Early fibrinogen concentrate therapy for major haemorrhage in trauma (E-FIT 1): results from a UK multi-centre, randomised, double blind, placebo-controlled pilot trial.

BACKGROUND: There is increasing interest in the timely administration of concentrated sources of fibrinogen to patients with major traumatic bleeding. Following evaluation of early cryoprecipitate in the CRYOSTAT 1 trial, we explored the use of fibrinogen concentrate, which may have advantages of more rapid administration in acute haemorrhage. The aims of this pragmatic study were to assess the feasibility of fibrinogen concentrate administration within 45 minutes of hospital admission and to quantify efficacy in maintaining fibrinogen levels ≥ 2 g/L during active haemorrhage. METHODS: We conducted a blinded, randomised, placebo-controlled trial at five UK major trauma centres with adult trauma patients with active bleeding who required activation of the major haemorrhage protocol. Participants were randomised to standard major haemorrhage therapy plus 6 g of fibrinogen concentrate or placebo. RESULTS: Twenty-seven of 39 participants (69%; 95% CI, 52-83%) across both arms received the study intervention within 45 minutes of admission. There was some evidence of a difference in the proportion of participants with fibrinogen levels ≥ 2 g/L between arms (p = 0.10). Fibrinogen levels in the fibrinogen concentrate (FgC) arm rose by a mean of 0.9 g/L (SD, 0.5) compared with a reduction of 0.2 g/L (SD, 0.5) in the placebo arm and were significantly higher in the FgC arm (p < 0.0001) at 2 hours. Fibrinogen levels were not different at day 7. Transfusion use and thromboembolic events were similar between arms. All-cause mortality at 28 days was 35.5% (95% CI, 23.8-50.8%) overall, with no difference between arms. CONCLUSIONS: In this trial, early delivery of fibrinogen concentrate within 45 minutes of admission was not feasible. Although evidence points to a key role for fibrinogen in the treatment of major bleeding, researchers need to recognise the challenges of timely delivery in the emergency setting. Future studies must explore barriers to rapid fibrinogen therapy, focusing on methods to reduce time to randomisation, using 'off-the-shelf' fibrinogen therapies (such as extended shelf-life cryoprecipitate held in the emergency department or fibrinogen concentrates with very rapid reconstitution times) and limiting the need for coagulation test-based transfusion triggers. TRIAL REGISTRATION: ISRCTN67540073 . Registered on 5 August 2015

Multiple imputation strategies for missing event times in a multi-state model analysis

In clinical studies, multi-state model (MSM) analysis is often used to describe the sequence of events that patients experience, enabling better understanding of disease progression. A complicating factor in many MSM studies is that the exact event times may not be known. Motivated by a real dataset of patients who received stem cell transplants, we considered the setting in which some event times were exactly observed and some were missing. In our setting, there was little information about the time intervals in which the missing event times occurred and missingness depended on the event type, given the analysis model covariates. These additional challenges limited the usefulness of some missing data methods (maximum likelihood, complete case analysis, and inverse probability weighting). We show that multiple imputation (MI) of event times can perform well in this setting. MI is a flexible method that can be used with any complete data analysis model. Through an extensive simulation study, we show that MI by predictive mean matching (PMM), in which sampling is from a set of observed times without reliance on a specific parametric distribution, has little bias when event times are missing at random, conditional on the observed data. Applying PMM separately for each sub-group of patients with a different pathway through the MSM tends to further reduce bias and improve precision. We recommend MI using PMM methods when performing MSM analysis with Markov models and partially observed event times

Endothelial failure and rejection in recipients of corneas from the same donor

ObjectiveTo determine whether patients who receive corneas from the same donor have similar risks of endothelial failure and rejection.Methods and AnalysisPatients with Fuchs endothelial dystrophy (FED) and pseudophakic bullous keratopathy (PBK) who received their first corneal transplant between 1999 and 2016 were analysed. Patients receiving corneas from donors who donated both corneas for the same indication were defined as ‘paired’. Gray’s test was used to compare the cumulative incidence of endothelial failure and rejection within 5 years post-transplant for ‘paired’ and ‘unpaired’ groups. Cox regression models were fitted to determine whether there was an association between recorded donor characteristics (endothelial cell density (ECD), age and sex and endothelial graft failure and rejection.Results10 838 patients were analysed of whom 1536 (14%) were paired. The unpaired group comprised 1837 (69%) recipients of single corneal donors and 7465 (69%) donors who donated both corneas for another indication. ECD was lower for unpaired single cornea donors (p&lt;0.01). There was no significant difference in endothelial graft failure or rejection between paired and unpaired groups for FED (p=0.37, p=0.99) or PBK (p=0.88, p=0.28) nor for donor ECD, age, sex and paired donation after adjusting for transplant factors (across all models p&gt;0.16 for ECD, p&gt;0.32 for donor age, p&gt;0.14 for sex match and p&gt;0.17 for the donor effect).ConclusionThe absence of a significant difference in graft outcome for corneal transplants for FED and PBK between paired and unpaired donors may reflect a homogeneous donor pool in the UK.</jats:sec

Multiple imputation of missing data under missing at random: including a collider as an auxiliary variable in the imputation model can induce bias

Epidemiological studies often have missing data, which are commonly handled by multiple imputation (MI). In MI, in addition to those required for the substantive analysis, imputation models often include other variables ("auxiliary variables"). Auxiliary variables that predict the partially observed variables can reduce the standard error (SE) of the MI estimator and, if they also predict the probability that data are missing, reduce bias due to data being missing not at random. However, guidance for choosing auxiliary variables is lacking. We examine the consequences of a poorly chosen auxiliary variable: if it shares a common cause with the partially observed variable and the probability that it is missing (i.e., it is a "collider"), its inclusion can induce bias in the MI estimator and may increase the SE. We quantify, both algebraically and by simulation, the magnitude of bias and SE when either the exposure or outcome is incomplete. When the substantive analysis outcome is partially observed, the bias can be substantial, relative to the magnitude of the exposure coefficient. In settings in which a complete records analysis is valid, the bias is smaller when the exposure is partially observed. However, bias can be larger if the outcome also causes missingness in the exposure. When using MI, it is important to examine, through a combination of data exploration and considering plausible casual diagrams and missingness mechanisms, whether potential auxiliary variables are colliders

Retransplantation for graft failure in chronic hepatitis C infection: A good use of a scarce resource?

AIM: To investigate the outcome of patients with hepatitis C virus (HCV) infection undergoing liver retransplantation

Analyses using multiple imputation need to consider missing data in auxiliary variables.

Auxiliary variables are used in multiple imputation (MI) to reduce bias and increase efficiency. These variables may often themselves be incomplete. We explored how missing data in auxiliary variables influenced estimates obtained from MI. We implemented a simulation study with three different missing data mechanisms for the outcome. We then examined the impact of increasing proportions of missing data and different missingness mechanisms for the auxiliary variable on bias of an unadjusted linear regression coefficient and the fraction of missing information. We illustrate our findings with an applied example in the Avon Longitudinal Study of Parents and Children. We foundthat where complete records analyses were biased, increasing proportions of missing data in auxiliary variables, under any missing data mechanism, reduced the ability of MI including the auxiliary variable to mitigate this bias. Where there was no bias in the complete records analysis, inclusion of a missing not at random auxiliary variable in MI introduced bias of potentially important magnitude (up to 17% of the effect size in our simulation). Careful consideration of the quantity and nature of missing data in auxiliary variables needs to be made when selecting them for use in MI models

Multiple imputation of missing data under missing at random: compatible imputation models are not sufficient to avoid bias if they are mis-specified

OBJECTIVES: Epidemiological studies often have missing data, which are commonly handled by multiple imputation (MI). Standard (default) MI procedures use simple linear covariate functions in the imputation model. We examine the bias that may be caused by acceptance of this default option and evaluate methods to identify problematic imputation models, providing practical guidance for researchers. STUDY DESIGN AND SETTING: Using simulation and real data analysis, we investigated how imputation model mis-specification affected MI performance, comparing results with complete records analysis (CRA). We considered scenarios in which imputation model mis-specification occurred because (i) the analysis model was mis-specified or (ii) the relationship between exposure and confounder was mis-specified. RESULTS: Mis-specification of the relationship between outcome and exposure, or between exposure and confounder, can cause biased CRA and MI estimates (in addition to any bias in the full-data estimate due to analysis model mis-specification). MI by predictive mean matching can mitigate model mis-specification. Methods for examining model mis-specification were effective in identifying mis-specified relationships. CONCLUSION: When using MI methods that assume data are MAR, compatibility between the analysis and imputation models is necessary, but not sufficient to avoid bias. We propose a step-by-step procedure for identifying and correcting mis-specification of imputation models