtures and cardiometabolic outcomes. To evaluate consistency of the DAG, we tested the suggested conditional independence statements using linear and logistic regression analyses with data from the San Luis Valley Diabetes Study (SLVDS; n=1795). We calculated the proportion of statements supported by the data and compared this to the proportion of conditional independence statements supported by 1,000 DAGs with the same structure but randomly permuted nodes. Next, we used our DAG to identify minimally sufficient adjustment sets needed to estimate the association between metal mixtures and cardiometabolic outcomes (i.e., cardiovascular disease, fasting glucose, and systolic blood pressure). We applied them to the SLVDS using Bayesian kernel machine regression, linear mixed effects, and Cox proportional hazards models. Results From the 42 articles included in the review, we developed an evidence-based DAG with 74 testable conditional independence statements (43 % supported by SLVDS data). We observed evidence for an association between As and Mn and fasting glucose. Conclusions We developed, tested, and applied an evidence-based approach to analyze associations between metal mixtures and cardiometabolic health.

Publicly Available June 26, 2023

Energy-efficient model “DenseNet201 based on deep convolutional neural network” using cloud platform for detection of COVID-19 infected patients

Sachin Kumar, Vijendra Pratap Singh, Saurabh Pal, Priya Jaiswal

Article number: 20210047

More Download PDF

Abstract

Objective The outbreak of the coronavirus caused major problems in more than 151 countries around the world. An important step in the fight against coronavirus is the search for infected people. The goal of this article is to predict COVID-19 infectious patients. Methods We implemented DenseNet201, available on cloud platform, as a learning network. DenseNet201 is a 201-layer networkthat. is trained on ImageNet. The input size of pre-trained DenseNet201 images is 224 × 224 × 3. Results Implementation of DenseNet201 was effectively performed based on 80 % of the training X-rays and 20 % of the X-rays of the test phases, respectively. DenseNet201 shows a good experimental result with an accuracy of 99.24 % in 7.47 min. To measure the computational efficiency of the proposed model, we collected more than 6,000 noise-free data infected by tuberculosis, COVID-19, and uninfected healthy chests for implementation. Conclusions DenseNet201 available on the cloud platform has been used for the classification of COVID-19-infected patients. The goal of this article is to demonstrate how to achieve faster results.

Publicly Available May 17, 2023

Identification of time delays in COVID-19 data

Nicola Guglielmi, Elisa Iacomini, Alex Viguerie

Article number: 20220117

More Download PDF

Abstract

Objective COVID-19 data released by public health authorities is subject to inherent time delays. Such delays have many causes, including delays in data reporting and the natural incubation period of the disease. We develop and introduce a numerical procedure to recover the distribution of these delays from data. Methods We extend a previously-introduced compartmental model with a nonlinear, distributed-delay term with a general distribution, obtaining an integrodifferential equation. We show this model can be approximated by a weighted-sum of constant time-delay terms, yielding a linear problem for the distribution weights. Standard optimization can then be used to recover the weights, approximating the distribution of the time delays. We demonstrate the viability of the approach against data from Italy and Austria. Results We find that the delay-distributions for both Italy and Austria follow a Gaussian-like profile, with a mean of around 11 to 14 days. However, we note that the delay does not appear constant across all data types, with infection, recovery, and mortality data showing slightly different trends, suggesting the presence of independent delays in each of these processes. We also found that the recovered delay-distribution is not sensitive to the discretization resolution. Conclusions These results establish the validity of the introduced procedure for the identification of time-delays in COVID-19 data. Our methods are not limited to COVID-19, and may be applied to other types of epidemiological data, or indeed any dynamical system with time-delay effects.

Publicly Available May 12, 2023

A country-specific COVID-19 model

Gunter Meissner, Hong Sherwin

Article number: 20220112

More Download PDF

Abstract

Objectives To dynamically measure COVID-19 transmissibility consistently normalized by population size in each country. Methods A reduced-form model enhanced from the classical SIR is proposed to stochastically represent the Reproduction Number and Mortality Rate, directly measuring the combined effects of viral evolution and population behavioral response functions. Results Evidences are shown that this e(hanced)-SIR model has the power to fit country-specific empirical data, produce interpretable model parameters to be used for generating probabilistic scenarios adapted to the still unfolding pandemic. Conclusions Stochastic processes embedded within compartmental epidemiological models can produce measurables and actionable information for surveillance and planning purposes.

May 3, 2023

Incidence and trend of leishmaniasis and its related factors in Golestan province, northeastern Iran: time series analysis

Mostafa Majidnia, Ali Hosseinzadeh, Ahmad Khosravi

Article number: 20220124

Abstract

Objectives Leishmaniasis is a parasitic disease whose transmission depends on climatic conditions and is more important in northeast Iran. This study aimed to investigate the time trend of leishmaniasis and present a prediction model using meteorological variables in Golestan province. Methods The 10-year data on leishmaniasis (2010–2019) were collected from the portal of the Ministry of Health and the National Meteorological Organization. First, the disease incidence (per 100,000 population) in different cities of the Golestan province was estimated. Then, the geographical distribution and disease clusters map were prepared at the province level. Finally, by using the Jenkins box model time series analysis method, the disease incidence was predicted for the period 2020 to 2023 of the total province. Results From 2010 to 2019, 8,871 patients with leishmaniasis were identified. The mean age of patients was 21.0 ± 18.4 years. The highest mean annual incidence was in Maravah-Tappeh city (188 per 100,000 population). The highest and lowest annual incidence was in 2018 and 2017, respectively. The average 10-year incidence was 48 per 100,000 population. The daily meteorological variables like monthly average wind speed, sunshine, temperature, and mean soil temperature at depth of 50 cm were significantly associated with the incidence of the disease. The estimated threshold for an epidemic was 40.3 per 100,000 population. Conclusions According to the results, leishmaniasis incidence cases apears in July and with a peak in November. The incidence rate was highest in Maravah-Tapeh and Gonbad-Kavous, and lowest in Kordkoy counties. The study showed that there were two peaks in 2010 and 2018 and also identified a downward trend in the disease between 2010 and 2013 with a clear decrease in the incidence. Climate conditions had an important effect on leishmaniasis incidence. These climate and epidemiological factors such as migration and overcrowding could provide important input to monitor and predict disease for control strategies.

Publicly Available April 17, 2023

Application of machine learning tools for feature selection in the identification of prognostic markers in COVID-19

Sprockel Diaz Johm Jaime, Hector Fabio Restrepo Guerrero, Juan Jose Diaztagle Fernandez

Article number: 20220132

More Download PDF

Abstract

Objective To identify prognostic markers by applying machine learning strategies to the feature selection. Methods An observational, retrospective, multi-center study that included hospitalized patients with a confirmed diagnosis of COVID-19 in three hospitals in Colombia. Eight strategies were applied to select prognostic-related characteristics. Eight logistic regression models were built from each set of variables and the predictive ability of the outcome was evaluated. The primary endpoint was transfer to intensive care or in-hospital death. Results The database consisted of 969 patients of which 486 had complete data. The main outcome occurred in 169 cases. The development database included 220 patients, 137 (62.3%) were men with a median age of 58.2, 39 (17.7%) were diabetic, 62 (28.2%) had high blood pressure, and 32 (14.5%) had chronic lung disease. Thirty-three variables were identified as prognostic markers, and those selected most frequently were: LDH, PaO2/FIO2 ratio, CRP, age, neutrophil and lymphocyte counts, respiratory rate, oxygen saturation, ferritin, and HCO3. The eight logistic regressions developed were validated on 266 patients in whom similar results were reached (accuracy: 65.8–72.9%). Conclusions The combined use of strategies for selecting characteristics through machine learning techniques makes it possible to identify a broad set of prognostic markers in patients hospitalized for COVID-19 for death or hospitalization in intensive care.

Publicly Available January 26, 2023

A study of the impact of policy interventions on daily COVID scenario in India using interrupted time series analysis

Subhankar Chattopadhyay, Debika Ghosh, Raju Maiti, Samarjit Das, Atanu Biswas, Bibhas Chakraborty

Article number: 20220113

More Download PDF

Abstract

Objectives The rapid increase both in daily cases and daily deaths made the second wave of COVID-19 pandemic in India more lethal than the first wave. Record number of infections and casualties were reported all over India during this period. Delhi and Maharashtra are the two most affected places in India during the second wave. So in response to this, the Indian government implemented strict intervention policies (“lockdowns”, “social distancing” and “vaccination drive”) in every state during this period to prohibit the spread of this virus. The objective of this article is to conduct an interrupted time series (ITS) analysis to study the impact of the interventions on the daily cases and deaths. Methods We collect daily data for Delhi and Maharashtra before and after the intervention points with a 14-day (incubation period of COVID-19) observation window. A segmented linear regression analysis is done to study the post-intervention slopes as well as whether there were any immediate changes after the interventions or not. We also add the counterfactuals and delayed time effects in the analysis to investigate the significance of our ITS design. Results Here, we observe the post-intervention trends to be statistically significant and negative for both the daily cases and the daily deaths. We also find that there is no immediate change in trend after the start of intervention, and hence we study some delayed time effects which display how changes in the trends happened over time. And from the Counterfactuals in our study, we can have an idea what would have happened to the COVID scenario had the interventions not been implemented. Conclusions We statistically try to figure out different circumstances of COVID scenario for both Delhi and Maharashtra by exploring all possible ingredients of ITS design in our analysis in order to present a feasible design to show the importance of implementation of proper intervention policies for tackling this type of pandemic which can have various highly contagious variants.

Publicly Available January 17, 2023

Measuring COVID-19 spreading speed through the mean time between infections indicator

Gabriel Pena, Verónica Moreno, Nestor Ruben Barraza

Article number: 20220106

More Download PDF

Abstract

Objectives To introduce a novel way of measuring the spreading speed of an epidemic. Methods We propose to use the mean time between infections (MTBI) metric obtained from a recently introduced nonhomogeneous Markov stochastic model. Different types of parameter calibration are performed. We estimate the MTBI using data from different time windows and from the whole stage history and compare the results. In order to detect waves and stages in the input data, a preprocessing filtering technique is applied. Results The results of applying this indicator to the COVID-19 reported data of infections from Argentina, Germany and the United States are shown. We find that the MTBI behaves similarly with respect to the different data inputs, whereas the model parameters completely change their behaviour. Evolution over time of the parameters and the MTBI indicator is also shown. Conclusions We show evidence to support the claim that the MTBI is a rather good indicator in order to measure the spreading speed of an epidemic, having similar values whatever the input data size.

January 12, 2023

Performance evaluation of ResNet model for classification of tomato plant disease

Sachin Kumar, Saurabh Pal, Vijendra Pratap Singh, Priya Jaiswal

Article number: 20210044

Abstract

Objectives The plant tomato (Solanum Lycopersicum) is vastly infected by various diseases. Exact diagnosis on time contributes a significant job to the good production of tomato crops. The key objective of this article is to recognize the infection in tomato leaves with better accuracy and in less time. Methods Nowadays deep convolutional neural networks have attained surprising outcomes in several applications, together with the categorization of tomato leaves infected with several diseases. Our work is based on deep CNN with different residual networks. Finally; we have performed tomato leaves disease classification by using pre-trained deep CNN with the residual network using MATLAB available on the cloud. Results We have used a dataset of tomato leaves for the experiments which contain six different types of diseases with one healthy tomato leaf class. We have collected 6,594 tomato leaves dataset from Plant Village and we did not collect actual tomato leaves for testing. The outcome obtained by ResNet-50 shows a significant result with 96.35% accuracy for 50% training and 50% testing data and if we focus on time consumption for the outcome then ResNet-18 consumes 12.46 min for 70% training and 30% testing. Conclusions After observation of several outcomes, we have concluded that ResNet-50 shows a better accuracy for 50% training and 50% testing of data and ResNet-18 shows better efficiency for 70% training and 30% testing of data for the same dataset on the cloud.

Publicly Available January 4, 2023

Energy- efficient model “Inception V3 based on deep convolutional neural network” using cloud platform for detection of COVID-19 infected patients

Sachin Kumar, Saurabh Pal, Vijendra Pratap Singh, Priya Jaiswal

Article number: 20210046

More Download PDF

Abstract

Objectives COVID-19 is frightening the health of billions of persons and speedily scattering worldwide. Medical studies have revealed that the majority of COVID-19 patients. X-ray of COVID-19 is extensively used because of their noticeably lower price than CT. This research article aims to spot the COVID-19 virus in the X-ray of the chest in less time and with better accuracy. Methods We have used the inception-v3 available on the cloud platform transfer learning model to classify COVID-19 infection. The online Inception v3 model can be reliable and efficient for COVID-19 disease recognition. In this experiment, we collected images of COVID-19-infected patients, then applied the online inception-v3 model to automatically extract features, and used a softmax classifier to classify the COVID-19 images. Finally, the experiment shows inception v3 is significant for COVID-19 image classification. Results Our results demonstrate that our proposed inception v3 model available on the cloud platform can detect 99.41% of COVID-19 cases between COVID-19 and Lung Mask diseases in 44 min only. We have also taken images of the normal chest for better outcomes. To estimate the computation power of the model, we collected 6018 COVID-19, Lung Masks, & Normal Chest images for experimentation. Our projected model offered a trustworthy COVID-19 classification by using chest X-rays. Conclusions In this research paper, the inception v3 model available on the cloud platform is used to categorize COVID-19 infection by X-ray images. The Inception v3 model available on the cloud platform is helpful to clinical experts to examine the enormous quantity of human chest X-ray images. Scientific and clinical experiments will be the subsequent objective of this paper.

Tutorial

October 20, 2023

On some pitfalls of the log-linear modeling framework for capture-recapture studies in disease surveillance

Yuzi Zhang, Lin Ge, Lance A. Waller, Robert H. Lyles

Article number: 20230019

Abstract

In epidemiological studies, the capture-recapture (CRC) method is a powerful tool that can be used to estimate the number of diseased cases or potentially disease prevalence based on data from overlapping surveillance systems. Estimators derived from log-linear models are widely applied by epidemiologists when analyzing CRC data. The popularity of the log-linear model framework is largely associated with its accessibility and the fact that interaction terms can allow for certain types of dependency among data streams. In this work, we shed new light on significant pitfalls associated with the log-linear model framework in the context of CRC using real data examples and simulation studies. First, we demonstrate that the log-linear model paradigm is highly exclusionary. That is, it can exclude, by design, many possible estimates that are potentially consistent with the observed data. Second, we clarify the ways in which regularly used model selection metrics (e.g., information criteria) are fundamentally deceiving in the effort to select a “best” model in this setting. By focusing attention on these important cautionary points and on the fundamental untestable dependency assumption made when fitting a log-linear model to CRC data, we hope to improve the quality of and transparency associated with subsequent surveillance-based CRC estimates of case counts.

Volume 12 (2023)

Issue 1

Volume 11 (2022)

Volume 10 (2021)

Volume 9 (2020)

CiteScore	1.8	2022, Scopus (Elsevier B.V., 2023)
SCImago Journal Rank	0.318	2022, SJR (Scimago Lab, 2023; Data Source: Scopus)
Source Normalized Impact per Paper	0.403	2022, CWTS Journal Indicators (CWTS B.V., 2023; Data Source: Scopus)

More information about journal rankings

Submit

Submission

You can easily submit your manuscript online. Simply go to...
http://mc.manuscriptcentral.com/dgem
...and you will be guided through the whole peer-reviewing and publishing process.

Your benefits of publishing with us

Rapid online ahead-of-print publication with short turnaround times
High quality manuscript processing
Optional open access publication
Accepted papers will be published online first as DOI-citable, forward-linked articles for quickest possible visibility for the scientific community
Every article easily discoverable because of Search Engine Optimization (SEO) and comprehensive abstracting and indexing services
Secure archiving by De Gruyter and the independent archiving service Portico

ARTICLE FORMATS

1. Original Research Article, including

Epidemiologic Method: describing novel statistical approaches for design and analysis of epidemiology studies, ideally with an illustrative epidemiologic data example
Epidemiology Case Study: reporting an epidemiology study with a robust design and analysis supported by state-of-the-art statistical methods
Abstract for Original Research Articles: Objectives, Methods, Results, and Conclusions.

2. Tutorial

This is a new category aimed at providing epidemiologists with the means to understand and implement an advanced epidemiologic method for study design and/or data analysis.
We welcome tutorial articles that introduce cutting-edge epidemiologic methods with a focus on the following aspects:

Clarification on the use or misuse of the method, and descriptions of purpose and typical applications for which the method is most appropriate.
Discussion on the advantages and limitations of the new method as compared standard epidemiologic method used in current practice.
Tools or software code to implement the cutting-edge method with a comprehensive and accessible case study.
Abstract for Tutorial: Method description, Advantages & Limitation, Case Example.

3. Perspective and Counterpoint Perspective Articles

Articles that do not fall into the Original Research Article or Tutorial may be considered for this category. This category typically considers papers that provide a critical review of epidemiologic evidence or discuss a unique perspective on how a method has been implemented in the current epidemiology literature.
Abstract for Perspective and Counterpoint Perspective Articles: Context, Methods, Findings, Perspective

Topics
• epidemiology
• genetic epidemiology
• infectious disease
• pharmaco-epidemiology
• ecologic studies
• environmental exposures
• screening
• surveillance
• social networks
• comparative effectiveness
• safety studies
• statistical modeling
• causal inference
• measurement error
• machine learning
• artificial intelligence
• study design
• cohort profile
• meta-analysis
• real-world evidence
• methodology
Submission process

Before preparing your manuscript please have a look at our Instructions for Authors
Submission of your paper via our submission management tool http://mc.manuscriptcentral.com/dgem
Peer review process (you will be guided through every step)
Decision on your paper
If accepted: you have the option to publish it open access
Publication online only

Please note:

Manuscripts must be written in clear and concise English
Methodologic contributions should be clearly relevant to the theory and practice of epidemiology
The provision of sample code is strongly suggested. With more technical contributions, authors are also encouraged to include a section or appendix on "Epidemiologic Practice" which summarizes, in a broadly accessible manner, how the methodology proposed (or a special case thereof) may be used in practice
Before submitting your article please have a look at our Ethical Guidelines and our Copyright Transfer Agreement
The Template for Ethical and Legal Declarations needs to be customized by the submitting author on behalf of all others and uploaded as a separate doc-file at the time of the original submission. Instructions on how to make declarations can be found here.
Once your article is accepted you have the option to publish it open access
Our Repository Policy allows you to distribute 30 PDF copies of your published article to colleagues (the PDF has to include the information that it is an author's copy). Please also feel free to distribute the link to the online abstract
If you have any general questions please visit our FAQ page for authors