Consulting Clinics

Through our consulting clinic program, we offer students one-on-one consultations with a CIDA graduate research assistant, during which we can assist with the following:  

• Advise on study design
  
• Construct data collection tools
  
• Develop an analysis plan 
  
• Learn how to implement your analysis an d interpret your findings
  
• Review your analysis code, procedures, tables, and figures 
  

We encourage students to attend as many clinics as are helpful throughout the duration of their project, and to come in the early phases of your study planning. 

MSA Sign Up

In addition to consultation clinics, medical students are eligible for a micro grant to support comprehensive statistical support on their research projects.  

Submission requirements:

• Attend at least one Biostatistical Consultation Clinic before you apply
  
• Data collection must be complete within 6 months of award date and analysis finalized within 1 year.

Evaluation criteria:

• A strong research plan with evidence that the study design was thoughtfully considered
  
• Demonstrated feasibility that data needed to answer your question can be collected
  
• Research questions requiring challenging statistical analyses (e.g. survival analysis, longitudinal data, etc.)

Submission Deadlines:  

• Fall applications are due by November 1, 2024.
  
• Spring applications are due by May 1, 2025.

Submit Grant Application

Best Practice for Improving Readability of Data

We are unable to address data format issues, and may need to ask you to reformat improperly-formatted datasets. Please be sure to follow these guidelines when you format your dataset:

• Single row for headings/column names. No repeated headings.
• Headings not too long—use short (1 or 2 words) column headings, then use a data dictionary to elaborate the short heading. We’ll be sure the long version from the data dictionary makes its way onto figures, etc.
• Include a separate document that defines values – a "data dictionary." See below for an example.
• We cannot analyze "free form" or "text string" columns (such as "other," "explain," or "notes"), although you can leave them in the dataset for reference.
• The computer ignores color, so don’t color-code data or the information that you color-coded will be lost.
• Stick to a coding convention. Entering "F" for one woman’s sex, "f" for another’s, and "Female" for another’s results in three types of females. Pick one convention and be consistent throughout a column. Capitalization matters.
• No "special" characters, such as text accents.
• File types that end with .xls, .xlsx, .csv, and .sas7bdat are good.
• Include patient IDs, provider IDs, etc.
• Do not include any Protected Health Information (PHI).
• Missing data should be left blank, rather than coded as "99," "-99," ".," etc.
• No characters in a numeric column/variable. If there are characters anywhere in a column (aside from the column name), the computer will treat the whole column as characters. Putting the word "missing" or "unknown" or the character "-" for missing values in a column will convert any numbers in that column to character expressions, which would be treated as categories, not numbers, in an analysis.
• For numeric variables, don’t include units in the cell values, as they are characters. Include the units in the data dictionary instead, and we’ll put them on figures, tables, etc.

NOTE: This list is not exhaustive.

Data Dictionary Example For a Ventricular Tachycardia Study

• PtID: patient ID
• Inst: institution ID
• Gender: gender of patient
• M=Male
• F=Female
• AblNum: ablation number:
• Numeric count
• Fascic: tachycardia type:
• 1=Fascicular VT
• 0=Other VT
• Recur: tachycardia recurrence:
• 1=VT recurrent
• 0=VT not recurrent
• Follow_Up: Follow up time after this ablation
  • Time started with a successful ablation and ended when VT recurred (1 above) or when follow up time ended without recurrent VT (0 above)
• Status: Final Patient Status:
  • 0=off meds, no VT
  • 1=off meds, intermittent VT
  • 2=on meds, no VT
  • 3=on meds, intermittent VT
  • 4=other
• Other Variables…(add other variables here)

It is important for you to organize your data in a way that facilitates transfer to our biostatisticians, or other investigators or computers. Well-defined and organized data minimizes confusion and incorrect data.

You are encouraged to use REDCap for data collection to minimize data entry errors or risks to patient confidentiality, and ease data transfer for statistical analysis.

Recommendations for Organizing Data

Our recommendations have demonstrated to be effective for moving data from point to point in a structured manner. A reasonable data organization scheme should minimize the amount of editing needed at the receiving side of your data transfer.

Table 1 illustrates three types of variables in a structure that lends itself to simple data transfer and minimal data editing.

• Identification (PatID) variables: uniquely identify aspects of an individual record (row of data), for instance, subject #, clinic #, or PatID.
• Time-stable variables: include characteristics that remain constant for individual subject if observed over time, for instance, baseline demographics (age, sex, race) or study group (A, B).
• Longitudinal variables: potentially change over time, for instance, weight, adolescent height, muscle tone, lab values (cholesterol, blood sugar, etc.).

In this example, the structure has one column available for identifying an individual (Subject), two columns for time-stable characteristics (Trt, Sex) and two columns for longitudinal characteristics (time, weight). Note the values of subject and time uniquely identify each row.

Other experimental designs will require different data structures, but each measured response must be uniquely associated with only one subject, visit or test.

Most statistical software packages (e.g. SAS, SPSS, Splus, R and Stata) require data represented in a rectangular format where each row is a unique observation and each column is a separate variable. When organizing data into a rectangular format: first each row contains one (and only one) unique observation. In the example each row contains a unique combination of subject, time, and treatment. Second, each column contains one (and only one) variable or response.

Table 1: Example of a Rectangular Table

PatID	Trt	Sex	Time	Weight
1	0	1	0	181.6
1	0	1	4	183.2
2	0	0	0	130.4
2	0	0	4
3	1	0	0	150.2
3	1	0	4	145
4	1	1	0	161.2
4	1	1	4	159.4

Codebook (in a separate worksheet):

Trt: Treatment, 0=Placebo, 1=Drug; Sex: 0= Women, 1=Men; Time: Time in Study in weeks; Weight: Body weight in pounds

Please note the following points, many of which are illustrated in Table 1:

1. Data table is rectangular, rows represent observations, and columns represent variables. Some columns identify observation and others contain a measured response. All data contained in one rectangular area.
2. Only Patient ID numbers are used, Protected Health Information (PHI) is not included. Names should not be included in your database for analysis to avoid unnecessary risks to patient confidentiality (see Tabl
3. Unique key to each row consists of two variables (columns) PatID and Time.
4. Characters (A, AB, O) and numeric values (0, 1, 2) are not mixed within one column. Where possible, a number has been chosen in place of a character. Definition of numbers, units for continuous data, and explanation for abbreviated variable titles should be provided separately in a codebook.
5. Missing data: Note that none of the variable values uniquely identify the subject and conditions where measurements taken are missing (ID, trt, time). A character value (e.g. "missing", "dk", "x") or numeric value zero (i.e., 0) should not be used to indicate missingness for a continuous variable (ex: variable "Weight" in Table 1).
6. Before data collection begins, your should give special attention to how an assay value below detection will be indicated in the data, and how it should be treated in the statistical analysis. Similarly for left-censored or right-censored values.
7. Column headers are variable names, not a description. Variable descriptions can be provided separately in a "codebook" (or a separate worksheet in same workbook). In general, variable names must:
   • Be 8 characters or less in length
   • Consist of one word (i.e. no spaces)
   • Be unique (not duplicated across multiple columns)
   • Begin with a letter, not a number
   • Contain no special characters: commas, quotes, apostrophes, period, underscore.
8. Avoid using punctuation or spaces (e.g. commas, quotes, <,>).
9. Avoid using special formatting like colored text, highlighted columns, italics, bolding, super or sub scripting, and the "comment" feature.
10. Store notes about patients in separate column from data used in analysis (e.g. "scheduled to come in again for repeat lab"). If information in text of notes needs to be analyzed, it should be coded into one (or more) variable column(s).

Identifiable PHI Information

1. Name
2. Fax number
3. Phone number
4. E-mail address
5. Account numbers
6. Social Security number
7. Medical Record number
8. Health Plan number
9. Certificate/license numbers
10. URL
11. IP address
12. Vehicle identifiers
13. Device ID
14. Biometric ID
15. Full face/identifying photo
16. Other unique identifying number, characteristic, or code
17. Postal address (geographic subdivisions smaller than state)
18. Date precision beyond year

If considered in enough detail before your data collection process begins, organization of the experimental data is relatively simple. Whether or not there are questions or confusion about how to efficiently organize and manage your data, consulting with a statistician before your experiment begins is a good idea. These matters can usually be resolved in a short time with satisfactory results for all concerned. Biostatisticians often oversee the data collection, storage, and retrieval systems for clinical studies. The study biostatistician is able to distinguish between essential and non-essential data, and can therefore limit the data collection systems to relevant information.

Limiting the amount of data collected means it is easier to assure data quality, minimize missing data, and pre-define the analysis data sets so that, upon study completion, data analysis is straightforward. Developing an effective data collection and management system is a key step in assuring ultimate integrity of your study. Dataset planning can be iterative, involving meetings between the Statistician, Investigator, and Informatics Manager.

Specific examples of instances in your planning phase where obtaining a statistician’s input would be beneficial:

• Design data collection forms
• Outline data collection/management systems (include variable name, specify variable type, e.g. date, numeric, open text)
• Design, implement, and conduct of data quality monitoring system for a study
• Outline how and when data abstraction should occur for interim analyses
• Provide input on parameters that would help to ensure data quality control

• All data should be securely stored, and access should be restricted to those individuals entering data.
• Properly dispose of paper and electronic files, keep paper copies in locked cabinet, and store electronic files on a secure-access central server.
• Keep in mind the Health Insurance Portability and Accountability Act (HIPAA)’s Minimum Necessary Principle when listing what variables to include in your database.
• Use or disclose only information necessary to the task. It is important to exclude unnecessary items that make information identifiable to ensure privacy, security and patient confidentiality.
• Identifiable information includes items listed in Table 2. If identifiable information is necessary for research (e.g. birth date, visit date, physical address), take necessary precautions to protect the database: strong passwords, anti-virus software, data backup, possibly encryption, and being very cautious with email.
• Refer to COMIRB and HIPAA for additional stipulations.

This is a statistical resource guide for MSA clinic attendees and other students that consult with CIDA. It summarizes a few statistical package installation guides, how to, and links to some helpful statistics resources.

Software Resource Guide