scholarly journals Can statistical adjustment guided by causal inference improve the accuracy of effect estimation? A simulation and empirical research based on meta-analyses of case–control studies

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Ruohua Yan ◽  
Tianyi Liu ◽  
Yaguang Peng ◽  
Xiaoxia Peng
Keyword(s):  
Causal Inference ◽  
Empirical Research ◽  
Case Control ◽  
Case Control Studies ◽  
Simulation Experiments ◽  
Confounding Bias ◽  
Statistical Adjustment ◽  
Adjustment Strategies ◽  
Effect Estimation ◽  
Meta Analyses

Abstract Background Statistical adjustment is often considered to control confounding bias in observational studies, especially case–control studies. However, different adjustment strategies may affect the estimation of odds ratios (ORs), and in turn affect the results of their pooled analyses. Our study is aimed to investigate how to deal with the statistical adjustment in case–control studies to improve the validity of meta-analyses. Methods Three types of adjustment strategies were evaluated including insufficient adjustment (not all preset confounders were adjusted), full adjustment (all confounders were adjusted under the guidance of causal inference), and improper adjustment (covariates other than confounders were adjusted). We carried out a series of Monte Carlo simulation experiments based on predesigned scenarios, and assessed the accuracy of effect estimations from meta-analyses of case–control studies by combining ORs calculated according to different adjustment strategies. Then we used the data from an empirical review to illustrate the replicability of the simulation results. Results For all scenarios with different strength of causal relations, combining ORs that were comprehensively adjusted for confounders would get the most precise effect estimation. By contrast, combining ORs that were not sufficiently adjusted for confounders or improperly adjusted for mediators or colliders would easily introduce bias in causal interpretation, especially when the true effect of exposure on outcome was weak or none. The findings of the simulation experiments were further verified by the empirical research. Conclusions Statistical adjustment guided by causal inference are recommended for effect estimation. Therefore, when conducting meta-analyses of case–control studies, the causal relationship formulated by exposure, outcome, and covariates should be firstly understood through a directed acyclic graph, and then reasonable original ORs could be extracted and combined by suitable methods.

scholarly journals Can statistical adjustment guided by causal inference improve the accuracy of effect estimation? A simulation and empirical research based on meta-analyses of case-control studies

2020 ◽  
Author(s):  
Ruohua Yan ◽  
Tianyi Liu ◽  
Yaguang Peng ◽  
Xiaoxia Peng
Keyword(s):  
Causal Inference ◽  
Empirical Research ◽  
Meta Analysis ◽  
Case Control ◽  
Case Control Studies ◽  
Simulation Experiments ◽  
Statistical Adjustment ◽  
Adjustment Strategies ◽  
Effect Estimation ◽  
Meta Analyses

Abstract Background: Statistical adjustment is often considered to control confounding bias in observational studies, especially case-control studies. However, different adjustment strategies may affect the estimation of odds ratios (ORs), and in turn affect the results of their pooled analyses. Our study is aimed to investigate how to deal with the statistical adjustment in case-control studies to improve the validity of meta-analyses.Methods: Three types of adjustment strategies were evaluated including insufficient adjustment (not all preset confounders were adjusted), full adjustment (all confounders were adjusted under the guidance of causal inference), and improper adjustment (covariates other than confounders were adjusted). We carried out a series of Monte Carlo simulation experiments based on predesigned scenarios, and assessed the accuracy of effect estimations from meta-analyses of case-control studies by combining ORs calculated according to different adjustment strategies. Then we used the data from an empirical review to illustrate the replicability of the simulation results.Results: For all scenarios with different strength of causal relations, combining ORs adjusted for confounders as far as possible would get the most precise effect estimation, regardless of the sampling approaches of case-control studies and the scale of meta-analysis. By contrast, combining ORs that were not sufficiently adjusted for confounders or improperly adjusted for mediators or colliders would easily introduce bias in causal interpretation, especially when the true effect of exposure on outcome was weak or none. The findings of the simulation experiments were further verified by the empirical research.Conclusions: Statistical adjustment guided by causal inference are recommended for effect estimation. Therefore, when conducting meta-analyses of case-control studies, the causal relationship formulated by exposure, outcome, and covariates should be firstly understood through a directed acyclic graph, and then reasonable original ORs could be extracted and combined by suitable methods.

scholarly journals Can statistical adjustment guided by causal inference improve the accuracy of effect estimation? A simulation and empirical research based on Meta-analyses of case-control studies

2020 ◽  
Author(s):  
Ruohua Yan ◽  
Tianyi Liu ◽  
Yaguang Peng ◽  
Xiaoxia Peng
Keyword(s):  
Causal Inference ◽  
Directed Acyclic Graph ◽  
Meta Analysis ◽  
Case Control ◽  
Case Control Studies ◽  
Confounding Bias ◽  
Statistical Adjustment ◽  
Adjustment Strategies ◽  
Effect Estimation ◽  
Meta Analyses

Abstract Background Statistical adjustment is often considered to control confounding bias in observational studies, especially case-control studies. However, different adjustment strategies may affect the estimations of odds ratios (ORs), and in turn affect the results of their pooled analyses. Our study is aimed to investigate how to deal with the statistical adjustment in case-control studies to improve the validity of Meta-analyses. Methods We carried out a series of Monte Carlo simulation experiments based on predesigned scenarios, and assessed the accuracy of effect estimations from Meta-analyses of case-control studies by combining ORs calculated according to different adjustment strategies. The strategies included fully adjustment of all preset confounders guided by causal inference, insufficiently adjustment of less confounders, and improperly adjustment of covariates other than confounders. Results For all scenarios with different strength of causal relations, combining ORs adjusted for confounders as far as possible would get the most precise effect estimation, regardless of the sampling approaches of case-control studies and the scale of Meta-analysis. By contrast, combining ORs that were not sufficiently adjusted for confounders or improperly adjusted for mediators or colliders would easily introduce bias in causal interpretation, especially when the true effect of exposure on outcome was weak or none. Conclusions Statistical adjustment guided by causal inference are recommended for effect estimation. Therefore, when conducting Meta-analyses of case-control studies, the causal relationship formulated by exposure, outcome, and covariates should be firstly understood through a directed acyclic graph, and then reasonable original ORs could be extracted and combined by suitable methods.

scholarly journals Relevance of hMLH1 -93G>A, 655A>G and 1151T>A polymorphisms with colorectal cancer susceptibility: a meta-analysis based on 38 case-control studies

2018 ◽  
Vol 64 (10) ◽  
pp. 942-951 ◽  
Author(s):  
Mohammad Zare ◽  
Jamal Jafari-Nedooshan ◽  
Mohammadali Jafari ◽  
Hossein Neamatzadeh ◽  
Seyed Mojtaba Abolbaghaei ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Cancer Susceptibility ◽  
Meta Analysis ◽  
Asian Population ◽  
Case Control ◽  
Biomedical Literature ◽  
Case Control Studies ◽  
Increased Risk ◽  
Comprehensive Search ◽  
Meta Analyses

SUMMARY OBJECTIVE: There has been increasing interest in the study of the association between human mutL homolog 1 (hMLH1) gene polymorphisms and risk of colorectal cancer (CRC). However, results from previous studies are inconclusive. Thus, a meta-analysis was conducted to derive a more precise estimation of the effects of this gene. METHODS: A comprehensive search was conducted in the PubMed, EMBASE, Chinese Biomedical Literature databases until January 1, 2018. Odds ratio (OR) with 95% confidence interval (CI) was used to assess the strength of the association. RESULTS: Finally, 38 case-control studies in 32 publications were identified met our inclusion criteria. There were 14 studies with 20668 cases and 19533 controls on hMLH1 −93G>A, 11 studies with 5,786 cases and 8,867 controls on 655A>G and 5 studies with 1409 cases and 1637 controls on 1151T>A polymorphism. The combined results showed that 655A>G and 1151T>A polymorphisms were significantly associated with CRC risk, whereas −93G>A polymorphism was not significantly associated with CRC risk. As for ethnicity, −93G>A and 655A>G polymorphisms were associated with increased risk of CRC among Asians, but not among Caucasians. More interestingly, subgroup analysis indicated that 655A>G might raise CRC risk in PCR-RFLP and HB subgroups. CONCLUSION: Inconsistent with previous meta-analyses, this meta-analysis shows that the hMLH1 655A>G and 1151T>A polymorphisms might be risk factors for CRC. Moreover, the −93G>A polymorphism is associated with the susceptibility of CRC in Asian population.

scholarly journals Causal inference in case-control studies

2020 ◽  
Author(s):  
Sokbae (Simon) Lee ◽  
Sung Jae Jun
Keyword(s):  
Causal Inference ◽  
Case Control ◽  
Case Control Studies

scholarly journals Application of Bee Products for Dermatological Problems

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Heidrun Männle ◽  
Karsten Münstedt
Keyword(s):  
Clinical Trials ◽  
Atopic Dermatitis ◽  
Randomized Clinical Trials ◽  
Case Control ◽  
Health Claims ◽  
Wound Infections ◽  
Herpes Viruses ◽  
Case Control Studies ◽  
Meta Analyses ◽  
Cutaneous Warts

Context: Bee products are frequently suggested as possible treatments for dermatological problems by protagonists of apitherapy, which is a discipline within the field of complementary and alternative medicine. Unfortunately, apitherapists do not support their health claims. This review was to identify potential uses of bee products in the field of dermatology. Evidence Acquisition: Randomized and non-randomized clinical trials, case-control studies, systematic reviews, and meta-analyses on the topics were identified using various search engines. Results: Evidence suggests that bee products may be a reasonable treatment option for wound infections, burns, radiodermatitis, infections with herpes viruses, atopic dermatitis, rosacea, scars, cutaneous warts, acne, psoriasis, facial wrinkles, and intertrigo. Conclusions: There are several applications for bee products in the field of dermatology, for instance treatment of wound infections with honey and herpes infections with propolis.

scholarly journals Association of microRNAs genes polymorphisms with arthritis: a systematic review and meta-analysis

2019 ◽  
Vol 39 (7) ◽  
Author(s):  
Yingqi Xiao ◽  
Hui Liu ◽  
Li Chen ◽  
Yang Wang ◽  
Xiang Yao ◽  
...  
Keyword(s):  
Rheumatoid Arthritis ◽  
Meta Analysis ◽  
Fixed Effect Model ◽  
Case Control ◽  
Cochrane Library ◽  
Random Effects Model ◽  
Inclusion Criteria ◽  
Case Control Studies ◽  
Effect Model ◽  
Meta Analyses

Abstract Objective: To investigate whether microRNAs genes’ polymorphisms are associated with arthritis. Methods: The PubMed, Cochrane Library et al. were systematically searched to identify case–control studies, systematic reviews and meta-analyses. A meta-analysis was performed to calculate odds ratios (ORs), and confidence intervals (CIs) at 95% using fixed-effect model or random-effects model. Results: Twenty-two case–control studies involving 10489 participants fulfilled the inclusion criteria. MiR-146a rs2910164 (G/C) was not significantly associated with the risk of rheumatoid arthritis (RA) in any model. Significant associations were found between miR-146a rs2910164 (G/C) and the risk of psoriatic arthritis (PsA) in the heterozygous model and the dominant model. The heterozygous model showed a significant association between the miR-146a rs2910164 (G/C) polymorphism and ankylosing spondylitis (AS). And there was no significant association of miR-146a rs2910164 (G/C) with risk of juvenile rheumatoid arthritis (JRA) at any model. Additionally, there was a significant association of miR-499 rs3746444 (T/C) with risk of RA at two genetic models, and with a moderate heterogeneity. When subgroup analysis by ethnicity, significant associations were almost found between miR-499 rs3746444 (T/C) and the risk of RA in any model in Caucasian populations, and there is no heterogeneity. Conclusions: The association of miR-146a rs2910164 (G/C) with RA was not found. And there was a significant association between miR-146a rs2910164(G/C) and PsA or AS. MiR-499 rs3746444 (T/C) was associated with RA in Caucasian populations. These findings did not support the genetic association between miR-146a rs2910164 (G/C) and JRA susceptibility, as well as the association of miR-196a-2 rs11614913 (C/T), miR-146a rs2431697, miR-146a rs57095329, miR-149 rs22928323 with arthritis.

scholarly journals Pooling analysis regarding the impact of human vitamin D receptor variants on the odds of psoriasis

2019 ◽  
Vol 20 (1) ◽  
Author(s):  
Juan Li ◽  
Li Sun ◽  
Jinghui Sun ◽  
Min Yan
Keyword(s):  
Vitamin D ◽  
Vitamin D Receptor ◽  
Meta Analysis ◽  
Genetic Effect ◽  
Analysis Data ◽  
Case Control ◽  
Case Control Studies ◽  
Significant Difference ◽  
Meta Analyses ◽  
The Impact

Abstract Background The study aims at scientifically investigating the genetic effect of four polymorphisms (rs7975232, rs1544410, rs2228570, and rs731236) within the human Vitamin D Receptor (VDR) gene on the odds of psoriasis through an updated meta-analysis. Methods We searched eight databases and screened the studies for pooling. Finally, a total of eighteen eligible case-control studies were included. BH (Benjamini & Hochberg) adjusted P-values of association (Passociation) and odd ratios (ORs) with the corresponding 95% confidence intervals (CIs) were calculated under the allele, homozygote, heterozygote, dominant, recessive, and carrier models. Results Compared with the negative controls, no statistically significant difference in the odds of psoriasis was detected for the cases under any genetic models (BH adjusted Passociation > 0.05). We also performed subgroup meta-analyses by the source of controls, ethnicity, country, Hardy-Weinberg equilibrium, and genotyping method. Similar results were observed in most subgroup meta-analyses (BH adjusted Passociation > 0.05). Besides, data of Begg’s and Egger’s tests excluded the significant publication bias; while the sensitivity analysis data further indicated the statistical reliability of our pooling results. Conclusion The currently available data fails to support a robust association between VDR rs7975232, rs1544410, rs2228570 and rs731236 polymorphisms and psoriasis susceptibility, which still required the support of more case-control studies.

scholarly journals Systematic Review and Meta-Analysis of Psychosocial Risk Factors for Stroke

2017 ◽  
Vol 37 (03) ◽  
pp. 294-306 ◽  
Author(s):  
Andrew Clegg ◽  
Kulsum Patel ◽  
Julie Lucas ◽  
Hannah Storey ◽  
Maree Hackett ◽  
...  
Keyword(s):  
Risk Factors ◽  
Meta Analysis ◽  
Case Control ◽  
Psychosocial Risk Factors ◽  
Psychosocial Risk ◽  
Systemic Review ◽  
Case Control Studies ◽  
Cochrane Database ◽  
Increased Risk ◽  
Meta Analyses

AbstractSeveral studies have assessed the link between psychosocial risk factors and stroke; however, the results were inconsistent. We have conducted a systemic review and meta-analysis of cohort or case-control studies to ascertain the association between psychosocial risk factors (psychological, vocational, behavioral, interpersonal, and neuropsychological) and the risk of stroke. Systematic searches were undertaken in MEDLINE, EMBASE, CINAHL, PsycINFO, and the Cochrane Database of Systematic Reviews between 2000 and January 2017. Two reviewers independently screened titles, abstracts, and full texts. One reviewer assessed quality and extracted data, which was checked by a second reviewer. For studies that reported risk estimates, a meta-analysis was performed. We identified 41 cohort studies and 5 case-control studies. No neuropsychological papers were found. Overall, pooled adjusted estimates showed that all other psychosocial risk factors were independent risk factors for stroke. Psychological factors increased the risk of stroke by 39% (hazard ratio [HR], 1.39; 95% confidence interval [CI], 1.27–1.51), vocational by 35% (HR, 1.35; 95% CI, 1.20–1.51), and interpersonal by 16% (HR, 1.16; 95% CI, 1.03–1.31), and the effects of behavioral factors were equivocal (HR, 0.94; 95% CI, 0.20–4.31). The meta-analyses were affected by heterogeneity. Psychosocial risk factors are associated with an increased risk of stroke.

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