Effects of intense assessment on statistical power in randomized controlled trials: Informed simulation study on depression

Schuster, Raphael; Schreyer, Manuela; Kaiser, Tim; Berger, Thomas; Klein, Jan Philipp; Moritz, Steffen; Laireiter, Anton-Rupert; Trutschnig, Wolfgang (February 2020). Effects of intense assessment on statistical power in randomized controlled trials: Informed simulation study on depression (PsyArXiv Preprints). PsyArXiv 10.31234/osf.io/chjur

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Clinical trials are mainly based on single point assessments of psychopathology. At the same time, automatized repeated assessments based on short scales are an increasing practice to account for daily fluctuations in disease symptoms (e.g. ecological momentary assessment, or time series-based analyses). This study investigated the impact of Intense Pre-Post-Assessment (IPA) on statistical power in randomized controlled trials (RCTs).
A simulation study, based on three scenarios and several empirical data sets, estimated the expected power gains of two- or fivefold pre-post-measurements of fluctuating disease symptoms. For each condition, patient data sets of various effect sizes were generated, and AN(C)OVAs were applied to the sample size of interest (N=50 – N=200).
Power increases ranged from 6% to 92%, with higher gains in more underpowered scenarios. ANCOVA with baseline as covariate profited from a more precise estimation of the baseline covariate, resulting in additional gains in statistical power. Ecological momentary assessment-like data sources resulted in highest absolute statistical power and outperformed traditional point assessments if fivefold IPA was applied. For example, ANCOVA of automatized PHQ-9 questionnaire data resulted in absolute power of 55 (for N=200 and d=0.3). Fivefold IPA, however, resulted in power of 88.9 to detect a similar effect.
IPA integrates short EMA-based assessments into RCT-based research designs. Sensitivity and efficiency of current RCTs could be improved by implementing a low number of automatized repeated assessments. Therefore, the merits of the suggested approach should be tested across various areas of clinical research (e.g. in neuroscience, or drug and psychotherapy research).

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