IPE 5

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IPE 5
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IPE 5
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  1. Definition of Biostatistics
    • Science that studies  biological phenomenons from a quantitative
    • point of view  (quantitative analysis
    • of biological phenomenons)
    •  
    • Inlcudes:
    • n GATHERING OF DATA
    • n DATA PROCESSING
    • n ANALYSIS AND INTERPRETATION 
    • n VALIDITY AND GRADE OF TRUST TRIALS
  2. Definition
    of “variable”
    • n ANY CHARACTERISTIC, STUDIED ON A POPULATION THAT
    • CAN CHANGE AND WHOSE CHANGE CAN BE MEASURED.
    • n Examples: age
  3. Classification
    of “variables”
    • Qualitative
    • (by order or name)
    • Quantitative
    • (continuous or non continous)
  4. Definition of qualitative variable
    examples
    (!)
    • characteristic of a population
    • that due to its peculiarity , can not be measured with numbers (it can not be
    • quantified by numbers).
    • Examples: sex, race,
    • religion,  civil status, level of
    • academic studies
  5. Qualitative
    variables: classification according to number of possible values it can
    have.
    Examples.
    • n DICOTHOMIC (choosing between two):  the
    • studied variable  can only have one value without hierarchy
    • between different values (ex.: man/woman; son/daughter).One value excludes
    • the other.
    • n POLICOTHOMIC OR MULTICOTHOMIC (choosing between more than two): the studied variable can have multiple
    • values
    • either with or without hierarchy among
    • them (socioeconomical level (low, medium, high ), blood
    • groups ( A/B/AB/0), colour of the hair
    • (black, brown, blonde)
  6. Qualitative
    variables classification depending on the
    existence of an order (
    ordinal,
    nominal). Examples.
    • Ordinal variable:  variable where its values  show an order,
    • sequence or natural progression.
    • Ex.:-order:
    • days of the week, months of the
    • year 
    •      -natural
    • progression or sequence:grade of undernourishment (light, moderate,
    • severe), answer to a treatment (good/ bad ), socioeconomical level (low,
    • medium, high), alcohol consumption (light, moderate, severe), dental hygiene (good/intermediate/bad)
    •  
    • Nominal variable: variable where its values
    • do not show hierarchy ( order, sequence or natural progression):
    • Ex.: names
    • of people, races, blood groups, civil status, types
    • of teeth( incisor, canine, premolar, molar)
  7.  Definition of quantitative variables
    examples
    • characteristic
    • of a population that can be measured with numbers.
    • n Ex.:number of children, number of teeth
  8.  Quantitative variables classification
    • continuous,
    • non continuous
  9. Definition
    of quantitative continuous variable.
    Examples.
    • n It can have a whole (entire) or fractioned value (inside a numeric scale)    (they refer to items that show a
    • continuum)
    • n Ex.: age, depth of a gum pocket (4mm, 5,5 mm, normal:3mm), mouth opening (3,5 cm, 2,7 cm )
  10. Definition
    of quantitative non continuous
    variable. Examples
    • n  It can
    • only have  a whole (entire) value (inside a numeric scale)     (they refer to items that can not
    • be   divided)
    • n Ex.: number of teeth (20/32)
  11. Classification
    of variables according to cause
    • n Independent: variable
    • causing  the effect (Streptoccoccus
    • mutans causes dental decay)
    •  
    • n Dependent: variable due to
    • an independent variable (dental decay is caused by Streptoccoccus mutans)
    • (dental pain is due to
    • infection)(gum
    • sore due to unadjusted dental prosthesis)
  12. Definition
    of “confusion variable”
    • Definition:
    • variables that have an influence on the effect, but they are not the main
    • cause.
    • Ex.:
    • stress can increase dental pain due to pulpitis. (stress is a confusion
    • variable) deficient dental hygiene can increase
    • periodontitis
    • (but the main cause are bacteria of the mouth), deficient dental hygiene is a

    • confusion
    • variable.
    •  
    • kann
    • auftreten, muss aber nicht, wenns auftritt verstärkt es den depending
    • variable
  13. Example for
    confusion, depending, indepedendent variable
    • confusion
    • variable: stress
    • depending:
    • dental pain
    • independent:
    • pulpitis
    •  
    • independent:
    • bacteria of the mouth
    • dependent:
    • perodontitis
    • confusion:
    • deficiet dental hygiene
  14. Frequency
    measures
    • =
    • parameter, we measure how often a disease occur in a population
    • incidence,
    • prevalence, -> morbidity,
    • mortality,

    • frequency,
    • percentage, proportion, ratio, relative risk, rate
  15. Definition
    of incidence and incidence rate
    • n Incidence: number of new
    • cases of a disease in a certain period of time (ex.: 100 new cases of
    • influenza per week)
    • n Incidence Rate: number of
    • new cases of a disease in a certain period of time/total population X 100 (
    • ex.: Incidence Rate of influenza in the population of this city is 8%)
  16. Definition
    of prevalence and prevalence rate
    • n Prevalence: total number of cases  (new and previous) in a determined period
    • of time.
    • n Prevalence Rate: total number of cases ( new and
    • previous) in a determined period of time/ total population X 100
    • n Ex.: prevalence of influenza infection is 5%( it
    • means that 5% of total population is affected)
  17. Definition
    of “absolute frecuency”
    • n They quantify the importance of a disease in a
    • community ( total number of people affected).
    • n Ex.: absolute frequency of Influenza in the
    • population of that city is 850 cases.
    • n disadvantage: Absolute data do not provide
    • information about the probability to develop the disease (relative risk).
  18. Definition
    of “relative frequency”
    • they
    • provide a more complete information about the event we are studying and about
    • the risk of the event or disease to
    • happen.(
    • because they compare absolute data with other data ).
  19.  Types of relative frequencies
    • proportions,percentages,
    • ratios and rates
  20. define proportion
    • n Relation between 2 events of the same type, in a
    • different geographical area

    • n Ex.: number of car accidents in Madrid/ number of
    • car accidents in Spain;

    • n Number of influenza cases in Valencia/ number of
    • influenza cases in Spain.
  21. define percentage
    • n Fraction of total population affected by a disease
    • or event, in relation to total population x 100

    •    (ex.: 35% of adults over 50 years old have
    • periodontitis)
  22. define ratio
    • n Relation between two events of the same type, one
    • of them exposed to the risk factor, and the other one  non exposed to the risk factor.

    • n Each event can be expressed as an absolute or
    • relative frecuency.

    • n Ex.: number of children with dental decay taking
    • sugar/number of children with dental decay not taking sugar.

    • n The most common ratio in epidemiological studies
    • is RELATIVE RISK (RR).

    n Relative risk is a ratio

    • n It intends to show the relation between risk
    • factors and appearance of diseases.

    • n Ex: relative risk to develop cancer of the
    • mouth  associated with unadjusted dental
    • prosthesis.

    • n Ex.: relative risk to develop periodontitis
    • associated with deficient oral hygiene.
  23. define rate
    • n Rate: number of events in relation to total
    • population X constant (100-1000)

    • n Ex.: mortality rate: number of people died in one
    • year/total population X 1000

    • n Ex.: birth rate: number of children born alive
    • during one year/total population X 1000

    • n Ex.: children mortality rate: number of children
    • under one year old died during one year/total of born alive in one year X 1000.


    • n Rates give us a more complete information about
    • the event we are studying, than absolute numbers.They  allow us to compare data from different
    • populations. Ex.: we can compare mortality rate in Africa (13,2 per 1000 ) and
    • in Europe (11,8 per 1000)
  24. types of rate
    • - Raw Rate: referred to total population.
    • Ex.: mortality rate in Spain is 8/1000. e.g. birth rate, mortaliy rate
    • - Specific Rate: referred to a certain part
    • of total population. Ex.: mortality rate in age group 40-50 years old in that
    • country is 4/1000.
    • e.g.
    • mortality by age group, morbidity by cause of the disease
  25. define relative risk
    relative risk = morbidity rate in the group of exposed people / morbidity rate in the goup of non exposed people -> a/a+b / c/c+d

    a/a+b: people with the disease in relation to total of people exposed to the risk factor

    c/c+d: people with the disease in relation to total of people non exposed to the risk factor

    RR= 1 indicates that the probability to develop the disease is the same in both groups (exposed and non exposed)

    RR> 1 indicates that exposed group has higher probability to develop disease than non exposed
  26. Association
    between variables
    • real
    • association, random errors (bias), misleading errors (sesgos), confusion
    • variables
  27. Epidemiological
    studies
    • Show if an
    • association exist or not between differen variables
  28. Precision
    • Absence of
    • error due to random (the larger the sample, the more precision; criteria =
    • logical)
  29. Definition
    of “null hypothesis”
    • (Ho): it
    • does not exist association or relation 
    • between 2 studied variables.
  30. Definition
    of “alternative hypothesis”
    • (Ha):  it exists association or relation between two
    • variables
  31. Definition
    of “hypothesis test”
    • = test of
    • statistical significance = comparison between the 2 hypothesis
  32. Definiton of
    "statistical significance"
    • appears at
    • the end of scientific articles when the author is trying to demonstrate that
    • his results have a “quality"
    • Big
    • statistical significance -> result is trustable/true
  33. Hypothesis
    test: how do they work?
    •  We look at the size (magnitude) of the
    • difference ( in the result) between the 2 groups we  are studying.
  34. Synonyms of
    statistical significance
    • -  we reject Null Hypothesis (=no association
    • between 2 variables)
    • -  we accept Alternative Hypothesis
    • -  enough evidence to doubt about Null
    • Hypothesis
    • -  result observed is not compatible with Null
    • Hypothesis
    • -  it is unprobable to obtain a result like
    • the one observed if Null Hypothesis would be true
    • -  It is unprobable ( unlikely) that the
    • result observed would be due to random
    • -  “P” is minor 0,05 ( p<0.05 )
  35. Definition
    of “p” value
    • probability
    • to accept “Alternative hypothesis” as being true, when the true hypothesis
    • could be  “null hypothesis”
    • To
    • accept/reject a hypothesis has a riskt what we quantify as p value
    • The smaller
    • the p value, the greater the statistical significance/the more secure is the
    • alternative hypothesis
  36. Interpretation
    of “p”<0.05
    •  we have a security of 95% of alternative
    • hypothesis being true
  37.  Interpretation of “p” value<0.01
    • we have a
    • security of 99% of alternative hypothesis being true
  38.  Interpretation of a small “p” value
    • the smaller
    • the “p” value is, the greater is the statistical significance of the result of
    • the study
  39.  Interpretation of “p” value>0.05
    • random can
    • not be excluded as the cause of the association between 2 variables, that is,
    • we can not reject Null hypothesis
    • that says
    • that both variables are not associated
  40. What does
    statistical significance depend on?
    • magnitude
    • of the difference (the larger the
    • difference, the higher the statistical significance), size of the sample (the
    • larger the size, the higher the statistical significance)
  41. What is the
    relation between “magnitude of the difference” and “statistical
    significance”
    • the larger
    • is the difference in the result observed at both groups we are studying, the
    • easier  is to demonstrate statistical
    • significance
  42.  What is the relation between “size of the
    sample” and “statistical significance”?
    • the larger
    • the size of the sample, the easier is to detect differences in the result
    • obtained in both groups we are studying, that is, the easier is to detect
    • statistical significance in the result of our study
  43. Types of
    error in association between variables
    • False
    • positive, False negative
  44. Definition
    of error Type I ( false positive)
    • This means
    • that we say that “it exists a relation between 2 variables”, but that is
    • false
    • We make
    • this error when we reject Null Hypothesis and we are wrong
    • we accept
    • Alternative Hypothesis but we are wrong. 

    • Example: We
    • make a study and our results show us that there is a relation between
    • sweets and
    • dental decay found at children. But at this particular case, we are wrong
  45. Definition
    of error Type II ( false negative)
    • This means
    • that we say that “it does not exist a relation between 2 variables”, but we
    • are wrong
    • We make
    • this error when we accept Null Hypothesis as real, but it is false.
    • we accept
    • Null Hypothesis but we are wrong
    • Example: We
    • make a study and our results show us that there is not a relation
    • between 
    • bad oral
    • hygiene and periodontitis found on adults. But in this particular case, we
    • are
    • wrong
  46. Definition
    of random error. Examples
    • To happen
    • by chance
    • Ex.: one
    • person could have a cancer by random: he is not exposed to risk factors, he
    • is not genetically predisposed, he has not family history related to that
    • disease…but cancer just happens.
  47. biological
    variability. example
    • In the
    • sample that we have chosen out of the general population, there exist a
    • certain biological   characteristic
    • that could influence the result of the study.
    • Ex.: a
    • sample of people receiving an antihypertensive treatment have responded very
    • well because they had a previous excellent condition of their vascular system
    • (arteries and veins), and not only because the medicine we have given to them
    • has such a fantastic effect to reduce hypertension.
  48. misleading
    error. example
    • Misleading
    • error (sesgo): in the study we are carrying out we introduce a condition  in the observers or in the method of study
    • we are using , that will alter the result of the study, thus , giving us a
    • wrong  information (false result)
    • Ex.: If we
    • want to diagnose dental decay in children, through the method of detecting a
    • “white spot” on the enamel of teeth, and we are using an observer who has
    • visual deficit , we are introducing a misleading error in the study.
  49. confusion
    variable. example
    •  Variables that can affect the result of the
    • study, but they are not the main cause of the result observed.
    • Ex.:poor
    • oral hygiene can be a confusion variable when we study periodontitis, where
    • the main cause of the disease are the bacteria of the mouth ( and not the
    • lack of dental hygiene ).

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