A method for assessing robustness of the results of a star-shaped network meta-analysis under the unidentifiable consistency assumption

Background In a star-shaped network, pairwise comparisons link treatments with a reference treatment (often placebo or standard care), but not with each other. Thus, comparisons between non-reference treatments rely on indirect evidence, and are based on the unidentifiable consistency assumption, limiting the reliability of the results. We suggest a method of performing a sensitivity analysis through data imputation to assess the robustness of results with an unknown degree of inconsistency. Methods The method involves imputation of data for randomized controlled trials comparing non-reference treatments, to produce a complete network. The imputed data simulate a situation that would allow mixed treatment comparison, with a statistically acceptable extent of inconsistency. By comparing the agreement between the results obtained from the original star-shaped network meta-analysis and the results after incorporating the imputed data, the robustness of the results of the original star-shaped network meta-analysis can be quantified and assessed. To illustrate this method, we applied it to two real datasets and some simulated datasets. Results Applying the method to the star-shaped network formed by discarding all comparisons between non-reference treatments from a real complete network, 33% of the results from the analysis incorporating imputed data under acceptable inconsistency indicated that the treatment ranking would be different from the ranking obtained from the star-shaped network. Through a simulation study, we demonstrated the sensitivity of the results after data imputation for a star-shaped network with different levels of within- and between-study variability. An extended usability of the method was also demonstrated by another example where some head-to-head comparisons were incorporated. Conclusions Our method will serve as a practical technique to assess the reliability of results from a star-shaped network meta-analysis under the unverifiable consistency assumption.This research was supported by a grant of the Korea Health Technology R&D Project through the Korea Health Industry Development Institute, funded by the Ministry of Health & Welfare, Republic of Korea (grant number: HI19C1178). The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript

Computing and Interpreting Specific Production Rates in a Chemostat in Steady State according to the Luedeking: Piret Model

The Luedeking–Piret model is an empirical relationship which is very widely used in cell cultures to evaluate specific production rates of some products (metabolites or others). It constitutes a very common method of calculation as much in fundamental as in applied research and especially for designing and optimizing industrial processes in very varied fields. However, this model appears to be frequently deficient and has to be greatly adapted, practically, one might say, for each individual case. Obviously, this is a very great drawback, requiring a great deal of time spent on it and one that greatly lessens the ‘universality’ of the model. This work reveals that it is possible to give the initial Luedeking–Piret model a much more general scope. The used method revealed metabolic switches that have never been suspected until now. Confirmation of the method would certainly give a precious general tool both to optimize production processes and to increase understanding of some physiological states of cells in chemostat.info:eu-repo/semantics/publishe