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Statistics in Pharmaceutical Sciences
PHR 112

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• Statistics and its application
• Variables and Attributes
• Classification and tabulation of data
• Populations and samples
• Frequency distributions
• Graphical presentation of data
• Describing and summarizing data: statistical averages & measures of
dispersion.
• Probability and probability distributions.
• Hypothesis testing: concepts, types, p-value.
• Test of significance: Parametric tests (t-test, One way ANOVA, multiple
comparison tests (Bonferoni, Duncan, Dunnet, Tukey), repeated measure
ANOVA;
• Non-parametric tests: Mann-whiteney, Wilcoxon rank test, Kruskal-Wallis test,
multiple comparison tests (Tukey), Friedman's test. Regression (simple linear
& nonparametric regression) and correlation (simple & rank correlation), Chi-
square & odds ratio.
Course Contents

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Course Plan
Topic and Test No. of Lectures Dates
- Statistics and its application, Variables and Attributes, Classification
and tabulation of data, Populations and samples
5
Test 1
- Frequency distributions, Graphical presentation of data, Describing
and summarizing data: statistical averages & measures of dispersion
5
Test 2
- Hypothesis testing: concepts, types, p-value, t-test 2
Test 3
Mid Term Examination
- One way ANOVA, multiple comparison tests (Bonferoni, Duncan,
Dunnet, Tukey), repeated measure ANOVA
5
Test 4
- Non-parametric tests: Mann-Whitney, Wilcoxon rank test, Kruskal-
Wallis test, multiple comparison tests (Tukey), Friedman's test.
Regression (simple linear & nonparametric regression) and correlation
(simple & rank correlation), Chi-square & odds ratio
5
Test 5
Presentation 2
Final Examination

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Statistics and its application

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1.1 What is Statistics?
Statistics is a scientific study of numerical data based on
natural phenomena.
It is also the science of collecting, organizing, interpreting
and reporting data.

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 Evaluate the activity of a drug; e.g.; effect of caffeine on attention;
compare the analgesic effect of a plant extract and NSAID
 To explore whether the changes produced by the drug are due to
the action of drug or by chance
 To compare the action of two or more different drugs or different
dosages of the same drug are studied using statistical methods.
 To find an association between disease and risk factors such as
Coronary artery disease and smoking
Pharmaceutical statistics is the application of statistics to matters
concerning the pharmaceutical industry. This can be from issues of
design of experiments, to analysis of drug trials, to issues of
commercialization of a medicine.
Statistics and its application

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Public health, including epidemiology, health services research,
nutrition, environmental health and healthcare policy & management.
Design and analysis of clinical trials in medicine
Population genetics, and statistical genetics in order to link variation
in genotype with a variation in phenotype. In biomedical research,
this work can assist in finding candidates for gene alleles that can
cause or influence predisposition to disease in human genetics
Analysis of genomics data. Example: from microarray or proteomics
experiments. Often concerning diseases or disease stages.
Systems biology for gene network inference or pathways analysis
Demographic studies: Age, gender, height, weight, BMI
Epidemiology: deficiency of iron in anemia, iodized salt and goiter,
hygiene and microbial disease
Statistics and its application

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Two Meanings
 Specific numbers
 Method of analysis
Statistics

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 Specific number
numerical measurement determined by a
set of data
Example: 65% student use facebook
account
Statistics

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Statistics
Method of analysis
A collection of methods for planning experiments,
obtaining data, and then organizing, summarizing,
presenting, analyzing, interpreting, and drawing
conclusions based on the data

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Population
the complete collection of all elements (scores, people,
measurements, and so on) to be studied. The collection is
complete in the sense that it includes all subjects to be
studied.
Population refers to all members of a defined group
Definition

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 Census
the collection of data from every element in a population
 Sample
a subcollection of elements drawn from a population
• Example; patients in a hospital would constitute the entire population
for a study of infection control in that hospital. However, for a study of
infected patients in the nation’s hospitals, the same group of patients
would be but a tiny sample. The same group can be a sample for one
question about its characteristics and a population for another
question.
Definition

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 Parameter
a numerical measurement describing
some characteristic of a population
Definition

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 Parameter
a numerical measurement describing
some characteristic of a population
population
parameter
Definition

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Statistic
a numerical measurement describing
some characteristic of a sample
sample
statistic
Definition

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Quantitative data
numbers representing counts or measurements
 Qualitative (or categorical or attribute) data
can be separated into different categories that are
distinguished by some nonnumeric characteristics
Definition

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Quantitative data
Cholesterol level in the blood
 Qualitative (categorical or attribute) data
The genders (male/female) of college graduates
Definition

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 Discrete
data result when the number of possible values is
either a finite number or a ‘countable’ number of
possible values
0, 1, 2, 3, . . .
 Continuous
(numerical) data result from infinitely many possible
values that correspond to some continuous scale
that covers a range of values without gaps,
interruptions, or jumps
2 3
Definition

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Discrete
The number of eggs that hens lay; for
example, 3 eggs a day.
 Continuous
The amounts of milk that cows produce;
for example, 2.343115 gallons a day.
Definition

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 nominal level of measurement
characterized by data that consist of names, labels,
or categories only. The data cannot be arranged in
an ordering scheme (such as low to high)
Example: survey responses yes, no, undecided
Definition

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 ordinal level of measurement
involves data that may be arranged in some order,
but differences between data values either cannot
be determined or are meaningless
Example: Course grades A, B, C, D, or F
Definition

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 interval level of measurement
like the ordinal level, with the additional property that
the difference between any two data values is
meaningful. However, there is no natural zero
starting point (where none of the quantity is present)
Example: Years 1000, 2000, 1776, and 1492
Definition

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 ratio level of measurement
the interval level modified to include the natural zero
starting point (where zero indicates that none of the
quantity is present). For values at this level,
differences and ratios are meaningful.
Example: Prices of college textbooks
Definition

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 Nominal - categories only
 Ordinal - categories with some order
 Interval - differences but no natural starting point
 Ratio - differences and a natural starting point
Levels of measurement

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Variables
A variable is just a term for an observation or reading giving
information on the study question to be answered. Blood pressure is a
variable giving information on hypertension. Blood uric acid level is a
variable giving information on gout.
Independent variable is a variable that, for the purposes of the study question
to be answered, occurs independently of the effects being studied.
A variable thought to be the cause of some effect. This term is usually used in
experimental research to denote a variable that the experimenter has
manipulated.
Dependent variable is a variable that depends on, or more exactly is
influenced by, the independent variable.
A variable thought to be affected by changes in an independent variable. You
can think of this variable as an outcome.

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Variables
In a study on gout, suppose we ask if blood uric acid (level) is a factor
in causing pain. We record blood uric acid level as a measurable
variable that occurs in the patient. Then we record pain as reported by
the patient. We believe blood uric acid level is predictive of pain. In this
relationship, the blood uric acid is the independent variable and pain is
the dependent variable.

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Control Groups and Placebos
A frequent mechanism to pinpoint the effect of a treatment
and to reduce bias is to provide a control group having all
the characteristics of the experimental group except the
treatment under study.
Example: Paracetamol tablet (drug group) and lactose
tablet (placebo); then compare their effect on fever
reducing property
Basics

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Case-Control Study
A case-control study is a study in which an experimental group of
patients is chosen for being characterized by some outcome factor, such
as having acquired a disease, and a control group lacking this factor is
matched patient for patient.
STUDY TYPES
Cohort Study
A cohort study starts by choosing groups that have already been
assigned to study categories, such as diseases or treatments, and
follows these groups forward in time to assess the outcomes.

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Randomized Controlled Trial
The soundest type of study is the randomized controlled trial (RCT), often
called a clinical trial. An RCT is a true experiment in which patients are
assigned randomly to a study category, such as clinical treatment, and
are then followed forward in time (making it a prospective study) and the
outcome is assessed.
STUDY TYPES
Paired and Crossover Designs
Some studies permit a design in which the patients serve as their own
controls, as in a “before-and-after” study or a comparison of two
treatments in which the patient receives both in sequence.

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STEPS THAT WILL AID IN PLANNING A STUDY
1. Start with objectives. Specify, clearly, unequivocally, a question to be answered about an
explicitly defined population.
2. Develop the background and relevance.
3. Plan your materials. From where will you obtain your equipment?
4. Plan your methods and data. Identify at least 1 measurable variable capable of answering
your question. Define the specific data that will satisfy your objectives and verify that your
methods will provide these data. Develop clearly specified null and alternate hypotheses.
5. Plan data recording. Develop a raw data entry sheet and a spreadsheet to transfer the raw
data to that will facilitate analysis by computer software.
6. Define the subject population, verify that your sampling procedures will sample
representatively.
7. Ensure that your sample size will satisfy your objectives.
8. Anticipate what statistical analysis will yield results that will satisfy your objectives.
9. Plan tests for sampling bias.
10. Plan the bridge from results to conclusions.
11. Anticipate the form in which your conclusions will be expressed
12. Now you can draft an abstract.