Semi-parametric modelling for costs of health care technologies

Cost data that arise in the evaluation of health care technologies usually exhibit highly skew, heavy-tailed and, possibly, multi-modal distributions. Distribution-free methods for analysing these data, such as the bootstrap, or those based on the asymptotic normality of sample means, may often lead to inefficient or misleading inferences. On the other hand, parametric models that fit the data (or a transformation of the data) equally well can produce very different answers. We consider a Bayesian approach, and model cost data with a distribution composed of a piecewise constant density up to an unknown endpoint, and a generalized Pareto distribution for the remaining tail.     

Testing equality of two functions using BARS

This article presents two methods of testing the hypothesis of equality of two functions H(0):f(1)(t)=f(2)(t) for all t, in a generalized non-parametric regression framework using a recently developed generalized non-parametric regression method called Bayesian adaptive regression splines (BARS). Of particular interest is the special case of testing equality of two Poisson process intensity functions lambda(1) (t)=lambda(2) (t), which arises frequently in neurophysiological applications. The first method uses Bayes factors, and the second method uses a modified Hotelling T(2) test. Both methods are applied to the analysis of 347 motor cortical neurons and, for certain choices of test criteria, the two methods lead to the same conclusions for all but 7 neurons. A small simulation study of power indicates that the Bayes factor can be somewhat more powerful in small samples. The T(2)-type test should be useful in screening large number of neurons for condition-related activity, while the Bayes factor will be especially helpful in assessing evidence in favour of H(0).     

Semi-parametric and non-parametric methods for clinical trials with incomplete data

Last observation carried forward (LOCF) and analysis using only data from subjects who complete a trial (Completers) are commonly used techniques for analysing data in clinical trials with incomplete data when the endpoint is change from baseline at last scheduled visit. We propose two alternative methods. The semi-parametric method, which cumulates changes observed between consecutive time points, is conceptually similar to the familiar life-table method and corresponding Kaplan-Meier estimation when the primary endpoint is time to event. A non-parametric analogue of LOCF is obtained by carrying forward, not the observed value, but the rank of the change from baseline at the last observation for each subject. We refer to this method as the LRCF method. Both procedures retain the simplicity of LOCF and Completers analyses and, like these methods, do not require data imputation or modelling assumptions. In the absence of any incomplete data they reduce to the usual two-sample tests. In simulations intended to reflect chronic diseases that one might encounter in practice, LOCF was observed to produce markedly biased estimates and markedly inflated type I error rates when censoring was unequal in the two treatment arms. These problems did not arise with the Completers, Cumulative Change, or LRCF methods. Cumulative Change and LRCF were more powerful than Completers, and the Cumulative Change test provided more efficient estimates than the Completers analysis, in all simulations. We conclude that the Cumulative Change and LRCF methods are preferable to LOCF and Completers analyses. Mixed model repeated measures (MMRM) performed similarly to Cumulative Change and LRCF and makes somewhat less restrictive assumptions about missingness mechanisms, so that it is also a reasonable alternative to LOCF and Completers analyses.     

Spline-based non-parametric regression for periodic functions and its application to directional tuning of neurons

The activity of neurons in the brain often varies systematically with some quantitative feature of a stimulus or action. A well-known example is the tendency of the firing rates of neurons in the primary motor cortex to vary with the direction of a subject's arm or wrist movement. When this movement is constrained to vary in only two dimensions, the direction of movement may be characterized by an angle, and the neuronal firing rate can be written as a function of this angle. The firing rate function has traditionally been fit with a cosine, but recent evidence suggests that departures from cosine tuning occur frequently. We report here a new non-parametric regression method for fitting periodic functions and demonstrate its application to the fitting of neuronal data. The method is an extension of Bayesian adaptive regression splines (BARS) and applies both to normal and non-normal data, including Poisson data, which commonly arise in neuronal applications. We compare the new method to a periodic version of smoothing splines and some parametric alternatives and find the new method to be especially valuable when the smoothness of the periodic function varies unevenly across its domain.     

Comparison of statistical methods for analysis of clustered binary observations

When correlated observations are obtained in a randomized controlled trial, the assumption of independence among observations within cluster likely will not hold because the observations share the same cluster (e.g. clinic, physician, or subject). Further, the outcome measurements of interest are often binary. The objective of this paper is to compare the performance of four statistical methods for analysis of clustered binary observations: namely (1) full likelihood method; (2) penalized quasi-likelihood method; (3) generalized estimating equation method; (4) fixed-effects logistic regression method. The first three methods take correlations into account in inferential processes whereas the last method does not. Type I error rate, power, bias, and standard error are compared across the four statistical methods through computer simulations under varying effect sizes, intraclass correlation coefficients, number of clusters, and number of observations per cluster, including large numbers 20 and 100 of observations per cluster. The results show that the performance of the full likelihood and the penalized quasi-likelihood methods is superior for analysis of clustered binary observations, and is not necessarily inferior to that of the fixed-effects logistic regression fit even when within-cluster correlations are zero.     

Heart rate variability predicts trauma patient outcome as early as 12 h: implications for military and civilian triage

BACKGROUND: Our previous work demonstrated dense physiological data capture in the intensive care unit (ICU), defined a new vital sign Cardiac Volatility Related Dysfunction (CVRD) reflecting reduced heart rate variability, and demonstrated CVRD predicts death during the hospital stay adjusting for age and injury severity score (ISS). We hypothesized a more precise definition of variability in integer heart rate improves predictive power earlier in ICU stay, without adjusting for covariates. METHODS: Approximately 120 million integer heart rate (HR) data points were prospectively collected and archived from 1316 trauma ICU patients, linked to outcome data, and de-identified. HR standard deviation was computed in each 5-min interval (HR(SD5)). HR(SD5) logistic regression identified ranges predictive of death. The study group was randomly divided. Integer heart rate variability (% time HR(SD5) in predictive distribution ranges) models were developed on the first set (N = 658) at 1, 2, 4, 6, 8, 12, and 24 h after ICU admission, and validated on the second set (N = 658). RESULTS: HR(SD5) is bimodal, predicts death at low (0.1-0.9 bpm) and survival at high (1.8-2.6 bpm) ranges. HRV predicts death as early as 12 h (ROC = 0.67). HRV in a moving 1-h window is a simple graphic display technique. CONCLUSIONS: Dense physiological data capture allows calculation of HRV, which: 1) Independently predicts hospital death in trauma patients at 12 h; 2) Shows early differences by mortality in groups of patients when viewed in a moving window; and 3) May have implications for military and civilian triage.     

Temporal characteristics of foot roll-over during barefoot jogging: reference data for young adults

The purpose of this study was to establish a representative reference dataset for temporal characteristics of foot roll-over during barefoot jogging, based on plantar pressure data collected from 220 healthy young adults. The subjects ran at 3.3 m s⁻¹ over a 16.5 m long running track, having a built-in pressure platform mounted on a force platform. The initial contact, final contact, time to peak pressure and the duration of contact at the lateral and medial heel, metatarsal heads I to V and the hallux were measured. Temporal plantar pressure variables were found to be reliable (93% of ICC coefficients above 0.75) and both gender and asymmetry influences could be neglected. Foot roll-over during jogging started with heel contact followed by a latero-medial contact of the metatarsals and finally the hallux. After heel off, the forefoot started to push off at the lateral metatarsals, followed by a more central push off over the second metatarsal and finally over the hallux. Based on the plantar pressure data, the stance phase during running was divided into four distinct phases: initial contact (8.2%), forefoot contact (11.3%), foot flat (25.3%) and forefoot push off (55.1%). These findings provide a reliable and representative reference dataset for temporal characteristics of foot roll-over during jogging of young adults that may also be relevant in the evaluation of running patterns.     

Quantitative perfusion measurements using pulsed arterial spin labeling: effects of large region-of-interest analysis

PURPOSE: To study arterial spin labeling (ASL) MRI techniques and to investigate various problematic issues that still hinder the accurate and robust quantitative analysis of ASL data. MATERIALS AND METHODS: A pulsed-ASL (PASL) sequence was implemented on a 3-T imaging system and a protocol was developed for the measurement of perfusion based on fitting to a standard kinetic model. Both numerical simulations and multi-inversion time MRI data were analyzed. The effect of fitting a kinetic curve to a large region of interest (ROI) with a distribution of arterial transit times was compared to a pixel-by-pixel (PBP) method. RESULTS: It was found that a significant underestimation of perfusion of approximately 17+/-6% (P<0.001) occurs in gray matter, when comparing an ROI with a PBP analysis over a group of 12 healthy subjects. CONCLUSION: Analysis of ASL data based on a large ROI may suffer from inaccuracies arising from a distribution of transit times, implying that averaging of ASL kinetic data over such regions should therefore be avoided. When possible, a PBP fit should be performed.     

Region-of-interest-based analysis of clustered BOLD MRI data in experimental arthritis

RATIONALE AND OBJECTIVES: BOLD MRI provides functional information based on minimal changes. Problems inherent in data processing of the very low signal-to-noise-ratio of BOLD experiments have created obstacles for validation of certain techniques using standard strength-field MR scanners. Measures of diagnostic accuracy of clustered data are directly related to the reading parameters used to define regions-of-interest (ROIs). Our primary aim was to determine the combination of ROI-related reading parameters that provides highest accuracy for discrimination of presence or absence of arthritis in acute and subacute stages of the disease using paired comparisons of BOLD MRI data. MATERIALS AND METHODS: Six male New Zealand white rabbits were injected with albumin into one knee and saline into the contralateral knee, 3 animals had albumin injected into only one of the knees, 2 had saline injected into one of the knees, and 3 animals were not injected. The rabbits' knees underwent BOLD MRI on days 1 and 28 after induction of arthritis, except for the knees of 3 animals (albumin- vs saline-injected knees, n = 2 animals; saline- vs noninjected knees, n = 1 animal) that died before expected and had only the first MRI examination done. Percentage of activated voxels and differences in on-and-off signal intensities were the BOLD MRI methods applied. Data were analyzed using anatomic-driven small ROI, voxel-chaser-driven small ROI and anatomic-driven large ROI techniques. RESULTS: Diagnostic areas-under-the curve (AUCs) were obtained only for acute arthritis and only when percentage of activated voxels was used. Low threshold, positive voxel activations and small ROIs generated the largest AUCs (AUC +/- SE, .911 +/- .092, P = .014) using either anatomic-driven or voxel-chaser-driven techniques. A sensitivity analysis confirmed the importance of threshold as a parameter for analysis. CONCLUSION: Low threshold, positive voxel activations and small ROIs constituted the set of reading parameters that provided the most accurate BOLD MRI results.     

重组毕赤酵母表达猪胰岛素前体代谢参数分析和动力学模型

采用发酵过程参数相关分析理论,分析了毕赤酵母在不同发酵阶段利用不同碳源时的生理代谢参数变化特征。同时,研究了不同比生长速率下重组毕赤酵母表达猪胰岛素前体补料分批发酵过程,建立蛋白表达阶段的细胞生长非结构模型,结果表明:比生长速率和甲醇浓度符合Monod方程,最大比生长速率(μmax)为0.101h-1,基质的半饱和常数(Ks)为0.252g/L,细胞得率系数YX/S=0.386g/g,细胞维持系数m=0.011g/(g·h)。当比生长速率为0.016h-1时猪胰岛素前体(PIP)最大比形成速率(qp,max)达0.098mg/(g·h)。该模型能较好预测毕赤酵母表达阶段细胞生长和PIP表达的发酵趋势,用其控制发酵过程,目标蛋白PIP产量为优化前的1.5倍,达0.976g/L。