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Successful Statistical Strategies for Patient Screening and Stratification

Purposeful consideration of statistical analysis can be hugely beneficial for clinical studies by increasing the potential to detect an efficacy signal and reducing the signal-to-noise ratio. Pre-screening and patient stratification are two methods that can be used to help drive efficiencies and boost the statistical power of clinical studies.

In this talk, Dr Elizabeth Baker, Director of Statistical Sciences at Cambridge Cognition, discussed “Successful statistical strategies for pre-screening and patient stratification”. The talk particularly focused on clinical studies with cognitive endpoints.

Dr Baker began by discussing how best to integrate statistical planning into a cognitive study design. The most important place to start is making sure the research question is well understood. This can then be used to derive the study objective, design, and statistical analysis. Dr Baker recommended using the ESTIMAND framework, which helps to elucidate the research question, fully derive the objective, and create a link between the estimate and analysis methods.

It is also important to account for sources of variability, which may be found within the population, the disorder being studied or between trial sites. With cognitive endpoint selection, objective measures of cognition must be used to ensure consistency between different people, sites and raters. The CANTABTM Spatial Working Memory task is an example of an objective tool that will reduce variability.

The second part of the talk focused on patient stratification.

For cognitive studies, stratification is of most benefit when there is significant heterogeneity in the population that is caused by a known factor. Stratification can help to ensure balance across the study design, for example by balancing recruitment across centres in multicentre trials and accounting for any between-centre differences in a stratified analysis.  

The EBBINGHAUS study investigated potential cognitive effects of cholesterol-lowering therapies. To ensure cognitive safety was appropriately assessed, it was important to stratify individuals by cholesterol level at screening to tangibly adjust for the risk of a cardiovascular event.

Another condition where patient stratification can be useful is schizophrenia, which has high variability in cognitive symptoms. One way to adjust for this variability is to stratify individuals by cognitive impairment. A recent post-hoc analysis has shown that people with a low level of cognitive impairment in schizophrenia responded differently to treatment than people with a high level of impairment. Stratification can therefore help to identify individuals who are more likely to respond.

The final part of the talk focused on pre-screening and included a deep dive into a study that had successfully used pre-screening: the Models of Patient Engagement for Alzheimer’s Disease (MOPEAD) study.

Pre-screening can help to increase chances of success in recruiting a particular patient population. It involves asking individuals to take an assessment prior to screening, for example a cognitive assessment. This helps to confirm that the individual meets the requirements for the target population before going through the longer screening process. In conditions where there is high heterogeneity in cognitive impairment such as Alzheimer’s disease, pre-screening can help to ensure that study participants are at a similar stage of disease. When partnered with remote testing, pre-screening can also reach participants from more diverse backgrounds.

Watch the full recording:

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