Internship in Biostatistics

Schistosomiasis is a parasitic disease caused by blood flukes of the genus Schistosoma with an acute phase followed by a chronic phase due to repeated exposure to the parasite if untreated. The main species causing human infections are Schistosoma mansoni and Schistosoma haematobium haematobium In Africa mainly and S. japonicum and S. Mekongki in Asia. Approximately 779 million people are at risk of acquiring the infection. In 2021, an estimated 136 million school-aged children and 115 million adults worldwide needed preventive chemotherapy (PC) in 51 countries. The African region has the highest disease burden with 41 out of 51 countries affected, and a proportion of 90.6% people requiring PC.

The main WHO elimination objective translates into eliminating “the morbidity associated with the disease in the target population by reducing the prevalence of moderate
- and heavy-intensity infections and the overall prevalence of infection, mainly by PC with praziquantel. However, the main hurdle to this strategy based on the prevalence of the disease are the diagnostic tests used to detect the presence of the parasite, which are inaccurate in terms of sensitivity especially when it comes to low prevalence. For S. mansoni, the conventional reference standard diagnostic test is duplicate Kato-Katz thick smears (KK) and the main parasitological method for detecting infection with S. haematobium is urine filtration and microscopy. These methods have been available for decades and are widely implemented because of their simplicity and low cost, but lack sensitivity.

To inform policy recommendations by the World Health Organization (WHO) expert group on diagnostic tools for human Schistosoma infections in the context of verification of transmission interruption, we performed a systematic review and meta-analysis that was published in 2023.

Meta analysis of available data on diagnostic tools accuracy is a means to evaluate current knowledge and to define research needs in this topic. Aggregated data from articles are usually pooled and analysed with random effects methods but these are less accurate when it comes to jointly evaluate 2 criteria such as sensitivity and specificity.

However this research do not allow to compare the new diagnostic tools together aside from with the conventional methods. In that case Network Meta Analysis methods could be used.

**Objectives**

The objectives of the current work are to assess and compare the sensitivity (Se) and specificity (Sp) of a wide range of diagnostic tools by using network meta analysis methods in order to evaluate the optimal tool for eendemic areas for shistosomiasis.

The steps to carry out the research are:

- Litterature search on statistical methods available for network meta analysis of diagnostic tools
- Comparison of the available methodologies
- Analysis of data with review manager (Cochrane collaboration)
- Analysis of data with Bayesian models
- Application of Network Meta Analysis methodologies to schistosomiaisis diagnostic tools

**Training and research environment**

Students have the opportunity to work in an interdisciplinary & international scientific environment, benefitting from diverse exchanges with other students and scientists in and across the department, and allowing further training in a variety of health-related domains, fostering the acquisition of horizontal skills around presentation & communication skills, scientific English, statistical data evaluation.

**Required Profile**
- Current master student in statistics, mathematics, applied mathematics or data science
- Good knowledge and hands-on experience of SAS or R
- Prior experience with data analysis of medical data
- Good writing skills in English
- Good organizational skills
- High motivation and discipline to work in a multi-disciplinary team.

**Main activities**:

- Litterature search on diagnostic tools for shistosomiasis
- Use of Revman to update data on the systematic review
- Extraction of data to further use with Network mea-analysis

**Scientific contact**:
Dr Michel Vaillant
Centre of Competences for Methodology and Statistics
Department of Medical Informatics

**Gender Equality**

The LIH is an equal opportunities employer. We are fully committed to removing any discriminatory barrier related to gender, and not only, in recruitment and career progression of our staff. The LIH is attentive to gender representation among its leadership staff and aims to eliminate obstacles to the recruitment and promotion of female leaders and their career development.

**In Short...**:

- Contract duration : 6 months
- Work hours : 40h/week
- Location : rue Thomas Edison 1 A-B - 1445 LUXEMBOURG
- Start date : ASAP
- Ref : JF/INT1024/MV/CCMS

**How to apply**:
Applications including a letter detailing your motivation and a curriculum vitae should be sent through our website.