| Working with human samples |
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Recruitment: enrolling patients with diverse backgrounds from multiple transplant centres remains a challenge
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Small sample size: acquiring a large number of study participants is necessary for significant findings
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Clinical data organisation: collecting, storing, and sorting a significant volume of longitudinal patient data can be difficult to manage
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Study timeline: retention of patients during follow-up is costly, time-consuming, and difficult
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Variable post-transplant drug regimens: immunosuppressive treatment, dosage, and management may change over time, presenting a confounding variable that is difficult to adjust for
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| Study Design |
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Lack of standardised study endpoints: patient/graft survival, acute rejection episodes, biochemical data (i.e., liver enzymes), disease recurrence and development, and non-immunological injury (nephrotoxicity) must all be assessed in relation to post-transplant outcomes
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Lack of control group: post-transplant control groups are difficult to define
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Identification of best markers: there is no standardised method for narrowing down the top candidate markers when using high-throughput approaches
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| Technical challenges |
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Isolating nucleic acids: circulating nucleic acids are characterised by low concentration and high degradation rates
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Experimental methods: utilising high throughput technologies can be costly and time-consuming
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Normalisation methods: multiple approaches can be used to normalise cell-free nucleic acids
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| Building predictive models |
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Team science: biostatisticians are needed to aid in the development and implementation of statistical and mathematical methods
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Overfitting: over-testing the training data can result in a model that appears very accurate but has memorised the key points in the data set rather than learned how to generalise, requiring an independent dataset to validate findings.
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