PUBLIC HEALTH 1 PG APA 3 references on SCIENTIFIC METHOD

Chapter 2 Types of Studies In Chapter 2: 2.1 Surveys 2.2 Comparative Studies Types of Studies •Surveys : describe population characteristics (e.g., a study of the prevalence of hypertension in a population) ⇒ §2.1 • Comparative studies: determine relationships between variables (e.g., a study to address whether weight gain causes hypertension) ⇒ §2.2 2.1 Surveys •Goal: to describe population characteristics • Studies a subset ( sample) of the population • Uses sample to make inferences about population • Sampling: –Saves time – Saves money – Allows resources to be devoted to greater scope and accuracy Illustrative Example: Youth Risk Behavior Surveillance (YRBS) YRBS monitors health behaviors in youth and young adults in the US. Six categories of health -risk behaviors are monitored. These include:

1. Behaviors that contribute to unintentional injuries and violence; 2. tobacco use; 3. alcohol and drug use; 4. sexual behaviors; 5. unhealthy dietary behaviors; and 6. physical activity levels and body weight. Illustrative Example: Youth Risk Behavior Surveillance (YRBS) The 2003 report used information from 15,240 questionnaires completed at 158 schools to infer health-risk behaviors for the public and private school student populations of the United States and District of Columbia. a The 15,240 students who completed the questionnaires comprise the sample. This information is used to infer the characteristics of the several million public and private school students in the United States for the period in question. aGrunbaum, J. A., Kann, L., Kinchen, S., Ross, J., Hawkins, J., Lowry, R., et al. (2004). Youth risk behavior surveillance —United States, 2003. MMWR Surveillance Summary, 53(2), 1 –96. Simple Random Sampling •Probability samples entail chance in the selection of individuals • This allows for generalizations to population • The most fundamental type of probability sample is the simple random sample (SRS) • SRS (defined): an SRS of size n is selected so that all possible combinations of n individuals from the population are equally likely to comprise the sample, SRSs demonstrate sampling independence Simple Random Sampling Method 1.Number population members 1, 2, . . ., N 2. Pick an arbitrary spot in the random digit table (Table A) 3. Go down rows or columns of table to select n appropriate tuples of digits (discard inappropriate tuples) • Alternatively , use a random number generator (e.g., www.random.org ) to generate n random numbers between 1 and N. Illustrative Example: Selecting a simple random sample Suppose a high school population has 600 students and you want to choose three students at random from this population. To select an SRS of n = 3:

1. Get a roster of the school. Assign each student a unique identifier 1 through 600. 2. Enter Table A at (say) line 15. Line 15 starts with these digits:

76931 95289 55809 19381 56686 Illustrative Example: Selecting a simple random sample 3. The first six triplets of numbers in this line are 769, 319, 528, 955, 809, and 193. 4. The first triplet (769) is excluded because there is no individual with that number in the population. The next two triplets (319 and 528) identify the first two students to enter the sample. The next two triplets (955 and 809) are not relevant. The last student to enter the sample is student 193. The final sample is composed of students with the IDs 319, 528, and 193. Cautions when Sampling •Undercoverage : groups in the source population are left out or underrepresented in the population list used to select the sample • Volunteer bias: occurs when self -selected participants are atypical of the source population • Nonresponse bias: occurs when a large percentage of selected individuals refuse to participate or cannot be contacted Other Types of Probability Samples •Stratified random samples • Cluster samples • Multistage sampling • These are advanced techniques not generally covered in introductory courses. §2.2 Comparative Studies • Comparative designs study the relationship between an explanatory variable and response variable . • Comparative studies may be experimental or non - experimental. • In experimental designs, the investigator assign the subjects to groups according to the explanatory variable (e.g., exposed and unexposed groups) • In nonexperimental designs , the investigator does not assign subjects into groups; individuals are merely classified as “exposed” or “non -exposed.” Figure 2.1 Experimental and non - experimental study designs Example of an Experimental Design •The Women's Health Initiative study randomly assigned about half its subjects to a group that received hormone replacement therapy (HRT). • Subjects were followed for ~5 years to ascertain various health outcomes, including heart attacks, strokes, the occurrence of breast cancer and so on. Example of a Nonexperimental Design •The Nurse's Health study classified individuals according to whether they received HRT.

• Subjects were followed for ~5 years to ascertain the occurrence of various health outcomes. Comparison of Experimental and Nonexperimental Designs •In both the experimental (WHI) study and nonexperimental (Nurse’s Health) study, the relationship between HRT (explanatory variable) and various health outcomes (response variables) was studied.

• In the experimental design, the investigators controlled who was and who was not exposed. • In the nonexperimental design, the study subjects (or their physicians) decided on whether or not subjects were exposed. Let’s focus on selected experimental design concepts and techniques •Experimental designs provide a paradigm for nonexperimental designs. Jargon • A subject ≡ an individual participating in the experiment • A factor ≡ an explanatory variable being studied; experiments may address the effect of multiple factors • A treatment ≡ a specific set of factors Illustrative Example: Hypertension Trial •A trial looked at two explanatory factors in the treatment of hypertension. • Factor A was a health -education program aimed at increasing physical activity, improving diet, and lowering body weight. This factor had two levels:

active treatment or passive treatment. • Factor B was pharmaceutical treatments at three levels: medication A, medication B, and placebo. Illustrative Example: Hypertension Trial •Because there were two levels of the health - education variable and three levels of pharmacological variable, the experiment evaluated six treatments, as shown in Table 2.2. • The response variable was “change in systolic blood pressure” after six months. One hundred and twenty subjects were studied in total, with equal numbers assigned to each group. • Figure 2.3 is a schematic of the study design. Table 2.2 Hypertension treatment trial with two factors and six treatments •Subjects = 100 individuals who participated in the study • Factor A = Health education (active, passive) • Factor B = Medication (Rx A, Rx B, or placebo) • Treatments = the six specific combinations of factor A and factor B Figure 2.3 Study design outline, hypertensive treatment trial illustrative example Three Important Experimentation Principles: •Controlled comparison • Randomized • Blinded “Controlled” Trail •The term “controlled” in this context means there is a non -exposed “control group” • Having a control group is essential because the effects of a treatment can be judged only in relation to what would happen in its absence • You cannot judge effects of a treatment without a control group because:

– Many factors contribute to a response – Conditions change on their own over time – The placebo effect and other passive intervention effects are operative Randomization •Randomization is the second principle of experimentation • Randomization refers to the use of chance mechanisms to assign treatments • Randomization balances lurking variables among treatments groups, mitigating their potentially confounding effects Randomization - Example Consider this study ( JAMA 1994;271: 595 -600 ) • Explanatory variable: Nicotine or placebo patch • 60 subjects (30 each group) • Response: Cessation of smoking (yes/no) Random Assignment Group 1 30 smokers Treatment 1 Nicotine Patch Compare Cessation rates Group 2 30 smokers Treatment 2 Placebo Patch Randomization – Example •Number subjects 01,…,60 • Use Table A (or a random number generator) to select 30 two -tuples between 01 and 60 • If you use Table A, arbitrarily select a different starting point each time • For example, if we start in line 19, we see 04247 38798 73286 Randomization, cont. •We identify random two- tuples, e.g., 04, 24, 73, 87, etc. • Random two- tuples greater than 60 are ignored • The first three individuals in the treatment group are 01, 24, and 29 • Keep selecting random two- tuples until you identify 30 unique individuals • The remaining subjects are assigned to the control group Blinding •Blinding is the third principle of experimentation • Blinding refers to the measurement of the response of a response made without knowledge of treatment type • Blinding is necessary to prevent differential misclassification of the response • Blinding can occur at several levels of a study designs – Single blinding - subjects are unaware of specific treatment they are receiving – Double blinding - subjects and investigators are blinded Ethics •Informed consent • Beneficence • Equipoise • Independent (IRB) over- sight