Algonquin College - Jan Ladas1 Community Dental Health Algonquin College

Algonquin College - Jan Ladas2 Statistical Significance Parametric Statistical Testing: Statistical techniques that are based on certain valid assumptions (normal distribution with equal variance) about the parameters of the population from which a large, randomized study sample was drawn. e.g.: - “t” test and “f” test - their value = parametric statistic - data are interval or ratio scaled.

Algonquin College - Jan Ladas3 Statistical Significance Non-Parametric: Less restrictive / distribution free Inferential techniques used when distribution is skewed or bimodal or when little is known about the observations Measurement scales are ordinal or nominal Study sample is small / variables are discrete e.g.: Chi Square – McNemar’s test for significance of changes. “Before and After”

Algonquin College - Jan Ladas4 Statistical Significance Two Groups Research Designs Student’s “t” test for statistical significance: Compares the means of 2 groups to determine if difference between them is real or a result of sampling fluctuation under conditions of ho (no difference) Best if applied to results where 2 groups received different experimental treatments Used when population standard deviation is not known

Algonquin College - Jan Ladas5 Statistical Significance E.G.: Test comparison to see if flavoured toothpaste affects brushing time (S1) 1 group given unflavoured paste (S2) 1 group given bubblegum flavour Compare mean brushing time for significant difference Ho : Mean of S1 = Mean of S2

Algonquin College - Jan Ladas6 Statistical Significance Student “t” test formula: Uses mean, variance and s.d. to calculate “t” values Uses tables with degrees of freedom and compare with P value Relates to normal curve and distribution

Algonquin College - Jan Ladas7 Statistical Significance Multiple Groups Research Design: Analyses of Variance Test for Statistical Significance = ANOVA – f value The test to determine whether differences in multiple group scores have occurred by chance (sample fluctuation) or by applied experimental manipulation

Algonquin College - Jan Ladas8 Statistical Significance ANOVA compares the variability in 2 ways: 1. Between Group Variance (BGV): Referred to as treatment effect Reflects the magnitude of the difference among the group means Number of d.f. for BGV is calculated by the formula K – 1 where K stands for the number of groups

Algonquin College - Jan Ladas9 Statistical Significance 2. Within Group Variance (WGV): Referred to as residual effect Pooled variance of all the groups in the design added together This variance represents the uncontrolled, unexplained variance due to the chance effect

Algonquin College - Jan Ladas10 Statistical Significance 1. Formula for d.f. for WGV is N (Number of subjects) – K (number of experimental groups) 2. f ratio calculated = BGV / WGV 3. With d.f. calculated and f value, tables are consulted to determine significance level If f value indicates significant differences, tests to determine areas of differences are used. Multiple t tests are not appropriate.

Algonquin College - Jan Ladas11 Statistical Significance Chi-square test: Compares observed and expected frequencies not means Best used with discrete variables where subjects can be assigned to mutually exclusive groups E.G.: Males, females, smoking, non-smoking Based on the normal curve and degree of freedom Chi-square value calculated and compared with pre- set critical value before accepting / rejecting Ho.

Algonquin College - Jan Ladas12 Correlation Refers to the linear relationship between two variables Statistical measure for determining the strength of the linear relationship Based on the number of variables, nature of variables (discrete or continuous) and the scale of measurement. (nominal, ordinal, interval and ratio)

Algonquin College - Jan Ladas13 Correlation Analyses Used to determine the relationship between variables each of which can be measured for each individual in the sample e.g.: - height and weight - profit and loss

Algonquin College - Jan Ladas14 Correlation Analyses Plus 1 Positive Correlation: Value of one variable increases as value of second variable increases Minus 1 Negative Correlation: Value of one variable increases as value of second variable decreases  + or – sign indicates the direction of the correlation  # (number) indicates the strength n.b.: the closer the value to or –1.00, the stronger the relationship. 0 = no relationship.

Algonquin College - Jan Ladas15 Correlation Analyses The analysis yields a measure called: Correlation Coefficient known as Pearson’s r. Measures the direction and strength of the relationship of the two variables to produce the numerical correlation ranging from –1.0 to +1.0

Algonquin College - Jan Ladas16 Epidemiology The study of the amount, causes, distribution and controls of diseases and health conditions among given populations. It attempts to determine which associated factors are important for prevention and control e.g.: Colorado Brown Stain Study

Algonquin College - Jan Ladas17 Characteristics of Epidemiology 1. Groups are studied, not individuals. “Well and ill” 2. Disease is multifactorial 3. A disease state depends on exposure to a specific agent, strength of the agent, susceptibility of the host and environmental conditions 4. Factors: Host – age, race, ethnic background, physiologic state, gender, culture Agent – chemical, microbial, physical or mechanical irritants, parasitic, viral or bacterial Environment – climate or physical environment, food sources, socioeconomic conditions

Algonquin College - Jan Ladas18 The Uses of the Science of Epidemiology 1. Description of normal biologic processes Examples: stages of growth, blood groups, and times and order of tooth eruption. 2. Understanding the natural history of diseases. Observations of disease progression and outcome in populations have enabled investigators to distinguish those diseases that are fatal or disabling from those that will resolve uneventfully.

Algonquin College - Jan Ladas19 The Uses of the Science of Epidemiology 3.Distribution of disease in the population. By age, gender, race, geographic region, and socioeconomic status. Demonstrates trends in disease prevalence and distribution. (Study of patterns among groups). 4.Studying non-disease entities. Suicide, injury.

Algonquin College - Jan Ladas20 The Uses of the Science of Epidemiology 5.Identifying the determinants of disease. Specific study designs can identify the risk factors and risk indicators associated with a disease and can lead to intervention strategies for prevention and control. 6.Testing hypotheses for disease prevention and control. Dental example: the various uses of fluorides to reduce caries incidence.

Algonquin College - Jan Ladas21 The Uses of the Science of Epidemiology 7.Planning and evaluating health care services. Data can be used to assist planning decisions on services and types of personnel required. Validates the effectiveness of treatment techniques and quality of treatment provided.

Algonquin College - Jan Ladas22 Epidemiologic Studies/Research Descriptive, Experimental, Analytic Every study/research, no matter how modest, needs a Protocol. A written plan containing the purpose and detailed operation of the study Helps it’s design Helps researchers to anticipate potential problems Helps in writing final report because the protocol forms the basis of the report n.b.: essential for research with humans

Algonquin College - Jan Ladas23 Research Process 1. Choosing the research question = hypothesis 2. Developing the protocol 3. Pre-testing the protocol – pilot study = small version of a proposed study 4. Conducting the study 5. Analyzing the findings – review data (biostatistics) 6. Disseminating the findings – distributing the results to target population so new knowledge can be utilized to benefit others

Algonquin College - Jan Ladas24 The Essential Features of a Protocol for Research with Humans 1.Precise definition of the research problem, the reasons for undertaking the research, and the review of pertinent literature. 2.Objectives of the study, or hypotheses to be tested and refuted. 3.Population to be studied, including its selection, source, size, method of sampling, and method of allocation to groups (if a clinical trial).

Algonquin College - Jan Ladas25 The Essential Features of a Protocol for Research with Humans 4. Data to be collected, describing each item needed to accomplish the objectives or to test the hypotheses. 5. Procedures to be carried out, how data will be obtained and by whom. 6. Data collection methods, with examples of all data collection forms or computer methods of data collection, and a list of all necessary supplies, equipment and instruments. Budget justifications.

Algonquin College - Jan Ladas26 The Essential Features of a Protocol for Research with Humans 7.Plans for data processing analysis and statistical distributions to be examined. 8.Time schedule for planning, obtaining informed consent, data collection and analysis and report writing. 9.An assessment of any ethical issues involved and obtaining consent.