Transcript Biostatistics Breakdown

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Biostatistics Breakdown
Common Statistical tests
Special thanks to:
Christyn Mullen, Pharm.D.
Clinical Pharmacy Specialist
John Peter Smith Hospital
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Objectives
• Briefly review important terms needed to
understand common types of statistical analysis
• Review the different types of data and how they
determine what type of statistical analysis is
appropriate to use
• Explore real examples of common statistical
analysis and their relevance to that particular
study
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Types of Variables
• Independent
▫ Variables that occur regardless of other variables
or factors
 Intervention in a trial
• Dependent
▫ Variables that are dependent upon other variables
or factors
 Outcome in a trial
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Interval
Arbitrary 0
Ex:
Temperature
(°F)
Continuous
Ratio
Absolute 0
Ex: Blood
glucose
Types of Data
Nominal
Categories of
data that do not
have a rank
Ordinal
Data measured
by a finite
number of
ranked
categories
Ex: Sex,
Smoking Status,
Race
Categorical
(Discrete)
Ex: NYHA
Classes I-IV
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Central Tendency
Continuous
• Mean
Ordinal
• Median
Nominal
• Mode
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Distribution
Normal
Distribution
• Parametric
Continuous Data
Non-Normal
Distribution
Ordinal or Nominal
Data
• Nonparametric
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Measures of Variability
Range
 Interval between lowest and highest values within a data
set
Interquartile Range
 Describes interval between 25th and 75th percentile (middle
50% of measures)
Standard Deviation
 Describes the distribution of values in a data set by
comparing each measured value to the mean (continuous
data only)
Variance
 Deviation from the mean
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Statistical Significance
• P-Value – indicates statistical significance
▫ A p-value < 0.05 means that 5% of the time, the null could be
rejected in error
• Confidence Interval (typically 95%)
▫ The range in which sample values are likely representative of the
true population
• Power
▫ The ability of a study to detect specified differences between
groups
▫ Increasing sample size can increase power
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• Student t-test
▫ Compares means of 2 groups
• ANOVA assumptions
1. Data have normal distribution
2. Each observation is independent of the others
3. The variances within the groups being compared are equal
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• Mann-Whitney and Wilcoxan Rank
▫ Non-parametric equivalent to t-test
• Kruskal- Wallis with multiple comparison correction
• Wilcoxan signed-rank
▫ Alternative to log-rank analysis used in Kaplan Meier
Regression
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• Chi-square (X2)
▫ Compares categorical variables to see if there is a difference
• Fisher’s exact test
▫ For a smaller sample size (n < 5)
• Mantel – Haenszel
▫ Adjusts for confounding variables
• McNemar
▫ Analyzes results from studies with related or dependent measures
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Regression
• Predicts the effect of independent variables
on the outcome (Framingham Risk Score)
• Multiple linear regression
▫ Used when outcome data is continuous
• Logistic regression
▫ Used when outcome data is categorical
(binary)
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Relative Risk
and
Odds Ratio
• Relative Risk
▫ Ratio of incidence of disease in exposed group
divided by incidence in unexposed group
 Cohort Studies
• Odds Ratio
▫ Odds of exposure in the group with the disease
divided by odds in control group
 Case-Control Studies (approximates relative risk b/c
patients already have the disease)

If the Confidence Interval includes 1, there is NO statistical
difference between groups
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Survival Analysis
• Kaplan- Meier Curve
▫ Assesses time to an event
▫ Log-Rank test will tell if differences
between 2 groups are significant
• Cox Proportional Hazard Model
▫ Assesses the effects of covariates (2 or more) on survival or time to an
event (adjusts for confounders)
▫ Uses Hazard Ratio as a function of relative risk
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Propensity Matching
• Used to decrease selection bias by matching participants based on
characteristics
▫ Matching can be done based on a score
▫ Can set number of significant digits depending on how precise
you want to be
• Allows for a more confident assessment of the intervention
• Instrumental variable analysis
▫ Gives each participant a probability of receiving an intervention
and then apply it to an entire group (grouped-treatment rate)
▫ Takes away selection bias based on prognosis or prescriber
preference
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References
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Allen, J. Applying study results to patient care: Glossary of study design and statistical terms. Pharmacists Letter..
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Israni, RK. ‘Guide to Biostatistics.” MedPageToday. 2007. http://medpagetoday.com
DeYoung GR. Understanding statistics: An approach for the clinician. Pharmacotherapy Self-Assessment
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Al-Qadheeb NS, et al. Impact of enteral methadone on the ability to wean off continuously infused opioids in
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Marcus M, et al. Kinematic shoulder MRI: The diagnostic value in acute shoulder dislocations. European
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Stefan MS, Rothberg MB, Priyaa, et al. Association between B-blocker therapy and outcomes in patients
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http://stat.ethz.ch/education/semesters/ss2011/seminar/contents/presentation_2.pdf. Accessed 20 Sept 2012.
http://www.gog.org/sdcstaff/MikeSill/Classes/STA575/Lectures/LectureNotesChp5.pdf. Accessed 25 Sept 2012.
https://statistics.laerd.com/spss-tutorials/mann-whitney-u-test-using-spss-statistics.php. Accessed 24 Sept 2012.
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