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Counterpoint: Keeping the Demons at Bay When Handling Time-Varying Exposures-Beyond Avoiding Immortal Person-Time.

ABSTRACT: The potential for immortal time bias is pervasive in epidemiologic studies with left truncation or time-varying exposures. Unlike other biases in epidemiologic research (e.g., measurement bias, confounding due to unmeasured factors, and selection based on unmeasured predictors of the outcome), immortal time bias can and should be avoided by the correct assignment of person-time during follow up. However, even when handing person-time correctly, allowing late entry into a study or into an exposure group can open the door to more insidious sources of bias, some of which we explore here. Clear articulation of the study question, including the treatment plans of interest, can provide navigation around these sources of bias and elucidate the assumptions needed for inference given the available data. Here, we use simulated data to illustrate the assumptions required under various approaches to estimate the effect of a time-varying treatment and describe how these assumptions relate to the assumptions necessary to estimate single sample rates and risks in settings with censoring and truncation.

SUBMITTER: Edwards JK

PROVIDER: S-EPMC7415259 | biostudies-literature | 2019 Jun

REPOSITORIES: biostudies-literature

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Counterpoint: Keeping the Demons at Bay When Handling Time-Varying Exposures-Beyond Avoiding Immortal Person-Time.

Edwards Jessie K JK Htoo Phyo T PT Stürmer Til T

American journal of epidemiology 20190601 6

The potential for immortal time bias is pervasive in epidemiologic studies with left truncation or time-varying exposures. Unlike other biases in epidemiologic research (e.g., measurement bias, confounding due to unmeasured factors, and selection based on unmeasured predictors of the outcome), immortal time bias can and should be avoided by the correct assignment of person-time during follow up. However, even when handing person-time correctly, allowing late entry into a study or into an exposur ...[more]

PMID: 31155642

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Project description:In their initial stages of discovery, prokaryotic toxin-antitoxin (TA) systems were confined to bacterial plasmids where they function to mediate the maintenance and stability of usually low- to medium-copy number plasmids through the post-segregational killing of any plasmid-free daughter cells that developed. Their eventual discovery as nearly ubiquitous and repetitive elements in bacterial chromosomes led to a wealth of knowledge and scientific debate as to their diversity and functionality in the prokaryotic lifestyle. Currently categorized into six different types designated types I-VI, type II TA systems are the best characterized. These generally comprised of two genes encoding a proteic toxin and its corresponding proteic antitoxin, respectively. Under normal growth conditions, the stable toxin is prevented from exerting its lethal effect through tight binding with the less stable antitoxin partner, forming a non-lethal TA protein complex. Besides binding with its cognate toxin, the antitoxin also plays a role in regulating the expression of the type II TA operon by binding to the operator site, thereby repressing transcription from the TA promoter. In most cases, full repression is observed in the presence of the TA complex as binding of the toxin enhances the DNA binding capability of the antitoxin. TA systems have been implicated in a gamut of prokaryotic cellular functions such as being mediators of programmed cell death as well as persistence or dormancy, biofilm formation, as defensive weapons against bacteriophage infections and as virulence factors in pathogenic bacteria. It is thus apparent that these antitoxins, as DNA-binding proteins, play an essential role in modulating the prokaryotic lifestyle whilst at the same time preventing the lethal action of the toxins under normal growth conditions, i.e., keeping the proverbial wolves at bay. In this review, we will cover the diversity and characteristics of various type II TA antitoxins. We shall also look into some interesting deviations from the canonical type II TA systems such as tripartite TA systems where the regulatory role is played by a third party protein and not the antitoxin, and a unique TA system encoding a single protein with both toxin as well as antitoxin domains.

Project description:ObjectiveTo compare theoretical strengths and limitations of common immortal time adjustment methods, propose a new approach using multiple imputation (MI), and provide practical guidance for using MI in precision medicine evaluations centered on a real-world case study.Study setting and designMethods comparison, guidance, and real-world case study based on previous literature. We compared landmark analysis, time-distribution matching, time-dependent analysis, and our proposed MI application. Guidance for MI spanned (1) selecting the imputation method; (2) specifying and applying the imputation model; and (3) conducting comparative analysis and pooling estimates. Our case study used a matched cohort design to evaluate overall survival benefits of whole-genome and transcriptome analysis, a precision medicine technology, compared to usual care for advanced cancers, and applied both time-distribution matching and MI. Bootstrap simulation characterized imputation sensitivity to varying data missingness and sample sizes.Data sources and analytic sampleCase study used population-based administrative data and single-arm precision medicine program data from British Columbia, Canada for the study period 2012 to 2015.Principal findingsWhile each method described can reduce immortal time bias, MI offers theoretical advantages. Compared to alternative approaches, MI minimizes information loss and better characterizes statistical uncertainty about the true length of the immortal time period, avoiding false precision. Additionally, MI explicitly considers the impacts of patient characteristics on immortal time distributions, with inclusion criteria and follow-up period definitions that do not inadvertently risk biasing evaluations. In the real-world case study, survival analysis results did not substantively differ across MI and time distribution matching, but standard errors based on MI were higher for all point estimates. Mean imputed immortal time was stable across simulations.ConclusionsPrecision medicine evaluations must employ immortal time adjustment methods for unbiased, decision-grade real-world evidence generation. MI is a promising solution to the challenge of immortal time bias.

Dataset Information

Counterpoint: Keeping the Demons at Bay When Handling Time-Varying Exposures-Beyond Avoiding Immortal Person-Time.

Publications

Counterpoint: Keeping the Demons at Bay When Handling Time-Varying Exposures-Beyond Avoiding Immortal Person-Time.

Similar Datasets

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