Bias In Observational Studies Research Articles

Association of serum uric acid with male sexual hormones and erectile dysfunction: a bidirectional 2-sample Mendelian randomization analysis.

Observational studies indicated that serum uric acid (SUA) was associated with male sexual hormones and erectile dysfunction (ED). However, their relationship was still heterogeneous. This study conducted 2-sample univariate mendelian randomization (UVMR) and multivariate mendelian randomization (MVMR) to explore the causal relationship between SUA and sexual hormones as well as ED. Genetic variants associated with SUA were derived from the UK Biobank database (N = 437 354). Outcomes from the IEU Open GWAS and summary data sets were sexual hormones (sex hormone-binding globulin [SHBG], testosterone, estradiol [E2], follicle-stimulating hormone, luteinizing hormone) and ED, with 3301 to 625 650 participants. UVMR analysis primarily utilized the inverse variance weighted method, complemented by MVMR analysis. Thorough sensitivity analyses were carried out to ensure the reliability of results. Moreover, mediation analysis was conducted to estimate the mediated effect between SUA and outcomes. The primary outcomes included results of UVMR and MVMR analysis and mediation analysis, along with sensitivity analyses involving the Cochran Q test, the MR Egger intercept test, leave-1-out analysis, and the MR-PRESSO method (mendelian randomization pleiotropy residual sum and outlier). UVMR analysis revealed that an elevated SUA level could decrease levels of SHBG (β = -0.10, P = 1.70 × 10-7) and testosterone (β = -0.10, P = 5.94 × 10-3) and had a positive causal effect on ED (odds ratio,1.10; P = .018). According to reverse mendelian randomization results, increased levels of SHBG (β = -0.06, P = 4.82 × 10-4) and E2 (β = -0.04, P = .037) could also reduce SUA levels. As shown by MVMR analysis, SUA had a negative effect on SHBG and testosterone levels (P <.05), while the significant causal relationship between SUA and ED disappeared. Furthermore, SHBG mediated 98.1% of the effect of SUA on testosterone levels. Results of other mendelian randomization analyses were not statistically significant. No pleiotropy was found by sensitivity analysis in this study. Given the causal relationship between SUA and sexual hormones, we must focus on SUA and E2 levels in men, especially patients with hypogonadism and ED. This study evaluated the causal effect of SUA on male sexual hormones and ED genetically for the first time, clarifying the common biases in observational studies and confirming the negative relationship between SUA and testosterone level. Limitations include a population based on European ancestry, some crossover of the samples, and unobserved confounding factors. Genetic studies provide evidence for the causal relationship between SUA and male sexual hormones (SHBG, testosterone, E2), while the relationship between SUA and ED should be further evaluated.

Comparative effectiveness of treatment approaches for early invasive breast cancer

BackgroundRandomized controlled trials (RCTs) suggested breast conserving therapy (BCT) and mastectomy have similar survival for early-stage breast cancer, whereas observational studies reported survival advantage for BCT. We aimed to address biases in observational studies to compare the effect of BCT and mastectomy on survival. MethodsWe emulated a target trial using institutional cancer registry. We included adult women diagnosed with early invasive first primary breast tumors ≤ 5 cm between July 2011 and December 2017. We used cloning, censoring, and weighting to estimate risk differences (RDs) and risk ratios (RRs) for all-cause mortality and recurrence or all-cause mortality between BCT and mastectomy (reference). ResultsOur study population comprised 534 observations with breast cancer. Median age was 56 years and 65 % were racial/ethnic minorities. The 8-year RD was 1.5 % (95 % confidence limits [CL]: −7.0 %, 9.8 %) and RR was 1.1 (95 % CL: 0.57, 2.2) for all-cause mortality. Results for recurrence or mortality were similar. ConclusionsOur results suggest that target trial emulation to mitigate selection and immortal-time biases in observational studies may generate estimates that are more compatible with RCTs when comparing the effects of BCT and mastectomy on survival. Studies with longer follow-up and more events are needed to confirm our findings.

A comparison of random forest-based missing imputation methods for covariates in propensity score analysis.

Propensity score analysis (PSA) is a prominent method to alleviate selection bias in observational studies, but missing data in covariates is prevalent and must be dealt with during propensity score estimation. Through Monte Carlo simulations, this study evaluates the use of imputation methods based on multiple random forests algorithms to handle missing data in covariates: multivariate imputation by chained equations-random forest (Caliber), proximity imputation (PI), and missForest. The results indicated that PI and missForest outperformed other methods with respect to bias of average treatment effect regardless of sample size and missing mechanisms. A demonstration of these five methods with PSA to evaluate the effect of participation in center-based care on children's reading ability is provided using data from the Early Childhood Longitudinal Study, Kindergarten Class of 2010-2011. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Association Between Healthy Behaviors and Health Care Resource Use With Subsequent Positive Airway Pressure Therapy Adherence in OSA

BackgroundThe healthy adherer effect (HAE) has gained increasing attention as potential source of bias in observational studies examining the association of positive airway pressure (PAP) adherence with health outcomes in obstructive sleep apnea (OSA). Research questionIs adherence to PAP associated with healthy behaviors and healthcare resource use prior to device prescription? MethodsData from the IRSR Pays de la Loire Sleep Cohort were linked to health administrative data to identify proxies of heathy behaviors (HB) including adherence to cardiovascular (CV) drugs (medication possession ratio, [MPR]), cancer screening tests, influenza vaccination, alcohol and smoking consumption, and drowsiness-related road accidents during the two years preceding PAP onset in OSA patients. Multivariable regression analyses were conducted to evaluate the association of HB with subsequent PAP adherence. Healthcare resource use was evaluated according to subsequent PAP adherence. FindingsWe included 2,836 patients who had started PAP therapy between 2012 and 2018 (65% of whom were PAP adherent with mean daily use ≥4h/night). Being adherent to CV active drugs (MPR≥80%) and non-smoker were associated with a higher likelihood of PAP adherence (odds ratio, OR [95% confidence interval]: 1.43 [1.15; 1.77] and 1.37 [1.10; 1.71] respectively). Patients with no history of drowsiness-related road accidents were more likely to continue PAP (OR: 1.39 [1.04; 1.87]). PAP adherent patients used less healthcare resources 2 years before PAP initiation, than non-adherents (mean number of outpatient consultations: 19.0 vs 17.2, P=.003; hospitalization days: 5.7 vs 5.0, P=.04; emergency room visit: 30.7 vs 24.0% P=.0002). InterpretationPatients who adhere to PAP therapy of OSA were more health seeking and less healthcare users prior to device initiation than non-adherent patients. Until the HAE associated with PAP adherence is better understood, caution is warranted when interpreting the association of PAP adherence with CV health outcomes and healthcare resource use in non-randomized cohorts.

Resampling-based confidence intervals and bands for the average treatment effect in observational studies with competing risks

The g-formula can be used to estimate the treatment effect while accounting for confounding bias in observational studies. With regard to time-to-event endpoints, possibly subject to competing risks, the construction of valid pointwise confidence intervals and time-simultaneous confidence bands for the causal risk difference is complicated, however. A convenient solution is to approximate the asymptotic distribution of the corresponding stochastic process by means of resampling approaches. In this paper, we consider three different resampling methods, namely the classical nonparametric bootstrap, the influence function equipped with a resampling approach as well as a martingale-based bootstrap version, the so-called wild bootstrap. For the latter, three sub-versions based on differing distributions of the underlying random multipliers are examined. We set up a simulation study to compare the accuracy of the different techniques, which reveals that the wild bootstrap should in general be preferred if the sample size is moderate and sufficient data on the event of interest have been accrued. For illustration, the resampling methods are further applied to data on the long-term survival in patients with early-stage Hodgkin’s disease.

Is there a correlation between peri-implantitis and systemic inflammation?

This systematic review investigates the association between peri-implantitis, an infectious/inflammatory disease sharing clinical and radiographic characteristics with periodontitis, and systemic inflammation. This study, adhering to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, systematically reviewed available evidence up to February 9, 2023. Searches were carried out across eight electronic databases (Cochrane Central Register of Controlled Trials, MEDLINE, EMBASE, Web of Science, Dentistry & Oral Sciences Source, Scopus, LILACS, and China Online), ClinicalTrials.gov, WHO International Clinical Trials Registry Platform (ICTRP), and gray literature. The review encompasses human studies, including randomised controlled trials, non-randomised intervention studies, cohort studies, case-control, and cross-sectional studies, yielded 27 full-text articles, with 11 clinical studies meeting inclusion criteria, and 9 articles included in the meta-analysis. Two independent reviewers carried out data extraction using the PECOS /PICOS tool (Patients, Exposure, Comparison, Outcomes, Study designs). The evaluation of the quality and risk of bias in observational studies, randomised controlled trials, and non-randomised studies of interventions was conducted utilising the Newcastle-Ottawa Scale (NOS), the revised Cochrane tool (RoB 2), and the ROBINS-I tool, respectively. Qualitative and quantitative analyses, including weighted mean differences (WMDs) and standard mean differences (SMD), were conducted using Stata/MP 17.0. Heterogeneity was assessed with the Q-statistic method. Pooled estimates, addressing potential inter-study heterogeneity, were calculated with random effects models. Significance criteria were set at p < 0.05. Publication bias was examined via funnel plot and Egger's test. Sensitivity analyses were predefined. Meta-analyses adhered to GRADE approach for inflammatory biomarkers/outcomes evaluation. Patients with peri-implantitis exhibited elevated levels of serum C-reactive protein (CRP) (standard mean difference (SMD): 4.68, 98.7% CI: 2.12 to 7.25), interleukin-6 (IL-6) (weighted mean difference (WMD): 6.27 pg/mL, 0% CI: 5.01 to 7.54), and white blood cell counts (WMD: 1.16 * 103/mL, 0% CI: 0.61 to 1.70) compared to those without peri-implantitis. The findings underscore a significant link between peri-implantitis and heightened systemic inflammation, emphasising the need for further research to elucidate the precise nature of this association.

Preventing immortal time bias in observational studies: a matter of design.

Preventing immortal time bias in observational studies: a matter of design.

Is there a sex difference in mortality rates in paediatric intensive care units?: a systematic review.

Mortality rates in infancy and childhood are lower in females than males. However, for children admitted to Paediatric Intensive Care Units (PICU), mortality has been reported to be lower in males, although males have higher admission rates. This female mortality excess for the subgroup of children admitted in intensive care is not well understood. To address this, we carried out a systematic literature review to summarise the available evidence. Our review studies the differences in mortality between males and females aged 0 to <18 years, while in a PICU, to examine whether there was a clear difference (in either direction) in PICU mortality between the two sexes, and, if present, to describe the magnitude and direction of this difference. Any studies that directly or indirectly reported the rates of mortality in children admitted to intensive care by sex were eligible for inclusion. The search strings were based on terms related to the population (those admitted into a paediatric intensive care unit), the exposure (sex), and the outcome (mortality). We used the search databases MEDLINE, Embase, and Web of Science as these cover relevant clinical publications. We assessed the reliability of included studies using a modified version of the risk of bias in observational studies of exposures (ROBINS-E) tool. We considered estimating a pooled effect if there were at least three studies with similar populations, periods of follow-up while in PICU, and adjustment variables. We identified 124 studies of which 114 reported counts of deaths by males and females which gave a population of 278,274 children for analysis, involving 121,800 (44%) females and 156,474 males (56%). The number of deaths and mortality rate for females were 5,614 (4.61%), and for males 6,828 (4.36%). In the pooled analysis, the odds ratio of female to male mortality was 1.06 [1.01 to 1.11] for the fixed effect model, and 1.10 [1.00 to 1.21] for the random effects model. Overall, males have a higher admission rate to PCU, and potentially lower overall mortality in PICU than females. www.crd.york.ac.uk/PROSPERO/display_record.php?RecordID=203009, identifier (CRD42020203009).

Unintentional unblinding in rheumatic disease trials.

The practice of blinding treatment assignment in randomised controlled trials mitigates important biases in observational studies. Unblinding, whereby study participants or investigators become aware of treatment assignments, is an important threat to the validity of trial results. Rheumatology studies might be particularly susceptible to unblinding because rheumatic disease therapies often cause high rates of idiosyncratic side-effects and frequently rely on subjective endpoints. Despite this susceptibility, the degree to which unblinding occurs in randomised controlled trials in rheumatic diseases has rarely been assessed during trials or acknowledged as a limitation. Rheumatologists should be aware of this important threat to the validity of trial results, assessments of unblinding should be undertaken, and strategies to prevent unblinding should be deployed when feasible.

Identifying Optimal Methods for Addressing Confounding Bias When Estimating the Effects of State-level Policies.

Policy evaluation studies that assess how state-level policies affect health-related outcomes are foundational to health and social policy research. The relative ability of newer analytic methods to address confounding, a key source of bias in observational studies, has not been closely examined. We conducted a simulation study to examine how differing magnitudes of confounding affected the performance of 4 methods used for policy evaluations: (1) the two-way fixed effects difference-in-differences model; (2) a 1-period lagged autoregressive model; (3) augmented synthetic control method; and (4) the doubly robust difference-in-differences approach with multiple time periods from Callaway-Sant'Anna. We simulated our data to have staggered policy adoption and multiple confounding scenarios (i.e., varying the magnitude and nature of confounding relationships). Bias increased for each method: (1) as confounding magnitude increases; (2) when confounding is generated with respect to prior outcome trends (rather than levels), and (3) when confounding associations are nonlinear (rather than linear). The autoregressive model and augmented synthetic control method had notably lower root mean squared error than the two-way fixed effects and Callaway-Sant'Anna approaches for all scenarios; the exception is nonlinear confounding by prior trends, where Callaway-Sant'Anna excels. Coverage rates were unreasonably high for the augmented synthetic control method (e.g., 100%), reflecting large model-based standard errors and wide confidence intervals in practice. In our simulation study, no single method consistently outperformed the others, but a researcher's toolkit should include all methodologic options. Our simulations and associated R package can help researchers choose the most appropriate approach for their data.

Introduction of a tool to assess Risk of Bias in Non-randomized Studies-of Exposure (2022)

This article introduces the contents of the latest edition Risk of Bias in Non-randomized Studies-of Exposure (ROBINS-E) published in June 2022 [ROBINS-E (2022)], and gives some examples about its usage. ROBINS-E is a tool for assessing the risk of bias in non-randomized studies-of exposure. Compared with ROBINS-E (2019), ROBINS-E (2022) adds more bias for observational studies, covers a more comprehensive range of bias, and adds the assessment of the external authenticity of the study. ROBINS-E (2022) adds a preliminary evaluation process to improve the efficiency of evaluation. In addition, ROBINS-E (2022) visualizes and instrumentalizes the use of signal problems in the form of path graph, making it more convenient to use. ROBINS-E (2022), although more consideration has been given to the issue of co-exposure, still does not address the problem of effect modification in co-exposure, and there is still room to expand the applicable research.

Development of the TrAnsparent ReportinG of observational studies Emulating a Target trial (TARGET) guideline

BackgroundObservational studies are increasingly used to inform health decision-making when randomised trials are not feasible, ethical or timely. The target trial approach provides a framework to help minimise common biases...

Mendelian randomization shows depression increases the risk of type 2 diabetes.

Introduction: Type 2 diabetes (T2D) is associated with severe mental illnesses (SMIs), such as schizophrenia, bipolar disorder, and depression. However, causal relationships between SMIs and T2D remain unclear owing to potential bias in observational studies. We aimed to characterize the causal effect of SMI liability on T2D using two-sample Mendelian randomization (MR). Methods: The causality between liability to SMI and T2D was investigated using the inverse-variance weighted (IVW), MREgger, MR-Egger with a simulation extrapolation, weighted median, and the MR pleiotropy residual sum and outlier method. Similarly, we performed additional MR which can detect the reverse causation effect by switching exposure and outcome for T2D liability for SMI. To further consider pleiotropic effects between SMIs, multivariable MR analysis was performed after accounting for the other traits. Results: In the univariable IVW method, depression showed a causal effect on T2D (odds ratio [OR]: 1.128, 95% confidence interval [CI]: 1.024-1.245, p = 0.014). Multinomial MR more strongly supported these results (IVW OR: 1.197, 95% CI: 1.069, 1.340, p = 0.002; MR-Egger OR: 1.198, 95% CI: 1.062, 1.349, p = 0.003). Bidirectional MR showed absence of reversecausality between depression and T2D. However, causal relationship of bipolar and schizophrenia on T2D was not detected. Discussion: Careful attention is needed for patients with depression regarding T2D prevention and treatment.

Essential Concepts for Reducing Bias in Observational Studies.

Randomized controlled trials (RCTs) are the gold standard study design for clinical research, as prospective randomization, at least in theory, balances any differences that can exist between groups (including any differences not measured as part of the study) and isolates the studied treatment effect. Any remaining imbalances after randomization are attributable to chance. However, there are many barriers to conducting RCTs within pediatric populations, including lower disease prevalence, high costs, inadequate funding, and additional regulatory requirements. Researchers thus frequently use observational study designs to address many research questions. Observational studies, whether prospective or retrospective, do not involve randomization and thus have more potential for bias when compared with RCTs because of imbalances that can exist between comparison groups. If these imbalances are associated with both the exposure of interest and the outcome, then failure to account for these imbalances may result in a biased conclusion. Understanding and addressing differences in sociodemographic and/or clinical characteristics within observational studies are thus necessary to reduce bias. Within this Method/ology submission we describe techniques to minimize bias by controlling for important measurable covariates within observational studies and discuss the challenges and opportunities in addressing specific variables.

Multi-morbidity and its association with common cancer diagnoses: a UK Biobank prospective study

BackgroundWhilst multi-morbidity is known to be a concern in people with cancer, very little is known about the risk of cancer in multi-morbid patients. This study aims to investigate the risk of being diagnosed with lung, colorectal, breast and prostate cancer associated with multi-morbidity.MethodsWe investigated the association between multi-morbidity and subsequent risk of cancer diagnosis in UK Biobank. Cox models were used to estimate the relative risks of each cancer of interest in multi-morbid participants, using the Cambridge Multimorbidity Score. The extent to which reverse causation, residual confounding and ascertainment bias may have impacted on the findings was robustly investigated.ResultsOf the 436,990 participants included in the study who were cancer-free at baseline, 21.6% (99,965) were multi-morbid (≥ 2 diseases). Over a median follow-up time of 10.9 [IQR 10.0–11.7] years, 9,019 prostate, 7,994 breast, 5,241 colorectal, and 3,591 lung cancers were diagnosed. After exclusion of the first year of follow-up, there was no clear association between multi-morbidity and risk of colorectal, prostate or breast cancer diagnosis. Those with ≥ 4 diseases at recruitment had double the risk of a subsequent lung cancer diagnosis compared to those with no diseases (HR 2.00 [95% CI 1.70–2.35] p for trend < 0.001). These findings were robust to sensitivity analyses aimed at reducing the impact of reverse causation, residual confounding from known cancer risk factors and ascertainment bias.ConclusionsIndividuals with multi-morbidity are at an increased risk of lung cancer diagnosis. While this association did not appear to be due to common sources of bias in observational studies, further research is needed to understand what underlies this association.

Telemedicine in the diagnosis and management of temporomandibular disorders: A systematic review conducted according to PRISMA guidelines and the Cochrane Handbook for Systematic Reviews of Interventions.

Telemedicine (T-Med) has always been an important tool in the arsenal of clinicians worldwide. This technique has become increasingly popular in recent years, especially in light of the COVID-19 pandemic, which has made it difficult for some people to access traditional dental care. The current review aimed to analyse the usage of telemedicine in the diagnosis and management of temporomandibular disorders (TMDs) and its impact on general health. An extensive search of databases was conducted using keywords such as, "telemedicine," "teledentistry," "TMJ" and "temporomandibular disorders," resulting in a total of 482 papers to be available from which eligible studies were selected. The Risk of Bias in Observational Studies of Exposures (ROBINS-E) tool was used to evaluate methodological quality of included studies. Two studies were selected which fulfilled the eligibility criteria. All assessed studies indicated varying degrees of positive outcomes for patients who were intervened for TMDs using T-Med. T-Med shows promising results for the diagnosis and management of TMDs, especially since the advent of the COVID-19 pandemic and thereafter. Long-term clinical trials with larger samples are needed to further ascertain validity in this regard.

Simple design and analysis strategies for solving problems in observational orthopaedic clinical research.

Randomized controlled trials are the gold standard to establishing causal relationships in clinical research. However, these studies are expensive and time consuming to conduct. This article aims to provide orthopaedic surgeons and clinical researchers with methodology to optimize inference and minimize bias in observational studies that are often much more feasible to undertake. To mitigate the risk of bias arising from their nonexperimental design, researchers must first understand the ways in which measured covariates can influence treatment, outcomes, and missingness of follow-up data. With knowledge of these relationships, researchers can then build causal diagrams to best understand how to control sources of bias. Some common techniques for controlling for bias include matching, regression, stratification, and propensity score analysis. Selection bias may result from loss to follow-up and missing data. Strategies such as multiple imputation and time-to-event analysis can be useful for handling missingness. For longitudinal data, repeated measures allow observational studies to best summarize the impact of the intervention over time. Clinical researchers familiar with fundamental concepts of causal inference and techniques reviewed in this article will have the power to improve the quality of inferences made from clinical research in orthopaedic trauma surgery.

Quantifying the impact of unmeasured confounding in observational studies with the E value

The E value method deals with unmeasured confounding, a key source of bias in observational studies. The E value method is described and its use is shown in a worked...

Bias in observational studies on the effectiveness of in hospital use of hydroxychloroquine in COVID-19.

During the first waves of the coronavirus pandemic, evidence on potential effective treatments was urgently needed. Results from observational studies on the effectiveness of hydroxychloroquine (HCQ) were conflicting, potentially due to biases. We aimed to assess the quality of observational studies on HCQ and its relation to effect sizes. PubMed was searched on 15 March 2021 for observational studies on the effectiveness of in-hospital use of HCQ in COVID-19 patients, published between 01/01/2020 and 01/03/2021 on. Study quality was assessed using the ROBINS-I tool. Association between study quality and study characteristics (journal ranking, publication date, and time between submission and publication) and differences between effects sizes found in observational studies compared to those found in RCTs, were assessed using Spearman's correlation. Eighteen of the 33 (55%) included observational studies were scored as critical risk of bias, eleven (33%) as serious risk and only four (12%) as moderate risk of bias. Biases were most often scored as critical in the domains related to selection of participants (n = 13, 39%) and bias due to confounding (n = 8, 24%). There were no significant associations found between the study quality and the characteristics nor between the study quality and the effect estimates. Overall, the quality of observational HCQ studies was heterogeneous. Synthesis of evidence of effectiveness of HCQ in COVID-19 should focus on RCTs and carefully consider the added value and quality of observational evidence.

The influence of immortal time bias in observational studies examining associations of antifibrotic therapy with survival in idiopathic pulmonary fibrosis: A simulation study.

Immortal time bias (ITB) has been overlooked in idiopathic pulmonary fibrosis (IPF). We aimed to identify the presence of ITB in observational studies examining associations between antifibrotic therapy and survival in patients with IPF and illustrate how ITB may affect effect size estimates of those associations. Immortal time bias was identified in observational studies using the ITB Study Assessment Checklist. We used a simulation study to illustrate how ITB may affect effect size estimates of antifibrotic therapy on survival in patients with IPF based on four statistical techniques including time-fixed, exclusion, time-dependent and landmark methods. Of the 16 included IPF studies, ITB was detected in 14 studies, while there were insufficient data for assessment in two others. Our simulation study showed that use of time-fixed [hazard ratio (HR) 0.55, 95% confidence interval (CI) 0.47-0.64] and exclusion methods (HR 0.79, 95% CI 0.67-0.92) overestimated the effectiveness of antifibrotic therapy on survival in simulated subjects with IPF, in comparison of the time-dependent method (HR 0.93, 95% CI 0.79-1.09). The influence of ITB was mitigated using the 1 year landmark method (HR 0.69, 95% CI 0.58-0.81), compared to the time-fixed method. The effectiveness of antifibrotic therapy on survival in IPF can be overestimated in observational studies, if ITB is mishandled. This study adds to the evidence for addressing the influence of ITB in IPF and provides several recommendations to minimize ITB. Identifying the presence of ITB should be routinely considered in future IPF studies, with the time-dependent method being an optimal approach to minimize ITB.