Three-step matching algorithm to enhance between-group comparability and minimize confounding in comparative effectiveness studies

We developed a three-step matching algorithm to enhance the between-group comparability for comparative drug effect studies involving prevalent new-users of the newer study drug versus older comparator drug(s). The three-step matching scheme is to match on: (1) index date of initiating the newer study drug to align the cohort entry time between study groups, (2) medication possession ratio measures that consider prior exposure to all older comparator drugs, and (3) propensity scores estimated from potential confounders. Our approach is illustrated with a comparative cardiovascular safety study of glucagon-like peptide-1 receptor agonist (GLP-1ra) versus sulfonylurea (SU) in type 2 diabetes patients using Taiwan’s National Health Insurance Research Database 2003–2015. 66% of 3195 GLP-1ra users had previously exposed to SU. The between-group comparability was well-achieved after implementing the matching algorithm (i.e., standardized mean difference < 0.2 for all baseline patient characteristics). Compared to SU, the use of GLP-1ra yielded a significantly reduced risk of the primary composite cardiovascular events (hazard ratio [95% confidence interval]: 0.71 [0.54–0.95], p = 0.022). Our matching scheme can enhance the between-group comparability in prevalent new-user cohort designs to minimize time-related bias, improve confounder adjustment, and ensure the reliability and validity of study findings.

Infections and antibiotics during fetal life and childhood and their relationship to juvenile idiopathic arthritis: a prospective cohort study

Background The aetiology of juvenile idiopathic arthritis (JIA) is poorly understood. It has been shown that use of antibiotics is associated with JIA. However, whether the association is due to increased occurrence of infection in these individuals is unknown. The purpose of this investigation was to measure the association between number of infections and use of antibiotics during childhood with development of JIA. Methods In ABIS (All Babies in Southeast Sweden) a population-based prospective birth cohort of 17,055 children, data were collected on infections and antibiotic exposure during pregnancy and childhood. 102 individuals with JIA were identified. Multivariable logistic regression analyses were performed, adjusting for confounding factors. Results Exposure to antibiotics during the periods 1–12 months, 1–3 years and 5–8 years was significantly associated with increased risk for JIA. The odds of developing JIA were three times higher in those exposed to antibiotics during the first 3 years of life compared with those not exposed (aOR 3.17; 95% CI 1.11–9.03, p = 0.031), and more than twice as high in those exposed to antibiotics during the first 5 years of life compared with those not exposed (aOR 2.18; 95% CI 1.36–3.50, p = 0.001). The odds of developing JIA were 78% higher in those exposed to antibiotics during the first 8 years of life compared with those not exposed (aOR 1.78; 95% CI 1.15–2.73, p = 0.009). Occurrence of infection during fetal life or childhood showed no significant association with the risk of developing JIA, after confounder adjustment. The cumulative number of courses of antibiotics was significantly higher during childhood for the individuals who developed JIA (p < 0.001). Penicillins were more frequently used than non-penicillins, but both had an equal effect on the risk of developing JIA. Conclusions Exposure to antibiotics early in life is associated with later onset of JIA in a large birth cohort from the general population. The relationship was dose dependent. These results suggest that further, more restrictive, antibiotic policies during the first years of life would be advisable.

The Triangulation WIthin a STudy (TWIST) framework for causal inference within pharmacogenetic research

In this paper we review the methodological underpinnings of the general pharmacogenetic approach for uncovering genetically-driven treatment effect heterogeneity. This typically utilises only individuals who are treated and relies on fairly strong baseline assumptions to estimate what we term the ‘genetically moderated treatment effect’ (GMTE). When these assumptions are seriously violated, we show that a robust but less efficient estimate of the GMTE that incorporates information on the population of untreated individuals can instead be used. In cases of partial violation, we clarify when Mendelian randomization and a modified confounder adjustment method can also yield consistent estimates for the GMTE. A decision framework is then described to decide when a particular estimation strategy is most appropriate and how specific estimators can be combined to further improve efficiency. Triangulation of evidence from different data sources, each with their inherent biases and limitations, is becoming a well established principle for strengthening causal analysis. We call our framework ‘Triangulation WIthin a STudy’ (TWIST)’ in order to emphasise that an analysis in this spirit is also possible within a single data set, using causal estimates that are approximately uncorrelated, but reliant on different sets of assumptions. We illustrate these approaches by re-analysing primary-care-linked UK Biobank data relating to CYP2C19 genetic variants, Clopidogrel use and stroke risk, and data relating to APOE genetic variants, statin use and Coronary Artery Disease.

2dFDR: a new approach to confounder adjustment substantially increases detection power in omics association studies

One challenge facing omics association studies is the loss of statistical power when adjusting for confounders and multiple testing. The traditional statistical procedure involves fitting a confounder-adjusted regression model for each omics feature, followed by multiple testing correction. Here we show that the traditional procedure is not optimal and present a new approach, 2dFDR, a two-dimensional false discovery rate control procedure, for powerful confounder adjustment in multiple testing. Through extensive evaluation, we demonstrate that 2dFDR is more powerful than the traditional procedure, and in the presence of strong confounding and weak signals, the power improvement could be more than 100%.

The Triangulation WIthin A STudy (TWIST) framework for causal inference within Pharmacogenetic research

In this paper we review the methodological underpinnings of the general pharmacogenetic approach for uncovering genetically-driven treatment effect heterogeneity. This typically utilises only individuals who are treated and relies on fairly strong baseline assumptions to estimate what we term the `genetically mediated treatment effect' (GMTE). When these assumptions are seriously violated, we show that a robust but less efficient estimate of the GMTE that incorporates information on the population of untreated individuals can instead be used. In cases of partial violation, we clarify when Mendelian randomization and a modified confounder adjustment method can also yield consistent estimates for the GMTE. A decision framework is then described to decide when a particular estimation strategy is most appropriate and how specific estimators can be combined to further improve efficiency. Triangulation of evidence from different data sources, each with their inherent biases and limitations, is becoming a well established principle for strengthening causal analysis. We call our framework `Triangulation WIthin a STudy' (TWIST)' in order to emphasise that an analysis in this spirit is also possible within a single data set, using causal estimators that are approximately statistically uncorrelated, but reliant on different sets of assumptions. We illustrate these approaches by re-analysing primary-care-linked UK Biobank data relating to CYP2C19 genetic variants, Clopidogrel use and stroke risk, and data relating to APOE genetic variants, statin use and Coronary Artery Disease.

Smoking methylation marks for prediction of urothelial cancer risk

BackgroundSelf-reported information may not accurately capture smoking exposure. We aimed to evaluate whether smoking-associated DNA methylation markers improve urothelial cell carcinoma (UCC) risk prediction.MethodsConditional logistic regression was used to assess associations between blood-based methylation and UCC risk using two matched case-control samples, N=404 pairs from the Melbourne Collaborative Cohort Study (MCCS) and N=440 pairs from the Women’s Health Initiative (WHI) cohort, respectively. Results were pooled using fixed-effects meta-analysis. We developed methylation-based predictors of UCC and evaluated their prediction accuracy on two replication datasets using the area under the curve (AUC).ResultsThe meta-analysis identified associations (P<4.7×10−5) for 29 of 1,061 smoking-associated methylation sites, but these were substantially attenuated after adjustment for self-reported smoking. Nominally significant associations (P<0.05) were found for 387 (36%) and 86 (8%) of smoking-associated markers without/with adjustment for self-reported smoking, respectively, with same direction of association as with smoking for 387 (100%) and 79 (92%) markers. A Lasso-based predictor was associated with UCC risk in one replication dataset in MCCS (N=134, odds ratio per SD [OR]=1.37, 95%CI=1.00-1.90) after confounder adjustment; AUC=0.66, compared with AUC=0.64 without methylation information. Limited evidence of replication was found in the second testing dataset in WHI (N=440, OR=1.09, 95%CI=0.91-1.30).ConclusionsCombination of smoking-associated methylation marks may provide some improvement to UCC risk prediction. Our findings need further evaluation using larger datasets.ImpactDNA methylation may be associated with UCC risk beyond traditional smoking assessment and could contribute to some improvements in stratification of UCC risk in the general population.

Low somatosensory cortex excitability in the acute stage of low back pain causes chronic pain

ABSTRACTBACKGROUNDDetermining the mechanistic causes of complex biopsychosocial health conditions such as low back pain (LBP) is challenging, and research is scarce. Cross-sectional studies demonstrate altered excitability and organisation of the primary somatosensory and primary motor cortex in people with acute and chronic LBP, however, no study has explored these mechanisms longitudinally or attempted to draw causal inferences.METHODSProspective, longitudinal, cohort study including 120 people with an acute episode of LBP. Sensory evoked potential area measurements were used to assess primary and secondary somatosensory cortex excitability. Transcranial magnetic stimulation derived map volume was used to assess corticomotor excitability. Directed acyclic graphs identified variables potentially confounding the exposure-outcome relationship. The effect of acute-stage sensorimotor cortex excitability on six-month LBP outcome was estimated using multivariable regression modelling, with adjusted and unadjusted estimates reported. Sensitivity analyses were performed to explore the effect of unmeasured confounding and missing data.RESULTSLower primary (OR = 2.08, 95% CI = 1.22 to 3.57) and secondary (OR = 2.56, 95% CI = 1.37 to 4.76) somatosensory cortex excitability in the acute stage of LBP increased the odds of developing chronic pain at six-month follow-up. This finding was robust to confounder adjustment and unmeasured confounding (E-Value = 2.24 & 2.58, respectively). Corticomotor excitability in the acute stage of LBP was associated with higher pain intensity at 6-month follow-up (B = −0.15, 95% CI: −0.28 to −0.02) but this association did not remain after confounder adjustment.CONCLUSIONThese data provide the first evidence that low somatosensory cortex excitability in the acute stage of LBP is a cause of chronic pain. Interventions designed to increase somatosensory cortex excitability in acute LBP may be relevant to the prevention of chronic pain.

Rehospitalisation Risk Factors and Rates in Preterm Babies: A Systematic Review and Meta-Analysis

Objective: To systematically review evidence of risk factors and rates for rehospitalisations within one month of discharge for babies born at <37 weeks gestation.Design: Systematic review and meta-analysisData Sources: PubMed (including MEDLINE and life science journals), Web of Science and reference lists of included articles. Study Selection: Inclusion criteria were studies published in English or French between 01 January 2000 to 31 March 2019, recruiting from the year 2000 onwards evaluating risk factors for rehospitalisation within one month of discharge in preterm babies. Two reviewers independently selected relevant studies, extracted study details, baseline characteristics and results of risk factor analyses. Results: Across 14 included studies, five studied babies of <37 weeks gestation, seven studied late preterm babies (34-36 weeks gestation), and two studied very to moderate preterm babies (<34 weeks gestation). Important risk factors were low birth weight, respiratory morbidity, male sex and lower socioeconomic status in <37 week babies, and shorter length of stay among late preterm babies. Pooled rehospitalisation rates were 4.3% (95% CI 1.9-9.7) in <37 week babies and 6.6% (95% CI 3.2-13.4) in late preterm babies. There was high heterogeneity in risk factors included in analyses and studies often lacked clarity on variable measurement and confounder adjustment.Conclusion: We found evidence for clinical and socioeconomic risk factors, high heterogeneity and important limitations. Limitations included a lack of breadth in both the gestational age ranges and risk factors studied, as well as lack of clarity around variable measurement and confounder adjustment

The Double Stitch Everting Technique in the End-to-Side Microvascular Anastomosis: Validation of the Technique Using a Randomized N-of-1 Trial

Background The double stitch everting (DSE) technique, in which time is won by leaving the needle inside the vessel wall in-between stitching, is a modification of the end-to-side (ETS) anastomosis in the interest of reducing anastomosis time. This ensures proper wall eversion, intima-to-intima contact, and improved suture symmetry. Materials and Methods We designed an N-of-1 randomized trial with each microsurgeon as their own control. We included 10 microsurgeons of different levels of experience who were then asked to perform classic and DSE ETS anastomoses on the chicken leg and rat femoral models. Every anastomosis was cut and evaluated using blinded assessment. Two-way analysis of variance (ANOVA) and multivariable logistic regression were used to analyze the results and for confounder adjustment. Results A total of 210 anastomoses were performed, of which 177 on the chicken leg and 43 on the rat femoral artery and vein. From the 210 anastomoses, 111 were performed using the classic technique and 99 using the DSE technique. The mean anastomosis time was 28.8 ± 11.3 minutes in the classic group and 24.6 ± 12 minutes in the DSE group (p < 0.001, t-test). There was a significant reduction (p < 0.001, two-way ANOVA) in the number of mistakes when using the DSE technique (mean 5.5 ± 2.6) compared with those using the classic technique (mean 7.7 ± 3.4). Conclusion The DSE technique for ETS anastomoses improves anastomoses times in experienced and moderately experienced microsurgeons while also improving or maintaining suture symmetry and lowering the number of mistakes.