Bias due to coarsening of time intervals in the inference for the effectiveness of colorectal cancer screening.

BACKGROUND: Observational studies are frequently used to estimate the comparative effectiveness of different colorectal cancer (CRC) screening methods due to the practical limitations and time needed to conduct large clinical trials. However, time-varying confounders, e.g. polyp detection in the last screening, can bias statistical results. Recently, generalized methods, or G-methods, have been used for the analysis of observational studies of CRC screening, given their ability to account for such time-varying confounders. Discretization, or the process of converting continuous functions into discrete counterparts, is required for G-methods when the treatment and outcomes are assessed at a continuous scale. DEVELOPMENT: This paper evaluates the interplay between time-varying confounding and discretization, which can induce bias in assessing screening effectiveness. We investigate this bias in evaluating the effect of different CRC screening methods that differ from each other in typical screening frequency. APPLICATION: First, using theory, we establish the direction of the bias. Then, we use simulations of hypothetical settings to study the bias magnitude for varying levels of discretization, frequency of screening and length of the study period. We develop a method to assess possible bias due to coarsening in simulated situations. CONCLUSIONS: The proposed method can inform future studies of screening effectiveness, especially for CRC, by determining the choice of interval lengths where data are discretized to minimize bias due to coarsening while balancing computational costs.

Using Evidence Factors to Clarify Exposure Biomarkers.

A study has 2 evidence factors if it permits 2 statistically independent inferences about 1 treatment effect such that each factor is immune to some bias that would invalidate the other factor. Because the 2 factors are statistically independent, the evidence they provide can be combined using methods associated with meta-analysis for independent studies, despite using the same data twice in different ways. We illustrate evidence factors, applying them in a new way in investigations that have both an exposure biomarker and a coarse external measure of exposure to a treatment. To illustrate, we consider the possible effects of cigarette smoking on homocysteine levels, with self-reported smoking and a cotinine biomarker. We examine joint sensitivity of 2 factors to bias from confounding, a central aspect of any observational study.

EVIDENCE FACTORS IN A CASE-CONTROL STUDY WITH APPLICATION TO THE EFFECT OF FLEXIBLE SIGMOIDOSCOPY SCREENING ON COLORECTAL CANCER.

As in any observational study, in a case-control study a primary concern is potential unmeasured confounders. Bias, due to unmeasured confounders, can result in a false discovery of an apparent treatment effect when there is none. Replication of an observational study, which tries to provide multiple analyses of the data where the biases affecting each analysis are thought to be different, is one way to strengthen the evidence from an observational study. Evidence factors allow for internal replication by testing a hypothesis using multiple comparisons in a way that the comparisons yield independent evidence and differ in the sources of potential bias. We construct evidence factors in a case-control study in which there are two types of cases, "narrow" cases which are thought to be potentially more affected by the exposure and "marginal" cases which are thought to have more heterogeneous causes. We develop and study an inference procedure for using such evidence factors and apply it to a study of the effect of sigmoidoscopy screening on colorectal cancer.

Tight clustering for large datasets with an application to gene expression data.

This article proposes a practical and scalable version of the tight clustering algorithm. The tight clustering algorithm provides tight and stable relevant clusters as output while leaving a set of points as noise or scattered points, that would not go into any cluster. However, the computational limitation to achieve this precise target of tight clusters prohibits it from being used for large microarray gene expression data or any other large data set, which are common nowadays. We propose a pragmatic and scalable version of the tight clustering method that is applicable to data sets of very large size and deduce the properties of the proposed algorithm. We validate our algorithm with extensive simulation study and multiple real data analyses including analysis of real data on gene expression.

Impact of health utility after thrombotic complications following total hip and knee arthroplasty.

BACKGROUND: Medical complications and preexisting conditions frequently place the surgeon in the unenviable position of choosing between the serious and potentially life-threatening effects of thrombosis, or the issue of bleeding and surgical site complications. The aim of this research was to determine if health utility could be a consideration before choosing to therapeutically anticoagulate in the hip and knee arthroplasty patient. METHODS: The quantitative risks and effectiveness of treatment options for venous thromboembolism, atrial fibrillation, acute coronary syndromes, ischaemic stroke and mechanical heart valves were evaluated from previously published data. Previously calculated health utility values were obtained from published studies for both thrombotic events and anticoagulant-related complications at the site of arthroplasty. An impact score was generated through a multiplication of 1 - utility with the rate of the event occurring. RESULTS: Impact scores associated with revision surgery and deep infection at the arthroplasty site following anticoagulation are higher compared to impact scores in subsets of non-treated venous thromboembolism, low CHADS2 (congestive heart failure, hypertension, age >75, diabetes (1 point each), stroke/transient ischaemic attack (2 points)) atrial fibrillation and potentially some acute coronary syndrome patients. Some anticoagulation strategies in mechanical heart valves are of definite overall patient value due to the significant complications otherwise. CONCLUSION: Objective calculation of both the benefit and risks of anticoagulation in the post-operative patient is significantly altered by including the effect on patient's quality of life utility value. Therapeutic anticoagulation in some common situations is probable to be of more detriment than benefit when considering health utility.