Biostatistics & Data Science Services

Biostatistics

My Working Principles

Assistance

For each article and dataset pair, once a one-time advance payment is received, I remain by your side as your assistant until our work with that article and dataset is completed.

Research

Beyond the main objective of the article, I uncover additional findings related to the dataset that may be useful to you either immediately or later.

Educational Approach

From the fundamentals to advanced topics in biostatistics, I ensure that the people I work with gain knowledge of the subject, and I conduct sessions solely for educational purposes.

PubMed Quality

From the tests we use to the text, tables, and graphics, I produce high-quality content suitable for the medical field, reinforced with examples from PubMed publications I have been involved in or not.

Biostatistical Techniques

Some of the techniques (not limited to these) for general information:

  • Descriptive statistics (mean, median, standard deviation)
  • Normality tests (Shapiro–Wilk, Kolmogorov–Smirnov)
  • Correlation analyses (Pearson, Spearman; including Bland–Altman for clinical measurement agreement)
  • Regression models (linear, logistic; multivariable models as advanced)
  • Hypothesis tests
    • Pre–post comparisons: Paired t-test, Wilcoxon
    • Between groups: Student t-test, Mann–Whitney U
    • Multiple group comparison: ANOVA, Kruskal–Wallis
  • Time series analyses (ARIMA, trend and seasonal assessment; health data monitoring analyses)
  • Survival analyses (Kaplan–Meier, Cox regression; treatment duration effectiveness evaluation)
  • Multivariate data analyses (Principal Component Analysis – PCA; Factor analysis)
  • Clustering and classification methods (k-means, hierarchical clustering; advanced Random Forest)
  • ROC curve and cut-off optimization
  • Confidence interval calculations
  • Effect size analyses (Cohen’s d etc.)
  • Sample size and power analyses
  • Machine learning-based modeling (limited advanced methods such as support vector machines)
  • Missing data methods (multiple imputation and advanced techniques)
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