Health research is often concerned with the transition of health conditions and their relation with given exposures, therefore requiring longitudinal data. However, such data is not always available and resource‐intensive to collect. Our aim is to use a pseudo‐panel of independent cross‐sectional data (e.g., data of T0$T_0$ and T1$T_1$) to extrapolate and approximate longitudinal health trajectories (T0$T_0$–T1$T_1$). Methods will be illustrated by examples of studying contextual effects on health among refugees by calculating transition probabilities with associated variances. The data consist of two cross‐sectional health surveys among randomly selected refugee samples in reception (T0$T_0$) and accommodation centers (T1$T_1$) located in Germany's third‐largest federal state. Self‐reported measures of physical and mental health, health‐related quality of life, health care access, and unmet medical needs of 560 refugees were collected. Missing data were imputed by multiple imputation. For each imputed data set, transition probabilities were calculated based on (i) probabilistic discrete event systems with Moore‐Penrose generalized inverse matrix method (PDES‐MP) and (ii) propensity score matching (PSM). By application of sampling approaches, exploiting the fact that status membership is multinomially distributed, results of both methods were pooled by Rubin's Rule, accounting for within and between‐imputation variance. Most of the analyzed estimates of the transition probabilities and their variances are comparable between both methods. However, it seems that they handle sparse cells differently: either assigning an average value for the transition probability for all states with high certainty (i) or assigning a more extreme value for the transition probability with large variance estimate (ii).
Enzymatic debridement (ED) has become a reliable tool for eschar removal. Although ED application is simple, wound bed evaluation and therapy decision post-intervention are prone to subjectivity and failure. Experience in ED might be the key, but this has not been proven yet. The aim of this study was to assess interrater reliability (IR) in post-intervention wound bed evaluation and therapy decision as well as the impact of experience. In addition, the authors introduce video assessment as a valuable tool for post-ED decision-making and education. A video-based survey was conducted among physicians with various experiences in ED. The survey involved multiple-choice and 5-point Likert scale questions about professional status, experience in ED, confidence in post-ED wound bed evaluation, and therapy decision. Subsequently, videos of 15 mixed pattern to full-thickness burns immediately after removal of the enzyme complex were demonstrated. Participants were asked for evaluation of each burn wound, including bleeding pattern and consequent therapy decision. IR ≥ 80% was considered as a consensus. Responses were stratified according to participants’ experience in applying ED (<10, 10–19, 20–49, and ≥50 applications). IR was assessed by chi-square test (raw agreement [RA]; ≥80% was considered as a consensus) and by calculation of Krippendorff’s alpha. In addition, expert consensus for therapy decision was compared with the actual clinical course of each shown patient. Last, participants were asked for their opinion on video as an assessment tool for post-ED wound bed evaluation, decision-making, and training. Thirty-one physicians from 11 burn centers participated in the survey. The overall consensus (raw agreement [RA] ≥ 80%) in post-ED wound bed evaluation and therapy decision was achieved in 20 and 40%, respectively. Krippendorff’s alpha is given by 0.32 (95% confidence interval: 0.15, 0.49) and 0.31 (95% confidence interval: 0.16, 0.47), respectively. Subgroup analysis revealed that physicians with high experience in ED achieved significantly more consensus in post-intervention wound bed evaluation and therapy decision compared with physicians with moderate experience (60 vs 13.3%; P = .02 and 86.7 vs 33.3%; P = .04, respectively). Video analysis was considered a feasible (90.3%) and beneficial (93.5%) tool for post-intervention wound bed evaluation and therapy decision as well as useful for training purposes (100%). Reliability of wound bed evaluation and therapy decision after ED depends on the experience of the rating physician. Video analysis is deemed to be a valuable tool for ED evaluation, decision-making, and user training.
Background Go/no-go decisions after phase II and sample size chosen for phase III are usually based on phase II results (e.g., the treatment effect estimate of phase II). Due to the decision rule (only promising phase II results lead to phase III), treatment effect estimates from phase II that initiate a phase III trial commonly overestimate the true treatment effect. Underpowered phase III trials are the consequence. Optimistic findings may then not be reproduced, leading to the failure of potentially expensive drug development programs. For some disease areas these failure rates are described to be quite high: 62.5%. Methods We integrate the ideas of multiplicative and additive adjustment of treatment effect estimates after go decisions in a utility-based framework for optimizing drug development programs. The design of a phase II/III program, i.e., the “right amount of adjustment”, the allocation of the resources to phase II and III in terms of sample size, and the rule applied to decide whether to stop or to proceed with phase III influences its success considerably. Given specific drug development program characteristics (e.g., fixed and variable per patient costs for phase II and III, probable gain in case of market launch), optimal designs with respect to the maximal expected utility can be identified by the proposed Bayesian-frequentist approach. The method will be illustrated by application to practical examples characteristic for oncological studies. Results In general, our results show that the program set-ups with adjusted treatment effect estimate used for phase III planning are superior to the “naïve” program set-ups with respect to the maximal expected utility. Therefore, we recommend considering an adjusted phase II treatment effect estimate for the phase III sample size calculation. However, there is no one-fits-all design. Conclusion Individual drug development planning for a specific program is necessary to find the optimal design. The optimal choice of the design parameters for a specific drug development program at hand can be found by our user friendly R Shiny application and package (both assessable open-source via [1]).
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