ezz eazy biostatistics for crash course

بسم هللا الرحمن الرحيم

رب اشرح لي صدري ويسر لى أمرى

واحلل عقدة من لسانى يفقهوا قولي

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Why do we need Statistics?

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Understanding & defining the problem

What to measure? & how to measure?

Collection & Editing

Constructing tables, Graphs, Estimation, Looking for patterns, Testing Hypotheses, Predicting,…

Interpretation, New ideas & Solving the problem

What is the Steps for Scientific Research?

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Previously discussed problem

1st time (new)

problem

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What is the mean value of ...? What is the percentage of …? What is the your opinion regarding…?

Is the mean value of ... Equals …? Is the percentage of …Equals …? There is a relation between … and …? There is a difference between … and …?

We Answer Using: Estimation Likert Scale

We Answer Using: Testing Hypotheses

Sampling techniques

Dr Ezz H. Abdelfattah

Sampling techniques

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Conservative law

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En

Sample Size

n is the required size of the sample E is the maximum allowable error

For example if E=0.05 then:

Sample Size for Estimating the Population

Mean(µ) 2

E

Zn

Sample Size

n is the required size of the sample E is the maximum allowable error

For example if E=0.05 and =1.1, then with 95% Confidence, we have:

is the population standard deviation

Z is the standard Normal value (e.g. = 1.96 at confidence 95%)

Sample Size for Estimating the Population

Proportion() 2

)1(

E

Zppn

Sample Size

n is the required size of the sample E is the maximum allowable error

For example if E=0.05 and p =0.30 then with 95% Confidence, we have:

P is the sample proportion

Z is the standard Normal value (e.g. = 1.96 at confidence 95%)

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What is biostatistics?

Biostatistics provides a framework for the analysis of data. Through the application of statistic principles to the biologic sciences, biostatisticians are able to methodically distinguish between true differences among observations and random variations caused by chance alone.

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How is biostatistics useful?

From an application standpoint, knowledge of biostatistics and epidemiology permits one to make valid conclusions (information) from data sets.

Associations between risk factors and disease are determined with this information and, ultimately, are used to reduce illness and injury.

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What is the main subjects of Biostatistics?

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Independent, Dependent, Confounder and intermediate Variables

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What is the different Types of Variables?

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What is the measurement levels?

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Why is the measurement levels important?

Appropriate Statistical measurement or test is based on measurement level

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What is the difference between a and a ?

A sample is a subgroup of the population. Used to study the population

A population consists of all subjects (human or otherwise) that are being studied.

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What is the difference between a and a ?

A statistic is a characteristic or measure obtained by

using the data values from a sample.

A parameter is a characteristic or measure obtained by

using the data values from a specific population

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What is the difference between and Statistics?

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How can the Inferential Statistics draw a picture” about what we don’t have?

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Population Sample

Inferential statistics

Decision

Descriptive statistics

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How can the Inferential Statistics draw a picture” about what we don’t have?

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What is the tools of Descriptive Statistics?

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What is the main parts of Inferential Statistics?

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What is the difference between and ?

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What is the difference between and ?

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Comparative Studies

gather past data from selected cases and controls to determine differences, if any, in exposure to a suspected risk factor. These are commonly referred to as case–control studies

Retrospective studies

Prospective Studies

enroll group or groups of subjects and follow them over certain periods of time. examples include occupational mortality studies and clinical trials

What is the difference between Retrospective and Prospective studies?

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What is the different types of ?

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What is the difference between types of ?

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What is the appropriate test used in the ?

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What is the Analysis of ?

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The cohort study design focuses on a particular exposure rather than a particular disease as in case–control studies.

Basic survival analysis and Cox’s proportional hazards regression—were developed to deal with survival data resulting from prospective or cohort studies.

Survival analysis, which was developed to deal with data resulting from prospective studies, is also focused on the occurrence of an event, such as death or relapse of a disease, after some initial treatment—a binary outcome.

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What is the Analysis of ?

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The basic difference with the logistic regression analysis is that: For survival data, studies have staggered entry, and subjects are followed for varying lengths of time; they do not have the same probability for the event to occur. Second, each member of the cohort belongs to one of three types of termination: 1. Subjects still alive on the analysis date 2. Subjects who died on a known date within the study period 3. Subjects who are lost to follow-up after a certain date (This is known as Censoring).

In prospective studies, the important feature is not only the outcome event, such as death, but the time to that event, the survival time.

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What is Kaplan-Meier is used for?

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The Kaplan-Meier procedure is a method of estimating time-to-event models in the presence of censored cases.

Example. Does a new treatment for AIDS have any therapeutic benefit in extending life? We could conduct a study using two groups of AIDS patients, one receiving traditional therapy and the other receiving the experimental treatment. Constructing a Kaplan-Meier model from the data would allow us to compare overall survival rates between the two groups to determine whether the experimental treatment is an improvement over the traditional therapy. We can also plot the survival or hazard functions and compare them visually for more detailed information.

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What is Cox’s regression is used for?

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Cox Regression builds a predictive model for time-to-event data. The model produces a survival function that predicts the probability that the event of interest has occurred at a given time t for given values of the predictor variables.

The shape of the survival function and the regression coefficients for the predictors are estimated from observed subjects; the model can then be applied to new cases that have measurements for the predictor variables.

Example. Do men and women have different risks of developing lung cancer based on cigarette smoking? By constructing a Cox Regression model, with cigarette usage (cigarettes smoked per day) and gender entered as covariates, we can test hypotheses regarding the effects of gender and cigarette usage on time-to-onset for lung cancer.

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What is ROC is used for?

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ROC Curves procedure is a useful way to evaluate the performance of classification schemes in which there is one variable with two categories by which subjects are classified.

Example. It is in a researcher interest to correctly classify pregnant women into those women who will and will give vaginal delivery, so special methods are developed for making these decisions. ROC curves can be used to evaluate how well these methods perform.

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Example on ?

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Example on ?

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Example on ?

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What is the Phases of the ?

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What is the Statistical Significance? And what is its importance?

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How can we judge the existence of a Statistical Significance?

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How do we take a decision

(the P value)

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Answering a Statistical Question

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p-Value in Hypothesis Testing

• p-VALUE is the probability of observing a sample value as extreme as, or more extreme than, the value observed, given that the null hypothesis is true.

e.g: H0: Mean PB for male = Mean PB for female

• In testing a hypothesis, we can also compare the p-value to the significance level ().

• Decision rule using the p-value:

Reject H0 if p-value < significance level

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To perform a hypothesis test using the p-value approach

• If P-value ≤ , then the test is significant (reject H0) otherwise, the test is not significant (do not reject H0).

• Assume that we find that p-value = 0.03,

• Assume that want to use = 0.05

• then the test is significant, that is we reject the null hypothesis at = 0.05 because

P-value = 0.03 < 0.05

(Note that the test will not be significant at = 0.01)

P-value = 0.03 > 0.01 D Ezz Abdelfattah

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What does it mean when p-value < ?

(a) .05, we have strong evidence that H0 is not true.

(b) .01, we have very strong evidence that H0 is not true.

(c) .001, we have extremely strong evidence that H0 is not true.

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Answering a Statistical Question

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Answering a Statistical Question

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Answering a Statistical Question

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Answering a Statistical Question

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Answering a Statistical Question

Reference Book

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