Quantitative methods for the assessment of systematic error in observational studies: improving causal research

点击翻译按钮获取中文摘要

Results from epidemiological studies often appear contradictory, leading to some cynicism regarding medical research among the general public. Many of these contradictions might be avoided if the true extent of the uncertainties that affect such research was assessed and explained. Presently, uncertainty arising from studying finite samples is typically the only uncertainty presented (as confidence intervals). However, other sources of uncertainty may dwarf this uncertainty: (i) important unmeasured factors in the study population (unmeasured confounding); (ii) non-random inclusion of subjects (selection bias); (iii) errors in measurements of exposure and outcome (measurement error) (iv) absence of outcome/exposure data for some subjects (missing data). Recently new approaches, which take account of these sources of uncertainty, have been proposed, but face both operational (e.g. how to specify the likely magnitude of selection bias) and methodological (e.g. how to combine this information with the observed data) difficulties. We shall investigate three alternative approaches to improved quantification of uncertainty; classical sensitivity analysis; Monte Carlo sensitivity analysis and Bayesian bias analysis. We will explore the feasibility of these approaches in different contexts and provide practical guidelines to practitioners (including user-friendly software) on methods which are robust, transparent and accessible to a wide range of practitioners.