biostatistics

Types of Epidemiological Research

Dr. Ahmed ALbehairy, M.D

Consultant of Psychiatry, MOH, Egypt

Types of Epidemiological Research

• Descriptive.

• Analytical

• Experimental and clinical trials

• Meta-analysis.

Types of Analytical Epidemiological Studies

• Retrospective studies.

• Prospective studies.

• Historical prospective studies.

• Cross sectional, prevalence or a survey study.

Retrospective studies and /or Case Control Studies

• In this kind of studies, the subject under the study have the disease, and their past experiences are compared with other persons who do not have this or related disease.

• Selection of cases should consist of all newly diagnosed cases with specified parameters under study during a specified period of time.

• Controls are representative of the general population in terms of probability of exposure to the risk factor under the study.

Prospective studies and /or Cohort

• The philosophy of this approach is that exposed subject in the investigation are representatives of all exposed persons in regard with risk under consideration.

• Healthy individuals , cohorts are allocated and followed forward in time for development of specific disease.

• Types of cohort : Birth cohort, marriage , specific graduation.

Historical prospective studies.

• Include the follow up of healthy exposed and unexposed subjects, cohort, for the development of disease.

• However this cohort are allocated retrospectively through medical records.

Cross sectional, prevalence or a survey study.

• Both the risk factor and the disease are examined at the same time .

• Temporality of the risk is not evident .

PAST PRESENT FUTURE

retrosp

control prosp

hist.

prosp

types of analytic studies

Select

cases

Look for exposure

to risk factor

Select cohort according

to exposure

Follow up To record the

Disease level

According toExisting

Records, determine Exposure in

the past

Identify cohort

in the past

Development Of disease

Research /service project model

• Type of study.

• Budget.

• Site, community.

• Date, start, close.

• Criteria ( inclusion and exclusion).

• Procedure plan.

Discovering Biostatistics , Using SPSS

Dr. Ahmed ALbehairy, M.DConsultant of Psychiatry, MOH, Egypt

Introduction for Biostatistics

The main goal to improve patient care through more understanding of research and to be critical thinkers, do study design and do statistics.

Of course , you may need statistician to be with you in advanced issues.

Population vs. samples

As a researcher , we are interested in finding results that apply to entire population of people or things. ( we cannot collect data from every human being).

Therefore , we collect data from a small subset of population ( known as sample).

Source of Data in Population(Epidemiological data )

• Census.

• Vital statistics.

• Morbidity data.

• National health network.

Sampling

How to collect data that represent population ??????>>>> reduce

the population to a statistical model>>>>>so this statistical

model make predictions about the real – world phenomenon.

Sampling

Hypotheses • A hypothesis is a proposed explanation for

the occurrence of a phenomena that a researcher formulates prior to conducting an experiment.

• Types of hypotheses.??????

• How to test your hypotheses.????

Types of Hypotheses.??????

Null hypothesis

Vs.

Alternative hypothesis

Null hypothesis, Vs. Alternative hypothesis

If non directional , H0:µ or P = K ( i.e. mean has no diff. to a value). HΑ:P # K ( i.e. mean is not equal to a value).

if directional H0:P ≤ K HΑ:P <K

H0:P ≥ K HΑ:P >K

Testing Null Hypothesis

Testing Null Hypothesis

• N0:= hypothesis that there is no relation or difference .

• If significant , P >0.05, reject N0, , i.e. false hypothesis , there is a relation or there is a difference.

• If non significant ,accept N0, i.e. true hypothesis, there is no relation , there is no difference. ( it is not no relation , but it is only statistically non significant ).

Available data and hypothesis , what we will do ???

Statistical tools ,results to discuss

Statistics

• A branch of applied mathematics concerned with the collection and interpretation of quantitative data , and the use of probability theory to estimate population parameters.

• Concerned with treatment of quantitative information from groups of individuals.

What can statistics do?

• Provide objective criteria for evaluating hypothesis.

• Synthesis of information.

• Help to detect the pattern of data

( descriptive statistics).

• Help to evaluate argument ( research questions and hypothesis ).

Statistics Cannot?

• Tell the truth ( it can only give probability only ).

• Compensate poor design.

• Indicate clinical significance.

Statistics don not Prove any thing

• Statistics suggest a relationship.

• In order to make conclusion you need :- Multiple converging indicators.- Multiple confirmatory studies.- Temporal relationship.- Dose response.- Biological response.- Biological plausibility ( reasoning).

Think in Research? How to

• Hypotheses and introduction.• Is the research quantitative or qualitative .• Collecting data . Sampling , prepare tools and survey

method.• Preparing Data. Types of variables, Dependent,

Independent, Categorical ,Continuous.• Data entry.• Exploring Data. ( parametric, nonparametric ).• Descriptive statistics.• Inferential / analytical Statistics.• Results, discussion , conclusion .

Types of Variablesstring and numerical

• Qualitative:- categorical, - Nominal - Usually independent- Analyzed by

frequency table.- Example?

• Quantitative• Continuous• Scale, ordinal,• Usually dependent on

predictor• Analyzed by

examining central tendency (mean..etc.)

• examples

In Qualitative Research N.B:

• Prepare information as variables.

then

• Descriptive and analytical statistics.

Statistics & Population

• Descriptive : frequencies.

• Inferential .

• Periodic report : SWAT Analysis Strengths, weakness, opportunities, threats

• Ratio and percentages.


• Incidence rate =

no. of new cases at point of time * 100 or 1000

population at risk

no. of new cases during a period of time * 100 or 1000

population at risk


• Incidence rate of rare disease = no. of new cases during a period of time population at mid of the year during this period of time

• Incidence rate in outbreak situation = attack rate• Inception rate = new attacks of illness in

a population / year .( attacks may exceed the number of population).


• Prevalence rate = no. of existing cases at a point of time * 100 or 1000 total number of population

No of existing cases during a period of time * 100 or 1000 total number of population

Annual prevalence : total no. of disease at any time during a year.

Life time prevalence : total no. of individuals known to have the disease at least part of their life time.


• Segmentation: Divide populations into segments.

• Profiling: Develop profiles of hotspot segments.• Drill-down: drill-down dimensions and numerical

value ranges.• Variable selection: select variables used in

profiling and segmentation.• Ranking: Order segments based ranking

criteria.• Visualization: Visualize result statistics.

Statistics & Sample

Statistics & Sample• The sample can be summarized statistically by what is called

“mean”. The center of distribution of the scores.• It is hypothetical value of typical score X . ????• Sum of Deviances from the mean = total error = of course 0• To be considered mathematically Sum of squared error (SS) are done.• To avoid the effect number of sample on the error, to estimate the

error in the population ----variance = SS n-1 ?? df?• SD+ = Square root of variance.• SD, a measure of how well the mean represent the data.• Small SD, indicates that the data points are close to the mean.• Large SD, indicates that the data points are distant from the mean.• Larger SD , i.e. that the mean is not accurate representation of the

data . • Smaller SD, i.e. that the mean is of small fluctuation.

Statistics & Sample

• of course , in different sample the mean and SD , shows that the sample is not in normal distribution .

• By z score , ( when any sample can be reformed to a normal distribution , by making mean =0 and SD = 1 ) we can calculate the probability, cumulative percentage of any values in the data, and how the distribution.

• e.g when 95% z score lies between + 1.96 • Standard error.SE is SD of sample means. Small SE

indicates that most sample means are similar to the population mean, and so our sample is likely to be an accurate reflection of the population .

Statistics

Zscore: writing scoreN Valid 200

Missing 0Mean .0000000Std. Error of Mean .07071068Median .1292387Std. Deviation 1.00000000Minimum -2.29728E0Maximum 1.50075Percentiles 25 -7.9389478E-1

50 1.2923869E-175 7.6224449E-1

Statistics & Sample

• Another way to think in the sample and represent the data than mean is “linear model “. It s the basic of ANOVA & regression .

• Linear model is based on central tendency and means .

Descriptive Statistics

• Method of organizing and summarizing data in table , graph or numbers.

• Frequencies , %, cumulative %

• Mean , median ,mode

• SD , SE of mean. Level of confidence

• Skewness ,kurtosis , SE of skew , SE of kurtosis . Parametric / non parametric

Inferential analysis • It s a decision to choose the right way to do your analysis according

to :

1- parametric vs. non parametric.2- level of confidence.3- hypothesis4- type of independent variable/s.5- type of dependent variables/s6- number of group / means .7- related participants or not .(one or more)8- repeated means .8- difference , correlation, or regression.9- reliability and validity for scales .

Inferential analysis

decision tree

examples

biostatistics

Health & Medicine