scholarly journals Impact of Best-Fitted Control Selection on Effect Size: An Example in Functional GI Disorder Case–Control Studies

2021 ◽  
Vol 18 (19) ◽  
pp. 10296
Author(s):  
Peyman Adibi ◽  
Shahram Agah ◽  
Hassan Doosti ◽  
Awat Feizi
Keyword(s):  
Sample Size ◽  
Effect Size ◽  
Case Control ◽  
Effect Sizes ◽  
Anxiety And Depression ◽  
Control Group ◽  
Case Control Studies ◽  
Control Groups ◽  
Definition Of ◽  
Selection Of

Background: Effect sizes are the most useful quantities for communicating the practical significance of results and helping to facilitate cumulative science. We hypothesize that the selection of the best-fitted controls can significantly affect the estimated effect sizes in case–control studies. Therefore, we decided to exemplify and clarify this effect on effect size using a large data set. The objective of this study was to investigate the association among variables in functional gastrointestinal disorders (FGIDs) and mental health problems, common ailments that reduce the quality of life of a large proportion of the community worldwide. Method: In this methodological study, we constitute case and control groups in our study framework using the Epidemiology of Psychological, Alimentary Health and Nutrition (SEPAHAN) dataset of 4763 participants. We devised four definitions for control in this extensive database of FGID patients and analyzed the effect of these definitions on the odds ratio (OR): 1. conventional control: without target disorder/syndrome (sample size 4040); 2. without any positive criteria: criterion-free control (sample size 1053); 3. syndrome-free control: without any disorder/syndrome (sample size 847); 4. symptom-free control: without any symptoms (sample size 204). We considered a fixed case group that included 723 patients with a Rome III-based definition of functional dyspepsia. Psychological distress, anxiety, and depression were considered as dependent variables in the analysis. Logistic regression was used for association analysis, and the odds ratio and 95% confidence interval (95%CI) for OR were reported as the effect size. Results: The estimated ORs indicate that the strength of the association in the first case–control group is the lowest, and the fourth case–control group, including controls with completely asymptomatic people, is the highest. Ascending effect sizes were obtained in the conventional, criterion-free, syndrome-free, and symptom-free control groups. These results are consistent for all three psychological disorders, psychological distress, anxiety, and depression. Conclusions: This study shows that a precise definition of the control is mandatory in every case–control study and affects the estimated effect size. In clinical settings, the selection of symptomatic controls using the conventional definition could significantly diminish the effect size.

scholarly journals Case Control Studies in Medical Research

1970 ◽  
Vol 1 (3) ◽  
pp. 77-80
Author(s):  
Brijesh Sathian ◽  
Jayadevan Sreedharan ◽  
Ankush Mittal ◽  
Nishida Chandrasekharan ◽  
Suresh N Baboo ◽  
...  
Keyword(s):  
Comparative Studies ◽  
Case Control ◽  
Control Group ◽  
Case Control Studies ◽  
Control Groups ◽  
Bias Effect ◽  
Good Design ◽  
Study Designs ◽  
Control Designs ◽  
Selection Of

Case-control studies can yield important scientific findings with relatively little time, money, and effort compared with other study designs. Investigators implement case-control studies more frequently than any other analytical epidemiological study. Unfortunately, case-control designs also tend to be more susceptible to biases than other comparative studies. Although easier to do, they are also easier to do wrong. A good design should aim to minimise error and bias. All remaining sources of error and bias should be recognised and decisively evaluated.  Case-control studies that are well designed and carefully done can provide useful and reliable results. Investigators must, however, devote painstaking attention to the selection of control groups and to measurement of exposure information. When the number of cases is small, the ratio of controls to cases can be raised to improve the ability to find important differences. Although no ideal control group exists, readers need to think carefully about how representative the controls are. Poor choice of controls can lead to both wrong results and possible medical harm. Awareness of these key elements should help readers to identify the strengths and weaknesses of a properly reported study.Keywords: Bias; Effect size; ConfoundingDOI: http://dx.doi.org/10.3126/nje.v1i3.5569 Nepal Journal of Epidemiology 2011;1(3) 77-80

scholarly journals Dissecting autism and schizophrenia through neuroimaging genomics

Brain ◽  
2021 ◽  
Author(s):  
Clara A Moreau ◽  
Armin Raznahan ◽  
Pierre Bellec ◽  
Mallar Chakravarty ◽  
Paul M Thompson ◽  
...  
Keyword(s):  
High Risk ◽  
Psychiatric Symptoms ◽  
Clinical Manifestations ◽  
Autism Spectrum ◽  
Case Control ◽  
Effect Sizes ◽  
Case Control Studies ◽  
Top Down ◽  
Bottom Up ◽  
Genomic Variants

Abstract Neuroimaging genomic studies of autism spectrum disorder and schizophrenia have mainly adopted a ‘top-down’ approach, starting with the behavioural diagnosis, and moving down to intermediate brain phenotypes and underlying genetic factors. Advances in imaging and genomics have been successfully applied to increasingly large case-control studies. As opposed to diagnostic-first approaches, the bottom-up strategy starts at the level of molecular factors enabling the study of mechanisms related to biological risk, irrespective of diagnoses or clinical manifestations. The latter strategy has emerged from questions raised by top-down studies: Why are mutations and brain phenotypes over-represented in individuals with a psychiatric diagnosis? Are they related to core symptoms of the disease or to comorbidities? Why are mutations and brain phenotypes associated with several psychiatric diagnoses? Do they impact a single dimension contributing to all diagnoses? In the review, we aimed at summarizing imaging genomic findings in autism and schizophrenia as well as neuropsychiatric variants associated with these conditions. Top-down studies of autism and schizophrenia identified patterns of neuroimaging alterations with small effect-sizes and an extreme polygenic architecture. Genomic variants and neuroimaging patterns are shared across diagnostic categories suggesting pleiotropic mechanisms at the molecular and brain network levels. Although the field is gaining traction; characterizing increasingly reproducible results, it is unlikely that top-down approaches alone will be able to disentangle mechanisms involved in autism or schizophrenia. In stark contrast with top-down approaches, bottom-up studies showed that the effect-sizes of high-risk neuropsychiatric mutations are equally large for neuroimaging and behavioural traits. Low specificity has been perplexing with studies showing that broad classes of genomic variants affect a similar range of behavioral and cognitive dimensions, which may be consistent with the highly polygenic architecture of psychiatric conditions. The surprisingly discordant effect sizes observed between genetic and diagnostic first approaches underscore the necessity to decompose the heterogeneity hindering case-control studies in idiopathic conditions. We propose a systematic investigation across a broad spectrum of neuropsychiatric variants to identify putative latent dimensions underlying idiopathic conditions. Gene expression data on temporal, spatial and cell type organization in the brain have also considerable potential for parsing the mechanisms contributing to these dimensions phenotypes. While large neuroimaging genomic datasets are now available in unselected populations, there is an urgent need for data on individuals with a range of psychiatric symptoms and high-risk genomic variants. Such efforts together with more standardized methods will improve mechanistically informed predictive modeling for diagnosis and clinical outcomes.

scholarly journals Power and sample size for multivariate logistic modeling of unmatched case-control studies

2017 ◽  
Vol 28 (3) ◽  
pp. 822-834
Author(s):  
Mitchell H Gail ◽  
Sebastien Haneuse
Keyword(s):  
Logistic Regression ◽  
Sample Size ◽  
Case Control ◽  
Simulation Methods ◽  
Case Control Studies ◽  
Control Data ◽  
Logistic Analysis ◽  
Sample Size Calculations ◽  
Control Designs ◽  
Univariate Analyses

Sample size calculations are needed to design and assess the feasibility of case-control studies. Although such calculations are readily available for simple case-control designs and univariate analyses, there is limited theory and software for multivariate unconditional logistic analysis of case-control data. Here we outline the theory needed to detect scalar exposure effects or scalar interactions while controlling for other covariates in logistic regression. Both analytical and simulation methods are presented, together with links to the corresponding software.

SELECTION OF CONTROLS FOR HOSPITAL-BASED CASE-CONTROL STUDIES USING RETROSPECTIVE DATA ON THE GEOGRAPHIC DISTRIBUTION OF CASES AND CONTROLS

Epidemiology ◽  
2004 ◽  
Vol 15 (4) ◽  
pp. S153-S154 ◽  
Author(s):  
Tony Fletcher ◽  
Giovanni Leonardi ◽  
Kvetoslava Koppova ◽  
Rupert Hough ◽  
Peter Rudnai ◽  
...  
Keyword(s):  
Geographic Distribution ◽  
Case Control ◽  
Retrospective Data ◽  
Case Control Studies ◽  
Selection Of

scholarly journals Oral Contraceptive Use and Breast Cancer Risk Assessment: A Systematic Review and Meta-Analysis of Case-Control Studies, 2009–2020

Cancers ◽  
2021 ◽  
Vol 13 (22) ◽  
pp. 5654
Author(s):  
Agnieszka Barańska ◽  
Agata Błaszczuk ◽  
Wiesław Kanadys ◽  
Maria Malm ◽  
Katarzyna Drop ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Oral Contraceptive ◽  
Cancer Risk ◽  
Meta Analysis ◽  
Case Control ◽  
Cochrane Library ◽  
Control Group ◽  
Case Control Studies ◽  
Increased Risk ◽  
Breast Cancer Risk Assessment

To perform a meta-analysis of case-control studies that addressed the association between oral contraceptive pills (OC) use and breast cancer (BrCa), PubMED (MEDLINE), Embase, and the Cochrane Library were searched to identify case-control studies of OC and BrCa published between 2009 and 2020. We used the DerSimonian–Laird method to compute pooled odds ratios (ORs) and confidence intervals (CIs), and the Mantel–Haenszel test to assess the association between OC use and cancer. Forty-two studies were identified that met the inclusion criteria and we included a total of 110,580 women (30,778 into the BrCa group and 79,802 into the control group, of which 15,722 and 38,334 were using OC, respectively). The conducted meta-analysis showed that the use of OC was associated with a significantly increased risk of BrCa in general, OR = 1.15, 95% CI: 1.01 to 1.31, p = 0.0358. Regarding other risk factors for BrCa, we found that increased risk was associated significantly with early menarche, nulliparous, non-breastfeeding, older age at first parity, postmenopause, obesity, smoking, and family history of BrCa. Despite our conclusion that birth control pills increase the cancer risk being supported by extensive previous studies and meta-analyzes, further confirmation is required.

scholarly journals Statistical Power in Content Analysis Designs: How Effect Size, Sample Size and Coding Accuracy Jointly Affect Hypothesis Testing ‐ A Monte Carlo Simulation Approach.

2021 ◽  
Vol 3 (1) ◽  
pp. 61-89
Author(s):  
Stefan Geiß
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Content Analysis ◽  
Sample Size ◽  
Effect Size ◽  
Statistical Power ◽  
Effect Sizes ◽  
Sample Sizes ◽  
Expected Effect ◽  
Sample Size Effect

Abstract This study uses Monte Carlo simulation techniques to estimate the minimum required levels of intercoder reliability in content analysis data for testing correlational hypotheses, depending on sample size, effect size and coder behavior under uncertainty. The ensuing procedure is analogous to power calculations for experimental designs. In most widespread sample size/effect size settings, the rule-of-thumb that chance-adjusted agreement should be ≥.80 or ≥.667 corresponds to the simulation results, resulting in acceptable α and β error rates. However, this simulation allows making precise power calculations that can consider the specifics of each study’s context, moving beyond one-size-fits-all recommendations. Studies with low sample sizes and/or low expected effect sizes may need coder agreement above .800 to test a hypothesis with sufficient statistical power. In studies with high sample sizes and/or high expected effect sizes, coder agreement below .667 may suffice. Such calculations can help in both evaluating and in designing studies. Particularly in pre-registered research, higher sample sizes may be used to compensate for low expected effect sizes and/or borderline coding reliability (e.g. when constructs are hard to measure). I supply equations, easy-to-use tables and R functions to facilitate use of this framework, along with example code as online appendix.

scholarly journals Validasi Modul Pelatihan Pendampingan Klien pada Pekerja Sosial

2019 ◽  
Vol 3 (2) ◽  
pp. 66
Author(s):  
Yova Tri Yolanda ◽  
Muhana Sofiati Utami
Keyword(s):  
Social Worker ◽  
Effect Size ◽  
Test Sample ◽  
Control Group ◽  
Control Groups ◽  
Large Effect Size ◽  
Control Group Design ◽  
Sample Data ◽  
Post Test ◽  
And Control

Purpose of this research is to validated the module Client Facilitating Training to increase social worker knowledge about stress  and skill for facilitating client. Training included psychoeducation about stress and management stress, basic of client facilitating method and communication skill in facilitating process. Subjects of this research are 12 social worker and divided to experiment and control group. This research using quasi experiment with non control group design with pretest and post test sample. Data collected by management stres questionnaire, facilitating scale and field data collected by observer and supervisor. Results indicated that there is a significant differences between experiment and control groups in stress and management stress (Z=-3,017; p<0,05) with large effect size of 0,87 and Facilitatting skill (Z= -1,354; p<0,05) with large effect size of 0,84. Client Facilitating Training is valid to improve social worker knowledge stress  and management stress  and facilitating skill.

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