Weather explains the decline and rise of insect biomass over 34 years.

Insects have a pivotal role in ecosystem function, thus the decline of more than 75% in insect biomass in protected areas over recent decades in Central Europe1 and elsewhere2,3 has alarmed the public, pushed decision-makers4 and stimulated research on insect population trends. However, the drivers of this decline are still not well understood. Here, we reanalysed 27 years of insect biomass data from Hallmann et al.1, using sample-specific information on weather conditions during sampling and weather anomalies during the insect life cycle. This model explained variation in temporal decline in insect biomass, including an observed increase in biomass in recent years, solely on the basis of these weather variables. Our finding that terrestrial insect biomass is largely driven by complex weather conditions challenges previous assumptions that climate change is more critical in the tropics5,6 or that negative consequences in the temperate zone might only occur in the future7. Despite the recent observed increase in biomass, new combinations of unfavourable multi-annual weather conditions might be expected to further threaten insect populations under continuing climate change. Our findings also highlight the need for more climate change research on physiological mechanisms affected by annual weather conditions and anomalies.

The contribution of insects to global forest deadwood decomposition.

The amount of carbon stored in deadwood is equivalent to about 8 per cent of the global forest carbon stocks1. The decomposition of deadwood is largely governed by climate2-5 with decomposer groups-such as microorganisms and insects-contributing to variations in the decomposition rates2,6,7. At the global scale, the contribution of insects to the decomposition of deadwood and carbon release remains poorly understood7. Here we present a field experiment of wood decomposition across 55 forest sites and 6 continents. We find that the deadwood decomposition rates increase with temperature, and the strongest temperature effect is found at high precipitation levels. Precipitation affects the decomposition rates negatively at low temperatures and positively at high temperatures. As a net effect-including the direct consumption by insects and indirect effects through interactions with microorganisms-insects accelerate the decomposition in tropical forests (3.9% median mass loss per year). In temperate and boreal forests, we find weak positive and negative effects with a median mass loss of 0.9 per cent and -0.1 per cent per year, respectively. Furthermore, we apply the experimentally derived decomposition function to a global map of deadwood carbon synthesized from empirical and remote-sensing data, obtaining an estimate of 10.9 ± 3.2 petagram of carbon per year released from deadwood globally, with 93 per cent originating from tropical forests. Globally, the net effect of insects may account for 29 per cent of the carbon flux from deadwood, which suggests a functional importance of insects in the decomposition of deadwood and the carbon cycle.

Hill-Chao numbers allow decomposing gamma multifunctionality into alpha and beta components.

Biodiversity-ecosystem functioning (BEF) research has provided strong evidence and mechanistic underpinnings to support positive effects of biodiversity on ecosystem functioning, from single to multiple functions. This research has provided knowledge gained mainly at the local alpha scale (i.e. within ecosystems), but the increasing homogenization of landscapes in the Anthropocene has raised the potential that declining biodiversity at the beta (across ecosystems) and gamma scales is likely to also impact ecosystem functioning. Drawing on biodiversity theory, we propose a new statistical framework based on Hill-Chao numbers. The framework allows decomposition of multifunctionality at gamma scales into alpha and beta components, a critical but hitherto missing tool in BEF research; it also allows weighting of individual ecosystem functions. Through the proposed decomposition, new BEF results for beta and gamma scales are discovered. Our novel approach is applicable across ecosystems and connects local- and landscape-scale BEF assessments from experiments to natural settings.

A transformation perspective on marginal and conditional models.

Clustered observations are ubiquitous in controlled and observational studies and arise naturally in multicenter trials or longitudinal surveys. We present a novel model for the analysis of clustered observations where the marginal distributions are described by a linear transformation model and the correlations by a joint multivariate normal distribution. The joint model provides an analytic formula for the marginal distribution. Owing to the richness of transformation models, the techniques are applicable to any type of response variable, including bounded, skewed, binary, ordinal, or survival responses. We demonstrate how the common normal assumption for reaction times can be relaxed in the sleep deprivation benchmark data set and report marginal odds ratios for the notoriously difficult toe nail data. We furthermore discuss the analysis of two clinical trials aiming at the estimation of marginal treatment effects. In the first trial, pain was repeatedly assessed on a bounded visual analog scale and marginal proportional-odds models are presented. The second trial reported disease-free survival in rectal cancer patients, where the marginal hazard ratio from Weibull and Cox models is of special interest. An empirical evaluation compares the performance of the novel approach to general estimation equations for binary responses and to conditional mixed-effects models for continuous responses. An implementation is available in the tram add-on package to the $\texttt{R}$ system and was benchmarked against established models in the literature.

Individual participant data meta-analysis with mixed-effects transformation models.

One-stage meta-analysis of individual participant data (IPD) poses several statistical and computational challenges. For time-to-event outcomes, the approach requires the estimation of complicated nonlinear mixed-effects models that are flexible enough to realistically capture the most important characteristics of the IPD. We present a model class that incorporates general normally distributed random effects into linear transformation models. We discuss extensions to model between-study heterogeneity in baseline risks and covariate effects and also relax the assumption of proportional hazards. Within the proposed framework, data with arbitrary random censoring patterns can be handled. The accompanying $\textsf{R}$ package tramME utilizes the Laplace approximation and automatic differentiation to perform efficient maximum likelihood estimation and inference in mixed-effects transformation models. We compare several variants of our model to predict the survival of patients with chronic obstructive pulmonary disease using a large data set of prognostic studies. Finally, a simulation study is presented that verifies the correctness of the implementation and highlights its efficiency compared to an alternative approach.

Enhancing the structural diversity between forest patches-A concept and real-world experiment to study biodiversity, multifunctionality and forest resilience across spatial scales.

Intensification of land use by humans has led to a homogenization of landscapes and decreasing resilience of ecosystems globally due to a loss of biodiversity, including the majority of forests. Biodiversity-ecosystem functioning (BEF) research has provided compelling evidence for a positive effect of biodiversity on ecosystem functions and services at the local (α-diversity) scale, but we largely lack empirical evidence on how the loss of between-patch ß-diversity affects biodiversity and multifunctionality at the landscape scale (γ-diversity). Here, we present a novel concept and experimental framework for elucidating BEF patterns at α-, ß-, and γ-scales in real landscapes at a forest management-relevant scale. We examine this framework using 22 temperate broadleaf production forests, dominated by Fagus sylvatica. In 11 of these forests, we manipulated the structure between forest patches by increasing variation in canopy cover and deadwood. We hypothesized that an increase in landscape heterogeneity would enhance the ß-diversity of different trophic levels, as well as the ß-functionality of various ecosystem functions. We will develop a new statistical framework for BEF studies extending across scales and incorporating biodiversity measures from taxonomic to functional to phylogenetic diversity using Hill numbers. We will further expand the Hill number concept to multifunctionality allowing the decomposition of γ-multifunctionality into α- and ß-components. Combining this analytic framework with our experimental data will allow us to test how an increase in between patch heterogeneity affects biodiversity and multifunctionality across spatial scales and trophic levels to help inform and improve forest resilience under climate change. Such an integrative concept for biodiversity and functionality, including spatial scales and multiple aspects of diversity and multifunctionality as well as physical and environmental structure in forests, will go far beyond the current widely applied approach in forestry to increase resilience of future forests through the manipulation of tree species composition.

On the relevance of prognostic information for clinical trials: A theoretical quantification.

The question of how individual patient data from cohort studies or historical clinical trials can be leveraged for designing more powerful, or smaller yet equally powerful, clinical trials becomes increasingly important in the era of digitalization. Today, the traditional statistical analyses approaches may seem questionable to practitioners in light of ubiquitous historical prognostic information. Several methodological developments aim at incorporating historical information in the design and analysis of future clinical trials, most importantly Bayesian information borrowing, propensity score methods, stratification, and covariate adjustment. Adjusting the analysis with respect to a prognostic score, which was obtained from some model applied to historical data, received renewed interest from a machine learning perspective, and we study the potential of this approach for randomized clinical trials. In an idealized situation of a normal outcome in a two-arm trial with 1:1 allocation, we derive a simple sample size reduction formula as a function of two criteria characterizing the prognostic score: (1) the coefficient of determination R2 on historical data and (2) the correlation ρ between the estimated and the true unknown prognostic scores. While maintaining the same power, the original total sample size n planned for the unadjusted analysis reduces to ( 1 - R 2 ρ 2 ) × n $(1 - R^2 \rho ^2) \times n$ in an adjusted analysis. Robustness in less ideal situations was assessed empirically. We conclude that there is potential for substantially more powerful or smaller trials, but only when prognostic scores can be accurately estimated.

The Impact of Prepartum Platelet Count on Postpartum Blood Loss and Its Association with Coagulation Factor XIII Activity.

Background: Postpartum hemorrhage is a leading cause of maternal morbidity and mortality worldwide. Contradictory information exists regarding the relevance of prepartum platelet count on postpartum hemorrhage. We have shown prepartum coagulation factor XIII to be associated with postpartum blood loss; however, little is known about the association of platelet count with factor XIII activity. Our objectives were, first, to evaluate the impact of prepartum platelet count on measured postpartum blood loss in the context of prepartum measurements of coagulation factors I, II, and XIII and, second, to evaluate the association of platelet count with coagulation factor XIII, both pre- and postpartum. Material and Methods: This is a secondary analysis of a prospective cohort study (PPH 1,300 study) which analyzed the impact of prepartum blood coagulation factors on postpartum blood loss in 1,300 women. Blood loss was quantified using a validated technique. The impact of prepartum platelet count on measured blood loss was assessed by continuous outcome logistic regression; the association of platelet count with factor XIII activity by Spearman rank correlation. Results: Prepartum platelet count was significantly associated with measured postpartum blood loss: every one unit (G/L) increase in prepartum thrombocytes was associated with an odds ratio of 1.002 (95% confidence interval, 1.001-1.004, p = 0.005) to keep blood loss below any given cut-off level. This means that the probability of postpartum hemorrhage decreases with increasing prepartum platelet levels. Moreover, a significant association of platelet count with factor XIII activity was shown (Spearman rank correlation coefficient for prepartum values 0.228, p < 0.001, and for postpartum values 0.293, p < 0.001). Discussion/Conclusion: The significant association of prepartum platelet count and postpartum blood loss as well as the association of platelet count with blood coagulation factor XIII activity support the likely role of platelets in preventing postpartum hemorrhage and support the new guidelines for the treatment of postpartum hemorrhage in Germany, Austria, and Switzerland, which calls for optimizing platelet counts peripartally in case of postpartum hemorrhage. A possible effect of platelets on the level of circulating factor XIII cannot be ruled out and should prompt further investigation.

An empirical comparison of two novel transformation models.

Continuous response variables are often transformed to meet modeling assumptions, but the choice of the transformation can be challenging. Two transformation models have recently been proposed: semiparametric cumulative probability models (CPMs) and parametric most likely transformation models (MLTs). Both approaches model the cumulative distribution function and require specifying a link function, which implicitly assumes that the responses follow a known distribution after some monotonic transformation. However, the two approaches estimate the transformation differently. With CPMs, an ordinal regression model is fit, which essentially treats each continuous response as a unique category and therefore nonparametrically estimates the transformation; CPMs are semiparametric linear transformation models. In contrast, with MLTs, the transformation is parameterized using flexible basis functions. Conditional expectations and quantiles are readily derived from both methods on the response variable's original scale. We compare the two methods with extensive simulations. We find that both methods generally have good performance with moderate and large sample sizes. MLTs slightly outperformed CPMs in small sample sizes under correct models. CPMs tended to be somewhat more robust to model misspecification and outcome rounding. Except in the simplest situations, both methods outperform basic transformation approaches commonly used in practice. We apply both methods to an HIV biomarker study.

Baseline-adjusted proportional odds models for the quantification of treatment effects in trials with ordinal sum score outcomes.

BACKGROUND: Sum scores of ordinal outcomes are common in randomized clinical trials. The approaches routinely employed for assessing treatment effects, such as t-tests or Wilcoxon tests, are not particularly powerful in detecting changes in relevant parameters or lack the ability to incorporate baseline information. Hence, tailored statistical methods are needed for the analysis of ordinal outcomes in clinical research. METHODS: We propose baseline-adjusted proportional odds logistic regression models to overcome previous limitations in the analysis of ordinal outcomes in randomized clinical trials. For the validation of our method, we focus on common ordinal sum score outcomes of neurological clinical trials such as the upper extremity motor score, the spinal cord independence measure, and the self-care subscore of the latter. We compare the statistical power of our models to other conventional approaches in a large simulation study of two-arm randomized clinical trials based on data from the European Multicenter Study about Spinal Cord Injury (EMSCI, ClinicalTrials.gov Identifier: NCT01571531). We also use the new method as an alternative analysis of the historical Sygen®clinical trial. RESULTS: The simulation study of all postulated trial settings demonstrated that the statistical power of the novel method was greater than that of conventional methods. Baseline adjustments were more suited for the analysis of the upper extremity motor score compared to the spinal cord independence measure and its self-care subscore. CONCLUSIONS: The proposed baseline-adjusted proportional odds models allow the global treatment effect to be directly interpreted. This clear interpretation, the superior statistical power compared to the conventional analysis approaches, and the availability of open-source software support the application of this novel method for the analysis of ordinal outcomes of future clinical trials.

Correction to: Baseline-adjusted proportional odds models for the quantification of treatment effects in trials with ordinal sum score outcomes.

An amendment to this paper has been published and can be accessed via the original article.

Higher incidence of retinopathy of prematurity in extremely preterm infants associated with improved survival rates.

AIM: This study assessed possible reasons for the increasing incidence of retinopathy of prematurity (ROP) since mid-2015 at our institution. METHODS: Retrospective analysis of all preterm infants born July 2013 to June 2017 with a gestational age (GA) <29 completed weeks admitted to the neonatal intensive care unit at the University Hospital Zurich during the first 28 days of life. The primary outcome measures were severest ROP stage. Statistical analysis was performed using generalised additive models in R. RESULTS: During the study period, survival increased in extremely preterm infants. Significant predictors for severest ROP stage were GA, days of mechanical ventilation and multiple gestation (P = .0322). A composite of severe comorbidities had no significant effect on severest ROP stage. GA was identified as the only significant risk factor the for severest ROP stage (P = .0045). CONCLUSION: Increased survival rate of extremely preterm infants was associated with an increased incidence of ROP at our institution. Despite the increase, the incidence is still very low compared with other countries. No other additive factors were identified.

An experimental test of the habitat-amount hypothesis for saproxylic beetles in a forested region.

The habitat-amount hypothesis challenges traditional concepts that explain species richness within habitats, such as the habitat-patch hypothesis, where species number is a function of patch size and patch isolation. It posits that effects of patch size and patch isolation are driven by effects of sample area, and thus that the number of species at a site is basically a function of the total habitat amount surrounding this site. We tested the habitat-amount hypothesis for saproxylic beetles and their habitat of dead wood by using an experiment comprising 190 plots with manipulated patch sizes situated in a forested region with a high variation in habitat amount (i.e., density of dead trees in the surrounding landscape). Although dead wood is a spatio-temporally dynamic habitat, saproxylic insects have life cycles shorter than the time needed for habitat turnover and they closely track their resource. Patch size was manipulated by adding various amounts of downed dead wood to the plots (~800 m³ in total); dead trees in the surrounding landscape (~240 km2 ) were identified using airborne laser scanning (light detection and ranging). Over 3 yr, 477 saproxylic species (101,416 individuals) were recorded. Considering 20-1,000 m radii around the patches, local landscapes were identified as having a radius of 40-120 m. Both patch size and habitat amount in the local landscapes independently affected species numbers without a significant interaction effect, hence refuting the island effect. Species accumulation curves relative to cumulative patch size were not consistent with either the habitat-patch hypothesis or the habitat-amount hypothesis: several small dead-wood patches held more species than a single large patch with an amount of dead wood equal to the sum of that of the small patches. Our results indicate that conservation of saproxylic beetles in forested regions should primarily focus on increasing the overall amount of dead wood without considering its spatial arrangement. This means dead wood should be added wherever possible including in local landscapes with low or high dead-wood amounts. For species that have disappeared from most forests owing to anthropogenic habitat degradation, this should, however, be complemented by specific conservation measures pursued within their extant distributional ranges.

Autoregressive transitional ordinal model to test for treatment effect in neurological trials with complex endpoints.

BACKGROUND: A number of potential therapeutic approaches for neurological disorders have failed to provide convincing evidence of efficacy, prompting pharmaceutical and health companies to discontinue their involvement in drug development. Limitations in the statistical analysis of complex endpoints have very likely had a negative impact on the translational process. METHODS: We propose a transitional ordinal model with an autoregressive component to overcome previous limitations in the analysis of Upper Extremity Motor Scores, a relevant endpoint in the field of Spinal Cord Injury. Statistical power and clinical interpretation of estimated treatment effects of the proposed model were compared to routinely employed approaches in a large simulation study of two-arm randomized clinical trials. A revisitation of a key historical trial provides further comparison between the different analysis approaches. RESULTS: The proposed model outperformed all other approaches in virtually all simulation settings, achieving on average 14 % higher statistical power than the respective second-best performing approach (range: -1 %, +34 %). Only the transitional model allows treatment effect estimates to be interpreted as conditional odds ratios, providing clear interpretation and visualization. CONCLUSION: The proposed model takes into account the complex ordinal nature of the endpoint under investigation and explicitly accounts for relevant prognostic factors such as lesion level and baseline information. Superior statistical power, combined with clear clinical interpretation of estimated treatment effects and widespread availability in commercial software, are strong arguments for clinicians and trial scientists to adopt, and further extend, the proposed approach.

Oxaliplatin added to fluorouracil-based preoperative chemoradiotherapy and postoperative chemotherapy of locally advanced rectal cancer (the German CAO/ARO/AIO-04 study): final results of the multicentre, open-label, randomised, phase 3 trial.

BACKGROUND: Preoperative chemoradiotherapy with infusional fluorouracil, total mesorectal excision surgery, and postoperative chemotherapy with fluorouracil was established by the German CAO/ARO/AIO-94 trial as a standard combined modality treatment for locally advanced rectal cancer. Here we compare the previously established regimen with an investigational regimen in which oxaliplatin was added to both preoperative chemoradiotherapy and postoperative chemotherapy. METHODS: In this multicentre, open-label, randomised, phase 3 study we randomly assigned patients with rectal adenocarcinoma, clinically staged as cT3-4 or any node-positive disease, to two groups: a control group receiving standard fluorouracil-based combined modality treatment, consisting of preoperative radiotherapy of 50·4 Gy in 28 fractions plus infusional fluorouracil (1000 mg/m(2) on days 1-5 and 29-33), followed by surgery and four cycles of bolus fluorouracil (500 mg/m(2) on days 1-5 and 29); or to an investigational group receiving preoperative radiotherapy of 50·4 Gy in 28 fractions plus infusional fluorouracil (250 mg/m(2) on days 1-14 and 22-35) and oxaliplatin (50 mg/m(2) on days 1, 8, 22, and 29), followed by surgery and eight cycles of oxaliplatin (100 mg/m(2) on days 1 and 15), leucovorin (400 mg/m(2) on days 1 and 15), and infusional fluorouracil (2400 mg/m(2) on days 1-2 and 15-16). Randomisation was done with computer-generated block-randomisation codes stratified by centre, clinical T category (cT1-3 vs cT4), and clinical N category (cN0 vs cN1-2) without masking. The primary endpoint was disease-free survival, defined as the time between randomisation and non-radical surgery of the primary tumour (R2 resection), locoregional recurrence after R0/1 resection, metastatic disease or progression, or death from any cause, whichever occurred first. Survival and cumulative incidence of recurrence analyses followed the intention-to-treat principle; toxicity analyses included all patients treated. Enrolment of patients in this trial is completed and follow-up is ongoing. This study is registered with ClinicalTrials.gov, number NCT00349076. FINDINGS: Of the 1265 patients initially enrolled, 1236 were assessable (613 in the investigational group and 623 in the control group). With a median follow-up of 50 months (IQR 38-61), disease-free survival at 3 years was 75·9% (95% CI 72·4-79·5) in the investigational group and 71·2% (95% CI 67·6-74·9) in the control group (hazard ratio [HR] 0·79, 95% CI 0·64-0·98; p=0·03). Preoperative grade 3-4 toxic effects occurred in 144 (24%) of 607 patients who actually received fluorouracil and oxaliplatin during chemoradiotherapy and in 128 (20%) of 625 patients who actually received fluorouracil chemoradiotherapy. Of 445 patients who actually received adjuvant fluorouracil and leucovorin and oxaliplatin, 158 (36%) had grade 3-4 toxic effects, as did 170 (36%) of 470 patients who actually received adjuvant fluorouracil. Late grade 3-4 adverse events in patients who received protocol-specified preoperative and postoperative treatment occurred in 112 (25%) of 445 patients in the investigational group, and in 100 (21%) of 470 patients in the control group. INTERPRETATION: Adding oxaliplatin to fluorouracil-based neoadjuvant chemoradiotherapy and adjuvant chemotherapy (at the doses and intensities used in this trial) significantly improved disease-free survival of patients with clinically staged cT3-4 or cN1-2 rectal cancer compared with our former fluorouracil-based combined modality regimen (based on CAO/ARO/AIO-94). The regimen established by CAO/ARO/AIO-04 can be deemed a new treatment option for patients with locally advanced rectal cancer. FUNDING: German Cancer Aid (Deutsche Krebshilfe).

Association of extinction risk of saproxylic beetles with ecological degradation of forests in Europe.

To reduce future loss of biodiversity and to allocate conservation funds effectively, the major drivers behind large-scale extinction processes must be identified. A promising approach is to link the red-list status of species and specific traits that connect species of functionally important taxa or guilds to resources they rely on. Such traits can be used to detect the influence of anthropogenic ecosystem changes and conservation efforts on species, which allows for practical recommendations for conservation. We modeled the German Red List categories as an ordinal index of extinction risk of 1025 saproxylic beetles with a proportional-odds linear mixed-effects model for ordered categorical responses. In this model, we estimated fixed effects for intrinsic traits characterizing species biology, required resources, and distribution with phylogenetically correlated random intercepts. The model also allowed predictions of extinction risk for species with no red-list category. Our model revealed a higher extinction risk for lowland and large species as well as for species that rely on wood of large diameter, broad-leaved trees, or open canopy. These results mirror well the ecological degradation of European forests over the last centuries caused by modern forestry, that is the conversion of natural broad-leaved forests to dense conifer-dominated forests and the loss of old growth and dead wood. Therefore, conservation activities aimed at saproxylic beetles in all types of forests in Central and Western Europe should focus on lowlands, and habitat management of forest stands should aim at increasing the amount of dead wood of large diameter, dead wood of broad-leaved trees, and dead wood in sunny areas.

Cranial dural arteriovenous shunts. Part 4. Clinical presentation of the shunts with leptomeningeal venous drainage.

Cranial dural arteriovenous fistulae have been classified into high- and low-risk lesions mainly based on the pattern of venous drainage. Those with leptomeningeal venous drainage carry a higher risk of an aggressive clinical presentation. Recently, it has been proposed that the clinical presentation should be considered as an additional independent factor determining the clinical course of these lesions. However, dural shunts with leptomeningeal venous drainage include a very wide spectrum of inhomogeneous lesions. In the current study, we correlated the clinical presentation of 107 consecutive patients harboring cranial dural arteriovenous shunts with leptomeningeal venous drainage, with their distinct anatomic and angiographic features categorized into eight groups based on the "DES" (Directness and Exclusivity of leptomeningeal venous drainage and features of venous Strain) concept. We found that among these groups, there are significant angioarchitectural differences, which are reflected by considerable differences in clinical presentation. Leptomeningeal venous drainage of dural sinus shunts that is neither direct nor exclusive and without venous strain manifested only benign symptoms (aggressive presentation 0%). On the other end of the spectrum, the bridging vein shunts with direct and exclusive leptomeningeal venous drainage and venous strain are expected to present aggressive symptoms almost always and most likely with bleeding (aggressive presentation 91.5%). Important aspects of the above correlations are discussed. Therefore, the consideration of leptomeningeal venous drainage alone, for prediction of the clinical presentation of these shunts appears insufficient. Angiographic analysis based on the above concept, offers the possibility to distinguish the higher- from the lower-risk types of leptomeningeal venous drainage. In this context, consideration of the clinical presentation as an additional independent factor for the prediction of their clinical course seems superfluous and possibly misleading. Topography is connected to the clinical presentation of the dural shunts inasmuch as the former determines the venous anatomy and the angioarchitectural features of the lesions.

Heterogeneous treatment effect estimation for observational data using model-based forests.

The estimation of heterogeneous treatment effects has attracted considerable interest in many disciplines, most prominently in medicine and economics. Contemporary research has so far primarily focused on continuous and binary responses where heterogeneous treatment effects are traditionally estimated by a linear model, which allows the estimation of constant or heterogeneous effects even under certain model misspecifications. More complex models for survival, count, or ordinal outcomes require stricter assumptions to reliably estimate the treatment effect. Most importantly, the noncollapsibility issue necessitates the joint estimation of treatment and prognostic effects. Model-based forests allow simultaneous estimation of covariate-dependent treatment and prognostic effects, but only for randomized trials. In this paper, we propose modifications to model-based forests to address the confounding issue in observational data. In particular, we evaluate an orthogonalization strategy originally proposed by Robinson (1988, Econometrica) in the context of model-based forests targeting heterogeneous treatment effect estimation in generalized linear models and transformation models. We found that this strategy reduces confounding effects in a simulated study with various outcome distributions. We demonstrate the practical aspects of heterogeneous treatment effect estimation for survival and ordinal outcomes by an assessment of the potentially heterogeneous effect of Riluzole on the progress of Amyotrophic Lateral Sclerosis.

Case studies in reproducibility.

Reproducible research is a concept of providing access to data and software along with published scientific findings. By means of some case studies from different disciplines, we will illustrate reasons why readers should be given the possibility to look at the data and software independently from the authors of the original publication. We report results of a survey comprising 100 papers recently published in Bioinformatics. The main finding is that authors of this journal share a culture of making data available. However, the number of papers where source code for simulation studies or analyzes is available is still rather limited.