In many clinical trials, frequent longitudinal data is collected from each patient. For example in chronic pain trials, daily pain measurements of the patients can be collected during several weeks which leads to a large number of highly correlated post-baseline measurements for each patient.
Blinded sample size re-estimation or continuous monitoring of the variance (Friede and Miller, 2012) can deal with situations where uncertainty regarding the true variance exists. In trials with longitudinal data, the situation is common that at interim looks a restricted number of patients have completed the study but a large number has started treatment and first post-baseline data is collected but endpoint data is not yet available. Nevertheless, it is reasonable that the partial data available from these patients gives useful information about the variance of the endpoint (Wüst and Kieser, 2003; Wachtlin and Kieser, 2013).
In this talk, we first quantify the gain of including partial data from patients when estimating the variance. Variability of sample size is often reduced but the amount of reduction depends on the correlation between measurements. Then, our main interest is to investigate the usefulness of a parametric model assumption for the covariance structure. We quantify the gain from the model assumption when the assumed model is correct and discuss consequences when a wrong model is assumed.
Friede T, Miller F (2012). Blinded continuous monitoring of nuisance parameters in clinical trials. Journal of the Royal Statistical Society Series C 61:601–618.
Wachtlin D, Kieser M (2013). Blinded Sample Size Recalculation in Longitudinal Clinical Trials Using Generalized Estimating Equations. Therapeutic Innovation & Regulatory Science 47:460-467.
Wüst K, Kieser M (2003). Blinded sample size recalculation for normally distributed outcomes using long- and short-term data. Biometrical Journal 45:915–930.