DPRESS: Localizing estimates of predictive uncertainty

<p>Abstract</p> <p>Background</p> <p>The need to have a quantitative estimate of the uncertainty of prediction for QSAR models is steadily increasing, in part because such predictions are being widely distributed as tabulated values disconnected from the models used to generate them. Classical statistical theory assumes that the error in the population being modeled is independent and identically distributed (IID), but this is often not actually the case. Such inhomogeneous error (heteroskedasticity) can be addressed by providing an individualized estimate of predictive uncertainty for each particular new object <it>u</it>: the standard error of prediction <it>s</it>_ucan be estimated as the non-cross-validated error <it>s</it>_t*for the closest object <it>t</it>* in the training set adjusted for its separation <it>d </it>from <it>u </it>in the descriptor space relative to the size of the training set.</p> <p><display-formula><graphic file="1758-2946-1-11-i1.gif"/></display-formula></p> <p>The predictive uncertainty factor <it>γ</it>_t*is obtained by distributing the internal predictive error sum of squares across objects in the training set based on the distances between them, hence the acronym: <it>D</it>istributed <it>PR</it>edictive <it>E</it>rror <it>S</it>um of <it>S</it>quares (DPRESS). Note that <it>s</it>_t*and <it>γ</it>_t*are characteristic of each training set compound contributing to the model of interest.</p> <p>Results</p> <p>The method was applied to partial least-squares models built using 2D (molecular hologram) or 3D (molecular field) descriptors applied to mid-sized training sets (<it>N </it>= 75) drawn from a large (<it>N </it>= 304), well-characterized pool of cyclooxygenase inhibitors. The observed variation in predictive error for the external 229 compound test sets was compared with the uncertainty estimates from DPRESS. Good qualitative and quantitative agreement was seen between the distributions of predictive error observed and those predicted using DPRESS. Inclusion of the distance-dependent term was essential to getting good agreement between the estimated uncertainties and the observed distributions of predictive error. The uncertainty estimates derived by DPRESS were conservative even when the training set was biased, but not excessively so.</p> <p>Conclusion</p> <p>DPRESS is a straightforward and powerful way to reliably estimate individual predictive uncertainties for compounds outside the training set based on their distance to the training set and the internal predictive uncertainty associated with its nearest neighbor in that set. It represents a sample-based, <it>a posteriori </it>approach to defining applicability domains in terms of localized uncertainty.</p

Developing and Evaluating Prediction Models in Rehabilitation Populations

This article presents a 3-part framework for developing and evaluating prediction models in rehabilitation populations. First, a process for developing and refining prognostic research questions and the scientific approach to prediction models is presented. Primary components of the scientific approach include the study design and sampling of patients, outcome measurement, selecting predictor variable(s), minimizing methodologic sources of bias, assuring a sufficient sample size for statistical power, and selecting an appropriate statistical model. Examples focus on prediction modeling using samples of rehabilitation patients. Second, a brief overview for statistically building and validating multivariable prediction models is provided, which includes the following 7 steps: data inspection, coding of predictors, model specification, model estimation, model performance, model validation, and model presentation. Third, we propose a set of primary considerations for evaluating prediction model studies using specific quality indicators as criteria to help stakeholders evaluate the quality of a prediction model study. Lastly, we offer perspectives on the future development and use of rehabilitation prediction models

On the need of validating inpatient registers.

Study design:Register study.Objectives:To design and implement a validation process to check the completeness of the Hospital Discharge Register (HDR) held by the Swedish National Board of Health and Welfare.Setting:Sweden.Methods:An accurate traumatic spinal cord injury prevalence group (n=495) was acquired from the Swedish Spinalis Clinic. A register control was performed on the group by raising three questions to check the validity of the HDR: Is an inpatient stay registered in association with the injury date? Is the reported first length of stay plausible, given the level and extent of injury? Are all the anticipated care and/or rehabilitation providers represented in the HDR?Results:For 62% (of 413 cases) the first registered hospitalization date correlated with the injury date. For the other 38%, hospitalization was reported to start between 2 and 8651 days after injury. Considering the level and extent of injury, individuals were reported to have unrealistically short initial hospitalization. The prevalence group visited 42 different hospitals and 47 clinics. Five rehabilitation clinics, though, were not reported.Conclusions:The HDR is a valuable source when conducting epidemiological and health services research. However, using the register without any validation process could, as detected in the investigated diagnosis group, lead to a severe underestimation of the inpatient usage. The study showed that systematic errors could be detected by means of extensive knowledge of the diagnosis group.Spinal Cord advance online publication, 13 May 2008; doi:10.1038/sc.2008.42

From unresponsive wakefulness to minimally conscious PLUS and functional locked-in syndromes: recent advances in our understanding of disorders of consciousness.

Functional neuroimaging and electrophysiology studies are changing our understanding of patients with coma and related states. Some severely brain damaged patients may show residual cortical processing in the absence of behavioural signs of consciousness. Given these new findings, the diagnostic errors and their potential effects on treatment as well as concerns regarding the negative associations intrinsic to the term vegetative state, the European Task Force on Disorders of Consciousness has recently proposed the more neutral and descriptive term unresponsive wakefulness syndrome. When vegetative/unresponsive patients show minimal signs of consciousness but are unable to reliably communicate the term minimally responsive or minimally conscious state (MCS) is used. MCS was recently subcategorized based on the complexity of patients' behaviours: MCS+ describes high-level behavioural responses (i.e., command following, intelligible verbalizations or non-functional communication) and MCS- describes low-level behavioural responses (i.e., visual pursuit, localization of noxious stimulation or contingent behaviour such as appropriate smiling or crying to emotional stimuli). Finally, patients who show non-behavioural evidence of consciousness or communication only measurable via para-clinical testing (i.e., functional MRI, positron emission tomography, EEG or evoked potentials) can be considered to be in a functional locked-in syndrome. An improved assessment of brain function in coma and related states is not only changing nosology and medical care but also offers a better-documented diagnosis and prognosis and helps to further identify the neural correlates of human consciousness

Recovery from mild traumatic brain injury: a focus on fatigue.

Contains fulltext : 50465.pdf (publisher's version ) (Closed access)BACKGROUND: Fatigue is one of the most frequently reported symptoms after Mild Traumatic Brain Injury (MTBI). To date, systematic and comparative studies on fatigue after MTBI are scarce, and knowledge on causal mechanisms is lacking. OBJECTIVES: To determine the severity of fatigue six months after MTBI and its relation to outcome. Furthermore, to test whether injury indices, such as Glasgow Coma Scale scores, are related to higher levels of fatigue. METHODS: Postal questionnaires were sent to a consecutive group of patients with an MTBI and a minor-injury control group, aged 18-60, six months after injury. Fatigue severity was measured with the Checklist Individual Strength. Postconcussional symptoms and limitations in daily functioning were assessed using the Rivermead Post Concussion Questionnaire and the SF-36. RESULTS: A total of 299 out of 618 eligible (response rate 52%) MTBI patients and 287 out of 482 eligible (response rate 60%) minor-injury patients returned the questionnaire. Ninety-five MTBI patients (32%) and 35 control patients (12%) were severely fatigued. Severe fatigue was highly associated with the experience of other symptoms, limitations in physical and social functioning, and fatigue related problems like reduced activity. Of various trauma severity indices, nausea and headache experienced on the ED were significantly related to higher levels of fatigue at six months. CONCLUSIONS: In conclusion, one third of a large sample of MTBI patients experiences severe fatigue six months after injury, and this experience is associated with limitations in daily functioning. Our finding that acute symptoms and mechanism of injury rather than injury severity indices appear to be related to higher levels of fatigue warrants further investigation