2018
DOI: 10.1186/s12874-018-0500-3
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Bias due to censoring of deaths when calculating extra length of stay for patients acquiring a hospital infection

Abstract: BackgroundIn many studies the information of patients who are dying in the hospital is censored when examining the change in length of hospital stay (cLOS) due to hospital-acquired infections (HIs). While appropriate estimators of cLOS are available in literature, the existence of the bias due to censoring of deaths was neither mentioned nor discussed by the according authors.MethodsUsing multi-state models, we systematically evaluate the bias when estimating cLOS in such a way. We first evaluate the bias in a… Show more

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Cited by 6 publications
(6 citation statements)
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“…Time-to-discharge was presented with the use of the Kaplan-Meier approach. We assigned the 'worst outcome' for individuals who died before day 28 and therefore these patients were managed as those with the longest hospital stay (more than 28 days) 22 . Non-inferiority was assessed for the secondary outcome of time to hospital discharge.…”
Section: Discussionmentioning
confidence: 99%
“…Time-to-discharge was presented with the use of the Kaplan-Meier approach. We assigned the 'worst outcome' for individuals who died before day 28 and therefore these patients were managed as those with the longest hospital stay (more than 28 days) 22 . Non-inferiority was assessed for the secondary outcome of time to hospital discharge.…”
Section: Discussionmentioning
confidence: 99%
“…Time-todischarge secondary outcome was compared between the two groups with the use of the Kaplan-Meier approach and cumulative incidence curves were compared between the two groups. We assigned the 'worst outcome' for individuals who died before day 28 and thus these patients were right-censored at the longest hospital stay [24]. Mann Whitney or t-test were used for the investigation of differences in ΔPaO 2 /FiO 2 and ΔWHO scale between the two groups based on the absence or presence of normality.…”
Section: Discussionmentioning
confidence: 99%
“…We used a multistate model to estimate the change in LOS as an effect of the intermediate state (HO E. coli/Klebsiella spp. bacteremia; Figure 1) [22,23]. In brief, the multistate model accounts for the time-dependent exposure (occurrence of HO E. coli/Klebsiella spp.…”
Section: Discussionmentioning
confidence: 99%