Biostatistics Online Modules
The Mayo Clinic Center for Clinical and Translational Science (CCaTS) offers a broad range of statistics-related online modules.
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JMP Modules (502E00JMPC0001)
JMP, a statistical software package from the SAS Institute, is designed for dynamic data visualization. It allows study teams to obtain descriptive statistics and perform simple data analysis.
For those who need personalized assistance, the CCaTS Service Center offers one-on-one statistical and epidemiologic consultations.
All modules in this series are presented by Ross A. Dierkhising, M.S., a master's-level biostatistician who also consults through CCaTS' Biostatistics, Epidemiology and Research Design (BERD) Resource.
The JMP curriculum includes the following content:
- 'JMP Dataset Creation.' Upon completion of this module, learners will be able to:
- Create a new dataset by entering data into a JMP table
- Define column properties for a variable
- Import a dataset from another file (such as Excel)
- Export a JMP dataset to another file type (such as Excel)
- 'Creating New Variables in JMP Data Sets Using Formulas.' Upon completion of this module, learners will be able to:
- Describe the functions of the formula editor
- Calculate the difference between two numeric variables
- Calculate body mass index from weight and height
- Use if-then statements
- Calculate a time interval
- 'JMP Dataset Manipulations.' Upon completion of this module, learners will be able to:
- Subset rows from a dataset
- Sort data by one or more variables
- Concatenate two datasets
- Check for duplicate subjects in a dataset
- Join two datasets
- 'Computation of Descriptive Statistics and How to Save Results in JMP.' Upon completion of this module, learners will be able to:
- Describe how variable modeling types determine which statistics are computed
- Identify where to find specific descriptive statistics in the output
- Use a "by" variable to obtain descriptive statistics within groups
- Save output in various formats
- Journal an output to collate results
- 'Analysis of Means Using JMP.' Upon completion of this module, learners will be able to:
- Conduct a one-sample test of a mean
- Conduct a two-sample test of means
- Compare means from more than two independent groups
- Compare two dependent means
- 'Analysis of Proportions Using JMP.' Upon completion of this module, learners will be able to:
- Conduct a one-sample test of a proportion
- Conduct a two-sample test of proportions
- Compare proportions from more than two independent groups
- Conduct a one-sample test for a multinomial distribution
- Compare multinomial distributions from independent groups
- Compare two dependent proportions
- 'Agreement Analysis Using JMP.' Upon completion of this module, learners will be able to assess agreement between measurements by estimating the kappa statistic for categorical variables and creating a Bland-Altman plot for continuous variables.
- 'Linear Regression and Correlation Using JMP.' Upon completion of this module, learners will be able to:
- Fit a simple linear regression model with a continuous or categorical predictor
- Estimate correlation coefficients
- Fit a multiple linear regression model
- 'Logistic Regression and ROC Curves Using JMP.' Upon completion of this module, learners will be able to:
- Fit a simple logistic regression model with a continuous or categorical predictor
- Construct an receiver operating characteristic (ROC) curve from a model with one continuous predictor and assess cutoff values
- Fit a multiple logistic regression model
- Construct an ROC curve from a model with multiple predictors and assess cutoff values
- 'Survival (Time to Event) Analysis Using JMP.' Upon completion of this module, learners will be able to:
- Estimate Kaplan-Meier survival and failure curves
- Properly estimate the median time to event
- Compare Kaplan-Meier curves between groups
- Fit a univariate Cox proportional hazards model with a continuous or categorical predictor
- Fit a multivariable Cox proportional hazards model
- Recognize predictors that JMP cannot handle (time-dependent covariates)
'Working With the Statistician' (502E00CMS090018)
Most researchers rely on statisticians to help them analyze their results. What information should you take to your statistical consultation? You and your statistician will both be glad you learned the answer in this module.
Presenter: Felicity T. Enders, Ph.D.
'Data-Monitoring Committees' (502E00CMS110027)
The Mayo Clinic Institutional Review Board recently issued a guidance document on Data and Safety Monitoring Plan (DSMP) guidelines (Guidance IRB 10379.002). Elements of a DSMP include study-stopping and participant-stopping rules.
All randomized clinical trials must begin with the assumption of clinical equipoise — that either treatment represents a viable alternative. As a study accrues participants, researchers garner information regarding the clinical equipoise assumption and must reach decisions regarding the continuation of the study.
This module addresses many of the logistical aspects associated with interim monitoring of clinical studies, with emphasis on efficacy evaluation, while providing guidance on appropriate statistical considerations for the repeated evaluation of the study data.
Presenter: Rickey E. Carter, Ph.D.
'Research Protocols — Guides to Success'
Scientific advances are based on reproducible science. At the heart of reproducible science is the research protocol. This module discusses guidelines for protocol preparation consistent with regulatory requirements and best practices. Participants also learn to differentiate a research protocol from a grant application.
The module makes recommendations on the importance for specific and measurable outcome measures in the context of mandatory reporting requirements (for example, Clinicaltrials.gov results reporting). Additionally, it highlights institutional resources that can assist investigators with protocol preparation.
Presenter: Rickey E. Carter, Ph.D.
'Beyond Bar and Line Graphs: Time for a New Data Presentation Paradigm'
Non-Mayo participants: Enroll here
Data presentation is an essential skill for scientists. Figures are critically important because they often show the data supporting key findings. However, a visually appealing figure is of little value if it is not appropriate for the type of data being presented. This module teaches participants how to select the right type of figures when presenting continuous data in small sample size studies.
The module's systematic review of research articles published in top physiology journals shows that most papers presented continuous data in bar and line graphs. This is a problem, as many different data distributions can lead to the same bar or line graph. The full data may suggest different conclusions from the summary statistics. Papers rarely include scatter plots, box plots and histograms that allow readers to critically evaluate continuous data.
The module directs learners to several resources that allow them to quickly make univariate scatter plots for small sample size studies, including Excel templates and instructions for GraphPad Prism. It also provides links to blog posts from other investigators that show how to make univariate scatter plots and box plots in R. Finally, the module discusses steps that students and investigators can take to improve the quality of data presentation in published papers.
Presenter: Tracey L. Weissgerber, Ph.D.