+ - 0:00:00
Notes for current slide
Notes for next slide

Data Management & Analysis In Research

A practical approach


Faculty of Family Medicine Workshop

April 28, 2023

Dr Samuel Blay Nguah FWACP FGCPS

1 / 50

Workshop outline

Data Management

  • Databases software
  • Database design & validation
  • Data verification
    • Double
    • Single
  • Data warehousing
  • Data migration
  • Data cleaning

Data Analysis

  • Planning your analysis
    • Text, dummy tables and figures
  • Software
  • Data cleaning and missing data
  • Descriptive statistics
    • Continuous variables
    • Categorical variables
  • Inferential statistics
    • Hypothesis testing
    • P-values
    • Confidence interval
  • Graphical presentations
2 / 50

Our study for the day

Aim

  • Determine if New Drug has a better BP lowering effect after 2 weeks of administration compared to the Control Drug

Study type

  • Randomized Controlled Trial

Variable to be collected

  • Age in years
  • Sex
  • Initial BP
  • Final BP
3 / 50

Our study for the day

Study questions

  • How much does the New drug lower the BP?
  • How much does the Control drug lower the BP?
  • Which of the two drugs lowers the BP better?
  • Is there a difference in the BP lowering effect of the two drugs?
  • Is the BP lowering effect related to the age of the patient?
  • Is the BP lowering effect related to the sex of the patient?
4 / 50

Our study for the day

5 / 50

Data Management

6 / 50

Data management software

7 / 50

8 / 50

9 / 50

Database design, validation and verification

Validation

  • Limits
  • Valid ranges
  • Allowable values
  • Some software better than others

Cleaning

  • Regular review of filled questionnaires
  • Weekly checking of entered data for correctness

Verification

  • Single entry
    • 10% verification
    • Whole database verification
  • Double entry
    • Create identical database
    • Double enter data
    • Picks data entry errors
    • Compare the data from both databases
    • Identify discrepancies
    • Correct errors as necessary
10 / 50

Data Warehousing, migration & cleaning

Warehousing

  • Backup the data regularly – 3 copies
  • Backup with versions and dates
  • Keep in the appropriate format
    • Microsoft Excel
    • Text files
    • PDF
    • Tiff

Migration

  • Moving data around
  • Should be in stable state
  • Not all software requires this

Cleaning

  • Involves picking out erroneous data
  • Picks up
    • Data collection errors
    • Data entry errors
  • Strategy depends on
    • Continuous variable
    • Discrete variable
    • Categorical
    • Etc
11 / 50

Data Analysis

12 / 50

Data Types

13 / 50

Variable types

Independent (predictor) variable

  • Potentially influences, affects or predicts another variable
  • E.g: How age influences income make age the independent variable

Dependent (predicted) variable

  • Potentially predicted, influenced and affected by another variable
  • E.g: How age influences income make income the dependent variable
14 / 50

Data analysis

Software

  • R - Analysis only
  • Microsoft Excel - Entry and analysis
  • Stata - Analysis only
  • SPSS - Entry and analysis
15 / 50

Know your data

Variables

  • id = Study ID
  • treat = Treatment given
  • age = Age of participant
  • sex = Sex of patient
  • bp1 = Initial mean arterial BP
  • bp2 = Final mean arterial BP
id treat age sex bp1 bp2
C1 0 63 F 97.4 93.1
C2 0 NA F 97.2 92.4
C6 0 62 F 103.4 99.7
C7 0 61 F 290.1 88.4
C9 0 73 F 96.4 91.1
C10 0 57 F 98.6 90.5
C13 0 61 F 97.4 93.8
C14 0 999 F 97.4 92.6
A18 0 51 F 92.2 86.2
A20 0 65 F 96.9 90.4
A21 NA 65 F 102.6 91.5
C3 0 54 M 98.8 94.6
C4 0 69 M 98.4 92.3
C5 0 75 M 89.8 89.3
C8 0 59 M 93.7 90.4
16 / 50

Summarizing data

      id                treat             age             sex           
 Length:50          Min.   :0.0000   Min.   : 45.00   Length:50         
 Class :character   1st Qu.:0.0000   1st Qu.: 57.75   Class :character  
 Mode  :character   Median :1.0000   Median : 63.00   Mode  :character  
                    Mean   :0.5714   Mean   : 81.00                     
                    3rd Qu.:1.0000   3rd Qu.: 65.00                     
                    Max.   :1.0000   Max.   :999.00                     
                    NA's   :1        NA's   :2                          
      bp1             bp2       
 Min.   : 87.5   Min.   :78.00  
 1st Qu.: 96.0   1st Qu.:85.20  
 Median : 98.0   Median :88.40  
 Mean   :102.4   Mean   :88.61  
 3rd Qu.:101.2   3rd Qu.:92.30  
 Max.   :290.1   Max.   :99.70  
 NA's   :1       NA's   :1
17 / 50

Pre-processing of data

Data cleaning

  • Involves picking out erroneous data
  • Picks up
    • Data collection errors
    • Data entry errors
    • Strategy depends on
    • Continuous variable OR Discrete variable

Steps (personal)

  • Check study id
    • Any duplication in whole data?
    • Any duplication in study id?
    • Any missing study id?
    • Sort them out if possible
  • General overview of data
    • single categorical variables
    • Single continuous variables
    • Combination of variables – Categorical
    • Combination of variables - Continuous
18 / 50

Pre-processing of data

Missing data

  • Know the pattern
  • Know how to deal with them
    • Dropping observations
    • Imputation
      • Commonest observation
      • Median/Mean
      • MICE
19 / 50

Missing pattern

20 / 50

Pre-processing of data

Generating new variables

  1. Convert data to appropriate types
    • sex to categorical variable (factor)
    • treat to categorical variable
  2. Fill missing data with appropriate data
  3. Correct abnormal values in BPs
  4. Generate variable(s)
    • bp_diff = Difference in BP
    • age_group:
      • Elderly: >60 years
      • Middle age: <=60 years
21 / 50

Data summary after cleaning

      id                 treat         age            sex          bp1        
 Length:50          Old Drug:22   Min.   :45.00   Female:26   Min.   : 87.50  
 Class :character   New Drug:28   1st Qu.:57.25   Male  :24   1st Qu.: 95.62  
 Mode  :character                 Median :63.00               Median : 97.70  
                                  Mean   :61.48               Mean   : 98.30  
                                  3rd Qu.:65.00               3rd Qu.: 99.40  
                                  Max.   :75.00               Max.   :111.70  
      bp2           bp_diff            age_group 
 Min.   :78.00   Min.   : 0.500   Middle age:17  
 1st Qu.:85.22   1st Qu.: 4.800   Elderly   :33  
 Median :88.15   Median : 8.250                  
 Mean   :88.60   Mean   : 9.704                  
 3rd Qu.:92.10   3rd Qu.:13.700                  
 Max.   :99.70   Max.   :26.300
22 / 50

Descriptive statistics

Categorical variable

  • Frequency tables – univariate
  • Contingency tables – bivariate
    • Row percentage
    • Column percentage
  • Graphical representations
    • Bar chart
    • Pie Chart
    • Others
  • Odds & Odds Ratio
  • Risk & Risk Ratio

Continuous Variable

  • Measures of central tendency
    • Mean
      • Arithmetic Mean
      • Geometric mean
      • Harmonic mean
    • Median
    • Mode
  • Measures of dispersion
    • Standard deviation
    • Variance
    • Interquartile range
    • Range
23 / 50

Categorical variables

Univariate analysis

Table 1: Univariate categorical table
Characteristic N = 50
Treatment Type, n (%)
    Old Drug 22 (44.0)
    New Drug 28 (56.0)
Sex, n (%)
    Female 26 (52.0)
    Male 24 (48.0)
Age Grouping, n (%)
    Middle age 17 (34.0)
    Elderly 33 (66.0)

Bivariate analysis

Table 2: Bivariate categorical table
Treatment Type Total
Old Drug New Drug
Sex
    Female 11 (42%) 15 (58%) 26 (100%)
    Male 11 (46%) 13 (54%) 24 (100%)
Total 22 (44%) 28 (56%) 50 (100%)
24 / 50

Categorical variables

Bivariate tables

Table 3: Bivariate categorical table
Characteristic 1">Overall, N = 501 Treatment given
Old Drug, N = 22 New Drug, N = 28
Sex, n (%)
    Female 26 (52.0) 11 (50.0) 15 (53.6)
    Male 24 (48.0) 11 (50.0) 13 (46.4)
Age Grouping, n (%)
    Middle age 17 (34.0) 7 (31.8) 10 (35.7)
    Elderly 33 (66.0) 15 (68.2) 18 (64.3)
1 n (%)
25 / 50

Categorical variables plots

Pie Chart

Bar Chart - Univariate

26 / 50

Categorical variables plots

Barchart - Bivariate

27 / 50

Measures of Asociation

Risk

  • Risk (probability, likelihood)
    • Probability of outcome in a specified period
    • If 200 children in a boarding school ate rice and 28 had diarrhea then

R𝑒=28200=0.14=14%

Odds

=Risk of getting the disease(Risk of not getting the disease) Oddse=0.141−0.14=0.140.86≈0.16

28 / 50

Measures of association

Risk Ratio (RR)

=Incidence in the exposedIncidence in the non−exposed=Re𝑅𝑢

  • Used for estimation of causal relationship
  • Can be calculated in cohort studies
  • Higher RR => Better causal relationship
    • RR=1 => No evidence of association
    • RR ≠ 1 => Exposure is harmful or protective
29 / 50

Measures of association

Odds Ratio (OR)

=Odds in the exposedOdds in the non−exposed=OddseOdds𝑢

  • Used for estimation of causal relationship
  • Can be calculated in case-control studies
  • Higher RR => Better causal relationship
    • OR=1 => No evidence of association
    • OR ≠ 1 => Exposure is harmful or protective
30 / 50

Descriptive statistics

Measures of central tendency

  • Mean
  • Median
  • Mode

Measure of Dispersion

  • Range – Minimum to maximum
  • Interquartile range
    • p25 - p75
  • Quartiles
    • Minimum, p25, p75, maximum
  • Standard Deviation
  • Variance
31 / 50

Descriptive Statistics

Table 4: Univariate descriptive statistics
Characteristic N = 50
Initial blood pressure (mmHg)
    Median (IQR) 97.7 (95.6, 99.4)
    Mean (SD) 98.3 (5.2)
    Range 87.5, 111.7
Blood Pressure after treatment
    Median (IQR) 88.2 (85.2, 92.1)
    Mean (SD) 88.6 (4.6)
    Range 78.0, 99.7
Table 5: Bivariate descriptive statistics
Characteristic Old Drug, N = 22 New Drug, N = 28
Initial blood pressure (mmHg)
    Median (IQR) 97.4 (95.5, 98.8) 98.3 (95.9, 101.7)
    Mean (SD) 97.1 (3.6) 99.2 (6.0)
    Range 89.8, 103.4 87.5, 111.7
Blood Pressure after treatment
    Median (IQR) 91.9 (90.4, 93.6) 85.4 (84.4, 87.2)
    Mean (SD) 92.2 (3.3) 85.8 (3.3)
    Range 86.2, 99.7 78.0, 94.2
32 / 50

Descriptive Statistics

Histogram

Boxplot

33 / 50

Descriptive Statistics - plots

Scatter plot

Boxplot

34 / 50

Inferential statistics

35 / 50

Sample vrs. Population

36 / 50

Statistic vrs. Parameter

37 / 50

Sample variation

38 / 50

Estimates

  • Point estimates
  • Interval estimates

Confidence interval

The 95% confidence interval is the interval that is likely to contain the population parameter 95% of the time.

39 / 50

95% Confidence interval

40 / 50

p-value

  • Well known in research
  • Very often misinterpreted and overemphasized

It is the probability of having a statistic as extreme as the one observed from the sample if the null hypothesis is true. It determines the strength of support for the null hypothesis.

  • The nearer p-value is to 1 the better the data at hand or test statistic supports the null value.
41 / 50

Hypothesis testing

  1. State (Null) hypothesis (H0)
  2. Decide on significance level (α) usually 0.05
  3. Determine sample size
  4. Collect data (Evidence)
  5. Apply appropriate statistical test
  6. Compute the probability value (p-value)
  7. We compare the p-value with α
    1. If p < α Then: Reject the H0 (Guilty)
    2. If p >= α Then: Refuse to reject the H0 (Not guilty)

Generally:

  • Lower p-value: More confident of rejecting the H0
  • Note that failure to reject H0 does not mean H0 is true. It means we do not have enough evidence to reject it
42 / 50

Statistical Tests

43 / 50

Answering our questions from our study

44 / 50

How much does the New drug lower the BP?

How much does the Control drug lower the BP?

Which of the two drugs lowers the BP better?

Characteristic 1">Old Drug, N = 221 2">95% CI2 1">New Drug, N = 281 2">95% CI2
Change in Blood Pressure 4.9 (2.3) 3.9, 5.9 13.4 (5.7) 11, 16
1 Mean (SD)
2 CI = Confidence Interval
45 / 50

Is there a difference in the BP lowering effect of the two drugs?

H0: No difference in BP lowering effect between the two drugs

Characteristic 1">Old Drug, N = 221 1">New Drug, N = 281 2">p-value2
Change in Blood Pressure 4.9 (2.3) 13.4 (5.7) <0.001
1 Mean (SD)
2 Welch Two Sample t-test

Conclusion: Based on the p-value we reject the H0 and thus conclude there is a significant difference between the BP lowering effect of the New drug and Control

46 / 50

Is the BP lowering effect related to the age of the patient?

H0: BP lowering effect is not related to age

Characteristic Beta 1">95% CI1 p-value
Age in years 0.08 -0.19, 0.36 0.5
1 CI = Confidence Interval

Conclusion: BP lowering effect not significantly related to the age of the patient.

47 / 50
Table 6: Overall data outlook
Characteristic 1">Overall, N = 501 Drug 2">p-value2
1">Old Drug, N = 221 1">New Drug, N = 281
Age in years 61.5 (6.5) 62.1 (6.6) 61.0 (6.5) 0.6
Sex 0.8
    Female 26.0 (52.0%) 11.0 (50.0%) 15.0 (53.6%)
    Male 24.0 (48.0%) 11.0 (50.0%) 13.0 (46.4%)
Initial blood pressure (mmHg) 98.3 (5.2) 97.1 (3.6) 99.2 (6.0) 0.13
Blood Pressure after treatment 88.6 (4.6) 92.2 (3.3) 85.8 (3.3) <0.001
Change in Blood Pressure 9.7 (6.2) 4.9 (2.3) 13.4 (5.7) <0.001
Age Grouping 0.8
    Middle age 17.0 (34.0%) 7.0 (31.8%) 10.0 (35.7%)
    Elderly 33.0 (66.0%) 15.0 (68.2%) 18.0 (64.3%)
1 Mean (SD); n (%)
2 Welch Two Sample t-test; Pearson’s Chi-squared test
48 / 50

Summary...

Data Management

  • Database design
  • Data entry
  • Data validation
  • Data verification
  • Data cleaning
  • Data warehousing
  • Data transfer

Data Analysis

  • Statistical software
  • Data cleaning
  • Data analysis
    • Continuous variables
    • Categorical variables
    • Descriptive vrs. Inferential
  • Presentation of results
    • Tables
    • Figures
49 / 50

Thank you!!!

50 / 50

Workshop outline

Data Management

  • Databases software
  • Database design & validation
  • Data verification
    • Double
    • Single
  • Data warehousing
  • Data migration
  • Data cleaning

Data Analysis

  • Planning your analysis
    • Text, dummy tables and figures
  • Software
  • Data cleaning and missing data
  • Descriptive statistics
    • Continuous variables
    • Categorical variables
  • Inferential statistics
    • Hypothesis testing
    • P-values
    • Confidence interval
  • Graphical presentations
2 / 50
Paused

Help

Keyboard shortcuts

↑, ←, Pg Up, k Go to previous slide
↓, →, Pg Dn, Space, j Go to next slide
Home Go to first slide
End Go to last slide
Number + Return Go to specific slide
b / m / f Toggle blackout / mirrored / fullscreen mode
c Clone slideshow
p Toggle presenter mode
t Restart the presentation timer
?, h Toggle this help
oTile View: Overview of Slides
sToggle scribble toolbox
Esc Back to slideshow