An urn-based Bayesian block bootstrap

Block bootstrap has been introduced in the literature for resampling dependent data, i.e. stationary processes. One of the main assumptions in block bootstrapping is that the blocks of observations are exchangeable, i.e. their joint distribution is immune to permutations. In this paper we propose a new Bayesian approach to block bootstrapping, starting from the construction of exchangeable blocks. Our sampling mechanism is based on a particular class of reinforced urn processes

When to start antiretroviral therapy in children aged 2-5 years: a collaborative causal modelling analysis of cohort studies from southern Africa

BACKGROUND There is limited evidence on the optimal timing of antiretroviral therapy (ART) initiation in children 2-5 y of age. We conducted a causal modelling analysis using the International Epidemiologic Databases to Evaluate AIDS-Southern Africa (IeDEA-SA) collaborative dataset to determine the difference in mortality when starting ART in children aged 2-5 y immediately (irrespective of CD4 criteria), as recommended in the World Health Organization (WHO) 2013 guidelines, compared to deferring to lower CD4 thresholds, for example, the WHO 2010 recommended threshold of CD4 count <750 cells/mm(3) or CD4 percentage (CD4%) <25%. METHODS AND FINDINGS ART-naïve children enrolling in HIV care at IeDEA-SA sites who were between 24 and 59 mo of age at first visit and with ≥1 visit prior to ART initiation and ≥1 follow-up visit were included. We estimated mortality for ART initiation at different CD4 thresholds for up to 3 y using g-computation, adjusting for measured time-dependent confounding of CD4 percent, CD4 count, and weight-for-age z-score. Confidence intervals were constructed using bootstrapping. The median (first; third quartile) age at first visit of 2,934 children (51% male) included in the analysis was 3.3 y (2.6; 4.1), with a median (first; third quartile) CD4 count of 592 cells/mm(3) (356; 895) and median (first; third quartile) CD4% of 16% (10%; 23%). The estimated cumulative mortality after 3 y for ART initiation at different CD4 thresholds ranged from 3.4% (95% CI: 2.1-6.5) (no ART) to 2.1% (95% CI: 1.3%-3.5%) (ART irrespective of CD4 value). Estimated mortality was overall higher when initiating ART at lower CD4 values or not at all. There was no mortality difference between starting ART immediately, irrespective of CD4 value, and ART initiation at the WHO 2010 recommended threshold of CD4 count <750 cells/mm(3) or CD4% <25%, with mortality estimates of 2.1% (95% CI: 1.3%-3.5%) and 2.2% (95% CI: 1.4%-3.5%) after 3 y, respectively. The analysis was limited by loss to follow-up and the unavailability of WHO staging data. CONCLUSIONS The results indicate no mortality difference for up to 3 y between ART initiation irrespective of CD4 value and ART initiation at a threshold of CD4 count <750 cells/mm(3) or CD4% <25%, but there are overall higher point estimates for mortality when ART is initiated at lower CD4 values. Please see later in the article for the Editors' Summary.

On the use of Cramér-Rao minimum variance bounds for the design of magnetic resonance spectroscopy experiments

Localized Magnetic Resonance Spectroscopy (MRS) is in widespread use for clinical brain research. Standard acquisition sequences to obtain one-dimensional spectra suffer from substantial overlap of spectral contributions from many metabolites. Therefore, specially tuned editing sequences or two-dimensional acquisition schemes are applied to extend the information content. Tuning specific acquisition parameters allows to make the sequences more efficient or more specific for certain target metabolites. Cramér-Rao bounds have been used in other fields for optimization of experiments and are now shown to be very useful as design criteria for localized MRS sequence optimization. The principle is illustrated for one- and two-dimensional MRS, in particular the 2D separation experiment, where the usual restriction to equidistant echo time spacings and equal acquisition times per echo time can be abolished. Particular emphasis is placed on optimizing experiments for quantification of GABA and glutamate. The basic principles are verified by Monte Carlo simulations and in vivo for repeated acquisitions of generalized two-dimensional separation brain spectra obtained from healthy subjects and expanded by bootstrapping for better definition of the quantification uncertainties.

Design and analysis for three level hierarchical structures with count response

In numerous intervention studies and education field trials, random assignment to treatment occurs in clusters rather than at the level of observation. This departure of random assignment of units may be due to logistics, political feasibility, or ecological validity. Data within the same cluster or grouping are often correlated. Application of traditional regression techniques, which assume independence between observations, to clustered data produce consistent parameter estimates. However such estimators are often inefficient as compared to methods which incorporate the clustered nature of the data into the estimation procedure (Neuhaus 1993).1 Multilevel models, also known as random effects or random components models, can be used to account for the clustering of data by estimating higher level, or group, as well as lower level, or individual variation. Designing a study, in which the unit of observation is nested within higher level groupings, requires the determination of sample sizes at each level. This study investigates the design and analysis of various sampling strategies for a 3-level repeated measures design on the parameter estimates when the outcome variable of interest follows a Poisson distribution. ^ Results study suggest that second order PQL estimation produces the least biased estimates in the 3-level multilevel Poisson model followed by first order PQL and then second and first order MQL. The MQL estimates of both fixed and random parameters are generally satisfactory when the level 2 and level 3 variation is less than 0.10. However, as the higher level error variance increases, the MQL estimates become increasingly biased. If convergence of the estimation algorithm is not obtained by PQL procedure and higher level error variance is large, the estimates may be significantly biased. In this case bias correction techniques such as bootstrapping should be considered as an alternative procedure. For larger sample sizes, those structures with 20 or more units sampled at levels with normally distributed random errors produced more stable estimates with less sampling variance than structures with an increased number of level 1 units. For small sample sizes, sampling fewer units at the level with Poisson variation produces less sampling variation, however this criterion is no longer important when sample sizes are large. ^ 1Neuhaus J (1993). “Estimation efficiency and Tests of Covariate Effects with Clustered Binary Data”. Biometrics , 49, 989–996^

Evolving biomarkers improve prediction of long-term mortality in patients with stable coronary artery disease: the BIO-VILCAD score.

OBJECTIVE Algorithms to predict the future long-term risk of patients with stable coronary artery disease (CAD) are rare. The VIenna and Ludwigshafen CAD (VILCAD) risk score was one of the first scores specifically tailored for this clinically important patient population. The aim of this study was to refine risk prediction in stable CAD creating a new prediction model encompassing various pathophysiological pathways. Therefore, we assessed the predictive power of 135 novel biomarkers for long-term mortality in patients with stable CAD. DESIGN, SETTING AND SUBJECTS We included 1275 patients with stable CAD from the LUdwigshafen RIsk and Cardiovascular health study with a median follow-up of 9.8 years to investigate whether the predictive power of the VILCAD score could be improved by the addition of novel biomarkers. Additional biomarkers were selected in a bootstrapping procedure based on Cox regression to determine the most informative predictors of mortality. RESULTS The final multivariable model encompassed nine clinical and biochemical markers: age, sex, left ventricular ejection fraction (LVEF), heart rate, N-terminal pro-brain natriuretic peptide, cystatin C, renin, 25OH-vitamin D3 and haemoglobin A1c. The extended VILCAD biomarker score achieved a significantly improved C-statistic (0.78 vs. 0.73; P = 0.035) and net reclassification index (14.9%; P < 0.001) compared to the original VILCAD score. Omitting LVEF, which might not be readily measureable in clinical practice, slightly reduced the accuracy of the new BIO-VILCAD score but still significantly improved risk classification (net reclassification improvement 12.5%; P < 0.001). CONCLUSION The VILCAD biomarker score based on routine parameters complemented by novel biomarkers outperforms previous risk algorithms and allows more accurate classification of patients with stable CAD, enabling physicians to choose more personalized treatment regimens for their patients.

Sportmotorische Leistungsfähigkeit und schulische Leistung – Zum mediierenden Effekt der exekutiven Funktionen

Introduction: Die sportmotorische Leistungsfähigkeit (SMLF) hängt nicht nur positiv mit der körperlichen Gesundheit zusammen, sondern gilt auch als Prädiktor für die schulische Leistung (SL) (van der Niet, Hartmann, Smith, & Visscher, 2014). Um die Frage zu beantworten, wie denn zwei auf den ersten Blick so distale Merkmale zusammenhängen sollen, werden unterschiedliche erklärende Variablen diskutiert, wobei die kognitive Stimulationshypothese die exekutiven Funktionen (EF) als mediierende Variable im Zusammenhang zwischen SMLF und SL postuliert. Die Annahme hierbei ist, dass die mit komplexen motorischen Kontrollprozessen einhergehende kognitive Beanspruchung bei einem wiederholten Ausführen von nicht-automatisierten sportbezogenen Handlungen zu einer Aktivierung und somit Förderung der EF führt (Best, 2010). EF, verstanden als höhere kognitive Prozesse, die ein zielorientiertes und situationsangepasstes Handeln erlauben, sind für den schulischen Erfolg von zentraler Bedeutung und gleichzeitig wichtige Prädiktoren der SL (Diamond, 2013). Obwohl diese Mediation seit einigen Jahren in der Literatur diskutiert wird, wurde sie bis heute noch nicht mit Hilfe längsschnittlicher Daten geprüft. Daher wird im Folgenden der mediierende Effekt der EF im Zusammenhang zwischen SMLF und SL getestet. Methods: Im Rahmen der Studie Sport und Kognition 5.0 wurden insgesamt 237 Primarschulkinder (52.3% ♀; 11.31 ± 0.62 Jahre) zu drei Messzeitpunkten in ihrer SMLF (T1) und ihren EF (T2) getestet. Zusätzlich wurde die SL (T3) mittels objektiver Schulleistungstests (Mathematik und Deutsch) erhoben. Um die Hauptfragestellung zu prüfen, ob die SL vorwiegend mediiert über die EF durch die SMLF vorhergesagt werden kann, wurde eine bootstrapping-basierte Mediationsanalyse in AMOS 22 durchgeführt. Results: Das theoretisch abgeleitete Strukturgleichungsmodell (2 (22, N = 237) = 30.357, p = .110; CFI = .978) weist eine zufriedenstellende Anpassungsgüte auf. Erwartungsgemäss zerfällt der Zusammenhang innerhalb des Mediationsmodells zwischen der SMLF und der SL, alsbald die EF ins Modell aufgenommen werden (β = .16, p = .634). Sowohl der Zusammenhang zwischen der SMLF und den EF (β = .38, p = .039), als auch der Zusammenhang zwischen den EF und der SL fallen signifikant aus (β = .91, p = .001) und ergeben dabei eine volle Mediation über den indirekten (p = .021) und totalen Effekt (p = .001). Discussion/Conclusion: Die erstmals vorliegenden längsschnittlichen Daten bestätigen den Zusammenhang zwischen SMLF und SL bei einer Mediation über die EF und decken sich mit den, aus einem querschnittlichen Design stammenden, Befunden von van der Niet et al. (2014). Zur Steigerung der schulischen Leistung sollten zukünftige Schulsportinterventionen die SMLF von Kindern erhöhen und dabei die EF bei der Auswahl von sportlichen Aufgaben mitberücksichtigen. References: Best, J. R. (2010). Effects of physical activity on children’s executive function: Contributions of experimental research on aerobic exercise. Developmental Review, 30, 331-351. Diamond, A. (2013). Executive functions. Annual Review of Psychology, 64, 135-168. van der Niet, A. G., Hartmann, E., Smith, J. & Visscher, C. (2014). Modeling relationships between physical fitness, executive functioning, and academic achievement in primary school children. Psychology of Sport & Exercise, 15(4), 319-325.

Determinanten kognitiver Angstmanifestation während schulischer Tests: Eine Längsschnittstudie

Theoretischer Hintergrund und Fragestellung: Schulische Tests dienen der Feststellung von Wissen und Können. Wie jede Messung kann auch diese durch Störvariablen verzerrt werden. Während Tests erlebte Angst ist ein solcher potentieller Störeinfluss: Angst kann Testleistungen beinträchtigen, da sie sich hinderlich auf die Informationsverarbeitung auswirken kann (Störung des Wissensabrufs und des Denkens; Zeidner, 1998). Dieser kognitiven Angstmanifestation (Rost & Schermer, 1997) liegt die angstbedingte automatische Aufmerksamkeitsorientierung auf aufgaben-irrelevante Gedanken während der Testbearbeitung zugrunde (Eysenck, Derakshan, Santos & Calvo, 2007). Es hat sich allerdings gezeigt, dass Angst nicht grundsätzlich mit Testleistungseinbußen einhergeht (Eysenck et al., 2007). Wir gehen davon aus, dass die Kapazität zur Selbstkontrolle bzw. Aufmerksamkeitsregulation (Baumeister, Muraven & Tice, 2000; Schmeichel & Baumeister, 2010) ein Faktor ist, der bedingt, wie stark kognitive Angstmanifestation während Tests und damit zusammenhängende Leistungseinbußen auftreten. Ängstliche Lernende mit höherer Aufmerksamkeitsregulationskapazität sollten ihrer automatischen Aufmerksamkeitsorientierung auf aufgaben-irrelevante Gedanken erfolgreicher entgegensteuern und ihre Aufmerksamkeit weiterhin auf die Aufgabenbearbeitung richten können. Dem entsprechend sollten sie trotz Angst weniger kognitive Angstmanifestation während Tests erleben als ängstliche Lernende mit geringerer Aufmerksamkeitsregulationskapazität. Auch die Selbstwirksamkeitserwartung und das Selbstwertgefühl sind Variablen, die in der Vergangenheit mit der Bewältigung von Angst und Stress in Verbindung gebracht wurden (Bandura, 1977; Baumeister, Campbell, Krueger & Vohs, 2003). Daher wurden diese Variablen als weitere Prädiktoren berücksichtigt. Es wurde die Hypothese getestet, dass die dispositionelle Aufmerksamkeitsregulationskapazität über die dispositionelle Selbstwirksamkeitserwartung und das dispositionelle Selbstwertgefühl hinaus Veränderungen in der kognitiven Angstmanifestation während Mathematiktests in einer Wirtschaftsschülerstichprobe vorhersagt. Es wurde des Weiteren davon ausgegangen, dass eine indirekte Verbindung zwischen der Aufmerksamkeitsregulationskapazität und der Veränderung in den Mathematiknoten, vermittelt über die Veränderung in der kognitiven Angstmanifestation, besteht. Methode: Einhundertachtundfünfzig Wirtschaftsschüler bearbeiteten im September 2011 (T1) einen Fragebogen, der die folgenden Messungen enthielt:-Subskala Kognitive Angstmanifestation aus dem Differentiellen Leistungsangstinventar (Rost & Schermer, 1997) bezogen auf Mathematiktests (Sparfeldt, Schilling, Rost, Stelzl & Peipert, 2005); Alpha = .90; -Skala zur dispositionellen Aufmerksamkeitsregulationskapazität (Bertrams & Englert, 2013); Alpha = .88; -Skala zur Selbstwirksamkeitserwartung (Schwarzer & Jerusalem, 1995); Alpha = .83; -Skala zum Selbstwertgefühl (von Collani & Herzberg, 2003); Alpha = .83; -Angabe der letzten Mathematikzeugnisnote. Im Februar 2012 (T2), also nach 5 Monaten und kurz nach dem Erhalt des Halbjahreszeugnisses, gaben die Schüler erneut ihre kognitive Angstmanifestation während Mathematiktests (Alpha = .93) und ihre letzte Mathematikzeugnisnote an. Ergebnisse: Die Daten wurden mittels Korrelationsanalyse, multipler Regressionsanalyse und Bootstrapping ausgewertet. Die Aufmerksamkeitsregulationskapazität, die Selbstwirksamkeitserwartung und das Selbstwertgefühl (alle zu T1) waren positiv interkorreliert, r= .50/.59/.59. Diese Variablen wurden gemeinsam als Prädiktoren in ein Regressionsmodell zur Vorhersage der kognitiven Angstmanifestation zu T2 eingefügt. Gleichzeitig wurde die kognitive Angstmanifestation zu T1 konstant gehalten. Es zeigte sich, dass die Aufmerksamkeitsregulationskapazität erwartungskonform die Veränderungen in der kognitiven Angstmanifestation vorhersagte, Beta = -.21, p= .02. Das heißt, dass höhere Aufmerksamkeitsregulationskapazität zu T1 mit verringerter kognitiver Angstmanifestation zu T2 einherging. Die Selbstwirksamkeitserwartung, Beta = .12, p= .14, und das Selbstwertgefühl, Beta = .05, p= .54, hatten hingegen keinen eigenen Vorhersagewert für die Veränderungen in der kognitiven Angstmanifestation. Des Weiteren ergab eine Mediationsanalyse mittels Bootstrapping (bias-corrected bootstrap 95% confidence interval, 5000 resamples; siehe Hayes & Scharkow, in press), dass die Aufmerksamkeitsregulationskapazität (T1), vermittelt über die Veränderung in der kognitiven Angstmanifestation, indirekt mit der Veränderung in der Mathematikleistung verbunden war (d.h. das Bootstrap-Konfidenzintervall schloss nicht die Null ein; CI [0.01, 0.24]). Für die Selbstwirksamkeitserwartung und das Selbstwertgefühl fand sich keine analoge indirekte Verbindung zur Mathematikleistung. Fazit: Die Befunde verweisen auf die Bedeutsamkeit der Aufmerksamkeitsregulationskapazität für die Bewältigung kognitiver Angstreaktionen während schulischer Tests. Losgelöst von der Aufmerksamkeitsregulationskapazität scheinen positive Erwartungen und ein positives Selbstbild keine protektive Wirkung hinsichtlich der leistungsbeeinträchtigenden kognitiven Angstmanifestation während Mathematiktests zu besitzen.

Is choking under pressure a consequence of skill-focus or increased distractibility? Results from a tennis serve task

It has been repeatedly demonstrated that athletes often choke in high pressure situations because anxiety can affect attention regulation and in turn performance. There are two competing theoretical approaches to explain the negative anxiety-performance relationship. According to skillfocus theories, anxious athletes’ attention is directed at how to execute the sport-specific movements which interrupts execution of already automatized movements in expert performers. According to distraction theories, anxious athletes are distractible and focus less on the relevant stimuli. We tested these competing assumptions in a between-subject design, as semi-professional tennis players were either assigned to an anxiety group (n = 25) or a neutral group (n = 28), and performed a series of second tennis serves into predefined target areas. As expected, anxiety was negatively related to serve accuracy. However, mediation analyses with the bootstrapping method revealed that this relationship was fully mediated by self-reported distraction and not by skill-focus.

Sportmotorische Leistungsfähigkeit und schulische Leistung – Zum mediierenden Effekt der exekutiven Funktionen

Die sportmotorische Leistungsfähigkeit (SMLF) gilt in jüngster Zeit als ein Prädiktor für schulische Leistung (SL) (Diamond, 2013). Um die Frage zu beantworten, wie denn zwei auf den ersten Blick so distale Merkmale zusammenhängen sollen, werden unterschiedliche erklärende Variablen diskutiert, wobei die kognitive Stimulationshypothese die exekutiven Funktionen (EF) als mediierende Variable im Zusammenhang zwischen SMLF und SL postuliert. Die Annahme hierbei ist, dass die mit komplexen motorischen Kontrollprozessen einhergehende kognitive Beanspruchung bei einem wiederholten Ausführen von nicht-automatisierten sportbezogenen Handlungen zu einer Aktivierung und somit Förderung der EF führt (Best, 2010). Der mediierende Effekt der EF im Zusammenhang zwischen der SMLF und der SL wird seit einigen Jahren in der Literatur diskutiert und wird im Folgenden innerhalb einer längsschnittlichen Untersuchung getestet. Im Rahmen der Studie SpuK wurden 237 Primarschulkinder (52.3% ♀; 11.31 ± 0.62 Jahre) zu drei Messzeitpunkten in ihrer SMLF (T1) und ihren EF (T2) getestet. Zur Ermittlung der SMLF wurden drei sportmotorische Tests in den Bereichen Koordination, Ausdauer und Schnellkraft durchgeführt. Die EF Inhibition, kognitive Flexibilität und Arbeitsgedächtnis wurden computerbasiert über den N-Back- und Flanker-Test operationalisiert. Zusätzlich wurde die SL (T3) mittels objektiver Schulleistungstests erhoben. Um die Hauptfragestellung zu prüfen, wurde eine bootstrapping basierte Mediationsanalyse in AMOS durchgeführt. Das Strukturgleichungsmodell (2 (22, N=237)=30.357, p=.110; CFI=.978) weist eine zufriedenstellende Anpassungsgüte auf. Erwartungsgemäss zerfällt der Zusammenhang innerhalb des Mediationsmodells zwischen der SMLF und der SL, alsbald die EF ins Modell aufgenommen werden (β=.16, p= .634). Sowohl der Zusammenhang zwischen der SMLF und den EF (β=.38, p= .039), als auch der Zusammenhang zwischen den EF und der SL fallen signifikant aus (β=.91, p=.001) und ergeben dabei eine volle Mediation über den indirekten (p=.021) und totalen Effekt (p=.001). Die vorliegenden längs-schnittlichen Daten bestätigen den Zusammenhang zwischen SMLF und SL bei einer Mediation über die EF und bestätigen somit die aus querschnittlichem Design stammenden Resultate von van der Niet et al. (2014). Literatur Best, J. R. (2010). Effects of physical activity on children’s executive function: Contributions of ex-perimental research on aerobic exercise. Developmental Review, 30, 331-351. Diamond, A. (2013). Executive functions. Annual Review of Psychology, 64, 135-168. van der Niet, A. G., Hartmann, E., Smith, J. & Visscher, C. (2014). Modeling relationships between physical fitness, executive functioning, and academic achievement in primary school chil-dren. Psychology of Sport & Exercise, 15(4), 319-325.

Polytomies and bayesian phylogenetic inference

Bayesian phylogenetic analyses are now very popular in systematics and molecular evolution because they allow the use of much more realistic models than currently possible with maximum likelihood methods. There are, however, a growing number of examples in which large Bayesian posterior clade probabilities are associated with very short edge lengths and low values for non-Bayesian measures of support such as nonparametric bootstrapping. For the four-taxon case when the true tree is the star phylogeny, Bayesian analyses become increasingly unpredictable in their preference for one of the three possible resolved tree topologies as data set size increases. This leads to the prediction that hard (or near-hard) polytomies in nature will cause unpredictable behavior in Bayesian analyses, with arbitrary resolutions of the polytomy receiving very high posterior probabilities in some cases. We present a simple solution to this problem involving a reversible-jump Markov chain Monte Carlo (MCMC) algorithm that allows exploration of all of tree space, including unresolved tree topologies with one or more polytomies. The reversible-jump MCMC approach allows prior distributions to place some weight on less-resolved tree topologies, which eliminates misleadingly high posteriors associated with arbitrary resolutions of hard polytomies. Fortunately, assigning some prior probability to polytomous tree topologies does not appear to come with a significant cost in terms of the ability to assess the level of support for edges that do exist in the true tree. Methods are discussed for applying arbitrary prior distributions to tree topologies of varying resolution, and an empirical example showing evidence of polytomies is analyzed and discussed.

Polytomies and bayesian phylogenetic inference

Bayesian phylogenetic analyses are now very popular in systematics and molecular evolution because they allow the use of much more realistic models than currently possible with maximum likelihood methods. There are, however, a growing number of examples in which large Bayesian posterior clade probabilities are associated with very short edge lengths and low values for non-Bayesian measures of support such as nonparametric bootstrapping. For the four-taxon case when the true tree is the star phylogeny, Bayesian analyses become increasingly unpredictable in their preference for one of the three possible resolved tree topologies as data set size increases. This leads to the prediction that hard (or near-hard) polytomies in nature will cause unpredictable behavior in Bayesian analyses, with arbitrary resolutions of the polytomy receiving very high posterior probabilities in some cases. We present a simple solution to this problem involving a reversible-jump Markov chain Monte Carlo (MCMC) algorithm that allows exploration of all of tree space, including unresolved tree topologies with one or more polytomies. The reversible-jump MCMC approach allows prior distributions to place some weight on less-resolved tree topologies, which eliminates misleadingly high posteriors associated with arbitrary resolutions of hard polytomies. Fortunately, assigning some prior probability to polytomous tree topologies does not appear to come with a significant cost in terms of the ability to assess the level of support for edges that do exist in the true tree. Methods are discussed for applying arbitrary prior distributions to tree topologies of varying resolution, and an empirical example showing evidence of polytomies is analyzed and discussed.

A Bootstrap-Regression Procedure to Capture Unit Specific Effects in Data Envelopment Analysis

The Data Envelopment Analysis (DEA) efficiency score obtained for an individual firm is a point estimate without any confidence interval around it. In recent years, researchers have resorted to bootstrapping in order to generate empirical distributions of efficiency scores. This procedure assumes that all firms have the same probability of getting an efficiency score from any specified interval within the [0,1] range. We propose a bootstrap procedure that empirically generates the conditional distribution of efficiency for each individual firm given systematic factors that influence its efficiency. Instead of resampling directly from the pooled DEA scores, we first regress these scores on a set of explanatory variables not included at the DEA stage and bootstrap the residuals from this regression. These pseudo-efficiency scores incorporate the systematic effects of unit-specific factors along with the contribution of the randomly drawn residual. Data from the U.S. airline industry are utilized in an empirical application.

Uncertainty analysis of cost effectiveness in a tailored intervention to promote screening for colorectal cancer

Background. Screening for colorectal cancer (CRC) is considered cost effective but screening compliance in the US remains low. There have been very few studies on economic analyses of screening promotion strategies for colorectal cancer. The main aim of the current study is to conduct a cost effectiveness analysis (CEA) and examine the uncertainty involved in the results of the CEA of a tailored intervention to promote screening for CRC among patients of a multispeciality clinic in Houston, TX. ^ Methods. The two intervention arms received a PC based tailored program and web based educational information to promote CRC screening. The incremental cost of implementing a tailored PC based program was compared to the website based education and the status quo of no intervention for each unit of effect after 12 months of delivering the intervention. Uncertainty analysis in the point estimates of cost and effect was conducted using nonparametric bootstrapping. ^ Results. The cost of implementing a web based educational intervention was $36.00 per person and the cost of the tailored PC based interactive intervention was $43.00 per person. The additional cost per person screened for the web-based strategy was $2374 and the effect of the tailored intervention was negative. ^

Improving the Quality of Life of spousal caregivers of stroke survivors: A cost effectiveness analysis

Back ground and Purpose. There is a growing consensus among health care researchers that Quality of Life (QoL) is an important outcome and, within the field of family caregiving, cost effectiveness research is needed to determine which programs have the greatest benefit for family members. This study uses a multidimensional approach to measure the cost effectiveness of a multicomponent intervention designed to improve the quality of life of spousal caregivers of stroke survivors. Methods. The CAReS study (Committed to Assisting with Recovery after Stroke) was a 5-year prospective, longitudinal intervention study for 159 stroke survivors and their spousal caregivers upon discharge of the stroke survivor from inpatient rehabilitation to their home. CAReS cost data were analyzed to determine the incremental cost of the intervention per caregiver. The mean values of the quality-of-life predictor variables of the intervention group of caregivers were compared to the mean values of usual care groups found in the literature. Significant differences were then divided into the cost of the intervention per caregiver to calculate the incremental cost effectiveness ratio for each predictor variable. Results. The cost of the intervention per caregiver was approximately $2,500. Statistically significant differences were found between the mean scores for the Perceived Stress and Satisfaction with Life scales. Statistically significant differences were not found between the mean scores for the Self Reported Health Status, Mutuality, and Preparedness scales. Conclusions. This study provides a prototype cost effectiveness analysis on which researchers can build. Using a multidimensional approach to measure QoL, as used in this analysis, incorporates both the subjective and objective components of QoL. Some of the QoL predictor variable scores were significantly different between the intervention and comparison groups, indicating a significant impact of the intervention. The estimated cost of the impact was also examined. In future studies, a scale that takes into account both the dimensions and the weighting each person places on the dimensions of QoL should be used to provide a single QoL score per participant. With participant level cost and outcome data, uncertainty around each cost-effectiveness ratio can be calculated using the bias-corrected percentile bootstrapping method and plotted to calculate the cost-effectiveness acceptability curves.^

Prediction of sepsis in the burn intensive care unit patient

Sepsis is a significant cause for multiple organ failure and death in the burn patient, yet identification in this population is confounded by chronic hypermetabolism and impaired immune function. The purpose of this study was twofold: 1) determine the ability of the systemic inflammatory response syndrome (SIRS) and American Burn Association (ABA) criteria to predict sepsis in the burn patient; and 2) develop a model representing the best combination of clinical predictors associated with sepsis in the same population. A retrospective, case-controlled, within-patient comparison of burn patients admitted to a single intensive care unit (ICU) was conducted for the period January 2005 to September 2010. Blood culture results were paired with clinical condition: "positive-sick"; "negative-sick", and "screening-not sick". Data were collected for the 72 hours prior to each blood culture. The most significant predictors were evaluated using logistic regression, Generalized Estimating Equations (GEE) and ROC area under the curve (AUC) analyses to assess model predictive ability. Bootstrapping methods were employed to evaluate potential model over-fitting. Fifty-nine subjects were included, representing 177 culture periods. SIRS criteria were not found to be associated with culture type, with an average of 98% of subjects meeting criteria in the 3 days prior. ABA sepsis criteria were significantly different among culture type only on the day prior (p = 0.004). The variables identified for the model included: heart rate>130 beats/min, mean blood pressure<60 mmHg, base deficit<-6 mEq/L, temperature>36°C, use of vasoactive medications, and glucose>150 mg/d1. The model was significant in predicting "positive culture-sick" and sepsis state, with AUC of 0.775 (p < 0.001) and 0.714 (p < .001), respectively; comparatively, the ABA criteria AUC was 0.619 (p = 0.028) and 0.597 (p = .035), respectively. SIRS criteria are not appropriate for identifying sepsis in the burn population. The ABA criteria perform better, but only for the day prior to positive blood culture results. The time period useful to diagnose sepsis using clinical criteria may be limited to 24 hours. A combination of predictors is superior to individual variable trends, yet algorithms or computer support will be necessary for the clinician to find such models useful. ^