Tim P. Morris

Asymptomatic internal carotid artery stenosis and cerebrovascular risk stratification

BACKGROUND The purpose of this study was to determine the cerebrovascular risk stratification potential of baseline degree of stenosis, clinical features, and ultrasonic plaque characteristics in patients with asymptomatic internal carotid artery (ICA) stenosis. METHODS This was a prospective, multicenter, cohort study of patients undergoing medical intervention for vascular disease. Hazard ratios for ICA stenosis, clinical features, and plaque texture features associated with ipsilateral cerebrovascular or retinal ischemic (CORI) events were calculated using proportional hazards models. RESULTS A total of 1121 patients with 50% to 99% asymptomatic ICA stenosis in relation to the bulb (European Carotid Surgery Trial [ECST] method) were followed-up for 6 to 96 months (mean, 48). A total of 130 ipsilateral CORI events occurred. Severity of stenosis, age, systolic blood pressure, increased serum creatinine, smoking history of more than 10 pack-years, history of contralateral transient ischemic attacks (TIAs) or stroke, low grayscale median (GSM), increased plaque area, plaque types 1, 2, and 3, and the presence of discrete white areas (DWAs) without acoustic shadowing were associated with increased risk. Receiver operating characteristic (ROC) curves were constructed for predicted risk versus observed CORI events as a measure of model validity. The areas under the ROC curves for a model of stenosis alone, a model of stenosis combined with clinical features and a model of stenosis combined with clinical, and plaque features were 0.59 (95% confidence interval [CI] 0.54-0.64), 0.66 (0.62-0.72), and 0.82 (0.78-0.86), respectively. In the last model, stenosis, history of contralateral TIAs or stroke, GSM, plaque area, and DWAs were independent predictors of ipsilateral CORI events. Combinations of these could stratify patients into different levels of risk for ipsilateral CORI and stroke, with predicted risk close to observed risk. Of the 923 patients with ≥ 70% stenosis, the predicted cumulative 5-year stroke rate was <5% in 495, 5% to 9.9% in 202, 10% to 19.9% in 142, and ≥ 20% in 84 patients. CONCLUSION Cerebrovascular risk stratification is possible using a combination of clinical and ultrasonic plaque features. These findings need to be validated in additional prospective studies of patients receiving optimal medical intervention alone.

PET-CT staging of DLBCL accurately identifies and provides new insight into the clinical significance of bone marrow involvement

We investigated whether positron emission tomography combined with computed tomography (PET-CT) identifies clinically important bone marrow involvement by diffuse large B-cell lymphoma (DLBCL) with sufficient accuracy to replace routine staging bone marrow biopsy. All patients from a single centre diagnosed as DLBCL since 2005 had data extracted from staging PET-CT, marrow biopsy, and treatment records. Of 130 patients, 35 (27%) were judged to have marrow involvement; 33 were identified by PET-CT compared with 14 by marrow histology. PET identified all clinically important marrow lymphoma, while biopsy did not upstage any patient. Sensitivity and specificity were 94% and 100% for PET-CT and 40% and 100% for marrow biopsy. As a secondary aim, we compared the prognosis of marrow involvement, as detected by PET-CT or biopsy. Cases with marrow deposits identified by PET-CT but not biopsy had progression-free survival (PFS) and overall survival similar to stage IV disease without involved marrow. Positive biopsy conferred significantly inferior PFS (P = .003); these cases frequently had other markers of poor-risk disease. These data confirm that in experienced hands PET-CT has a high level of accuracy for identifying marrow disease in DLBCL, and provide new insight into the nature and clinical significance of marrow involvement.

Improper analysis of trials randomised using stratified blocks or minimisation

Many clinical trials restrict randomisation using stratified blocks or minimisation to balance prognostic factors across treatment groups. It is widely acknowledged in the statistical literature that the subsequent analysis should reflect the design of the study, and any stratification or minimisation variables should be adjusted for in the analysis. However, a review of recent general medical literature showed only 14 of 41 eligible studies reported adjusting their primary analysis for stratification or minimisation variables. We show that balancing treatment groups using stratification leads to correlation between the treatment groups. If this correlation is ignored and an unadjusted analysis is performed, standard errors for the treatment effect will be biased upwards, resulting in 95% confidence intervals that are too wide, type I error rates that are too low and a reduction in power. Conversely, an adjusted analysis will give valid inference. We explore the extent of this issue using simulation for continuous, binary and time-to-event outcomes where treatment is allocated using stratified block randomisation or minimisation.

Reporting and analysis of trials using stratified randomisation in leading medical journals: review and reanalysis

Objectives To assess how often stratified randomisation is used, whether analysis adjusted for all balancing variables, and whether the method of randomisation was adequately reported, and to reanalyse a previously reported trial to assess the impact of ignoring balancing factors in the analysis. Design Review of published trials and reanalysis of a previously reported trial. Setting Four leading general medical journals (BMJ, Journal of the American Medical Association, Lancet, and New England Journal of Medicine) and the second Multicenter Intrapleural Sepsis Trial (MIST2). Participants 258 trials published in 2010 in the four journals. Cluster randomised, crossover, non-randomised, single arm, and phase I or II trials were excluded, as were trials reporting secondary analyses, interim analyses, or results that had been previously published in 2010. Main outcome measures Whether the method of randomisation was adequately reported, how often balanced randomisation was used, and whether balancing factors were adjusted for in the analysis. Results Reanalysis of MIST2 showed that an unadjusted analysis led to larger P values and a loss of power. The review of published trials showed that balanced randomisation was common, with 163 trials (63%) using at least one balancing variable. The most common methods of balancing were stratified permuted blocks (n=85) and minimisation (n=27). The method of randomisation was unclear in 37% of trials. Most trials that balanced on centre or prognostic factors were not adequately analysed; only 26% of trials adjusted for all balancing factors in their primary analysis. Trials that did not adjust for balancing factors in their analysis were less likely to show a statistically significant result (unadjusted 57% v adjusted 78%, P=0.02). Conclusion Balancing on centre or prognostic factors is common in trials but often poorly described, and the implications of balancing are poorly understood. Trialists should adjust their primary analysis for balancing factors to obtain correct P values and confidence intervals and to avoid an unnecessary loss in power.

The risks and rewards of covariate adjustment in randomized trials: an assessment of 12 outcomes from 8 studies

BackgroundAdjustment for prognostic covariates can lead to increased power in the analysis of randomized trials. However, adjusted analyses are not often performed in practice.MethodsWe used simulation to examine the impact of covariate adjustment on 12 outcomes from 8 studies across a range of therapeutic areas. We assessed (1) how large an increase in power can be expected in practice; and (2) the impact of adjustment for covariates that are not prognostic.ResultsAdjustment for known prognostic covariates led to large increases in power for most outcomes. When power was set to 80% based on an unadjusted analysis, covariate adjustment led to a median increase in power to 92.6% across the 12 outcomes (range 80.6 to 99.4%). Power was increased to over 85% for 8 of 12 outcomes, and to over 95% for 5 of 12 outcomes. Conversely, the largest decrease in power from adjustment for covariates that were not prognostic was from 80% to 78.5%.ConclusionsAdjustment for known prognostic covariates can lead to substantial increases in power, and should be routinely incorporated into the analysis of randomized trials. The potential benefits of adjusting for a small number of possibly prognostic covariates in trials with moderate or large sample sizes far outweigh the risks of doing so, and so should also be considered.

Reappraising the concept of massive transfusion in trauma

IntroductionThe massive-transfusion concept was introduced to recognize the dilutional complications resulting from large volumes of packed red blood cells (PRBCs). Definitions of massive transfusion vary and lack supporting clinical evidence. Damage-control resuscitation regimens of modern trauma care are targeted to the early correction of acute traumatic coagulopathy. The aim of this study was to identify a clinically relevant definition of trauma massive transfusion based on clinical outcomes. We also examined whether the concept was useful in that early prediction of massive transfusion requirements could allow early activation of blood bank protocols.MethodsDatasets on trauma admissions over a 1 or 2-year period were obtained from the trauma registries of five large trauma research networks. A fractional polynomial was used to model the transfusion-associated probability of death. A logistic regression model for the prediction of massive transfusion, defined as 10 or more units of red cell transfusions, was developed.ResultsIn total, 5,693 patient records were available for analysis. Mortality increased as transfusion requirements increased, but the model indicated no threshold effect. Mortality was 9% in patients who received none to five PRBC units, 22% in patients receiving six to nine PRBC units, and 42% in patients receiving 10 or more units. A logistic model for prediction of massive transfusion was developed and validated at multiple sites but achieved only moderate performance. The area under the receiver operating characteristic curve was 0.81, with specificity of only 50% at a sensitivity of 90% for the prediction of 10 or more PRBC units. Performance varied widely at different trauma centers, with specificity varying from 48% to 91%.ConclusionsNo threshold for definition exists at which a massive transfusion specifically results in worse outcomes. Even with a large sample size across multiple trauma datasets, it was not possible to develop a transportable and clinically useful prediction model based on available admission parameters. Massive transfusion as a concept in trauma has limited utility, and emphasis should be placed on identifying patients with massive hemorrhage and acute traumatic coagulopathy.

Tuning multiple imputation by predictive mean matching and local residual draws

BackgroundMultiple imputation is a commonly used method for handling incomplete covariates as it can provide valid inference when data are missing at random. This depends on being able to correctly specify the parametric model used to impute missing values, which may be difficult in many realistic settings. Imputation by predictive mean matching (PMM) borrows an observed value from a donor with a similar predictive mean; imputation by local residual draws (LRD) instead borrows the donor’s residual. Both methods relax some assumptions of parametric imputation, promising greater robustness when the imputation model is misspecified.MethodsWe review development of PMM and LRD and outline the various forms available, and aim to clarify some choices about how and when they should be used. We compare performance to fully parametric imputation in simulation studies, first when the imputation model is correctly specified and then when it is misspecified.ResultsIn using PMM or LRD we strongly caution against using a single donor, the default value in some implementations, and instead advocate sampling from a pool of around 10 donors. We also clarify which matching metric is best. Among the current MI software there are several poor implementations.ConclusionsPMM and LRD may have a role for imputing covariates (i) which are not strongly associated with outcome, and (ii) when the imputation model is thought to be slightly but not grossly misspecified. Researchers should spend efforts on specifying the imputation model correctly, rather than expecting predictive mean matching or local residual draws to do the work.

Assessing potential sources of clustering in individually randomised trials

BackgroundRecent reviews have shown that while clustering is extremely common in individually randomised trials (for example, clustering within centre, therapist, or surgeon), it is rarely accounted for in the trial analysis. Our aim is to develop a general framework for assessing whether potential sources of clustering must be accounted for in the trial analysis to obtain valid type I error rates (non-ignorable clustering), with a particular focus on individually randomised trials.MethodsA general framework for assessing clustering is developed based on theoretical results and a case study of a recently published trial is used to illustrate the concepts. A simulation study is used to explore the impact of not accounting for non-ignorable clustering in practice.ResultsClustering is non-ignorable when there is both correlation between patient outcomes within clusters, and correlation between treatment assignments within clusters. This occurs when the intraclass correlation coefficient is non-zero, and when the cluster has been used in the randomisation process (e.g. stratified blocks within centre) or when patients are assigned to clusters after randomisation with different probabilities (e.g. a surgery trial in which surgeons treat patients in only one arm). A case study of an individually randomised trial found multiple sources of clustering, including centre of recruitment, attending surgeon, and site of rehabilitation class. Simulations show that failure to account for non-ignorable clustering in trial analyses can lead to type I error rates over 20% in certain cases; conversely, adjusting for the clustering in the trial analysis gave correct type I error rates.ConclusionsClustering is common in individually randomised trials. Trialists should assess potential sources of clustering during the planning stages of a trial, and account for any sources of non-ignorable clustering in the trial analysis.

Analysis of multicentre trials with continuous outcomes: when and how should we account for centre effects?

In multicentre trials, randomisation is often carried out using permuted blocks stratified by centre. It has previously been shown that stratification variables used in the randomisation process should be adjusted for in the analysis to obtain correct inference. For continuous outcomes, the two primary methods of accounting for centres are fixed-effects and random-effects models. We discuss the differences in interpretation between these two models and the implications that each pose for analysis. We then perform a large simulation study comparing the performance of these analysis methods in a variety of situations. In total, we assessed 378 scenarios. We found that random centre effects performed as well or better than fixed-effects models in all scenarios. Random centre effects models led to increases in power and precision when the number of patients per centre was small (e.g. 10 patients or less) and, in some scenarios, when there was an imbalance between treatments within centres, either due to the randomisation method or to the distribution of patients across centres. With small samples sizes, random-effects models maintained nominal coverage rates when a degree-of-freedom (DF) correction was used. We assessed the robustness of random-effects models when assumptions regarding the distribution of the centre effects were incorrect and found this had no impact on results. We conclude that random-effects models offer many advantages over fixed-effects models in certain situations and should be used more often in practice.

Long‐term outcomes of augmentation ileocystoplasty in patients with spinal cord injury: a minimum of 10 years of follow‐up

Study Type – Therapy (case series)