Medical Statistics Part-I:Descriptive statistics

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    22-Aug-2014

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Medical statistics

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  • Descriptive Statistics in cardiovascular research MOST SIMPLE WAY OF DATA HANDLING
  • Statistics in general SUBCLAUSE Collection Analysis Interpretation Presentation USED FOR Reasoning Discussion Calculation Scientific Inference
  • STATISTICS IN GENERAL DESCRIPTIVE INFERENTIAL
  • Descriptive Statistics Data analysis begins with calculation of descriptive statistics for the research variables These statistics summarize various aspects about the data, giving details about the sample and providing information about the population from which he sample was drawn Each variables type determines the nature of descriptive statistics that one calculates and the manner in which one reports or displays those statistics Simply to describe what's going on in our data
  • inferential statistics Trying to reach conclusions that extend beyond the immediate data INFER alone= We use inferential statistics to try to infer from the sample data what the population might think/experience Make judgments of the probability that an observed difference between groups is a dependable one or one that might have happened by chance in this study Make inferences from our data to more general conditions http://www.socialresearchmethods.net/kb/statinf.php
  • KEYWORDS Population[Orientation] SAMPLE[Representive] VARIABLES[Characteristics] PARAMETERS[quantities that define a statistical model]
  • DISPLAY OF DESCRIPTIVE STATISTICS TABLES GRAGHS CHARTS CIRCLE DOT PLOTS BOX-AND-WHISKER PLOTS SCATTERPLOT SURVIVAL PLOTS BLAND-ALTMAN PLOTS
  • TABLE-1
  • Variables: DISCRETE Only certain values (fixed and readily Countable Examples of discrete variables commonly encountered in cardiovascular research include species, strain, racial/ethnic group, sex, education level, treatment group, hypertension status, and New York Heart Association class. CONTINUOUS Infinite number of values Fixed intervals between adjacent values They can be manipulated mathematically, taking sums and differences Age, height, weight, blood pressure, measures of cardiac structure and function, blood chemistries, and survival time
  • Discrete variables (categorical) NOMINAL (UNORDERED) Take values such as yes/no, Human/dog/mouse, female/male, treatment A/B/C; a nominal Variable that takes only 2 possible values is called binary. One May apply numbers as labels for nominal categories, but there Is no natural ordering ORDINAL (ORDERED) Take naturally ordered values such as New York Heart Association class (I, II, III, or IV), hypertension status (optimal, normal, high-normal,or hypertensive), or education level (less than high school,high school, college, graduate school
  • Descriptive statistics for Discrete variables Absolute frequencies (raw counts) for each category Relative frequencies (proportions or percentages of the total Number of observations) Cumulative frequencies for successive categories of ordinal variables
  • Collection Formal Sampling Recording Responses To Experimental Conditions Observing A Process Repeatedly Over Time
  • Descriptive statistics for continuous variables Location statistics MEAN MEDIAN MODE, QUANTILES Dispersion statistics[CENTRAL TENDENCY] VARIANCE= STANDARD DEVIATION=S=S RANGE INTERQUARTILE RANGE Shape statistics SKEWNESS KURTOSIS
  • ROBUST MEDIAN is robust :Not strongly affected by outliers or by extreme changes to a small portion MEAN is sensitive (not robust) to those conditions MODE is robust to outliers, but it may be affected by data collection operations, such as rounding or digit preference, that alter data precision.
  • QUANTILES Quintiles combine aspects of ordered data and cumulative frequencies The p-th quantile (0p1) 100p is an integer, the quantiles are called percentiles Median, or 0.50 quantile, is the 50th percentile, the 0.99 quantile is the 99th percentile Three specific percentiles are widely used in descriptive statistics, [100p is an integer multiple of 25] Q1first quartile (25th percentile, 0.25 quantile) Q2second quartile (50th percentile, 0.50 quantile), median Q3third quartile (75th perce ntile,0.75 quantile)
  • INTERQUARTILE RANGE[IQR] It is a single number defined as IQ of RQ-3Q1 Variance and standard deviation are affected (increased) by the presence of extreme observations, the IQR is not; it is robust
  • SKEWNESS[skewness coefficient] For a given data Distribution is symmetric (skewness=0) A more pronounced tail in 1 direction than the other (left tail, skewness0) If skewness=0, the mean= median Right- (left-) skewed distribution has its mean value greater (less than) the median
  • Kurtosis a measure of the peakedness of a distribution A gaussian distribution (also called normal) with a bell-shaped frequency curve has kurtosis 0 Positive kurtosis indicates a sharper peak with longer/fatter tails and relatively more variability due to extreme deviations Negative kurtosis coefficient indicates broader shoulders with shorter/thinner tails
  • GRAPHS[complementary to tabular]
  • DOT PLOT of Continuous variable(BMI) The dot plot is a simple graph that is used mainly with small data sets to show individual values of sample data in 1 dimension
  • Box-and-whisker plot= box plot graph Graph displays values of quartiles (Q1, Q2, Q3) by a rectangular box. The ends of the box correspond to Q1 and Q3, such that the length of the box is the interquartile range (IQRQ3Q1). There is a line drawn inside the box at the median, Q2, and there is a symbol plotted at the mean.Traditionally, whiskers (thin lines) extend out to, at most,1.5 times the box length from both ends of the box: they connect all values outside the box that are not 1.5 IQR away from the box, and they must end at an observed value.Beyond the whiskers are outliers, identified individually by symbols such as circles or asterisks
  • Univariate Analysis: Look one variable at a time for 3 features Distribution Of Frequency in % /bar diagram/histogram Central Tendency Mean Median Mode Dispersion Range Standard Deviation Variance
  • Correlation[r] is a single 1 number that shows the degree of relationship between 2 variables -1 to +1
  • r is also called Karl Pearsons coefficient of correlation
  • It is just beginning With best wishes

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