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Estimating inestimable standard errors in population pharmacokinetic studies: the bootstrap with Winsorization.

A simulation study was performed to determine how inestimable standard errors could be obtained when population pharmacokinetic analysis is performed with the NONMEM software on data from small sample size phase I studies. Plausible sets of concentration-time data for nineteen subjects were simulated using an incomplete longitudinal population pharmacokinetic study design, and parameters of a drug in development that exhibits two compartment linear pharmacokinetics with single dose first order input. They were analyzed with the NONMEM program. Standard errors for model parameters were computed from the simulated parameter values to serve as true standard errors of estimates. The nonparametric bootstrap approach was used to generate replicate data sets from the simulated data and analyzed with NONMEM. Because of the sensitivity of the bootstrap to extreme values, winsorization was applied to parameter estimates. Winsorized mean parameters and their standard errors were computed and compared with their true values as well as the non-winsorized estimates. Percent bias was used to judge the performance of the bootstrap approach (with or without winsorization) in estimating inestimable standard errors of population pharmacokinetic parameters. Winsorized standard error estimates were generally more accurate than non-winsorized estimates because the distribution of most parameter estimates were skewed, sometimes with heavy tails. Using the bootstrap approach combined with winsorization, inestimable robust standard errors can be obtained for NONMEM estimated population pharmacokinetic parameters with > or = 150 bootstrap replicates. This approach was also applied to a real data set and a similar outcome was obtained. This investigation provides a structural framework for estimating inestimable standard errors when NONMEM is used for population pharmacokinetic modeling involving small sample sizes.

Clinical Trials, Phase I as Topic↗
PubMed1999

Visual analysis of serial T2-weighted MRI in multiple sclerosis: intra- and interobserver reproducibility.

We evaluated the effect of consensus formation and training on the agreement between observers in scoring the number of new and enlarging multiple sclerosis (MS) lesions on serial T2-weighted MRI studies. The baseline and month 9 MRI studies of 16 patients with a range of MRI activity were used (dual-echo conventional spin-echo sequence, TR 2000, TE 34 and 90 ms, 5 mm contiguous slices, inplane resolution 1 mm). First, the serial studies were visually analysed for the presence of new and enlarging lesions, on two occasions, by five experienced observers, without adopting any consensus strategy and in isolation. Next, the observers met to identify the common sources of inconsistencies in reporting between observers and formulate consensus rules. Finally, a further independent reading session was performed on the same MRI dataset, this time applying the consensus rules. Agreement between observers was assessed using kappa scores. Without the consensus rules, interobserver kappa scores for the first and second reading sessions for new lesions were only 0.51 and 0.39 respectively; agreement for enlarging lesions was even worse. The mean intraobserver kappa score for new lesions was higher at 0.72, reflecting the fact that the observers were consistently applying their individual assessment strategies. Application of the consensus rules did not lead to a significant improvement in inter observer kappas; the kappa scores adopting the guidelines were 0.46 and 0.21 for new and enlarging lesions respectively. Consensus guidelines thus did not improve the reproducibility of visual analysis of serial T2-weighted MRI, and the level of agreement between observers remained only moderate. Suboptimal repositioning is likely to be a major source of residual variability and this suggests a future role for image registration strategies; until then, a single observer, or pair of observers working in consensus, should be used in MS studies.

Analysis of Variance↗
PubMed2005

Early invasive treatment (endarterectomy vs. stenting) of moderate-to-severe carotid stenosis in patients with transient ischaemic attack or minor stroke.

Carotid endarterectomy (CA) is an effective treatment for the secondary prevention of stroke in patients with carotid stenosis >50%. More recently, carotid angioplasty with stenting (CAS) has been introduced and found to be effective in case series and small clinical trials. Although CAS has been shown not to be inferior to CA, the comparative effects of early treatment (i.e., during the first month after transient ischaemic attack (TIA) or minor stroke) with these techniques are unknown. Early treatment is advocated, as recurrent stroke tends to present frequently in this time period. On this background, we designed a randomised clinical trial comparing the efficacy and safety of CA vs. CAS in patients who had suffered TIA or minor stroke in the antecedent month. The study design and methods of this multicentre pragmatic randomised parallel-group open trial are presented here.

Carotid Stenosis↗
PubMed1985

A review of the literature evaluating the Defensive Driving Course.

The Defensive Driving Course (DDC), like other post-licensure driver training programs, is intended to reduce the rate of motor vehicle crashes among those who take the course. A review of the literature revealed 14 controlled studies of the effects of DDC. About a third of these studies provided methodologically strong tests of DDC, but the remainder had design flaws that made their findings questionable or inadequate as tests of DDC. Only among the flawed tests were there large, positive effects of DDC. In the methodologically strong tests, DDC had no consistent effect on crashes but did decrease the frequency of traffic violations by about 10%. The failure of violation reductions to be translated into crash reductions may indicate that the violation reduction is an artifact of traffic record procedures or that the changes in driver behavior, if real, were insufficient to modify individual crash likelihoods. In either event, the best available evidence does not support the hypothesis that DDC decreases the likelihood of motor vehicle crashes.

Accidents, Traffic↗