Here is the design of a clinical trial:

Official Title: Amplify Gait to Improve Locomotor Engagement in Spinal Cord Injury (AGILE SCI Trial)

Included conditions:
- Incomplete Spinal Cord Injury

Study Armgroups:
- {'label': 'Treadmill group', 'type': 'ACTIVE_COMPARATOR', 'description': 'Participants randomized to the Treadmill group will complete high intensity gait training on a treadmill.', 'interventionNames': ['Device: Gait training performed on a treadmill']}
- {'label': 'Movement Amplification group', 'type': 'EXPERIMENTAL', 'description': 'The locomotor training protocol described for the Treadmill group will be used for the Movement Amplification group with one exception. The Movement Amplification group will perform all gait training within the movement amplification environment.', 'interventionNames': ['Device: Gait training performed in a Movement Amplification Environment']}

Interventions:
- {'type': 'DEVICE', 'name': 'Gait training performed on a treadmill', 'description': 'Participants randomized to the Control group will complete high intensity gait training on a treadmill.', 'armGroupLabels': ['Treadmill group'], 'otherNames': ['Treadmill Group']}
- {'type': 'DEVICE', 'name': 'Gait training performed in a Movement Amplification Environment', 'description': 'The Experimental group will perform all gait training within the movement amplification environment. To create the movement amplification environment, the investigators have constructed a cable-driven robot, the Agility Trainer. The Agility Trainer applies small forces to the pelvis that increase the difficulty to maintain forward walking', 'armGroupLabels': ['Movement Amplification group'], 'otherNames': ['Movement Amplification Group']}

Primary Outcomes:
- {'measure': 'Daily Stepping', 'description': 'The investigators will assess the amount of daily stepping in the home and community during three 1-week periods. Daily stepping will be measured and recorded using an activity monitor.', 'timeFrame': '10 Week Change from Baseline, 3 Month Change from Baseline'}
- {'measure': 'Lane Width Optimization Test', 'description': "The Lane width Optimization test will be used to quantify the capacity of individuals to control their lateral center of mass motion during treadmill walking. Participants will walk on the treadmill for 2 minutes. During the test a narrow lane will be projected on the treadmill belt surface. Individuals will be asked to maintain their body position within the lane during walking. If successful the lane will become progressively more narrow. If unsuccessful, the lane will become progressively wider. The width of the lane at the completion of the test will provide a quantitative measure of the individual's ability to control the center of mass motion during walking.", 'timeFrame': '5 Week Change from Baseline, 10 Week Change from Baseline, 3 Month Change from Baseline'}

Please estimate the sample size based on the assumption: 
The significance level (α) is set at 0.05, and the power is set at 80%. We also account for a 20% dropout rate based on a recent study with a 12% dropout rate.
Statistical analysis and sample size
      Our primary clinical outcome measures are the FGA, to assess functional walking balance, and the 10MWT, to assess walking speed. Our primary biomechanical gait assessment measure is the minimum lateral COM excursion during walking. Our primary measure of walking participation is the average number of steps taken per day.
      To test between group differences in walking balance following intervention, we will compare changes in participantsâ COM lateral excursion, calculated across the four assessment periods (BSL, Mid, Post, Follow Up) and between intervention groups (control and experimental). To account for the correlation that arises from measuring multiple data points within each participant over time (i.e., data measured from the mid- assessment period from a specific participant will bear more resemblance to data measured from the BSL- assessment period from the same participant than a different participant), we will use a linear mixed effects model. An indicator variable will be used for group assignment, 1â=âexperimental, 0â=âcontrol, to test for the effect of intervention while controlling for potential confounders such as initial walking speed, location of the iSCI, and age. The strength of the linear mixed effects models is that it can accommodate multiple data points from a single participant and can also manage missing data when data is missing at random. A similar strategy will be applied to examine changes in preferred walking biomechanical characteristics (average and variability of step width, step length, and minimum lateral MOS) and clinical outcome measures (10MWT, FGA, ABC etc.) across the four assessment periods and between intervention groups.
      To test if changes in steps per day are different following the two interventions, changes in participantsâ average steps per day across the three assessment periods (BSL, Post, Follow Up) and between intervention groups will be compared using linear mixed effects model. Potential confounders will be accounted for in the model to ensure the robustness of the findings.
      We estimated our target sample size using our primary biomechanical gait assessment measure, walking balance, which we quantified by evaluating participantsâ maximum ability to control mediolateral COM excursion during treadmill walking with visual feedback (Fig.Â 2b). We performed a power analysis to determine the minimum sample size needed to detect a 20% difference in improvements in walking balance between groups. We estimated the population variability from 13 people with iSCI performing unassisted, preferred-speed, treadmill walking who had a lateral COM excursion per stride of 0.083âÂ±â0.014Â m (meanâÂ±âstandard deviation) [67]. To estimate effect size, we used pre- and post-intervention data from pilot testing of three people with iSCI who received 16â18 locomotor training sessions performed in the movement amplification environment. These people decreased their lateral COM excursion during walking by 32â36%. Thus, at Î±â=â0.05 and a power of 80%, we estimate that we will need a minimum sample size of 7 people per group. A recent locomotor training intervention in people with iSCI had a 12% drop out rate [21]. Thus, we plan to enroll 36 people with a conservative estimate that we will lose 20% of participants to drop out, which would ultimately yield 14 people per group.