Here is the design of a clinical trial:

Official Title: Prospective Exploration of the Effect of Adiposity and Associated Microbial Factors on the Healing and Progression of Diabetic Foot Ulcers in Tanzania

Included conditions:
- Diabetic Foot Ulcer

Study Armgroups:
- {'label': 'Normal adiposity group', 'description': 'Body fat \\< 25% and \\<32% in men and women respectively, will be categorized as high adiposity. This is according to the The American Council on Exercise criteria.', 'interventionNames': ['Other: Normal standard care']}
- {'label': 'High adiposity group', 'description': 'Body fat ≥ 25% and ≥32% in men and women respectively, will be categorized as high adiposity. This is according to the The American Council on Exercise criteria.', 'interventionNames': ['Other: Normal standard care']}

Interventions:
- {'type': 'OTHER', 'name': 'Normal standard care', 'description': 'Both groups will receive standardized treatment and care for diabetic foot ulcers', 'armGroupLabels': ['High adiposity group', 'Normal adiposity group']}

Primary Outcomes:
- {'measure': 'Proportion (%) of patients with complete wound healing at 24 weeks', 'description': 'Complete healing will be defined based on the criteria of the wound healing society, as 100% re-epithelialization of the wound surface (complete wound closure) with a complete absence of drainage.', 'timeFrame': '24 weeks'}

Please estimate the sample size based on the assumption: 
The power is set at 80% with a 95% confidence interval. A maximum of 20% loss to follow-up is assumed, considering competing risks such as lower limb amputation and death.
Sample size calculations and analysis plan
        
          
            
Sample size calculation: We estimated the required sample size based on power calculations. A 20% difference in DFU healing at 24 weeks, between the high and normal adiposity groups will be considered as a clinically meaningful difference. To attain a power of 80% at 95% CI and aiming to recruit equal numbers of patients with DFU in the two groups (normal and high adiposity), a total sample size of 188 subjects is required. This calculation is based on the assumptions that the proportions (cumulative incidences) of complete wound healing at 24 weeks in normal and high adiposity groups are 55% and 35%, respectively. This assumption was based on clinical records of patients with complete wound healing at 24 weeks, who were attending DFU clinics in Dar es Salaam (unpublished data). These patients were predominantly overweight and obese hence the proportion (35%) has been used for the high adiposity group. Assuming a maximum of 20% loss to follow-up and considering the presence of competing risks (lower limb amputation and death) we plan to recruit a total of 300 patients with DFU (150 in each group) to maintain the power that might be weakened by a decrease in sample size due to loss to follow-up, and also to maintain the effect size that might be affected by competing risks (lower limb amputation and death).
          
          
            
Primary outcome analysis: The cumulative incidence competing risk method will be used. This method takes into account all the different events/outcomes that may affect the outcome of interest,66 in this case, amputation and death.
            At 24 weeks, we will summarise the outcome of categorised study participants (normal and raised adiposity groups) by using the proportions of complete wound healing in the two groups. These proportions will be estimated from the cumulative incidence (rate) of healing at 24 weeks (with death and amputation as competing risks) and compare the incidences (proportions) between normal and raised adiposity groups (using Ï2 or Greyâs test).67 In this regard healing estimates and cumulative incidence curves of the time to healing, in the presence of competing risks will be provided with 95% CIs. Participants who will not experience any adverse event (amputation or death) before and at the end of initial follow-up (24 weeks) will be censored.
            To find out whether there is any significant difference in healing between patients with normal and those with high adiposity, we will use the subdistribution proportional hazards mode in a multivariate survival analysis to compare the two groups by examining the difference in the hazards of healing between normal and high adiposity groups (adjusting for known covariates including baseline ulcer characteristics that describe severity including ulcer surface area, stage, presence of infection and others). Similar analysis will be done to compare the outcome of grouped study participants based on ulcer-related events that are our secondary end points (outcome measures).
          
          
            
Descriptive analysis: Exploratory analysis beyond the primary study design will be done among the incident cases at baseline and different follow-up points.
            Descriptive and inferential statistics will be used to determine the characteristics of DFUs in patients with T2DM. Measured variables will include type and load/density of microorganisms and their characteristics (dominance, diversity and antimicrobial sensitivity pattern). Related factors are systemic factors such as adiposity, circulatory insufficiency, impaired LBM, poor glycaemic control and behavioural characteristics like cigarette smoking, dietary habits and physical activity type/levels. Local factors including ulcer types/grades/classes will also be considered. Univariate analysis that includes measures of distribution, central tendency and dispersion will be used. Frequency of occurrences of different types and characteristics of microorganisms will be measured using proportions/means/median and compared using appropriate statistical tests, depending on the nature of the analysed data (t-test, analysis of variance and Ï2).
            Linear and logistic regression models will also be used to determine systemic factors that predict or explain local DFU characteristics for continuous and categorical data, respectively.
          
          
            
Predictive validity testing: Given its longitudinal nature, this study will offer an opportunity to evaluate the utility/ability of various assessment methods (physical examination, ABI, TBI, PVR and combined ABI and PVR) in predicting wound healing in an African population. We will calculate sensitivity and specificity of different methods (feeling peripheral pulses and ABI with PVWs) against ulcer outcomes (healed or not healed) at different follow-up points. Both independent and combined validity of ABI and PVW will be evaluated against ulcer outcomes. The receiver operating characteristics curve will be used to compare the diagnostic performance of the different assessment methods in predicting wound healing and also to set up reasonable cut-off points for the different tests.
            Sensitivity, specificity, positive and negative predictive values, as well as accuracy will be calculated.