Here is the design of a clinical trial:

Official Title: Musical and Psychomotor Interventions for Cognitive, Sensorimotor, and Cerebral Decline in Patients With Mild Cognitive Impairment (COPE): a Study Protocol for a Multicentric Randomized Controlled Study

Included conditions:
- Mild Cognitive Impairment

Study Armgroups:
- {'label': 'Music practice', 'type': 'EXPERIMENTAL', 'description': 'Patients will receive Music Practice interventions of 45 minutes twice a week over 6 months, provided by a professional musician', 'interventionNames': ['Behavioral: Music practice']}
- {'label': 'Psychomotor therapy', 'type': 'EXPERIMENTAL', 'description': 'Patients will receive Psychomotor interventions of 45 minutes twice a week over 6 months, provided by a professional psychomotor therapist', 'interventionNames': ['Behavioral: Psychomotor therapy']}
- {'label': 'Passive control group', 'type': 'NO_INTERVENTION', 'description': 'Healthy passive controls will pass all measurements without any intervention. The control group participants must adhere to the same inclusion and exclusion criteria as the experimental groups, except for an MCI diagnosis. Control participants will be matched to the experimental groups for age, gender and education level.'}

Interventions:
- {'type': 'BEHAVIORAL', 'name': 'Music practice', 'description': 'Patients will be trained to play a simple instrument (tongue-drum) in a group setting using different musical styles.', 'armGroupLabels': ['Music practice']}
- {'type': 'BEHAVIORAL', 'name': 'Psychomotor therapy', 'description': 'Patients will be trained in body awareness and a wide range of of movement activities.', 'armGroupLabels': ['Psychomotor therapy']}

Primary Outcomes:
- {'measure': 'Cognitive Telephone Screening Instrument (COGTEL)', 'description': 'This test can be applied by telephone or face-to-face (in this study we will apply the face-to-face method). The outcome consists of increase or less decrease (experimental group 1 or 2 vs. the control group) of the total weighted score at the COGTEL test directly after the 6 months interventions as compared to directly before the interventions.\n\nThe COGTEL test provides a main weighted score of core cognitive function, it comprises 6 subtests covering prospective memory, short- and long-term verbal memory, working memory (digit span), verbal fluency and inductive reasoning.\n\nCOGTEL main weighted score: COGTEL Total score = 7.2 x Prospective Memory score + 1.0 x Verbal Short-Term Memory score + 0.9 x Verbal Long-Term Memory score + 0.8 x Working Memory score + 0.2 x Verbal Fluency score + 1.7 x Inductive Reasoning score\n\n(Kliegel, Martin, \\& Jager, 2007, doi:10.3200/JRLP.141.2.147-172) (lhle et al., 2017, doi:10.1159/000479680)', 'timeFrame': '6 months'}

Please estimate the sample size based on the assumption: 
The statistical power analysis assumes an alpha of 0.05 and a power of 0.80. The MMSE score is highly correlated with the COGTEL score (r = 0.93). An attrition rate of 20% is considered.
Statistical analysis plan and sample size calculation
      
        Basic statistical analyses
        First we will use linear mixed model equations (lme) using the open-source software R [144] to evaluate the longitudinal effects for all variables separately comparing both experimental groups to the control group and to each other. These lme are flexible and powerful statistical models that consider several levels of clustering, continuous, ordinal and categorical explanatory variables. They support unbalanced data and allow to integrate demographic data into the models. Our 2 patient groups may turn out unbalanced due to attrition. Moreover, because we are interested in development, we will incorporate the baseline scores in the models. This will allow correcting for differences between the groups at baseline.
        We will also compare those results with more traditional multivariate analyses of covariance.
        If the data are not normally distributed, and if data transformation cannot cope with this non-normality, we will either use robust analyses (weighting outliers or other influential observations) or use non-parametric approaches.
        In testing multiple contrasts, adjustments of the alpha criterion for multiple comparisons will be applied.
        Concerning the primary objective, the analysis will consist in evaluating increase or less decrease (experimental group 1 or 2 vs. the control group) of the total weighted score at the COGTEL test [79, 80] at t1 and t2 compared to baseline and to the passive control group, by means of lme and MANCOVA analyses, using a two-sided alpha criterion of 0.05.
      
      
        Advanced multivariate statistical analyses
        Finally, we will perform an exploratory analysis of the data using a multivariate data-driven approach. Analyzing separately behavioral data and several kinds of brain imaging data, cross-sectionally or over time, provides valuable information on these specific data. Additionally, combining diverse behavioral and different kinds of brain data within data-driven multivariate analyses may unravel hidden "covert" relationships [145, 146]. Specifically, we will perform multivariate data-driven analyses using multimodal Independent Component Analysis (ICA) [145, 146] and Partial Least of Squares (PLS) [147, 148] which have both been used successfully on multimodal brain imaging data.
        Two methods will be used:
        1) Joint, fusion, and linked ICA [149, 150] represent several blends of multimodal extensions of ICA that allow combining different types of data such as functional and structural neuroimaging data. The results of these analyses should reveal interactions between different types of information and consequently allow us to investigate differences between our groups / experimental conditions (interventions) [145, 149]. In addition, we will also decompose the fMRI data into functional brain networks using innovation-driven co-activation patterns (iCAPs), a recent approach of dynamic functional connectivity [151] that that can quantify temporal interactions between different networks.
        2) PLS is another powerful technique that can identify components of multivariate relationships between imaging and behavioral data [148, 152], notably unified PLS correlation models [148]. This PLS technique allows revealing hidden relationships between all battery tests and MRI measurements over time and potentially show whether these relationships differ between the groups (machine-learning). We will also use PLS regression models to fit the best interventional neurobehavioral model for predicting the rehabilitation of elderly on a specific behavioral outcome i.e. evolution of the working memory performance. Implementations of these approaches are publicly available in addition to several in-house extensions that allow using them in the most flexible way, including machine-learning approaches [153, 154].
        These advanced analyses will be performed in close collaboration with Prof. D. Van de Ville (computer scientist), professor of bioengineering at the EPFL (Ãcole polytechnique fÃ©dÃ©rale de Lausanne) and the University of Geneva and internationally recognized for his expertise in advanced techniques of MRI-analysis, specifically of resting-state and morphometric changes. Prof. Van De Ville and Prof. James worked together for many years.
      
      
        Statistical power of the study
        We performed a statistical power analysis for sample size estimation, based on the main outcome of this project, a significant group improvement at the COGTEL test. Data used for this analysis stem from a study on MCI patients by Biasutti et Mangiacotti (2018 [62], comparing musical instrumental practice (nâ=â15) to soft gymnastic activities (nâ=â14). In this study, the main outcome was a change over time in the MMSE score. Between baseline and the end of their twice weekly 3-month interventions, similar to the interventions in the current study, the MMSE performance did not change in the soft gymnastics group whereas it improved significantly in the musical instrumental practice group (F(1,33)â=â13.906; pâ<â0.001; pÎ·2â=â0.296). Such a partial Î·2 value corresponds to Cohen's effect size f of 0.65 which is considered large [155]. With an alphaâ=â0.05 and powerâ=â0.80, projected sample size required for this effect size is nâ=â12 (GPower 3.1) [156].
        The MMSE being highly correlated to the COGTEL (râ=â0.93) [80], our proposed sample size of nâ=â16 per group (or nâ=â12 considering an attrition rate of 20%) seems reasonable for observing a significant COGTEL effect.
        We recruit in two university hospital memory clinics; sample size was in the COVID context also determined according to their databases and recommendations, to ensure feasibility, but we will respect our power calculation above.
        In general, it is difficult to recruit older adults for interventional neuroimaging studies. MRI measurements represent an impediment for older adults, even if they are mentally fit. Those studies therefore suffer from under recruitment. Even with fewer healthy older individuals than the number of patients suggested in the current protocol, many authors could still publish significant findings [157â163].
      
      
        Attrition / Drop-outs
        A total of 16 volunteers will be recruited for each of the experimental patient groups and the control group. Thus, the intended total number of participants will be 48. Given the unclear future of the covid pandemic and the natural attrition in our patient population, our minimum aim is a total of at least 12 participants in each group at the completion of the six-month interventions and measurements, which corresponds to our power analyses (see section Statistical power of the study). We maintain the 16-person target for the control group. Thus, we predict a 20% attrition rate among the patients and will over-recruit 32 participants. We will not be able to compensate for dropouts because the interventions are ongoing.