Here is the design of a clinical trial:

Official Title: Integrative Medicine for Pain in Patients With Advanced Cancer Trial (IMPACT)

Included conditions:
- Cancer Pain

Study Armgroups:
- {'label': 'Acupuncture', 'type': 'EXPERIMENTAL', 'description': 'Participants will receive up to 10 treatments in the first 10 weeks (+/- 4 days) and then receive monthly booster treatments (+/- 7 days) for up to 26 weeks.', 'interventionNames': ['Procedure: Acupuncture']}
- {'label': 'Massage', 'type': 'ACTIVE_COMPARATOR', 'description': 'Participants will receive up to 10 treatments in the first 10 weeks (+/- 4 days) and then receive monthly booster treatments (+/- 7 days) for up to 26 weeks.', 'interventionNames': ['Procedure: Massage']}

Interventions:
- {'type': 'PROCEDURE', 'name': 'Acupuncture', 'description': 'Acupuncture, a therapy of traditional Chinese medicine (TCM), involves penetrating the skin with thin, solid, metallic needles that are manipulated by hand or electrical stimulation. If the patient has an electronically charged device, they will not receive TENS stimulation.', 'armGroupLabels': ['Acupuncture']}
- {'type': 'PROCEDURE', 'name': 'Massage', 'description': 'Massage, which involves the manual manipulation of muscles and other soft tissue areas of the body, is one of the earliest known forms of pain relief.', 'armGroupLabels': ['Massage']}

Primary Outcomes:
- {'measure': 'Worst Pain', 'description': 'Worst Pain Item from the short-form Brief Pain Inventory (BPI). The short-form BPI will be used to quantify pain severity and pain interference. The BPI contains 4 pain severity items and 7 pain interference items, all rated on a scale from 0 to 10 (higher ratings indicate worse pain intensity/interference).', 'timeFrame': '26 weeks from randomization'}

Please estimate the sample size based on the assumption: 
The assumptions include a two-sided alpha of 0.05, 80% power, 20% lost to follow-up, and a correlation between baseline and 26-week BPI Worst Pain of 0.5.
Power analysis and sample size
        For our sample size/power considerations, we calculated the smallest standardised effect size (ie, Cohenâs d) we will be able to detect with 80% power, given our sample size of 300 and other assumptions. Using the âpower.mmrmâ function from the R package âlongpower,â we applied the formulas in Lu et al,71 to derive the smallest detectable effect size for the coefficient of the time-by-arm interaction term in our LMM, given our study design and assumptions, which we transformed to represent the standardised mean difference (ie, Cohenâs d) between the two arms at 26 weeks postrandomisation. Based on our prior experience72 73 and given that patients living with advanced cancer may have unanticipated health issues (eg, hospitalisations, death), we conservatively anticipate lost to follow-up to be 20% by 26 weeks. Assuming this 20% lost to follow-up, correlation between baseline and 26-week BPI Worst Pain of 0.5, and two-sided alpha of 0.05, and with 150 participants in each of the two active intervention arms, we will have 80% power to detect an effect size of 0.35 (standardised mean difference, Cohenâs d) at 26 weeks post-randomisation between acupuncture vs massage. Based on our own preliminary data in patients with stage IV cancer who experienced moderate to severe pain (N=284), the mean BPI Worst Pain score was 6.3 with SD of 1.7. A difference of 1 on the BPI Worst Pain score (considered a clinically meaningful difference in pain) based on SD of 1.7 equals an effect size (Cohenâs d) of 0.59. In this study, we have 99% power to detect this clinically meaningful mean difference of 1 point (Cohenâs d of 0.59) on the BPI Worst Pain score. Our trial is more than sufficiently powered to detect a clinically meaningful difference between acupuncture and massage at 26 weeks.