Revision of topics for CMED 305 Final Exam

The exam duration: 1hour 30 min. Marks :25

All MCQ’s.

You should choose the correct answer.

No major calculations, but simple maths IQ is required.

No need to memorize the formulas.

Bring your own calculator.

Cell phones are not allowed to use as a calculator.

Research Methodology:

Incidence and Prevalence   (2)

Study Designs (Casecontrol, Cohort,Experimental,Cross sectional) (3)

Odds Ratio and Relative Risk (2)

Designing questionnaire and Study Tools for data collection (1)

Data Interpretation (2)

Biostatistics Topics: ( 40 questions) Sampling Techniques (4) Sample size (2) Type of data & graphical presentation(4) Summary and Variability measures (7) Normal distribution (2) Statistical significance using p-values (6) Statistical significance using confidence intervals (5) Statistical tests for quantitative variables (5) Statistical tests for qualitative variables (4) Spss software (1)

Probability Sampling Simple random

sampling Stratified random

sampling Systematic random

sampling Cluster (area) random

sampling Multistage random

sampling

Non-Probability Sampling

Deliberate (quota) sampling

Convenience sampling Purposive sampling Snowball sampling Consecutive sampling

Estimation of Sample Size by Three ways:

By using

(1) Formulae (manual calculations)

(2) Sample size tables or Nomogram

(3) Softwares

Nominal – qualitative classification of equal value: gender, race, color, city

Ordinal - qualitative classification which can be rank ordered: socioeconomic status of families

Interval - Numerical or quantitative data: can be rank ordered and sizes compared : temperature

Ratio - Quantitative interval data along with ratio: time, age.

QUALITATIVE DATA (Categorical data) DISCRETE QUANTITATIVE CONTINOUS QUANTITATIVE

Categorical data --- Bar diagram (one or two groups) --- Pie diagram Continuous data --- Histogram --- Frequency polygon (curve) --- Stem-and –leaf plot --- Box-and-whisker plot --- Scatter diagram

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Arithmetic Mean

Median

Mode

Describing Data Numerically

Variance

Standard Deviation

Range

Interquartile Range

Geometric Mean

Skewness

Central Tendency Variation ShapeQuartiles

Harmonic Mean

DISTRIBUTION OF DATA IS SYMMETRIC ---- USE MEAN & S.D.,

DISTRIBUTION OF DATA IS SKEWED

---- USE MEDIAN & QUARTILES(IQR)

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Bell-Shaped (also known as symmetric” or “normal”)

Skewed:positively (skewed to

the right) – it tails off toward larger values

negatively (skewed to the left) – it tails off toward smaller values

VARIANCE: Deviations of each observation from the

mean, then averaging the sum of squares of these deviations.

STANDARD DEVIATION: “ ROOT- MEANS-SQUARE-DEVIATIONS”

Standard error of the mean (sem):

Comments:n = sample sizeeven for large s, if n is large, we can get good

precision for semalways smaller than standard deviation (s)

s sems

nx

Standard error of mean is calculated by:

s sems

nx

Many biologic variables follow this pattern Hemoglobin, Cholesterol, Serum

Electrolytes, Blood pressures, age, weight, height

One can use this information to define what is normal and what is extreme

In clinical medicine 95% or 2 Standard deviations around the mean is normalClinically, 5% of “normal” individuals

are labeled as extreme/abnormal We just accept this and move on.

Symmetrical about mean, Mean, median, and mode are equal Total area under the curve above the

x-axis is one square unit 1 standard deviation on both sides of

the mean includes approximately 68% of the total area2 standard deviations includes

approximately 95% 3 standard deviations includes

approximately 99%

Sample

z =x - xs

Population

z = x - µ

Measures of Positionz score

- 3 - 2 - 1 0 1 2 3

Z

Unusual Values

Unusual Values

OrdinaryValues

Interpreting Z Scores

‘No difference ‘ or ‘No association’

Alternative hypothesisAlternative hypothesisLogical alternative to the null hypothesis

‘There is a difference’ or ‘Association’

HypothesisHypothesis

simple, specific, in advance

Every decisions making process will commit two types of errors.

“We may conclude that the difference is significant when in fact there is not real difference in the population, and so reject the null hypothesis when it is true. This is error is known as type-I error, whose magnitude is denoted by the Greek letter ‘α’.

On the other hand, we may conclude that the difference is not significant, when in fact there is real difference between the populations, that is the null hypothesis is not rejected when actually it is false. This error is called type-II error, whose magnitude is denoted by ‘β’.

Disease (Gold Standard)

Present

Correct

Negative

Total

PositiveTest

False Negative

a+b

a+b+c+d

Total

Correct

a+c

b+d

c+d

False Positive

Result

Absent

a b

c d

P S

Investigation

S

Sampling

P valueConfidence intervals!!!

Inference

Results

This level of uncertainty is called type 1 error or a false-positive rate (

More commonly called a p-value In general, p ≤ 0.05 is the agreed upon

level In other words, the probability that the

difference that we observed in our sample occurred by chance is less than 5%Therefore we can reject the Ho

-1.96 +1.96

Rejection Nonrejection region Rejection region region

Z/2 = 1.96Reject H0 if Z < -Z /2 or Z > Z /2

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Testing significance at 0.05 level

Stating the Conclusions of our Results

When the p-value is small, we reject the null hypothesis or, equivalently, we accept the alternative hypothesis. “Small” is defined as a p-value , where

acceptable false (+) rate (usually 0.05). When the p-value is not small, we

conclude that we cannot reject the null hypothesis or, equivalently, there is not enough evidence to reject the null hypothesis. “Not small” is defined as a p-value > , where =

acceptable false (+) rate (usually 0.05).

EstimationTwo forms of estimation

• Point estimation = single value, e.g., x-bar is unbiased estimator of μ

• Interval estimation = range of values confidence interval (CI). A confidence interval consists of:

Mean, , is unknown

Population Random SampleI am 95%

confident that is between 40 &

60.

Mean X = 50

Estimation Process

Sample

Different Interpretations of the 95% confidence interval

• “We are 95% sure that the TRUE parameter value is in the 95% confidence interval”

• “If we repeated the experiment many many times, 95% of the time the TRUE parameter value would be in the interval”

• “the probability that the interval would contain the true parameter value was 0.95.”

Most commonly used CI:

CI 90% corresponds to p 0.10

CI 95% corresponds to p 0.05

CI 99% corresponds to p 0.01

Note:

p value only for analytical studies

CI for descriptive and analytical studies

CHARACTERISTICS OF CI’S--The (im) precision of the estimate is

indicated by the width of the confidence interval.

--The wider the interval the less precision THE WIDTH OF C.I. DEPENDS ON: ---- SAMPLE SIZE ---- VAIRABILITY ---- DEGREE OF CONFIDENCE

Comparison of p values and confidence interval

• p values (hypothesis testing) gives you the probability that the result is merely caused by chance or not by chance, it does not give the magnitude and direction of the difference

• Confidence interval (estimation) indicates estimate of value in the population given one result in the sample, it gives the magnitude and direction of the difference

Z-test:Study variable: QualitativeOutcome variable: Quantitative or QualitativeComparison: two means or two proportionsSample size: each group is > 50Student’s t-test:Study variable: QualitativeOutcome variable: QuantitativeComparison: sample mean with population

mean; two means (independent samples); paired samples.

Sample size: each group <50 ( can be used even for large sample size)

Chi-square test:Study variable: QualitativeOutcome variable: QualitativeComparison: two or more proportionsSample size: > 20Expected frequency: > 5Fisher’s exact test:Study variable: QualitativeOutcome variable: QualitativeComparison: two proportionsSample size:< 20Macnemar’s test: (for paired samples)Study variable: QualitativeOutcome variable: QualitativeComparison: two proportionsSample size: Any

1.1. Test for single meanTest for single mean 2. Test for difference in means2. Test for difference in means 3. Test for paired observationTest for paired observation

Student ‘s t-test will be used: --- When Sample size is small , for

mean values and for the following situations:

(1) to compare the single sample mean

with the population mean (2) to compare the sample means of two independent samples (3) to compare the sample means of

paired samples

Statistical tests for qualitative (categorical) data

When both the study variables and outcome variables are categorical (Qualitative):

Apply (i) Chi square test(ii) Fisher’s exact test (Small samples)(iii) Mac nemar’s test ( for paired

samples)

Wishing all of you Best of Luck !