Varianzunterschiede in klinischen Studien

Die hier vorgelegte Arbeit befasst sich im Wesentlichen mit Varianzunterschieden und Varianzfehlspezifikationen von Zielgrößen klinischer Studien. Im Mittel und im Median waren nur sehr geringe Abweichungen der Varianz in der zweiten Hälfte realer Studien zu erkennen. Die vorgefundene Verteilung der Varianzquotienten unterschied sich jedoch signifikant von der unter der Annahme keines Unterschieds hergeleiteten theoretischen Mischverteilung. Grafische Methoden deuteten auf eine größere Anzahl an extremen Verhältnissen hin, was durch das 5%-Perzentil (Faktor 2/3) und das 95%-Perzentil (Faktor 1.8) bestätigt wurde. Die Untersuchung mit einem hierarchischen Modell korrigierte den Gesamtmittelwert der Verhältnisse der Standardabweichungen auf 1.03 und zeigte, dass Variablen innerhalb einer Studie zu gleichartigem Verhalten tendierten. Bei den erhobenen Eigenschaften könnte nur durch ungleichmäßige Rekrutierung (p=0.09) und Amendments (p=0.13) ein Einfluss auf die Varianzungleichheit vermutet werden. Anschließend wurden statistische Eigenschaften von neuen Fallzahladaptionsprozeduren für normalverteilte (t-Test) und binäre (Chi-Quadrat-Test) Endpunkte unter der Nullhypothese keines Gruppenunterschieds bestimmt. Die Prozeduren sollten im Verlauf der Studie mehrfach und verblindet die Varianzannahme überprüfen und ggf. die Fallzahl korrigieren. Im Falle stetiger Endpunkte wurde das Niveau eingehalten, bei binären ist zu vermuten, dass die Liberalität des Chi-Quadrat-Tests für die Niveauverletzungen verantwortlich ist. Korrektur für Verblinden führte erwartungsgemäß zu einer leicht unterschätzten Fallzahl, Kontrollgrenzen für die Power verhinderten eine zu häufige Rekalkulation, bewirkten aber u.U. eine verzerrte Fallzahlschätzung. Abhängig vom Vorhandensein anderer Merkmale konnte die Variabilität der Fallzahl durch sie erhöht aber auch verringert werden. Mindestfallzahlen führten teilweise zu starker Überschätzung der Fallzahl, wirkten sich aber ansonsten nicht nachteilig aus. Die Auswirkungen für binäre Variablen konnten sich von denen für die stetigen unterscheiden. Im dritten Teil wurde eine Möglichkeit der alternativen Auswertung von Studien vorgestellt, in denen eine Veränderung der Varianz der Zielgröße angenommen wurde. Anhand der Ergebnisse aus dem ersten Teil konnten verschiedene Szenarien Varianzinflation und/oder Mittelwertverschiebung über den Zeitverlauf einer Studie simuliert und der Fehler 1. und 2. Art der gewöhnlichen Pooling-Prozedur, des Kombinationstests von Fisher und einer Abschlusstestprozedur ermittelt werden. Keines der Verfahren verhielt sich antikonservativ. Die Prozeduren auf Basis des Kombinationstests wiesen verglichen mit der Poolingprozedur bei mittelgroßer Effektstärke eine um bis zu 7%-Punkte größere Power auf, bei veränderten Mittelwerten lagen sie bis zu 20%-Punkten höher. Bei großen und kleinen Effektstärken war der Vorteil dagegen gering, oder der Poolingtest war geringfügig besser

Влияние стартового состава ядерного топлива на концентрацию изотопов урана и плутония в облученном ядерном топливе реактора ВВЭР-1000

Объектом исследования является ядерный реактор ВВЭР с электрической мощностью 1000 МВт. Целью работы является определение влияние стартового состава ядерного топлива на концентрацию изотопов урана и плутония в облученном ядерном топливе реактора ВВЭР-1000.The object of the study is a VVER nuclear reactor with an electric capacity of 1,000 MW. The aim of the work is to determine the influence of the starting composition of nuclear fuel on the concentration of uranium and plutonium isotopes in irradiated nuclear fuel of the VVER-1000 reactor

Age- and sex-specific prevalence and ten-year risk for cardiovascular disease of all 16 risk factor combinations of the metabolic syndrome - A cross-sectional study

<p>Abstract</p> <p>Background</p> <p>Based on the AHA/NHLBI-definition three out of five cardiometabolic traits must be present for the diagnosis of the metabolic syndrome (MetS), resulting in 16 different combination types. The associated cardiovascular risk may however be different and specific combination may be indicative of an increased risk, furthermore little is known to which extent these 16 combinations contribute to the overall prevalence of MetS. Here we assessed the prevalence of all 16 combination types of MetS, analyzed the impact of age and gender on prevalence rates, and estimated the 10-year risk of fatal and non-fatal myocardial infarction (MI) of each MetS combination type.</p> <p>Methods</p> <p>We used data of the German Metabolic and Cardiovascular Risk Project (GEMCAS), a cross-sectional study, performed during October 2005, including 35,869 participants (aged 18-99 years, 61% women). Age-standardized prevalence and 10-year PROCAM and ESC risk scores for MI were calculated.</p> <p>Results</p> <p>In both men and women the combination with elevated waist-circumference, blood pressure and glucose (WC-BP-GL) was the most frequent combination (28%), however a distinct unequal distribution was observed regarding age and sex. Any combination with GL was common in the elderly, whereas any combination with dyslipidemia and without GL was frequent in the younger. Men without MetS had an estimated mean 10-year risk of 4.7% (95%-CI: 4.5%-4.8%) for MI (PROCAM), whereas the mean 10-year risk of men with MetS was clearly higher (age-standardized 7.9%; 7.8-8.0%). In women without MetS the mean 10-year risk for MI was 1.1%, in those with MetS 2.3%. The highest impact on an estimated 10-year risk for MI (PROCAM) was observed with TG-HDL-GL-BP in both sexes (men 14.7%, women 3.9%). However, we could identify combinations with equal risks of non-fatal and fatal MI compared to participants without MetS.</p> <p>Conclusions</p> <p>We observed large variations in the prevalence of all 16 combination types and their association to cardiovascular risk. The importance of different combinations of MetS changes with age and between genders putting emphasis on a tailored approach towards very young or very old subjects. This knowledge may guide clinicians to effectively screen individuals and prioritize diagnostic procedures depending on age and gender.</p

Dyslipidemia in primary care – prevalence, recognition, treatment and control: data from the German Metabolic and Cardiovascular Risk Project (GEMCAS)

<p>Abstract</p> <p>Background</p> <p>Current guidelines from the European Society of Cardiology (ESC) define low thresholds for the diagnosis of dyslipidemia using total cholesterol (TC) and LDL-cholesterol (LDL-C) to guide treatment. Although being mainly a prevention tool, its thresholds are difficult to meet in clinical practice, especially primary care.</p> <p>Methods</p> <p>In a nationwide study with 1,511 primary care physicians and 35,869 patients we determined the prevalence of dyslipidemia, its recognition, treatment, and control rates. Diagnosis of dyslipidemia was based on TC and LDL-C. Basic descriptive statistics and prevalence rate ratios, as well as 95% confidence intervals were calculated.</p> <p>Results</p> <p>Dyslipidemia was highly frequent in primary care (76% overall). 48.6% of male and 39.9% of female patients with dyslipidemia was diagnosed by the physicians. Life style intervention did however control dyslipidemia in about 10% of patients only. A higher proportion (34.1% of male and 26.7% female) was controlled when receiving pharmacotherapy. The chance to be diagnosed and subsequently controlled using pharmacotherapy was higher in male (PRR 1.15; 95%CI 1.12–1.17), in patients with concomitant cardiovascular risk factors, in patients with hypertension (PRR 1.20; 95%CI 1.05–1.37) and cardiovascular disease (PRR 1.46; 95%CI 1.29–1.64), previous myocardial infarction (PRR 1.32; 95%CI 1.19–1.47), and if patients knew to be hypertensive (PRR 1.18; 95%CI 1.04–1.34) or knew about their prior myocardial infarction (PRR 1.17; 95%CI 1.23–1.53).</p> <p>Conclusion</p> <p>Thresholds of the ESC seem to be difficult to meet. A simple call for more aggressive treatment or higher patient compliance is apparently not enough to enhance the proportion of controlled patients. A shift towards a multifactorial treatment considering lifestyle interventions and pharmacotherapy to reduce weight and lipids may be the only way in a population where just to be normal is certainly not ideal.</p

The statistical analysis of a clinical trial when a protocol amendment changed the inclusion criteria

Abstract Background Sometimes, protocol amendments that change the inclusion and exclusion criteria are required in clinical trials. Then, the patient populations before and after the amendment may differ. Methods We propose to perform separate statistical tests for the different phases, i.e. for the patients recruited before and after the amendment, and to combine the tests using Fisher's combination test. After a significant combination test a multiple testing procedure can be applied to identify the phase(s) to which a proof of efficacy refers. We assume that the amendment(s) are not based on any type of unblinded data. The proposed method is investigated within a simulation study. Results The proposed combination approach is superior to the 'naïve' strategy to ignore the differences between the phases and pooling the data to perform just one statistical test. This superiority disappears when there are hardly any differences between the two phases. Conclusion When one or more protocol amendments change the inclusion and exclusion criteria, one should realize that the populations may differ. In this case, separate tests for the different phases together with a combination test are a powerful method that can be applied in a variety of settings. The (first) amendment should specify the combination test to be applied in order to combine the different phases.</p

Impact of time since last caloric intake on blood glucose levels

Blood glucose (BG) is usually measured after a caloric restriction of at least 8 h; however evidence-based recommendations for the duration of a fasting status are missing. Here we analyze the effect of fasting duration on levels of BG to determine the minimal fasting duration to achieve comparable BG levels to conventional fasting measurements. We used data of a cross-sectional study on primary care patients, performed in October 2005. We included 28,024 individuals (age-range 18–99 years; 63% women) without known diabetes mellitus and without missing data for BG and fasting status. We computed general linear models, adjusting for age, sex, time of blood withdrawal, systolic blood pressure, waist circumference, total- and HDL-cholesterol, physical activity, smoking, intake of beta-blocker and alcohol. We tested the intra-individual variability with respect to fasting status. Overall, the mean BG differed only slightly between individuals fasting ≥8 h and those fasting <8 h (men: 5.1 ± 0.8 mmol/L versus 5.2 ± 1.2 mmol/L; women: 4.9 ± 0.7 mmol/L, 5.0 ± 1.0 mmol/L). After 3 h of fasting differences of BG diminished in men to −0.08 mmol/L (95%-CI: −0.15; −0.01 mmol/L), in women to −0.07 mmol/L (−0.12; −0.03 mmol/L) compared to individuals fasting ≥8 h. Noteworthy, age, time of day of blood withdrawal, physical activity, and intake of hard liquor influenced BG levels considerably. Our data challenge the necessity for a fasting duration of ≥8 h when measuring blood glucose, suggesting a random sampling or a fasting duration of 3 h as sufficient. Rather, our study indicates that essentially more effort on the assessment of additional external/internal factors on BG levels is necessary

Phylogeography of the second plague pandemic revealed through analysis of historical Yersinia pestis genomes

The second plague pandemic, caused by Yersinia pestis, devastated Europe and the nearby regions between the 14th and 18th centuries AD. Here we analyse human remains from ten European archaeological sites spanning this period and reconstruct 34 ancient Y. pestis genomes. Our data support an initial entry of the bacterium through eastern Europe, the absence of genetic diversity during the Black Death, and low within-outbreak diversity thereafter. Analysis of post-Black Death genomes shows the diversification of a Y. pestis lineage into multiple genetically distinct clades that may have given rise to more than one disease reservoir in, or close to, Europe. In addition, we show the loss of a genomic region that includes virulence-related genes in strains associated with late stages of the pandemic. The deletion was also identified in genomes connected with the first plague pandemic (541–750 AD), suggesting a comparable evolutionary trajectory of Y. pestis during both events