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BackgroundAnimal studies have revealed seasonal patterns in cerebrospinal fluid (CSF) monoamine (MA) turnover. In humans, no study had systematically assessed seasonal patterns in CSF MA turnover in a large set of healthy adults.Methodology/principal findingsStandardized amounts of CSF were prospectively collected from 223 healthy individuals undergoing spinal anesthesia for minor surgical procedures. The metabolites of serotonin (5-hydroxyindoleacetic acid, 5-HIAA), dopamine (homovanillic acid, HVA) and norepinephrine (3-methoxy-4-hydroxyphenylglycol, MPHG) were measured using high performance liquid chromatography (HPLC). Concentration measurements by sampling and birth dates were modeled using a non-linear quantile cosine function and locally weighted scatterplot smoothing (LOESS, span = 0.75). The cosine model showed a unimodal season of sampling 5-HIAA zenith in April and a nadir in October (p-value of the amplitude of the cosine = 0.00050), with predicted maximum (PC(max)) and minimum (PC(min)) concentrations of 173 and 108 nmol/L, respectively, implying a 60% increase from trough to peak. Season of birth showed a unimodal 5-HIAA zenith in May and a nadir in November (p = 0.00339; PC(max) = 172 and PC(min) = 126). The non-parametric LOESS showed a similar pattern to the cosine in both season of sampling and season of birth models, validating the cosine model. A final model including both sampling and birth months demonstrated that both sampling and birth seasons were independent predictors of 5-HIAA concentrations.ConclusionIn subjects without mental illness, 5-HT turnover shows circannual variation by season of sampling as well as season of birth, with peaks in spring and troughs in fall.

Abstract Background Costs of in vitro fertilisation (IVF) are high, which is partly due to the use of follicle stimulating hormone (FSH). FSH is usually administered in a standard dose. However, due to differences in ovarian reserve between women, ovarian response also differs with potential negative consequences on pregnancy rates. A Markov decision-analytic model showed that FSH dose individualisation according to ovarian reserve is likely to be cost-effective in women who are eligible for IVF. However, this has never been confirmed in a large randomised controlled trial (RCT). The aim of the present study is to assess whether an individualised FSH dose regime based on an ovarian reserve test (ORT) is more cost-effective than a standard dose regime. Methods/Design Multicentre RCT in subfertile women indicated for a first IVF or intracytoplasmic sperm injection cycle, who are aged Discussion The results of this study will be integrated into a decision model that compares cost-effectiveness of the three dose-adjustment strategies to a standard dose strategy. The study outcomes will provide scientific foundation for national and international guidelines. Trial registration NTR2657

Abstract Background In in vitro fertilization (IVF) and intracytoplasmatic sperm injection (ICSI) treatment a large drop is present between embryo transfer and occurrence of pregnancy. The implantation rate per embryo transferred is only 30%. Studies have shown that minor intrauterine abnormalities can be found in 11–45% of infertile women with a normal transvaginal sonography or hysterosalpingography. Two randomised controlled trials have indicated that detection and treatment of these abnormalities by office hysteroscopy after two failed IVF cycles leads to a 9–13% increase in pregnancy rate. Therefore, screening of all infertile women for intracavitary pathology prior to the start of IVF/ICSI is increasingly advocated. In absence of a scientific basis for such a policy, this study will assess the effects and costs of screening for and treatment of unsuspected intrauterine abnormalities by routine office hysteroscopy, with or without saline infusion sonography (SIS), prior to a first IVF/ICSI cycle. Methods/design Multicenter randomised controlled trial in asymptomatic subfertile women, indicated for a first IVF/ICSI treatment cycle, with normal findings at transvaginal sonography. Women with recurrent miscarriages, prior hysteroscopy treatment and intermenstrual blood loss will not be included. Participants will be randomised for a routine fertility work-up with additional (SIS and) hysteroscopy with on-the-spot-treatment of predefined intrauterine abnormalities versus the regular fertility work-up without additional diagnostic tests. The primary study outcome is the cumulative ongoing pregnancy rate resulting in live birth achieved within 18 months of IVF/ICSI treatment after randomisation. Secondary study outcome parameters are the cumulative implantation rate; cumulative miscarriage rate; patient preference and patient tolerance of a SIS and hysteroscopy procedure. All data will be analysed according to the intention-to-treat principle, using univariate and multivariate logistic regression and cox regression. Cost-effectiveness analysis will be performed to evaluate the costs of the additional tests as routine procedure. In total 700 patients will be included in this study. Discussion The results of this study will help to clarify the significance of hysteroscopy prior to IVF treatment. Trial registration NCT01242852

Abstract Background Environmental epidemiology and biomonitoring studies typically rely on biological samples to assay the concentration of non-persistent exposure biomarkers. Between-participant variations in sampling conditions of these biological samples constitute a potential source of exposure misclassification. Few studies attempted to correct biomarker levels for this error. We aimed to assess the influence of sampling conditions on concentrations of urinary biomarkers of select phenols and phthalates, two widely-produced families of chemicals, and to standardize biomarker concentrations on sampling conditions. Methods Urine samples were collected between 2002 and 2006 among 287 pregnant women from Eden and Pélagie cohorts, from which phthalates and phenols metabolites levels were assayed. We applied a 2-step standardization method based on regression residuals. First, the influence of sampling conditions (including sampling hour, duration of storage before freezing) and of creatinine levels on biomarker concentrations were characterized using adjusted linear regression models. In the second step, the model estimates were used to remove the variability in biomarker concentrations due to sampling conditions and to standardize concentrations as if all samples had been collected under the same conditions (e.g., same hour of urine collection). Results Sampling hour was associated with concentrations of several exposure biomarkers. After standardization for sampling conditions, median concentrations differed by ‒ 38 % for 2,5-dichlorophenol to +80 % for a metabolite of diisodecyl phthalate. However, at the individual level, standardized biomarker levels were strongly correlated (correlation coefficients above 0.80) with unstandardized measures. Conclusions Sampling conditions, such as sampling hour, should be systematically collected in biomarker-based studies, in particular when the biomarker half-life is short. The 2-step standardization method based on regression residuals that we proposed in order to limit the impact of heterogeneity in sampling conditions could be further tested in studies describing levels of biomarkers or their influence on health.

Abstract Background Rankings based on outcome are often used to present health care provider performance. These rankings do however not reflect that part of the variation in outcome between providers is caused by natural variation, and not by any differences in quality of care. The aim of this study is to compare standard methods for ranking with a novel method that takes into account natural variation. Methods We used data on the number of treatment cycles and the number of pregnancies of 13 Dutch IVF clinics from 2004. We calculated the Expected Rank (ER), an estimate of the true rank of a provider, accounting for natural variation. We rescaled the ER to obtain the Percentile based on ER (PCER), that can be interpreted as the probability that a clinic is worse than a randomly selected other clinic. We also calculated a measure for rankability ρ, which is the part of variation between providers that is due to true differences (as opposed to natural variation). Results The expected ranks ranged from 1.4 to 11.9 instead of the original ranks 1–13. The ER showed that some clinics performed very similar, which would be disregarded when using standard ranks. The PCER ranged from 7% to 88%. Rankability was substantial (ρ = 0.9) Conclusion The Expected Rank provides a way to combine the attractiveness of a ranking, a single number and easy interpretation, with reliable analyses that does justice to the providers, and also allows individual comparisons.

Binary logistic regression is one of the most frequently applied statistical approaches for developing clinical prediction models. Developers of such models often rely on an Events Per Variable criterion (EPV), notably EPV ≥10, to determine the minimal sample size required and the maximum number of candidate predictors that can be examined. We present an extensive simulation study in which we studied the influence of EPV, events fraction, number of candidate predictors, the correlations and distributions of candidate predictor variables, area under the ROC curve, and predictor effects on out-of-sample predictive performance of prediction models. The out-of-sample performance (calibration, discrimination and probability prediction error) of developed prediction models was studied before and after regression shrinkage and variable selection. The results indicate that EPV does not have a strong relation with metrics of predictive performance, and is not an appropriate criterion for (binary) prediction model development studies. We show that out-of-sample predictive performance can better be approximated by considering the number of predictors, the total sample size and the events fraction. We propose that the development of new sample size criteria for prediction models should be based on these three parameters, and provide suggestions for improving sample size determination. [ABSTRACT FROM AUTHOR]

Objective: Amyotrophic lateral sclerosis (ALS) clinical trials based on single end points only partially capture the full treatment effect when both function and mortality are affected, and may falsely dismiss efficacious drugs as futile. We aimed to investigate the statistical properties of several strategies for the simultaneous analysis of function and mortality in ALS clinical trials. Methods: Based on the Pooled Resource Open-Access ALS Clinical Trials (PRO-ACT) database, we simulated longitudinal patterns of functional decline, defined by the revised amyotrophic lateral sclerosis functional rating scale (ALSFRS-R) and conditional survival time. Different treatment scenarios with varying effect sizes were simulated with follow-up ranging from 12 to 18 months. We considered the following analytical strategies: 1) Cox model; 2) linear mixed effects (LME) model; 3) omnibus test based on Cox and LME models; 4) composite time-to-6-point decrease or death; 5) combined assessment of function and survival (CAFS); and 6) test based on joint modeling framework. For each analytical strategy, we calculated the empirical power and sample size. Results: Both Cox and LME models have increased false-negative rates when treatment exclusively affects either function or survival. The joint model has superior power compared to other strategies. The composite end point increases false-negative rates among all treatment scenarios. To detect a 15% reduction in ALSFRS-R decline and 34% decline in hazard with 80% power after 18 months, the Cox model requires 524 patients, the LME model 794 patients, the omnibus test 526 patients, the composite end point 1,274 patients, the CAFS 576 patients and the joint model 464 patients. Conclusion: Joint models have superior statistical power to analyze simultaneous effects on survival and function and may circumvent pitfalls encountered by other end points. Optimizing trial end points is essential, as selecting suboptimal outcomes may disguise important treatment clues. [ABSTRACT FROM AUTHOR]

Objective: Amyotrophic lateral sclerosis (ALS) clinical trials based on single end points only partially capture the full treatment effect when both function and mortality are affected, and may falsely dismiss efficacious drugs as futile. We aimed to investigate the statistical properties of several strategies for the simultaneous analysis of function and mortality in ALS clinical trials., Methods: Based on the Pooled Resource Open-Access ALS Clinical Trials (PRO-ACT) database, we simulated longitudinal patterns of functional decline, defined by the revised amyotrophic lateral sclerosis functional rating scale (ALSFRS-R) and conditional survival time. Different treatment scenarios with varying effect sizes were simulated with follow-up ranging from 12 to 18 months. We considered the following analytical strategies: 1) Cox model; 2) linear mixed effects (LME) model; 3) omnibus test based on Cox and LME models; 4) composite time-to-6-point decrease or death; 5) combined assessment of function and survival (CAFS); and 6) test based on joint modeling framework. For each analytical strategy, we calculated the empirical power and sample size., Results: Both Cox and LME models have increased false-negative rates when treatment exclusively affects either function or survival. The joint model has superior power compared to other strategies. The composite end point increases false-negative rates among all treatment scenarios. To detect a 15% reduction in ALSFRS-R decline and 34% decline in hazard with 80% power after 18 months, the Cox model requires 524 patients, the LME model 794 patients, the omnibus test 526 patients, the composite end point 1,274 patients, the CAFS 576 patients and the joint model 464 patients., Conclusion: Joint models have superior statistical power to analyze simultaneous effects on survival and function and may circumvent pitfalls encountered by other end points. Optimizing trial end points is essential, as selecting suboptimal outcomes may disguise important treatment clues., Competing Interests: Disclosure RPAvE, MJCE, DR and SN report no disclosures. LHvdB reports grants from the Netherlands ALS Foundation, Netherlands Organization for Health Research and Development (Vici scheme) and Netherlands Organization for Health Research and Development (SOPHIA, STRENGTH, ALS-CarE project), funded through the EU Joint Programme – Neurodegenerative Disease Research (JPND), and served on the scientific advisory board of Biogen, Cytokinetics, Prinses Beatrix SpierFonds, and the Latran Foundation. The authors report no other conflicts of interest in this work.

Most of the discoveries from gene expression data are driven by a study claiming an optimal subset of genes that play a key role in a specific disease. Meta-analysis of the available datasets can help in getting concordant results so that a real-life application may be more successful. Sequential meta-analysis (SMA) is an approach for combining studies in chronological order while preserving the type I error and pre-specifying the statistical power to detect a given effect size. We focus on the application of SMA to find gene expression signatures across experiments in acute myeloid leukemia. SMA of seven raw datasets is used to evaluate whether the accumulated samples show enough evidence or more experiments should be initiated. We found 313 differentially expressed genes, based on the cumulative information of the experiments. SMA offers an alternative to existing methods in generating a gene list by evaluating the adequacy of the cumulative information.