Using standard missing data taxonomy, due to Rubin and co-workers, and simple algebraic derivations, it is argued that some simple but commonly used methods to handle incomplete longitudinal clinical trial data, such as complete case analyses and methods based on last observation carried forward, require restrictive assumptions and stand on a weaker theoretical foundation than likelihood-based methods developed under the missing at random (MAR) framework. Given the availability of flexible software for analyzing longitudinal sequences of unequal length, implementation of likelihood-based MAR analyses is not limited by computational considerations. While such analyses are valid under the comparatively weak assumption of MAR, the possibility of data missing not at random (MNAR) is difficult to rule out. It is argued, however, that MNAR analyses are, themselves, surrounded with problems and therefore, rather than ignoring MNAR analyses altogether or blindly shifting to them, their optimal place is within sensitivity analysis. The concepts developed here are illustrated using data from three clinical trials, where it is shown that the analysis method may have an impact on the conclusions of the study.

Summary1. Worldwide, the floristic composition of temperate forests bears the imprint of past land use for decades to centuries as forests regrow on agricultural land. Many species, however, display significant interregional variation in their ability to (re)colonize post-agricultural forests. This variation in colonization across regions and the underlying factors remain largely unexplored. 2. We compiled data on 90 species and 812 species · study combinations from 18 studies across Europe that determined species' distribution patterns in ancient (i.e. continuously forested since the first available land use maps) and post-agricultural forests. The recovery rate (RR) of species in each landscape was quantified as the log-response ratio of the percentage occurrence in post-agricultural over ancient forest and related to the species-specific life-history traits and local (soil characteristics and light availability) and regional factors (landscape properties as habitat availability, time available for colonization, and climate). 3. For the herb species, we demonstrate a strong (interactive) effect of species' life-history traits and forest habitat availability on the RR of post-agricultural forest. In graminoids, however, none of the investigated variables were significantly related to the RR. 4. The better colonizing species that mainly belonged to the short-lived herbs group showed the largest interregional variability. Their recovery significantly increased with the amount of forest habitat within the landscape, whereas, surprisingly, the time available for colonization, climate, soil characteristics and light availability had no effect. 5. Synthesis. By analysing 18 independent studies across Europe, we clearly showed for the first time on a continental scale that the recovery of short-lived forest herbs increased with the forest habitat availability in the landscape. Small perennial forest herbs, however, were generally unsuccessful in colonizing post-agricultural forest -even in relatively densely forested landscapes. Hence, our results stress the need to avoid ancient forest clearance to preserve the typical woodland flora.

Abstract. Vegetation history for the study region is reconstructed on the basis of pollen, charcoal and AMS ~4C investigations of lake sediments from Lago del Segrino (calcareous bedrock) and Lago di Muzzano (siliceous bedrock). Late-glacial forests were characterised by Betula and Pinus sylvestris. At the beginning of the Holocene they were replaced by temperate continental forest and shrub communities. A special type of temperate lowland forest, with Abies alba as the most important tree, was present in the period 8300 to 4500 B.P. Subsequently, Fagus, Quercus and Alnus glutinosa were the main forest components and A. aIba ceased to be of importance. Casmnea sativa and Juglans regia were probabIy introduced after forest clearance by fire during the first century A.D. On soils derived from siliceous bedrock, C. sativa was already dominant at ca. A.D. 200 (A.D. dates are in calendar years). In limestone areas, however, C. sativa failed to achieve a dominant role. After the introduction of C. sativa, the main trees were initially oak (Quercus spp.) and later the walnut (Juglans regia). Ostrya carpinifolia became the dominant tree around Lago del Segrino only in the last 100-200 years though it had spread into the area at ca. 5000 cal. B.C. This recent expansion of Ostrya is confirmed at other sites and appears to be controlled by human disturbances involving especially clearance. It is argued that these forests should not be regarded as climax communities. It is suggested that under undisturbed succession they would develop into mixed deciduous forests consisting of Fraxinus excelsior, Tilia, Ulmus, Quercus and Acer.

Models for incomplete longitudinal data under missingness not at random have gained some popularity. At the same time, cautionary remarks have been issued regarding their sensitivity to often unverifiable modeling assumptions. Consequently, there is evidence for a shift towards using ignorable methodology, supplemented with sensitivity analyses to explore the impact of potential deviations of this assumption in the direction of missingness at random. One such tool is local influence. It is shown that local influence tends to pick up a lot of different anomalies in the data at hand, not just deviations in the MNAR mechanism. This particular behavior is described and insight offered in terms of the non-standard behavior of the likelihood ratio test statistic for MAR missingness versus MNAR missingness within a model of the Diggle and Kenward type.

The process of monoisotopic mass determination, i.e., nomination of the correct peak of an isotopically resolved group of peptide peaks as a monoisotopic peak, requires prior information about the isotopic distribution of the peptide. This points immediately to the difficulty of monoisotopic mass determination, whereas a single mass spectrum does not contain information about the atomic composition of a peptide and therefore the isotopic distribution of the peptide remains unknown. To solve this problem a technique is required, which is able to estimate the isotopic distribution given the information of a single mass spectrum. Senko et al. calculated the average isotopic distribution for any mass peptide via the multinomial expansion (Yergey 1983) [1], using a scaled version of the average amino acid Averagine (Senko et al. 1995) [2]. Another method, introduced by Breen et al., approximates the result of the multinomial expansion by a Poisson model (Breen et al. 2000) [3]. Although both methods perform well, they have their specific limitations. In this manuscript, we propose an alternative method for the prediction of the isotopic distribution based on a model for consecutive ratios of peaks from the isotopic distribution, similar in spirit to the approach introduced by Gay et al. (1999) [5]. The presented method is computationally simple and accurate in predicting the expected isotopic distribution. Further, we extend our method to estimate the isotopic distribution of sulphur-containing peptides. This is important because the naturally occurring isotopes of sulphur have an impact on the isotopic distribution of a peptide. (J