biostatistics - acads – hassle mabuhay .•pictogram / cartogram •cluster diagram . horizontal

Download Biostatistics - Acads – HASSLE MABUHAY .•Pictogram / Cartogram •Cluster Diagram . Horizontal

Post on 09-Jul-2018

212 views

Category:

Documents

0 download

Embed Size (px)

TRANSCRIPT

  • Biostatistics

  • Quantitative Qualitative

    Observational Studies Descriptive

    Case Study/Report Case-Series

    Analytical Ecological Study Cross-sectional Study Longitudinal Study

    Case Control (Retrospective) Nested Case Control Cohort (Prospective) Historical Cohort

    Descriptive Qualitative Case Study Case Report/Series

    Traditional Qualitative

    Historical research Phenomenological research Grounded theory research Ethnographical research

    Meta-synthesis

    Experimental Studies Pre-Experimental Quasi-experimental True Experimental

    Meta-analysis

    Correlational Studies

    Multi-Method /Mixed Method

    Component Design Integrated Design

  • QUANTITATIVE Pre-experimental

    Uncontrolled Experimental Study

    (Single Group Post Test Only)

    Self Controlled Experimental Study

    (Within Group Pre-Post Test)

  • Quasi-experimental

    Controlled Study Between Groups Non-equivalent Post Test Only

    Between Groups Non-equivalent Pre-Post Test

    Trials with External Controls

  • Factorial Design

    True experimental Randomized Controlled Trial (RCT)

    Completely Randomized Design (CRD)

    Randomized Complete Block Design (RCBD)

  • Onset of Study

    Time

    Sequential Control (Crossover Design)

    X X X X

    Intervention

    X X X X Washout

    period

    Experimental subjects

    Controls

  • Pre Clinical Trial

  • Phases of a Full Clinical Trial

    Phase I: finalizes the treatment (e.g., to determine things like drug dose and safety)

    Phase II: seeks preliminary evidence of effectiveness

    Phase III: fully tests the treatment (randomized clinical trial or RCT)

    Phase IV: focuses on long-term consequences of the treatment

  • LEVELS OF MEASUREMENT

  • DESCRIPTIVE STATISTICS

    Descriptive Measures

    Tabular Presentation

    Graphical Presentation

    Textual Presentation

  • Descriptive Measures

    Measures of the Middle (Central Tendency)

    Measures of Variability (Dispersion)

    Measures of Position

    Measures of Association

    Measures of Ratios and Proportions

    Measures of Disease Frequency and Association

  • Central Tendency Mean

    Arithmetic Mean

    Weighted Mean

    Median

    Mode

  • Variability Range = (Highest Lowest)

    Variance = Mean of Squared Deviations

    Standard Deviation = Square Root of Variance

    Coefficient of Variation = standard deviation divided by mean times 100

    Kurtosis

    Skewness

  • KURTOSIS

  • Position Percentile

    Deciles

    Quartile

    Z score (Standard score)

  • Association Numerical & Numerical Pearsons Product Moment Correlation Coefficient

    Numerical & Binary Point-Biserial Correlation Coefficient

    Ordinal and Ordinal Spearmans Rank Correlation Coefficient

    Kendalls Tau Rank Correlation

    Ordinal and binary Rank Biserial Correlation

    Binary and Binary Phi Coefficient

  • Strength of Linear Association

    0

    1

    0.5

    0.4

    0.8

    weak relationship (0.01 0.39)

    strong relationship (0.80 0.99)

    moderate relationship (0.40 0.79)

    perfect linear relationship

    no linear relationship

  • Positive Correlation

    Negative Correlation

    r = 0.95 r = - 0.95

  • Ratios and Proportions Proportions

    Percentage

    Ratios

    Rates

  • Disease Frequency Frequency of Occurrence

    Incidence Rate

    Prevalence Rate

    Risk Ratios

  • Descriptive Measures

    Central Tendency: Mean, Median, Mode

    Dispersion: Variance, Standard Deviation, Coefficient of Variation, Kurtosis, Skewness

    Position: Percentiles, Deciles, Quartiles, Z Score

    Association: Correlation Coefficients

    Ratios and Proportions

    Disease Frequency: Incidence and Prevalence

  • tables

    Simple Frequency Tables Contingency Table / Cross-Tabulation Dummy Tables Master Tables Summary Tables

  • Graphical Presentation

    Bar Graphs

    Line Graphs

    Scatter/Dot Plots

    Pie/Circle Graphs

    Pictogram / Cartogram

    Cluster Diagram

  • Horizontal Bar Graph

  • Vertical Bar Graph

  • Histogram

  • Component Bar Graph

  • Grouped Bar Graph

  • Range Bar Graph

  • Box and Whisker Plot

  • Waterfall Chart, Flying Bricks Chart, Mario Chart or Bridge Chart

  • Line Graph

  • SHAPES OF DISTRIBUTION

    Symmetric (Normally distributed)

  • SHAPES OF DISTRIBUTION

    Asymmetric (skewed) Distributions

  • Area Chart

  • Dot Plot

  • Scatter Plot

  • Pie Chart

  • Exploded Pie Chart

  • Polar Area Chart

  • Radar Chart, Web chart, Spider chart, Star chart, Cobweb chart, Star plot, Irregular polygon, Polar chart,

    or Kiviat diagram

  • Nightingales Rose/Coxcomb Chart

  • Spie Chart

  • Multi-level Pie, Radial tree, or Ring chart

  • 3-D Pie

  • Doughnut Chart

  • Pictogram

  • Cartogram

  • Timeline

  • Stem and Leaf Plot

  • Cluster Diagrams

    Network

    Flow chart

    Organizational chart

    Tree map

    Fan chart

  • Network Diagram

  • Flowchart

  • Organizational Chart

  • Tree Diagram / Dendrogram

  • Fan Chart / Geneology Chart

  • Inferential Statistics 1. Students T-Test

    2. Chi-squared Test

    3. ANOVA

  • T Tests

    Small sample test

    With sample size

  • T Tests and Alternatives

    One sample T Test Sign Test

    Paired (Dependent) sample T Test

    Wilcoxon Signed Rank Test or Mann-Whitney U Test

    Two (Independent) samples T Test

    Wilcoxon Rank Sum Test

    Parametric Tests Non Parametric Tests

  • Chi Square Test

    Types - One Random Sample: Test of Goodness of Fit One Random Sample, Two Group Comparison: Test of independence Two Random Samples: Test of Homogeneity

  • Alternatives to Chi square Test

    Merge Columns or Rows

    Fishers Exact Test (applicable only to 2X2 table, more than 20%

    less than 5 and with zero)

    McNemars Test (Dependent samples)

  • One way ANOVA Compare two or more groups

    Example: Compare the waist-hip ratio among sedentary, semi-active and active people

    Require Post Hoc Tests when significant

    Example: Tukeys HSD

  • Alternatives One-way ANOVA : Kruskal-Wallis

    Dependent Samples: Friedman

  • Correlational Statistics

    Correlation Coefficients

    Regression Analysis

    Correlation Coefficients + Prediction

    Note: only establishes associations (functional relationships)

    Limited by data of sample

  • Multivariate Statistics * Many Groups of Data * Many Variables

    1. Inferential Methods: ANOVA & ANCOVA

    2. Regression Methods

    3. Classification Methods

    4. True Multivariate Methods:

    MANOVA & Canonical Correlation

    5. Meta-analysis

  • ANOVA Multiple Independent Single Dependent

    ANCOVA Single or Multiple Independent Single Dependent Co-variables (Confounders)

  • Linear Regression

    Independent Dependent

    Many

    Numerical or

    Categorical

    One

    Numerical

    Multiple Regression

    One or Many

    Numerical or

    Categorical

    One

    Binary

    Binary Logistic/Logit Regression

    One or Many

    Numerical or

    Categorical

    One

    Categorical

    Multinomial Logistic Regression

    One or Many

    Categorical

    One

    Categorical

    Log Linear Analysis

  • Multivariate Statistics

    Classification Methods

    DISCRIMINANT FUNCTION ANALYSIS

    Factor Analysis (example: Principal Component Analysis)

    Cluster Analysis

    CLASSIFICATION AND REGRESSION TREE ANALYSIS (CART)

  • MULTIVARIATE ANALYSIS OF VARIANCE

    Simply called as ANOVA with many dependent variables

    CANONICAL CORRELATION ANALYSIS

    the correlation of two canonical (latent) variables, one representing a set of independent variables, the other a set of dependent variables

  • META-ANALYSIS

    using statistical procedures to combine the results from different studies.

  • CORRELATIONAL

    STATISTICS

    INFERENTIAL

    STATISTICS

    MULTIVARIATE

    STATISTICS