HOW STATISTICS IMPACT PHARMACY PRACTICE?

HOW STATISTICS IMPACT PHARMACY PRACTICE? CPPD at NCCR 13 th June, 2013 Mohamed Izham M.I., PhD Professor in Social & Administrative Pharmacy Learning objective.. At the end of the presentation pharmacists and pharmacy personnel should be able to: 1. explain the biostatistical concepts 2. explain the differences of statistical tests 3. discuss how these tests and its findings can impact pharmacy practice 1

Outline Overview of Biostatistical Terms and Concepts Application of Statistical Tests The Research Process OBSERVATION Broad Area of Research Interest PRELIMINARY DATA GATHERING Interviews & Literature review PROBLEM DEFINITION Research problem delineated THEORETICAL FRAMEWORK Variables clearly identified DEDUCTION Hypotheses substantiated? Research Questions answered? GENERATION OF HYPOTHESES DATA COLLECTION, ANALYSIS & INTERPRETATION 2

RESEARCH PROCES 3

Health Statistics provide information for understanding, monitoring, improving and planning the use of resources to improve the lives of people, provide services and promote their well being. Numerical information (data) is everywhere Health statistics promote population health and enhance societal well being Statistical techniques are used to make decisions that affect our daily lives 4

Knowledge of statistical methods will help you understand how decisions are made and give you a better understanding of how they affect you No matter what line of work you select, you will find yourself faced with decisions where an understanding of data analysis is helpful. In order to make an informed decision, you will need to be able to: 1. Determine whether the existing information is adequate or additional information is required. 2. Gather additional information, if it is needed, in such a way that it does not provide misleading results. 3. Summarize the information in a useful and informative manner. 4. Analyze the available information. 5. Draw conclusions and make inferences while assessing the risk of an incorrect conclusion. 5

Why Use Statistics? Descriptive Statistics identify patterns leads to hypothesis generating Inferential Statistics distinguish true differences from random variation allows hypothesis testing Why Use Statistics? 1. "Credibility -- the ability to speak intelligently is highly valued, use of numbers in a coherent way is essential. 2. Enable us to make informed, intelligent and sometimes predictive decisions e.g. quality testing, predicting diseases, forecasting, diagnosis, treatment effectiveness 6

HYPOTESIS TESTING Explain the nature of relationships Establish differences among groups or the interdependence of two or more factor in a situation Explain the variance in the dependent variable or to predict organizational outcome. Research Variable Types Independent comes first influences or predicts Also called manipulated or experimental variable Can be an attribute Antecedent (precursor, originator) Dependent comes second if affected or predicted by independent variable Consequence 7

Independent Variable Dependent Variable e.g.: Gender Age Outcome variable Blood pressure Thinking ability Other Research Variable Types Control type of IV that is measured because it may influence the dependent variable. Effect of variable accounted for by inclusion in statistical analysis Confounding (extraneous) not measured but may have an influence on DV 8

Which is IV, DV, Control, CV? Alcohol intake Smoking habit Cancer of larynx People who drink alcohol, majority of them smoke, but People who smoke not necessarily drink alcohol Both alcohol and tobacco have been shown have significant relationship with Ca of larynx So if we study the relationship between Alcohol and Ca, we should control the 3 rd variable i.e. smoking habit 9

Different Types of Statistics Descriptive statistics Pictorial statistics Inferential statistics Types of Data Numerical Continuous Discrete Categorical Ordinal Nominal 10

Mean, Median, Mode 15/06/2013 Descriptive Statistics Identifies patterns in the data Identifies outliers Guides choice of statistical test The Normal Distribution Mean = median = mode Skew is zero 68% of values fall between 1 SD 95% of values fall between 2 SDs 99% of values fall between 3 SDs. 1 2 11

Mode Median Mean 15/06/2013 Skewness Curve A Curve B negative skew Inferential Statistics Used to determine the likelihood that a conclusion based on data from a sample is true Making inference: SAMPLE POPULATION 12

Terms p value: the probability that an observed difference could have occurred by chance Terms confidence interval: The range of values we can be reasonably certain includes the true value. 13

Types of Errors Conclusion No difference Difference Truth No difference TYPE I ERROR ( ) Power = 1- Difference TYPE II ERROR ( ) 14

Inferential Statistics General Purpose Explore Relationships between Variables Description (only) Specific Purpose Compare groups Find strength of relationship, relate variables Summarize data Type of Qs/Hypothesis Differences Associational Descriptive General Type of Statistics Difference Inferential Statistics (e.g. t-test, ANOVA) Associational Inferential Statistics (e.g. correlation, regression, Chi- Square) Descriptive Statistics (e.g. mean, SD, %, range) 15

What Test Should I Use? 1. What type of data? 2. Are the data normally distributed? 3. How many groups/samples? 4. Are the groups independent or dependent? 5. What is the sample size? When Should I Use Regression Analysis? Regression analysis can be used to identify the explanatory variables that can most effectively influence or control the value of the outcome variable. Regression analysis can also be used to estimate the future value of a variable. forecast 16

Simple linear regression Outcome variable is affected by a single factor Is this realistic? Is this true? Omitted variables bias Multiple regression Outcome variable is affected by variety of factors 17

When Should I Use Correlation Analysis? Correlation analysis measures the relationship between two items (x and y variables) The 2 variables are continuous measurement 18

When Should I Use Chi-Square Test? You would like to see if there is a relationship between two variables Both variables are categorical 19

20

When Should I Use Independent t-test? You have 2 groups of subjects or programs or services You would like to compare the means outcome (dependent) variable for two independent groups Outcome variable: continuous measurement 21

22

When Should I Use Paired t-test? A paired (samples) t-test is used when you have two related observations (i.e. two observations per subject) You want to see if the means on these two normally distributed interval variables differ from one another. 23

24

When Should I Use One-way ANOVA? You have 3 or more groups You wish to test for differences in the means of the outcome (dependent) variable 25

What Test Do I Use? 1. What type of data? Quantitative vs Qualitative (Interval & ratio vs Nominal & Ordinal) 2. Are the data normally distributed? Normal distribution vs Skewed 3. How many groups/samples? One group/sample vs 2 groups/samples vs more than 2 groups/samples 4. Are the groups independent or dependent? Separate groups (not related) vs pre-post (before and after) 5. What is the sample size? Large vs small 26

Statistical objective Process in Choosing Statistical Tests Level of measurement Number of groups compared Dependent observation (data) or independent observation Statistical tests Parametric test Non-parametric test METHODS TO TEST HYPOTHESES Scale of Measurement 2 treatment groups, different individuals 3+ groups, diff individuals Before and after a single treatment in same individuals Multiple treatments, same individuals Association between 2 variables Interval (and drawn from normally distributed populations)* Nominal Ordinal Survival time Unpaired t- test Chi-square analysis-ofcontingency table Mann- Whitney rank-sum test Log-rank test or Gehan's test (11) Analysis of variance (ANOVA) Chi-square analysis-ofcontingency table Kruskal-Wallis statistic Paired t-test McNemar's test Cochrane Q ** Wilcoxon signedrank test Linear regression and Pearson Repeatedmeasures ANOVA product-moment correlation; Bland- Altman analysis Friedman statistic Contingency coefficients ** Spearman rank correlation 27

Take Home Message Pharmacists should acquire the knowledge in Research methods Statistics (or biostatistics) For some, you need to have the skill in these 2 areas For a few, you need to master in these areas What are the impact on practice? Aware the standard or level of current practice Identify barriers in order to improve practice What kind of services should pharmacists focus on? Is there any significant difference between methods, between interventions or between procedures? Is drug, intervention, procedure or method A superior than drug, intervention, procedure or method B 28

What are the impact on practice? (cont.) Any observed link between drug and disease? Identifying determinants for a disease, drugrelated problem etc. Which drug, regimen, protocol is more costeffective? Making rational decisions and recommendations Making informed-decisions Evidence-based medicines and practice Lessons learned.. 29