Analytical challenges in estimating the effect of exposures that are bounded by follow-up time: experiences from the Blood Stream Infection—Focus on Outcomes study

Research output: Contribution to journalArticle (Academic Journal)peer-review

Abstract

Objective
To illustrate the challenges of estimating the effect of an exposure that is bounded by duration of follow-up on all-cause 28-day mortality, whilst simultaneously addressing missing data and time-varying covariates.

Study design and methods
BSI-FOO is a multicentre cohort study with the primary aim of quantifying the effect of modifiable risk factors, including time to initiation of therapy, on all-cause 28-day mortality in patients with bloodstream infection. The primary analysis involved two Cox proportional hazard models, first one for non-modifiable risk factors and second one for modifiable risk factors, with a risk score calculated from the first model included as a covariate in the second model. Modifiable risk factors considered in this study were recorded daily for a maximum of 28 days after infection. Follow-up was split at daily intervals from day 0 to 28 with values of daily collected data updated at each interval (i.e., one row per patient per day).

Analytical challenges
Estimating the effect of time to initiation of treatment on survival is analytically challenging since only those who survive to time t can wait until time t to start treatment, introducing immortal time bias. Time-varying covariates representing cumulative counts were used for variables bounded by survival time e.g. the cumulative count of days before first receipt of treatment. Multiple imputation using chained equations was used to impute missing data, using conditional imputation to avoid imputing non-applicable data e.g. ward data after discharge.

Conclusion
Using time-varying covariates represented by cumulative counts within a one row per day per patient framework can reduce the risk of bias in effect estimates. The approach followed uses established methodology and is easily implemented in standard statistical packages.
Original languageEnglish
Article number197
Number of pages10
JournalBMC Medical Research Methodology
Volume21
Issue number1
Early online date30 Sep 2021
DOIs
Publication statusE-pub ahead of print - 30 Sep 2021

Bibliographical note

Funding Information:
This research was funded by the National Institute for Health Research (NIHR) Programme Grants for Applied Research (RP-PG-0707–10043). This study was designed and delivered in collaboration with the Bristol Trials Centre, a UKCRC registered clinical trials unit (CTU), which is in receipt of National Institute for Health Research CTU support funding. The British Heart Foundation and NIHR Bristol Biomedical Research Unit for Cardiovascular Disease funded some staff time (RE, KP, CR). NIHR reviewed the study design but played no part in the collection, analysis or interpretation of data, and had no role in writing the report or in the decision to submit it.

Funding Information:
We wish to thank the clinical and administrative staff from all sites involved for their contribution to the delivery of the BSI-FOO study. We also wish to thank colleagues at the Bristol Trials Centre, University of Bristol, for their help and support with the manuscript preparation. The views expressed are those of the authors and not necessarily those of the NHS, the NIHR or the Department of Health and Social Care.

Publisher Copyright:
© 2021, The Author(s).

Fingerprint

Dive into the research topics of 'Analytical challenges in estimating the effect of exposures that are bounded by follow-up time: experiences from the Blood Stream Infection—Focus on Outcomes study'. Together they form a unique fingerprint.

Cite this