A new way to assess the performance of pre-clinical and clinical trials
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vienna A new method has been developed that aims to assess a study’s pre- or post-clinical performance in a systematic way.
The method uses computer simulations to assess how well a study is performing, based on factors like the size of the sample, number of participants and the number of studies.
A study with less than 50 participants will not be included in the simulation, but if a study has 50 participants or more, then the simulations will also be used to assess whether the study has achieved the desired effect.
The researchers believe that their method will be able to identify study trends that will improve its ability to detect important clinical trials and the ability to predict the impact of such trials on the health of the public.
“We are already seeing some studies that are performing well on a global scale and the main reason for that is the fact that we are able to analyse data from different sources.
However, we have to be cautious when we look at individual studies that have been published, because we know that there is a tendency for studies to be less than satisfactory,” said Dr Mladen Naljibovic, an assistant professor at the Institute of Epidemiology and Biostatistics, Vienna University, who worked on the project.
“We have to look at what we are seeing from the data from the studies and what is the reason behind that.”
To do this, researchers analyzed the number and types of studies conducted during the previous six months in the preclinical and human trials databases of the European Commission.
This database was created in 2009 and is currently used to analyse the quality of preclinical studies that were carried out during the period from 2008 to 2013.
The database includes data from all of the studies that received funding from the EU or from national and international bodies, and can be used for research purposes such as in the prevention of diseases or in the development of new treatments.
The researchers looked at the studies conducted from the beginning of 2008 to the end of March 2013, using a method called ‘quantitative data mining’, which involves analysing the data by using mathematical equations.
They then analysed the data for the number, type, duration and type of participants in the studies, using statistical methods and developed an algorithm that can identify trends in the data.
The method is based on mathematical algorithms that are used to model statistical phenomena such as how long it takes for a particular sample to produce an outcome, and the distribution of different factors that influence the outcomes of a study.
It also helps in detecting whether there are systematic differences in the results from studies that did not have a lot of participants.
Using this method, researchers identified the three main trends that were most evident in the large numbers of studies that had been published.
These were the number-of-participants (MOM) and number of trials (TOT) that are associated with a high risk of bias, the type of trials that were performed and the proportion of participants with preexisting conditions.
The results were also able to show the importance of the number in assessing the results of the study, and how this could be used in future studies to detect systematic differences.
The authors also found that the results can be linked to different types of disease outcomes, for example, the proportion with preexsistent conditions, the duration of the trial and the type and frequency of the intervention.
It is the first time that such a systematic method has ever been applied in a large scale, and Dr Nalju said that this approach has the potential to identify important clinical trial trends that can improve the overall quality of research.
As a result, the researchers are planning to start a study in a new European country, to test the new method in an upcoming study that will examine the effects of the MOM and TOT on patients with chronic diseases such as hypertension, diabetes and cancer.
### The article ‘Quantitative Data Mining: A method to predict study trends in preclinical trials and clinical studies’ by Mladen J. Naljić, Mladen S. Nalevski, Marja V. Miljanovic, Janneke J. van der Ploeg, M. E. S. Sijpers and M. N. Nalinovic was published in the journal Applied Physiology, Nutrition and Metabolism.
The research was supported by the European Research Council.
vienna A new method has been developed that aims to assess a study’s pre- or post-clinical performance in a systematic…