# Step 1 - Behavioral Science 1

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2015-04-25 15:53

## Section

Question | Answer |
---|---|

Case control studies | Observational and retrospective - Compares a group of people with disease to a group without, asks - “What happened?” |

Measure in Case control studies | Odds Ratio (OR) |

Cohort study | Observational and prospective - compares a group with a given risk factor to a group without to assess whether the risk factor ↑ the likelihood of disease |

Measure in Cohort study | Relative risk (RR) |

Cross-sectional study | Observational - Collects data from a group of people to assess frequency of disease (and related risk factors) at a particular point in time - asks “what is happening” |

Measure Cross-sectional study | Disease prevalence - can show risk factor association with disease but does not establish causality |

Twin concordance study | compares the frequency with which both monozygotic twins or both dizygotic twins develop a disease - measures heritability |

Adoption study | compares siblings raided by biologic vs. Adoptive parents - measures hertability and influence of environmental factors |

Clinical trial | experimental study involving humans, compares therapeutic benefits of 2 or more treatments or of treatment and placebo. Highest quality study when randomized, controlled and double-blinded |

Phase I trial | small number of patients, usually healthy volunteers - assesses safety, toxicity and pharmacokinetics |

Phase II trial | small number of patients with disease of interest - assesses treatment efficacy, optimal dosing and adverse effects |

Phase III trial | Large number of patients randomly assigned either to the treatment under investigation or to the best available treatment (or placebo) - compares new treatment to the current standard of care. Is more convincing if double blinded |

Meta-analysis | Pools data from several studies to come to an overall conclusion. Achieves greater statistical power and integrates results of similar studies, highest echelon of clinical evidence - may be limited by quality of individual studies or bias in study selection |

Sensitivity | Proportion of all people with disease who test positive or the ability of a test to detect a disease when it is present. - Value approaching 1 is desirable for ruling out disease and indicates a low false-negative rate. Used for screening in diseases with low prevalence. |

Sensitivity equation | TP / (TP+FN) |

Specificity equation | TN / (TN+FP) |

Specificity explanation | Proportion of all people without disease who test negative. or the ability of a tesl to indicate non-disease when disease is not present. Value approaching 1 is desirable for ruling in disease and indicates a low false-positive rate. - Used as a confirmatory lest after a positive screening test. |

PPV equation | TP/(TP+FP) |

NPV equation | TN / (FN+TN) |

Positive predictive value | Proportion of positive test results that are true positive.Probability that person actually has the disease given a positive test result. (Note: If the prevalence of a disease in a population is low, even tests with high specificry or high sensitivity will have low positive predictive values!) |

Negative predictive value | Proportion of negative test results that are true negative. Probability that person actually is disease free given a negative test result. |

Point prevalence equation | total cases in population at a given time / total population at risk at a given time |

Incidence equation | new cases in population over a given time period / total population at risk during that time (when calculating incidence remember that people with the disease are not in at risk population) |

Prevalence | incidence x disease duration (this means prevalence > incidence for chronic diseases |

Odds ratio (OR) for case-control studies | Odds of having disease in exposed group dividcd by odds of having disease in unexposed group |

Odds ratio equation | (a/b) / (c/d) = ad/bc - using table where a=(+disease+risk), b=(- disease, + risk) c = (- risk, +disease) d= (-risk, - disease) |

Relative risk (RR) for cohort studies | Relative probability of getting a disease in the exposed group compared to the unexposed group. Calculated as percent with disease In cxposed group divided by percent with disease in unexposed group. |

Relative risk (RR) equation | (a/(a+b)) /( c/(c+d)) |

Attributable risk | Thc difference in risk between exposed and unexposed groups, or the proportion of disease occurrences that are attributable to the exposure (e.g., smoking causes one-third of cases of pneumonia). |

Absolute risk reduction | The reduction in risk associated with a treatment as compared to a placebo. |

Number needed to treat | 1/absolute risk reduction |

Number needed to harm | 1/attributable risk |

Random error reduces | precision |

Systematic error reduces | accuracy |

Precision | I. The consistency and reproducibility of a test (reliability) 2. The absence of random variation in a test |

Accuracy | the trueness of a tests measurements (validity) |

Selection bias | nonrandom assignment to study group (c.g, Berkson’s bias) |

Recall bias | knowledge of presence of disorder alters recall by subjects |

Sampling bias | subjects are not representative relative to general population; therefore, results arc not generalizable |

Late-look bias | Information gathered at inappropriate time-e.g., using a survey to study a fatal disease |

Procedure bias | subjects in different groups arc not treated the same-e.g., more attention is paid to treatment group, stimulating greater compliance |

Confounding bias | occurs with 2 closed associated factors; the effect of 1 factor distorts or confuses the effect of the other |

Lead-time bias | early detection confused with ↑ survival; seen with improved screening (natural history of disease is not changed, but early detection makes it seem as though survival ↑) |

Pygmalion effect | occurs when a researcher's belief in the efficacy of a treatment changes the outcome of that treat |

Hawthorne effect | occurs when the group being studied changes its behavior owing to the knowledge of being studied |

Norma distribution | Gaussian = bell-shaped (mean = median = mode). |

Bimodaldistribution | simply 2 humps (2 modal peaks). |

Positive skew distribution | mean > median > mode. - Asymmetry with tail on right. |

Negative skew distribution | mean < median < mode. - Asymmetry with tail on left. |

least affected by outliers in the sample. | mode |

Null (H0) | Hypothesis of no difference (e.g there is no association between the diseases and the risk factor in the population) |

Alternative (H1) | Hypothesis that there is some difference (some association b/w disease and risk factor in population) |

Draw study result picture | 56 |

Type 1 error | Staling that there is an effect or difference when none exists\ (to mistakenly accept the experimental hypothesis and reject the null hypothesis) P =probability of making a type I error p is judged against alpha, a preset level of significance (usually < .05). "False-positive error." |

Type II error | Stating that there is not an effect or difference when one exists (to fail to reject the null hypothesis when in fact H0 is false) - Beta is probability of making a type II error “false-negative error” |

Power(1-beta) | Probability of rejecting null hypothesis when it is in fact false or the likelihood of finding a difference if one in fact exists - it depends on 1. Total number of endpoints experienced by population 2. Difference in compliance between treatment groups (differences in the mean values between groups) 3. Size of expected effect |

% of sample in one standard deviation | 68% |

% of sample in two standard deviations | 95% |

% of sample in three standard deviations | 99.7% |

Standard error of the mean | Standard deviation/ (sqrt(n)) |

Confidence interval | Range of values in which a specified probability of the means of repeated samples would be expected to fall - If CI for a mean of 2 variables include 0 then there is no significant difference and H0 is not rejected - if CIs b/w 2 groups overlap then these groups are not significantly different |

T-test checks | difference b/w means of 2 groups |

ANOVA checks | difference between means of 3+ groups |

Chi squared | checks difference between 2 or more percentages or proportions of categorical outcomes (not mean values) |

Correlation coefficient | r is always between -1 and +1. The closer the absolute value or r is to 1, the stronger the correlation b/w the 2 variables |

Coefficient of determination | r squared (value that is usually reported) |

Primary disease prevention | prevent disease occurence (HPV vaccination) |

Secondary disease prevention | early detection of disease (Pap smear) |

Tertiary disease prevention | reduce disability from disease (e.g. Chemotherapy) |

Diabetes prevention measures | Eye, foot exams; urine tests |

Drug use prevention measures | Hepatitis immunization; HIV, TB tests |

Alcoholism prevention measures | Influenza, pneumococcal immunizations; TB test |

Overweight prevention measures | blood sugar tests for diabetes |

Homeless, recent immigrant inmate prevention measures | TB test |

High-risk sexual behavior prevention measures | HIV, hepatitis B, syphilis, gonorrhea, chlamydia tests |

Reportable diseases | Hep, Hep, Hep, Hooray the SSSMMART Chick is Gone - Hep A, Hep B, Hep C, HIV, salmonella, Shigella, syphilis, Measles, Mumps, AIDS, rubella, TB, Chickenpox, Gonorrhea |

Leading cause of death of infants | Congenital anomalies, short gestation/low birth weight, sudden infant death syndrome, maternal complications of pregnancy, respiratory distress syndrome |

Cause of death 1-14 yo | Injuries, cancer, congenital anomalies, homicide, heart disease |

Cause of death 15-24 y | Injuries, homicide, suicide, cancer, heart disease |

Cause of death 25-64 y | Cancer, heart disease, injuries, suicide, stroke |

Cause of death 65+ | Heart disease, cancer, stroke, COPD, pneumonia, influenza |