Instrumental variable meta-analysis of randomised trials of epidural analgesia in labour to adjust for non-compliance

Objective: Intention-to-treat analysis of randomised controlled trials may cause bias towards the null where non-compliance with the allocated intervention occurs. Instrumental variable analysis allows estimation of the causal effect adjusted for non-compliance. The aim of this study is to compare intention-to-treat and instrumental variable meta-analysis of the association between epidural analgesia in labour and caesarean section. Study design and Setting: The study was restricted to 27 trials in a recent Cochrane Systematic Review. For trials with data on compliance, the association between epidural analgesia in labour and caesarean section was calculated using intention-to-treat analysis and instrumental variable analysis. Fixed-effects meta-analysis was used to calculate pooled risk ratios. Results: In 18 trials with data on compliance, 23% of women allocated to epidural analgesia did not comply and 27% of women allocated to the control received epidural analgesia. Data on outcomes in non-compliant groups were available for 10 trials. The pooled risk ratio for caesarean section following epidural analgesia in labour was 1.37 (95% CI 1.00-1.89, p=0.049) using instrumental variable analysis compared to 1.19 (95% CI 0.93-1.51, p=0.16) using intention-to-treat analysis. Conclusion: Intention-to-treat meta-analysis underestimates the effect of receiving epidural analgesia in labour on caesarean section compared to instrumental variable meta-analysis.NHMR

Circulating long noncoding RNA GAS5 levels are correlated to prevalence of type 2 diabetes mellitus

AbstractBackgroundDiabetes mellitus (DM), a metabolic disease, is characterized by impaired fasting glucose levels. Type 2 DM is adult onset diabetes. Long non-coding RNAs (lncRNAs) regulate gene expression and multiple studies have linked lncRNAs to human diseases.MethodsSerum samples obtained from 96 participating veterans at JAH VA were deposited in the Research Biospecimen Repository. We used a two-stage strategy to identify an lncRNA whose levels correlated with T2DM. Initially we screened five serum samples from diabetic and non-diabetic individuals using lncRNA arrays. Next, GAS5 lncRNA levels were analyzed in 96 serum samples using quantitative PCR. Receiver operating characteristic (ROC) analysis was performed to determine the optimal cutoff GAS5 for diagnosis of DM.ResultsOur results demonstrate that decreased GAS5 levels in serum were associated with diabetes in a cohort of US military veterans. The ROC analysis revealed an optimal cutoff GAS5 value of less than or equal to 10. qPCR results indicated that individuals with absolute GAS5<10ng/μl have almost twelve times higher odds of having diabetes (Exact Odds Ratio [OR]=11.79 (95% CI: 3.97, 37.26), p<0.001). Analysis indicated area under curve (AUC) of ROC of 0.81 with 85.1% sensitivity and 67.3% specificity in distinguishing non-diabetic from diabetic subjects. The positive predictive value is 71.4%.ConclusionlncRNA GAS5 levels are correlated to prevalence of T2DM.General SignificanceAssessment of GAS5 in serum along with other parameters offers greater accuracy in identifying individuals at-risk for diabetes

Salud es Vida: Development of a Cervical Cancer Education Curriculum for Promotora Outreach With Latina Farmworkers in Rural Southern Georgia

Methods: A systematic review was conducted to synthesize evidence from all prospective controlled studies on effectiveness of CHW programs in improving screening mammography rates. Studies reported in English and conducted in the United States were included if they: (i) evaluated a CHW intervention designed to increase screening mammography rates in women 40 years of age or older without a history of breast cancer; (ii) were a randomized controlled trial (RCT), case-controlled study, or quasi-experimental study; and (iii) evaluated a CHW intervention outside of a hospital setting. Results: Participation in a CHW intervention was associated with a statistically significant increase in receipt of screening mammography [risk ratio (RR): 1.06 (favoring intervention); 95% CI: 1.02-1.11, P = 0.003]. The effect remained when pooled data from only RCTs were included in meta-analysis (RR: 1.07; 95% CI: 1.03-1.12, P = 0.0005) but was not present using pooled data from only quasi-experimental studies (RR: 1.03; 95% CI: 0.89-1.18, P = 0.71). In RCTs, participants recruited from medical settings (RR: 1.41; 95% CI: 1.09-1.82, P = 0.008), programs conducted in urban settings (RR: 1.23; 95% CI: 1.09, 1.39, P = 0.001), and programs where CHWs were matched to intervention participants on race or ethnicity (RR: 1.58, 95% CI: 1.29-1.93, P = 0.0001) showed stronger effects on increasing mammography screening rates. Conclusions: CHW interventions are effective for increasing screening mammography in certain settings and populations. Impact: CHW interventions are especially associated with improvements in rate of screening mammography in medical settings, urban settings, and in participants who are racially or ethnically concordant with the CHW

Instrumental variable meta-analysis of individual patient data: application to adjust for treatment non-compliance

<p>Abstract</p> <p>Background</p> <p>Intention-to-treat (ITT) is the standard data analysis method which includes all patients regardless of receiving treatment. Although the aim of ITT analysis is to prevent bias due to prognostic dissimilarity, it is also a counter-intuitive type of analysis as it counts patients who did not receive treatment, and may lead to "bias toward the null." As treated (AT) method analyzes patients according to the treatment actually received rather than intended, but is affected by the selection bias. Both ITT and AT analyses can produce biased estimates of treatment effect, so instrumental variable (IV) analysis has been proposed as a technique to control for bias when using AT data. Our objective is to correct for bias in non-experimental data from previously published individual patient data meta-analysis by applying IV methods</p> <p>Methods</p> <p>Center prescribing preference was used as an IV to assess the effects of methotrexate (MTX) in preventing debilitating complications of chronic graft-versus-host-disease (cGVHD) in patients who received peripheral blood stem cell (PBSCT) or bone marrow transplant (BMT) in nine randomized controlled trials (1107 patients). IV methods are applied using 2-stage logistic, 2-stage probit and generalized method of moments models.</p> <p>Results</p> <p>ITT analysis showed a statistically significant detrimental effect with the use of day 11 MTX, resulting in cGVHD odds ratio (OR) of 1.34 (95% CI 1.02-1.76). AT results showed no difference in the odds of cGVHD with the use of MTX [OR 1.31 (95%CI 0.99-1.73)]. IV analysis further corrected the results toward no difference in the odds of cGVHD between PBSCT vs. BMT, allowing for a possibility of beneficial effects of MTX in preventing cGVHD in PBSCT recipients (OR 1.14; 95%CI 0.83-1.56).</p> <p>Conclusion</p> <p>All instrumental variable models produce similar results. IV estimates correct for bias and do not exclude the possibility that MTX may be beneficial, contradicting the ITT analysis.</p

Kernel Density Estimation of Reliability With Applications to Extreme Value Distribution

In the present study, we investigate kernel density estimation (KDE) and its application to the Gumbel probability distribution. We introduce the basic concepts of reliability analysis and estimation in ordinary and Bayesian settings. The robustness of top three kernels used in KDE with respect to three different optimal bandwidths is presented. The parametric, Bayesian, and empirical Bayes estimates of the reliability, failure rate, and cumulative failure rate functions under the Gumbel failure model are derived and compared with the kernel density estimates. We also introduce the concept of target time subject to obtaining a specified reliability. A comparison of the Bayes estimates of the Gumbel reliability function under six different priors, including kernel density prior, is performed. A comparison of the maximum likelihood (ML) and Bayes estimates of the target time under desired reliability using the Jeffrey\u27s non-informative prior and square error loss function is studied. In order to determine which of the two different loss functions provides a better estimate of the location parameter for the Gumbel probability distribution, we study the performance of four criteria, including the non-parametric kernel density criterion. Finally, we apply both KDE and the Gumbel probability distribution in modeling the annual extreme stream flow of the Hillsborough River, FL. We use the jackknife procedure to improve ML parameter estimates. We model quantile and return period functions both parametrically and using KDE, and show that KDE provides a better fit in the tails

Kernel Density Estimation of Reliability With Applications to Extreme Value Distribution

In the present study, we investigate kernel density estimation (KDE) and its application to the Gumbel probability distribution. We introduce the basic concepts of reliability analysis and estimation in ordinary and Bayesian settings. The robustness of top three kernels used in KDE with respect to three different optimal bandwidths is presented. The parametric, Bayesian, and empirical Bayes estimates of the reliability, failure rate, and cumulative failure rate functions under the Gumbel failure model are derived and compared with the kernel density estimates. We also introduce the concept of target time subject to obtaining a specified reliability. A comparison of the Bayes estimates of the Gumbel reliability function under six different priors, including kernel density prior, is performed. A comparison of the maximum likelihood (ML) and Bayes estimates of the target time under desired reliability using the Jeffrey\u27s non-informative prior and square error loss function is studied. In order to determine which of the two different loss functions provides a better estimate of the location parameter for the Gumbel probability distribution, we study the performance of four criteria, including the non-parametric kernel density criterion. Finally, we apply both KDE and the Gumbel probability distribution in modeling the annual extreme stream flow of the Hillsborough River, FL. We use the jackknife procedure to improve ML parameter estimates. We model quantile and return period functions both parametrically and using KDE, and show that KDE provides a better fit in the tails

Instrumental variable meta-analysis of randomised trials of epidural analgesia in labour to adjust for non-compliance

Objective: Intention-to-treat analysis of randomised controlled trials may cause bias towards the null where non-compliance with the allocated intervention occurs. Instrumental variable analysis allows estimation of the causal effect adjusted for non-compliance. The aim of this study is to compare intention-to-treat and instrumental variable meta-analysis of the association between epidural analgesia in labour and caesarean section. Study design and Setting: The study was restricted to 27 trials in a recent Cochrane Systematic Review. For trials with data on compliance, the association between epidural analgesia in labour and caesarean section was calculated using intention-to-treat analysis and instrumental variable analysis. Fixed-effects meta-analysis was used to calculate pooled risk ratios. Results: In 18 trials with data on compliance, 23% of women allocated to epidural analgesia did not comply and 27% of women allocated to the control received epidural analgesia. Data on outcomes in non-compliant groups were available for 10 trials. The pooled risk ratio for caesarean section following epidural analgesia in labour was 1.37 (95% CI 1.00-1.89, p=0.049) using instrumental variable analysis compared to 1.19 (95% CI 0.93-1.51, p=0.16) using intention-to-treat analysis. Conclusion: Intention-to-treat meta-analysis underestimates the effect of receiving epidural analgesia in labour on caesarean section compared to instrumental variable meta-analysis.NHMR