The method of inverse probability weighting (henceforth, weighting) can be used to adjust for measured confounding and selection bias under the four assumptions consistency, exchangeability, positivity, no misspecification model estimate weights. In recent years, several published estimates effect time-varying exposures have been based on weighted estimation parameters marginal structural models because, unlike standard statistical methods, appropriately confounders affected by prior...

Overadjustment is defined inconsistently. This term meant to describe control (eg, by regression adjustment, stratification, or restriction) for a variable that either increases net bias decreases precision without affecting bias. We define overadjustment as an intermediate (or descending proxy variable) on causal path from exposure outcome. unnecessary adjustment does not affect of the relation between and outcome but may its precision. use diagrams empirical example (the effect maternal...

Competing events can preclude the event of interest from occurring in epidemiologic data and be analyzed by using extensions survival analysis methods. In this paper, authors outline 3 regression approaches for estimating 2 key quantities competing risks analysis: cause-specific relative hazard (csRH) subdistribution (sdRH). They compare contrast structure risk sets interpretation parameters obtained with these also demonstrate use methods Women's Interagency HIV Study established 1993,...

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That conditioning on a common effect of exposure and outcome may cause selection, or collider-stratification, bias is not intuitive. We provide two hypothetical examples to convey concepts underlying due collider. In the first example, fever influenza consumption tainted egg-salad sandwich. second case-status genotype an environmental factor. both examples, imparts association between otherwise independent variables; we call this selection bias.

We use causal graphs and a partly hypothetical example from the Physicians' Health Study to explain why common standard method for quantifying direct effects (i.e. stratifying on intermediate variable) may be flawed. Estimating without bias requires that two assumptions hold, namely absence of unmeasured confounding (1) exposure outcome, (2) variable outcome. Recommendations include collecting incorporating potential confounders effect mediator as well clearly stating additional assumption...

Randomized trials remain the most accepted design for estimating effects of interventions, but they do not necessarily answer a question primary interest: Will program be effective in target population which it may implemented? In other words, are results generalizable? There has been very little statistical research on how to assess generalizability, or "external validity," randomized trials. We propose use propensity-score-based metrics quantify similarity participants trial and...

Abstract Purpose: Previous research identified differences in breast cancer–specific mortality across 4 intrinsic tumor subtypes: luminal A, B, basal-like, and human epidermal growth factor receptor 2 positive/estrogen negative (HER2+/ER−). Experimental Design: We used immunohistochemical markers to subtype 1,149 invasive cancer patients (518 African American, 631 white) the Carolina Breast Cancer Study, a population-based study of women diagnosed with cancer. Vital status was determined...

An estimated 650,000 Americans will have ESRD by 2010. Young adults with kidney failure often develop progressive chronic disease (CKD) in childhood and adolescence. The Chronic Kidney Disease Children (CKiD) prospective cohort study of 540 children aged 1 to 16 yr GFR between 30 75 ml/min per 1.73 m2 was established identify novel risk factors for CKD progression; the impact function decline on growth, cognition, behavior; evolution cardiovascular factors. Annually, a physical examination...

The sociology of science is dominated today by relativists who boldly argue that the content not primarily determined evidence from empirical world but instead socially constructed in laboratory. Making Science a serious critique sociologist social constructivitst position. It argues although focus scientific research, rate advance, and indeed everyday making are influenced variables processes, core constrained nature.

Selection bias due to loss follow up represents a threat the internal validity of estimates derived from cohort studies. Over past 15 years, stratification-based techniques as well methods such inverse probability-of-censoring weighted estimation have been more prominently discussed and offered means correct for selection bias. However, unlike correcting confounding using weighting, uptake competing has limited in applied epidemiologic literature. To motivate greater use methods, we causal...

Three assumptions sufficient to identify the average causal effect are consistency, positivity, and exchangeability (ie, "no unmeasured confounders no informative censoring," or "ignorability of treatment assignment measurement outcome"). The well known territory for epidemiologists biostatisticians. Briefly, be satisfied, these 2 that require exposed unexposed subjects, censored uncensored subjects have equal distributions potential outcomes, respectively. Indeed, so-called fundamental...

Robins' generalized methods (g methods) provide consistent estimates of contrasts (e.g. differences, ratios) potential outcomes under a less restrictive set identification conditions than do standard regression linear, logistic, Cox regression). Uptake g by epidemiologists has been hampered limitations in understanding both conceptual and technical details. We present simple worked example that illustrates basic concepts, while minimizing complications.

Increasingly, the statistical and epidemiologic literature is focusing beyond issues of internal validity turning its attention to questions external validity. Here, we discuss some challenges transporting a causal effect from randomized trial specific target population. We present an inverse odds weighting approach that can easily operationalize transportability. derive these weights in closed form illustrate their use with simple numerical example. how conditions required for...

Principled methods with which to appropriately analyze missing data have long existed; however, broad implementation of these remains challenging. In this and 2 companion papers (Am J Epidemiol. 2018;187(3):576–584 Am 2018;187(3):585–591), we discuss issues pertaining in the epidemiologic literature. We provide details regarding missing-data mechanisms nomenclature encourage conduct principled analyses through a detailed comparison multiple imputation inverse probability weighting. Data from...

Epidemiologic studies are frequently susceptible to missing information. Omitting observations with variables remains a common strategy in epidemiologic studies, yet this simple approach can often severely bias parameter estimates of interest if the values not completely at random. Even when missingness is random, complete-case analysis reduce efficiency estimated parameters, because large amounts available data simply tossed out incomplete observations. Alternative methods for mitigating...

Selection bias remains a subject of controversy. Existing definitions selection are ambiguous. To improve communication and the conduct epidemiologic research focused on estimating causal effects, we propose to unify various existing in literature by considering any away from true effect referent population (the before process), due selecting sample population, as bias. Given this unified definition, can be further categorized into two broad types: type 1 owing restricting one or more...