How effective are maternal effects at having effects?

The well studied trade-off between offspring size and offspring number assumes that offspring fitness increases with increasing per-offspring investment. Where mothers differ genetically or exhibit plastic variation in reproductive effort, there can be variation in per capita investment in offspring, and via this trade-off, variation in fecundity. Variation in per capita investment will affect juvenile performance directly—a classical maternal effect—while variation in fecundity will also affect offspring performance by altering the offsprings' competitive environment. The importance of this trade-off, while a focus of evolutionary research, is not often considered in discussions about population dynamics. Here, we use a factorial experiment to determine what proportion of variation in offspring performance can be ascribed to maternal effects and what proportion to the competitive environment linked to the size–number trade-off. Our results suggest that classical maternal effects are significant, but that in our system, the competitive environment, which is linked to maternal environments by fecundity, can be a far more substantial influence

Predictive performance of machine and statistical learning methods: impact of data-generating processes on external validity in the "large N, small p" setting

Machine learning approaches are increasingly suggested as tools to improve prediction of clinical outcomes. We aimed to identify when machine learning methods perform better than a classical learning method. We hereto examined the impact of the data-generating process on the relative predictive accuracy of six machine and statistical learning methods: bagged classification trees, stochastic gradient boosting machines using trees as the base learners, random forests, the lasso, ridge regression, and unpenalized logistic regression. We performed simulations in two large cardiovascular datasets which each comprised an independent derivation and validation sample collected from temporally distinct periods: patients hospitalized with acute myocardial infarction (AMI, n = 9484 vs. n = 7000) and patients hospitalized with congestive heart failure (CHF, n = 8240 vs. n = 7608). We used six data-generating processes based on each of the six learning methods to simulate outcomes in the derivation and validation samples based on 33 and 28 predictors in the AMI and CHF data sets, respectively. We applied six prediction methods in each of the simulated derivation samples and evaluated performance in the simulated validation samples according to c-statistic, generalized R-2, Brier score, and calibration. While no method had uniformly superior performance across all six data-generating process and eight performance metrics, (un)penalized logistic regression and boosted trees tended to have superior performance to the other methods across a range of data-generating processes and performance metrics. This study confirms that classical statistical learning methods perform well in low-dimensional settings with large data sets.Development and application of statistical models for medical scientific researc

Cost effectiveness of thrombolytic therapy with tissue plasminogen activator as compared with streptokinase for acute myocardial infarction

BACKGROUND. Patients with acute myocardial infarction who were treated with accelerated tissue plasminogen activator (t-PA) (given over a period of 1 1/2 hours rather than the conventional 3 hours, and with two thirds of the dose given in the first 30 minutes) had a 30-day mortality that was 15 percent lower than that of pati

Cost calculation and prediction in adult intensive care: A ground-up utilization study

Publisher's copy made available with the permission of the publisherThe ability of various proxy cost measures, including therapeutic activity scores (TISS and Omega) and cumulative daily severity of illness scores, to predict individual ICU patient costs was assessed in a prospective “ground-up” utilization costing study over a six month period in 1991. Daily activity (TISS and Omega scores) and utilization in consecutive admissions to three adult university associated ICUs was recorded by dedicated data collectors. Cost prediction used linear regression with determination (80%) and validation (20%) data sets. The cohort, 1333 patients, had a mean (SD) age 57.5 (19.4) years, (41% female) and admission APACHE III score of 58 (27). ICU length of stay and mortality were 3.9 (6.1) days and 17.6% respectively. Mean total TISS and Omega scores were 117 (157) and 72 (113) respectively. Mean patient costs per ICU episode (1991 $AUS) were$6801 ($10311), with median costs of$2534, range $106 to$95,602. Dominant cost fractions were nursing 43.3% and overheads 16.9%. Inflation adjusted year 2002 (mean) costs were $9343 ($ AUS). Total costs in survivors were predicted by Omega score, summed APACHE III score and ICU length of stay; determination R2, 0.91; validation 0.88. Omega was the preferred activity score. Without the Omega score, predictors were age, summed APACHE III score and ICU length of stay; determination R2, 0.73; validation 0.73. In non-survivors, predictors were age and ICU length of stay (plus interaction), and Omega score (determination R2, 0.97; validation 0.91). Patient costs may be predicted by a combination of ICU activity indices and severity scores.J. L. Moran, A. R. Peisach, P. J. Solomon, J. Martinhttp://www.aaic.net.au/Article.asp?D=200403

New insights into the genetic etiology of Alzheimer's disease and related dementias

Characterization of the genetic landscape of Alzheimer's disease (AD) and related dementias (ADD) provides a unique opportunity for a better understanding of the associated pathophysiological processes. We performed a two-stage genome-wide association study totaling 111,326 clinically diagnosed/'proxy' AD cases and 677,663 controls. We found 75 risk loci, of which 42 were new at the time of analysis. Pathway enrichment analyses confirmed the involvement of amyloid/tau pathways and highlighted microglia implication. Gene prioritization in the new loci identified 31 genes that were suggestive of new genetically associated processes, including the tumor necrosis factor alpha pathway through the linear ubiquitin chain assembly complex. We also built a new genetic risk score associated with the risk of future AD/dementia or progression from mild cognitive impairment to AD/dementia. The improvement in prediction led to a 1.6- to 1.9-fold increase in AD risk from the lowest to the highest decile, in addition to effects of age and the APOE ε4 allele