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 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 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 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.

The genetics of venous thromboembolism

2009 ◽  
Vol 102 (08) ◽  
pp. 360-370 ◽  
Author(s):  
Reya Gohil ◽  
George Peck ◽  
Pankaj Sharma
Keyword(s):  
Venous Thromboembolism ◽  
Meta Analysis ◽  
Random Effect ◽  
Case Control ◽  
Disease Association ◽  
Factor V ◽  
Case Control Studies ◽  
Factor V G1691a ◽  
Meta Analyses ◽  
Random Effect Models

SummaryWe conducted a systematic and comprehensive meta-analysis on all candidate genes to assess their genetic contribution to the aetiology of venous thromboembolism (VTE) (pulmonary embolism and deep venous thrombosis) in all ethnic groups. Electronic databases were searched until and including January 2008 for any candidate gene investigated in VTE. Odds ratios (OR) and 95% confidence intervals (CI) were determined for each gene disease association using fixed and random effect models. Our meta-analyses included ∼126,525 cases and 184,068 controls derived from 173 case-control studies, which included 21 genes (28 polymorphisms). Statistically significant associations with VTE were identified for factor V G1691A (OR 9.45; 95% CI 6.72–13.30, p<0.0001), factor V A4070G (OR 1.24; 95% CI

scholarly journals Evaluation of association studies and a systematic review and meta-analysis of CYP1A1 T3801C and A2455G polymorphisms in breast cancer risk

PLoS ONE ◽  
2021 ◽  
Vol 16 (4) ◽  
pp. e0249632
Author(s):  
Chen Yang ◽  
Xiao-Feng He
Keyword(s):  
Breast Cancer ◽  
Systematic Review ◽  
Sensitivity Analysis ◽  
Breast Cancer Risk ◽  
Association Studies ◽  
Meta Analysis ◽  
Case Control ◽  
Case Control Studies ◽  
Meta Analyses ◽  
Positive Results

Background Nine previous meta-analyses have been published to analyze the CYP1A1 T3801C and A2455G polymorphisms with BC risk. However, they did not assess the credibility of statistically significant associations. In addition, many new studies have been reported on the above themes. Hence, we conducted an updated systematic review and meta-analysis to further explore the above issues. Objectives To explore the association on the CYP1A1 T3801C and A2455G polymorphisms with BC risk. Methods Preferred Reporting Items for Systematic Reviews and Meta-Analyses (The PRISMA) were used. Results In this study, there were 63 case–control studies from 56 publications on the CYP1A1 T3801C polymorphism (including 20,825 BC cases and 25,495 controls) and 51 case–control studies from 46 publications on the CYP1A1 A2455G polymorphism (including 20,124 BC cases and 29,183 controls). Overall, the CYP1A1 T3801C polymorphism was significantly increased BC risk in overall analysis, especially in Asians and Indians; the CYP1A1 A2455G polymorphism was associated with BC risk in overall analysis, Indians, and postmenopausal women. However, when we used BFDP correction, associations remained significant only in Indians (CC vs. TT + TC: BFDP < 0.001) for the CYP1A1 T3801C polymorphism with BC risk, but not in the CYP1A1 A2455G polymorphism. In addition, when we further performed sensitivity analysis, no significant association in overall analysis and any subgroup. Moreover, we found that all studies from Indians was low quality. Therefore, the results may be not credible. Conclusion This meta-analysis strongly indicates that there is no significant association between the CYP1A1 T3801C and A2455G polymorphisms and BC risk. The increased BC risk may most likely on account of false-positive results.

Consumption of Artificially-sweetened Soft Drinks and Risk of Gastrointestinal Cancer: A Meta-analysis of Observational Studies

2021 ◽  
pp. 1-50
Author(s):  
Alfred Jatho ◽  
Jansen Marcos Cambia ◽  
Seung-Kwon Myung ◽  
Keyword(s):  
Cohort Studies ◽  
Observational Studies ◽  
Meta Analysis ◽  
Epidemiological Studies ◽  
Case Control ◽  
Soft Drinks ◽  
Case Control Studies ◽  
Gi Cancer ◽  
Increased Risk ◽  
Meta Analyses

Abstract Objective: There remain inconclusive findings from previous observational epidemiological studies on whether consumption of artificially-sweetened soft drinks (ASSDs) increases the risk of gastrointestinal (GI) cancer. We investigated the associations between the consumption of ASSDs and the risk of GI cancer using a meta-analysis. Design: Systematic review and meta-analysis. Setting: PubMed and EMBASE were searched using keywords until May 2020 to identify observational epidemiological studies on the association between the consumption of ASSDs and the risk of GI cancer. Subjects: Twenty-one case-control studies and 17 cohort studies with 12,397 cancer cases and 2,474,452 controls. Results: In the random-effects meta-analysis of all the studies, consumption of ASSDs was not significantly associated with the risk of overall GI cancer (odds ratio (OR)/relative risk (RR), 1.02; 95% CI, 0.92-1.14). There was no significant association between the consumption of ASSDs and the risk of overall GI cancer in the subgroup meta-analyses by study design (case-control studies: OR, 0.95; 95% CI, 0.82-1.11; cohort studies: RR, 1.14; 95% CI, 0.97-1.33). In the subgroup meta-analysis by type of cancer, consumption of ASSDs was significantly associated with the increased risk of liver cancer (OR/RR, 1.28; 95% CI,1.03-1.58). Conclusions: The current meta-analysis of observational epidemiological studies suggests that overall, there is no significant association between the consumption of ASSDs and the risk of GI cancer.

scholarly journals The risk of being culpable for or involved in a road crash after using cannabis: A systematic review and meta-analyses

2021 ◽  
Vol 7 ◽  
pp. 205032452110553
Author(s):  
Michael A. White ◽  
Nicholas R. Burns
Keyword(s):  
Odds Ratio ◽  
Meta Analysis ◽  
Case Control ◽  
Odds Ratios ◽  
Case Control Studies ◽  
Directional Bias ◽  
Increased Risk ◽  
Meta Analyses ◽  
Summary Odds Ratio ◽  
Average Measure

Background The development of drug driving policies should rest on sound epidemiological evidence as to the crash risks of driving after using psychoactive drugs. The findings from individual studies of the increased risk of crashing from the acute use of cannabis range in size from no increase (and perhaps even a protective effect) to a 10-fold increase. Coherent cannabis-driving policies cannot readily be developed from such an incoherent evidence base. A weighted average measure of risk, as provided by a meta-analysis, might be useful. However, if the range of risks found in the cannabis-crash studies reflects the different ways that a variety of biases are being expressed, then the simple application of a meta-analysis might provide little more than an average measure of bias. In other words, if the biases were predominantly inflationary, the meta-analysis would give an inflated estimate of crash risk; and if the biases were predominantly deflationary, the meta-analysis would give a deflated estimate of risk. Review We undertook a systematic search of electronic databases, and identified 13 culpability studies and 4 case–control studies from which cannabis-crash odds ratios could be extracted. Random-effects meta-analyses gave summary odds ratios of 1.37 (1.10–1.69) for the culpability studies and 1.45 (0.94–2.25) for the case–control studies. A tool was designed to identify and score biases arising from: confounding by uncontrolled covariates; inappropriate selection of cases and controls; and the inappropriate measurement of the exposure and outcome variables. Each study was scrutinised for the presence of those biases, and given a total ‘directional bias score’. Most of the biases were inflationary. A meta-regression against the total directional bias scores was performed for the culpability studies, giving a bias-adjusted summary odds ratio of 0.68 (0.45–1.05). The same analysis could not be performed for the case–control studies because there were only four such studies. Nonetheless, a monotonic relationship was found between the total bias scores and the cannabis-crash odds ratios, with Spearman's rho  =  0.95, p  =  0.05, indicating that the summary odds ratio of 1.45 is an overestimate. It is evident that the risks from driving after using cannabis are much lower than from other behaviours such as drink-driving, speeding or using mobile phones while driving. With the medical and recreational use of cannabis becoming more prevalent, the removal of cannabis-presence driving offences should be considered (while impairment-based offences would remain).

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