Nephrectomy Data Cleanup & Analysis and outcomes
Budget: $250 – $750 USD
I have a retrospective database of patients who underwent nephrectomy that now needs a rigorous clean-up, validation, and a full statistical dive. Your first task will be to audit the existing entries, resolve missing or inconsistent records, and structure the file so that it is publication-ready.
The dataset must ultimately include clear fields for age and gender, race and ethnicity, socioeconomic status, and several key clinical features—namely pre-existing conditions, surgical complications, recovery time, and longitudinal renal function. On the outcomes side I need treatment success rate, mortality rate, post-surgery quality of life, plus detailed renal metrics such as eGFR captured at specific time points.
Once the data are pristine, I want descriptive summaries, comparative statistics, and correlations that link demographics and clinical factors to each outcome. Multivariate modelling, survival analysis, or any other advanced method you judge appropriate is welcome, provided it is justified in the final write-up.
Please bring prior peer-reviewed medical research experience; I will reference your publications before we begin. Ideally you are comfortable in R, Stata, or SPSS and can provide both the annotated code and an interpretable report ready for manuscript insertion. Deliverables will be checked against reproducibility, clarity of variable definitions, and alignment with CONSORT and STROBE guidance where relevant.
The dataset must ultimately include clear fields for age and gender, race and ethnicity, socioeconomic status, and several key clinical features—namely pre-existing conditions, surgical complications, recovery time, and longitudinal renal function. On the outcomes side I need treatment success rate, mortality rate, post-surgery quality of life, plus detailed renal metrics such as eGFR captured at specific time points.
Once the data are pristine, I want descriptive summaries, comparative statistics, and correlations that link demographics and clinical factors to each outcome. Multivariate modelling, survival analysis, or any other advanced method you judge appropriate is welcome, provided it is justified in the final write-up.
Please bring prior peer-reviewed medical research experience; I will reference your publications before we begin. Ideally you are comfortable in R, Stata, or SPSS and can provide both the annotated code and an interpretable report ready for manuscript insertion. Deliverables will be checked against reproducibility, clarity of variable definitions, and alignment with CONSORT and STROBE guidance where relevant.