1. Biostatistics Basics: Part I Leroy R. Thacker, PhD Associate Professor Schools of Nursing and Medicine

2. Outline Characteristics of a good hypothesis Hypotheses Underlying statistical principals

3. Preliminary Thoughts First steps of a research study include: –Identifying a measurable/testable hypothesis –Choosing the appropriate study design –Choosing the appropriate set of variables –Conducting a power/sample size study Notice that all of these steps would go better/smoother with the assistance of a biostatistician. So consult with your friendly biostatistician early in the study planning process

4. Funny Thought/Setting the Tone "A judicious man looks on statistics not to get knowledge, but to save himself from having ignorance foisted on him." Thomas Carlyle

5. Characteristics of a Good Hypothesis Topic 1

6. Characteristics of a Good Hypothesis “A good hypothesis must be based on a good research question. It should also be simple, specific and stated in advance.” Plan your analysis based upon your hypothesis AND the types of data you will be working with Keep the PICOT criteria in mind when making your hypothesis –This is especially important should you move into funded research

7. Characteristics of a Good Hypothesis Population –What specific patient population are you interested in? Intervention –What is your investigational intervention? Comparison Group –What is the main alternative to compare with the intervention? Outcome of interest –What do you intend to accomplish, measure, improve or affect? Time –What is the appropriate follow-up time to assess the outcome? (This is optional)

8. Characteristics of a Good Hypothesis Hypothesis – “Daily eye of newt improves outcomes for high-risk cardiac patients” Not bad, in fact I see A LOT of hypotheses just like this –Do you hear a “But” in this statement? If not, you should! Why do you think I might have issue with this hypothesis based on the PICOT criteria in the last slide?

9. Characteristics of a Good Hypothesis Is the population defined? Is the intervention defined? Is the comparison group defined? Is the outcome defined? Is the time defined? –This is not always done, so don’t get bent out of shape if it isn’t there

10. Characteristics of a Good Hypothesis Other considerations: –What does “improve mean”? Clinically significant vs. statistically significant –How much change will occur? –Be specific

11. Characteristics of a Good Hypothesis Improved Final Hypothesis: “For high risk cardiac patients, daily 50mg eye of newt will produce a reduction in resting systolic BP, measured 7 days post-initiation of treatment of 20mmHg greater than seen in the placebo group” THIS is a good hypothesis, one that a biostatistician (or you with the correct software or formulas) could use to come up with a sample size/power calculation

12. Characteristics of a Good Hypothesis It is a simple hypothesis –A simple hypothesis contains one predictor and one outcome variable It is a specific hypothesis –A specific hypothesis leaves no ambiguity about the subjects, variables or how the tests of statistical significance will be applied It is (I assume) being made BEFORE the start of the study

13. Characteristics of a Good Hypothesis A simple hypothesis is easier to work with than a complex hypothesis –In contrast to a simple hypothesis which has one predictor and one outcome variable, a complex hypothesis will have more than one predictor variable and one dependent variable or one predictor variable and more than dependent variables A complex hypothesis is usually broken down into multiple simple hypotheses

14. Hypotheses Topic 2

15. Hypotheses Research questions typically come in two forms –Is there a difference between…? –Is there a relationship between…? Questions about differences involve comparing some characteristics between groups Questions about relationships involve assessing the relationship/association of one characteristic with another

16. Hypotheses Statistical hypotheses are phrased in a very specific way The process begins by creating the null hypothesis from the research hypothesis The null hypothesis is a hypothesis of “no difference” or “no relationship” –The null hypothesis will become the formal basis for testing statistical significance when your study ends and will help estimate the probability that your observed results may be due to chance

17. Hypotheses You will also need to create the alternative hypothesis This is a hypothesis that states that there is a difference or there is an association –The alternative hypothesis matches the research hypothesis if you are doing inference The possible outcomes are “Reject the null hypothesis” or “Fail to reject the null hypothesis” –Semantics – You NEVER accept the null hypothesis

18. Hypotheses Alternative hypotheses are either One-sided or Two-sided A one-sided alternative implies some kind of directionality while a two-sided alternative simply implies there is a difference, one way or the other The most conservative thing to do (and what most biostatisticians will do) is to use a two sided alternative

19. Underlying Statistical Principals Topic 3

20. Type 1 Error, Type II Error and Power Null Hypothesis True State of Nature Alternative Hypothesis True State of Nature Fail to reject the Null Hypothesis Correct Decision (1-α) True Negative Type II Error (Denoted by β) False Negative Reject the Null Hypothesis Type I Error (Denoted by α) (Size of the test) False Positive Correct Decision (Power = 1 – β) True Positive

21. Effect Size The likelihood that a study will be able to detect a difference or an association in a sample depends upon the magnitude of the difference/association in the population But, we don’t know the magnitude of the difference/association…we are trying to estimate it with the study! When planning the study, the investigator must determine “size” of the difference/association they expect to see or wish to determine

22. Effect Size This expected/desired difference/association is called the effect size The effect size may be determined by published data, pilot data the investigator has access to or by investigator discretion –Rank those three methods in order of preference Many studies produce multiple effect sizes because of multiple measures

23. α, β and Power In the planning stages of a study, the investigator establishes the maximum chance that they will tolerate of making a Type I error (false positive; α) and a Type II error (false negative; β) –Power is defined as 1-β and can be thought of as “doing what you ant to do”, that is, rejecting the null hypothesis when it is false Most studies set a significance level of 0.05 and a power of 0.80; there is nothing magical about these numbers –But, if you use other values than these you will be criticized!

24. Activities Scenario 1: An investigator is interested in looking at the association between suicide rates in patients with chronic pain and the effectiveness of their pain management. Draft a “good” research hypothesis using the PICOT criteria, state the null and alternative hypotheses based on the research hypothesis, propose a study design and sampling scheme to address the investigator’s question. Scenario 2: An investigator is interested in seeing if using high dose chemotherapy and autologous stem cell transplants as salvage therapy for primary mediastinal non-seminoma germ cell tumors will improve outcomes. Draft a “good” research hypothesis using the PICOT criteria and state both the null and alternative hypotheses based on the research hypothesis. As the investigator presented the question, do you think this is a simple or a complex hypothesis?

25. Summary When developing a research hypothesis use the PICOT criteria Make your hypothesis simple (if possible), specific, and make it BEFORE you collect your data Do your homework; read the literature and talk to your mentors to determine effect sizes for your studies Oh yeah, talk to a biostatistician early!

26. Questions?