In normal clinical treatment and experimentation, we generate many medical data. Therefore, how to choose the correct data processing analysis method and let the data reflect the state and indicators in the experiment more accurately, has great significance for our clinical research. The core idea of medical survey data analysis is to eliminate or control non-random errors in the processing, and to ensure that analysis can be based on real sampling errors. The analysis of medical survey data involves medical expertise, statistical expertise, experience and skills in processing data, and is a very high art. In data processing, the collection and entry of raw data, the management of data, the appropriate selection of statistical methods, and the proficient use of statistical software are all steps that must be taken seriously.
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From the entry of raw data to data analysis, our company will help you solve problems throughout the whole process. The raw data needs to be inputted into the computer before statistical analysis. The types of files vary in various formats, including database files, such as dBASE, FoxBASE, Lotus, EPIinfo, and etc.; Excel file; text file, such as word files, WPS files, and etc.; corresponding files of statistical application software, such as SPSS data files, SAS data files, Stata data files, and etc. Currently, most of the above file types can be converted to each other (data access). When entering data, the principles of easy enter, easy verification, easy conversion, and easy analysis should be followed. We will select the most scientific ones from the above methods for data analysis based on the type of data you provide; next, we will detail the types to which the various methods apply. In addition, our company can select the appropriate statistical analysis method for statistical analysis according to the customer's research problems, which can ensure the accuracy and professionalism of medical data processing and analysis.
In medical survey data analysis, the most commonly used multivariate statistical analysis and models include multivariate analysis of variance, covariance analysis, multivariate linear regression analysis, logistic regression analysis, Poisson regression analysis, less principal component analysis, cluster analysis, discriminant analysis, canonical correlation, the Logistic model and the Cox model. We can help you to choose an appropriate analysis method for your survey analysis.
(1) When individual data deviates significantly from group data, it is called outlier or extreme value. If there is outlier data, it can be divided into two cases. One is that if the data is confirmed to be logically incorrect and cannot be corrected, the data can be deleted directly. The other is that if there is no obvious logic error in the data, the data can be analyzed before and after the data is removed.
(2) When applying the parametric method for hypothesis testing, we often require the data to meet certain preconditions. For example, two independent samples comparing the t test or the analysis of variance of multiple independent samples require homogeneity of variance; therefore, it is necessary to test the homogeneity of variance. If you want to estimate the range of reference values using the normal distribution method, firstly, you much check whether the data is taken in a normal distribution. When establishing various multiple regression equations, it is often necessary to test the normality of multiple collinearity and residual distribution between variables.
Our experienced experts can help you to overcome problems mentioned above.We guarantee the confidentiality and sensitivity of our customers' data. We are committed to providing you with timely and high-quality deliverables. At the same time, we guarantee cost-effective, complete and concise reports.
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References:
1. Durand Wesley M, Stey Paul C, Chen Elizabeth S et al. (2018) ’Trend Analysis of Aggregate Outcomes in Complex Health Survey Data’. AMIA Jt Summits Transl Sci Proc: 349-358.
2. Barber Sarah, Brettell Rachel, Perera-Salazar Rafael et al. (2018) ’UK medical students' attitudes towards their future careers and general practice: a cross-sectional survey and qualitative analysis of an Oxford cohort’. BMC Med Educ, 18(1): 160.
3. Khalagi Kazem, Mansournia Mohammad Ali, Rahimi-Movaghar Afarin et al. (2016) ‘Assessing measurement error in surveys using latent class analysis: application to self-reported illicit drug use in data from the Iranian Mental Health Survey’. Epidemiol Health, 38: e2016013.