Creating a Database. Frank Friedenberg, MD

Transcription

1 Creating a Database Frank Friedenberg, MD

2 Classic Data Management Flow for Clinical Research Scientific Hypotheses Identify Specific Data Elements Required to Test Hypotheses Data Acquisition Instruments (optional) (paper forms) Computer Program for Data Collection and Organization (e.g. Access) Output to Analytical Software (e.g. SPSS)

3 Data Management Process Define data set to acquire, create codes (e.g. F=0, M=1) Paper Data forms Enter data into computer Accumulate in database Data quality control (check for missing, out of range and illogical responses) Transfer to statistical and presentation software Do statistical analysis Prepare graphs and tables

4 Differences between a clinical and research database Clinical database (e.g. PACS ) Report oriented so data is displayed for clinical decision making (e.g. abdominal X-ray report) Emphasis on displaying or reporting of individual data rather than accumulating multiple records Not a lot of variables recorded Research database Table oriented so that data is accumulated for eventual export to a statistical package for data analysis and reporting Less emphasis on individual records Create Forms to represent tables that are storing information

5 Example of a Form

6 Here is the Table Storing Results From the Form

7 Databases versus Excel Excel has some capabilities to sort data but its primary function is to create financial spreadsheets Can be used for small and limited research data sets and simple lists Databases (e.g. ACCESS) are designed to collect, sort, and manipulate data Can create Data Quality Control features that ensure valid data is entered and missing data is eliminated A relational database allows for linking of an unlimited number of tables and therefore an unlimited number of variables

8 Relational Database Example Subject Info Id Name Age 10 Smith Jones Doe 60 Anthropometrics ID Weight (lb) Weight (kg) Physical Activity ID KCAL KCAL/kg Treadmill Performance ID V02 V02/kg

9 Relationships Between Tables Subject ID Links All the Tables

10 Databases versus Excel Databases are more suitable for importing data from multiple sources Imports of different data types (e.g. SAS files and Dbase files) into different tables can be linked via common identifiers such as subject ID Merging multiple data sources into Excel can be a challenge

11 How is a database organized? Multiple linked tables Tables store records (rows in the database) Tables have a collection of fields (these are the columns) Patient identifiers Name, DOB, address,..are stored in separate fields

12 Table = Records and Fields Fields Records

13 Need to Code Data for Each Field

14 Advantages of a Database Collection of data in a centralized location Data stored so as to appear to users in one location A relational database brings it all together

15 Sharing and Exchanging Data Multiple users can access the same database via a network Can be local or over the internet Access via a client application or web interface Server allows remote access over the internet from anywhere

16 Database Design Considerations What to collect What questions are to be answered? Think of the data tables in your future publications Focus on the key data elements rather than collect as much as possible What statistical package will be used? May need to format the data to accommodate program it will be exported to For example, gender can be recorded in the database as M or F but statistical package may require 0 and 1

17 What needs to be in the research database? Research variables directly related to the hypotheses being tested-yes Clinical measures used for screening-maybe Blood work, ECG, medical history Administrative data-no Contact information Scheduling

18 Examples of Data Collection Forms Paper then into Excel; tedious, mistakes common Scantron - Must scan in; many opportunities for mistakes Directly into Excel -tedious Direct computer entry via forms Allow real-time validity/completeness checks, prompts Inexpensive but requires expertise and maintenance

19 Designing the Questions Try to collect continuous data convert to categorical during analysis period. (e.g. age) Use validated scales/instruments Don t build your own unless unavoidable Consider asking in more than one way a question concerning a critical variable under study. (allows for validity assessment) Avoid measurements that cluster at one position or one end of a scale E.g. measuring body temperature on healthy outpatients Pilot the form for patients, then revise

20 What is Data Coding? A group of letters, numbers, or symbols and the rules that form a link to a specific terminology (e.g. male =1, female =2) Coded references should be incorporated into a data dictionary Dictionaries should be based on standardized terminology e.g. if doing a study on cirrhotics categorize patient on MELD score or Child-Pugh classification

21 Why code? Forces data into analyzable structure, format Speeds data input/transcription Vastly simplifies data analysis/reporting

22 Messed Up Coding

23 Rules for Data Entry Decide which variables require a number or string code e.g. code subject s name or MRN as a string variable Continuous values are entered directly Missing values must be different values from a real response Common formats are 99 or bullets Don t use 99 if the variable is a continuous data field! Don t know is a response do not leave blank. Have a code for don t know Don t use 0 for missing, just leave blank Coding instructions should be in coding dictionary

24 Avoid open-ended questions in subject/patient surveys What is your gender? -correct responses could be man, woman, male, female What is your level of education? -the answers 9 th grade, did not finish high school, and no college education all are correct. -provide specific choices for reply. -electronic surveys avoid these problems

25 Use Pre-Coded Response Forms When Possible

26 Be Careful With Scales Subject forgot to put tick mark

27 Data Validation

28 Data in Spreadsheet Finding Errors Subject ID Gender Age 1001 Male Male Mael Female 54 5 Female Female Femele Male M Female F 54

29 Database Entry Parameters Avoid These Errors

30 Exploratory Data Analysis Explore why there is missing data Is this a software or coding glitch? Did you delete by accident? (always back-up) Examine for unusual consistency /inconsistency issues Is every patient measured one month 6 10 Good time to use histograms and calculate skewness Find inadmissible ranges and codes Patient coded as age 205 instead of 25.

31 Attributes of Successful Data Management Attention to detail Explicit structure and process Robust designs Anticipate failures, lapses and mistakes Design systems that identify and correct them