Prior Event Rate Ratio Adjustment for Hidden Confounding in Observational Studies of Treatment Effectiveness: A Pairwise Cox Likelihood Approach

Observational studies provide a rich source of information for assessing effectiveness of treatment interventions in many situations where it is not ethical or practical to perform randomized controlled trials. However, such studies are prone to bias from hidden (unmeasured) confounding. A promising approach to identifying and reducing the impact of unmeasured confounding is Prior Event Rate Ratio (PERR) adjustment, a quasi-experimental analytic method proposed in the context of electronic medical record database studies. In this paper we present a statistical framework for using a pairwise approach to PERR adjustment that removes bias inherent in the original PERR method. A flexible pairwise Cox likelihood function is derived and used to demonstrate the consistency of the simple and convenient PERR-ALT estimator. We show how to estimate standard errors and confidence intervals for treatment effect estimates based on the observed information, and provide R code to illustrate how to implement the method. Assumptions required for the pairwise approach (as well as PERR) are clarified, and the consequences of model misspecification are explored. Our results confirm the need for researchers to consider carefully the suitability of the method in the context of each problem. We illustrate the application of the method using data from a longitudinal cohort study of enzyme replacement therapy for lysosomal storage disorders.This research was funded by the Medical Research Council [grant number G0902158]. William Henley received additional support from the National Institute for Health Research (NIHR) Collaboration for Leadership in Applied Health Research and Care (CLAHRC) for the South West Peninsula. The views expressed in this publication are those of the authors and not necessarily those of the NHS, the NIHR or the Department of Health

Bias and sensitivity analysis when estimating treatment effects from the cox model with omitted covariates

This is the final version of the article. Available from the publisher via the DOI in this record.Omission of relevant covariates can lead to bias when estimating treatment or exposure effects from survival data in both randomized controlled trials and observational studies. This paper presents a general approach to assessing bias when covariates are omitted from the Cox model. The proposed method is applicable to both randomized and non-randomized studies. We distinguish between the effects of three possible sources of bias: omission of a balanced covariate, data censoring and unmeasured confounding. Asymptotic formulae for determining the bias are derived from the large sample properties of the maximum likelihood estimator. A simulation study is used to demonstrate the validity of the bias formulae and to characterize the influence of the different sources of bias. It is shown that the bias converges to fixed limits as the effect of the omitted covariate increases, irrespective of the degree of confounding. The bias formulae are used as the basis for developing a new method of sensitivity analysis to assess the impact of omitted covariates on estimates of treatment or exposure effects. In simulation studies, the proposed method gave unbiased treatment estimates and confidence intervals with good coverage when the true sensitivity parameters were known. We describe application of the method to a randomized controlled trial and a non-randomized study.We thank Prof. Robin Henderson for providing the leukaemia and deprivation data. We are grateful for the helpful comments of the editor, associate editor and two referees. This research was funded by the Medical Research Council [grant number G0902158]. William Henley and Stuart Logan were supported by the National Institute for Health Research (NIHR) Collaboration for Leadership in Applied Health Research and Care (CLAHRC) for the South West Peninsula. The views expressed in this publication are those of the authors and not necessarily those of the NHS, the NIHR or the Department of Health

Coastal clustering of HEV; Cornwall, UK.

PublishedBACKGROUND AND AIMS: Autochthonous hepatitis E virus (HEV) infection is a porcine zoonosis and increasingly recognized in developed countries. In most cases the route of infection is uncertain. A previous study showed that HEV was associated geographically with pig farms and coastal areas. AIM: The aim of the present research was to study the geographical, environmental and social factors in autochthonous HEV infection. METHODS: Cases of HEV genotype 3 infection and controls were identified from 2047 consecutive patients attending a rapid-access hepatology clinic. For each case/control the following were recorded: distance from home to nearest pig farm, distance from home to coast, rainfall levels during the 8 weeks before presentation, and socioeconomic status. RESULTS: A total of 36 acute hepatitis E cases, 170 age/sex-matched controls and 53 hepatitis controls were identified. The geographical spread of hepatitis E cases was not even when compared with both control groups. Cases were more likely to live within 2000 m of the coast (odds ratio=2.32, 95% confidence interval=1.08-5.19, P=0.03). There was no regional difference in the incidence of cases and controls between west and central Cornwall. There was no difference between cases and controls in terms of distance from the nearest pig farm, socioeconomic status or rainfall during the 8 weeks before disease presentation. CONCLUSION: Cases of HEV infection in Cornwall are associated with coastal residence. The reason for this observation is uncertain, but might be related to recreational exposure to beach areas exposed to HEV-contaminated 'run-off' from pig farms. This hypothesis merits further study.The European Centre for the Environment and Human Health (part of the Peninsula College of Medicine and Dentistry which is a joint entity of the University of Exeter, the University of Plymouth and the NHS in the South West) is supported by investment from the European Regional Development Fund and the European Social Fund Convergence Programme for Cornwall and the Isles of Scilly

A novel tumor suppressor gene ECRG4 interacts directly with TMPRSS11A (ECRG1) to inhibit cancer cell growth in esophageal carcinoma

<p>Abstract</p> <p>Background</p> <p>The esophageal carcinoma related gene 4 (ECRG4) was initially identified and cloned from human normal esophageal epithelium in our laboratory (GenBank accession no.<ext-link ext-link-id="AF325503" ext-link-type="gen">AF325503</ext-link>). ECRG4 has been described as a novel tumor suppressor gene associated with prognosis in esophageal squamous cell carcinoma (ESCC).</p> <p>Methods</p> <p>In this study, binding affinity assay in vitro and co-immunoprecipitation experiment in vivo were utilized to verify the physical interaction between ECRG4 and transmembrane protease, serine 11A (TMPRSS11A, also known as ECRG1, GenBank accession no. <ext-link ext-link-id="AF 071882" ext-link-type="gen">AF 071882</ext-link>). Then, p21 protein expression, cell cycle and cell proliferation regulations were examined after ECRG4 and ECRG1 co-transfection in ESCC cells.</p> <p>Results</p> <p>We revealed for the first time that ECRG4 interacted directly with ECRG1 to inhibit cancer cell proliferation and induce cell cycle G1 phase block in ESCC. Binding affinity and co-immunoprecipitation assays demonstrated that ECRG4 interacted directly with ECRG1 in ESCC cells. Furthermore, the ECRG4 and ECRG1 co-expression remarkably upregulatd p21 protein level by Western blot (P < 0.001), induced cell cycle G1 phase block by flow cytometric analysis (P < 0.001) and suppressed cell proliferation by MTT and BrdU assay (both P < 0.01) in ESCC cells.</p> <p>Conclusions</p> <p>ECRG4 interacts directly with ECRG1 to upregulate p21 protein expression, induce cell cycle G1 phase block and inhibit cancer cells proliferation in ESCC.</p

Discovering temporal regularities in retail customers’ shopping behavior

In this paper we investigate the regularities characterizing the temporal purchasing behavior of the customers of a retail market chain. Most of the literature studying purchasing behavior focuses on what customers buy while giving few importance to the temporal dimension. As a consequence, the state of the art does not allow capturing which are the temporal purchasing patterns of each customers. These patterns should describe the customerâ\u80\u99s temporal habits highlighting when she typically makes a purchase in correlation with information about the amount of expenditure, number of purchased items and other similar aggregates. This knowledge could be exploited for different scopes: set temporal discounts for making the purchases of customers more regular with respect the time, set personalized discounts in the day and time window preferred by the customer, provide recommendations for shopping time schedule, etc. To this aim, we introduce a framework for extracting from personal retail data a temporal purchasing profile able to summarize whether and when a customer makes her distinctive purchases. The individual profile describes a set of regular and characterizing shopping behavioral patterns, and the sequences in which these patterns take place. We show how to compare different customers by providing a collective perspective to their individual profiles, and how to group the customers with respect to these comparable profiles. By analyzing real datasets containing millions of shopping sessions we found that there is a limited number of patterns summarizing the temporal purchasing behavior of all the customers, and that they are sequentially followed in a finite number of ways. Moreover, we recognized regular customers characterized by a small number of temporal purchasing behaviors, and changing customers characterized by various types of temporal purchasing behaviors. Finally, we discuss on how the profiles can be exploited both by customers to enable personalized services, and by the retail market chain for providing tailored discounts based on temporal purchasing regularity

Pulmonary Hypertension: Intensification and Personalization of Combination Rx (PHoenix): A phase IV randomized trial for the evaluation of dose-response and clinical efficacy of riociguat and selexipag using implanted technologies

\ua9 2024 The Authors. Pulmonary Circulation published by John Wiley &amp; Sons Ltd on behalf of Pulmonary Vascular Research Institute.Approved therapies for the treatment of patients with pulmonary arterial hypertension (PAH) mediate pulmonary vascular vasodilatation by targeting distinct biological pathways. International guidelines recommend that patients with an inadequate response to dual therapy with a phosphodiesterase type-5 inhibitor (PDE5i) and endothelin receptor antagonist (ERA), are recommended to either intensify oral therapy by adding a selective prostacyclin receptor (IP) agonist (selexipag), or switching from PDE5i to a soluble guanylate-cyclase stimulator (sGCS; riociguat). The clinical equipoise between these therapeutic choices provides the opportunity for evaluation of individualized therapeutic effects. Traditionally, invasive/hospital-based investigations are required to comprehensively assess disease severity and demonstrate treatment benefits. Regulatory-approved, minimally invasive monitors enable equivalent measurements to be obtained while patients are at home. In this 2 7 2 randomized crossover trial, patients with PAH established on guideline-recommended dual therapy and implanted with CardioMEMS™ (a wireless pulmonary artery sensor) and ConfirmRx™ (an insertable cardiac rhythm monitor), will receive ERA + sGCS, or PDEi + ERA + IP agonist. The study will evaluate clinical efficacy via established clinical investigations and remote monitoring technologies, with remote data relayed through regulatory-approved online clinical portals. The primary aim will be the change in right ventricular systolic volume measured by magnetic resonance imaging (MRI) from baseline to maximal tolerated dose with each therapy. Using data from MRI and other outcomes, including hemodynamics, physical activity, physiological measurements, quality of life, and side effect reporting, we will determine whether remote technology facilitates early evaluation of clinical efficacy, and investigate intra-patient efficacy of the two treatment approaches