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Preparation for USMLE Step 1

Preparation for USMLE Step 1. Ronald J. Markert, PhD ronald.markert@wright.edu Adrienne Stolfi, MSPH adrienne.stolfi@wright.edu. Today ’ s Topics. Research designs and methods Measurement Hypothesis testing Risk Clinical diagnostic testing Statistical tests Miscellaneous.

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Preparation for USMLE Step 1

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  1. Preparation for USMLE Step 1 Ronald J. Markert, PhD ronald.markert@wright.edu Adrienne Stolfi, MSPH adrienne.stolfi@wright.edu

  2. Today’s Topics • Research designs and methods • Measurement • Hypothesis testing • Risk • Clinical diagnostic testing • Statistical tests • Miscellaneous

  3. Research Designs in Medicine I. Descriptive studies Case series II. Explanatory studies A. Experimental Randomized controlled trial (RCT)* B. Observational 1. Cohort* 2. Case-control (retrospective) 3. Cross-sectional ------------- *prospective

  4. prospective vs. retrospectiveprospective: characteristic to outcome RCT and cohort studyretrospective: outcome to characteristic case-control study

  5. Internal Validity: can results be believed? Is confounding avoided? Is bias avoided? External Validity (generalizability) Are results generalizable to your setting or interest?

  6. Systolic hypertension in elderly program (SHEP) JAMA (265:3255-64, 1991) 60 years of age and older with isolated systolic hypertension (average follow-up 4.5 years) antihypertensive medications placebo (n = 2365) (2371) stroke 96 149 but suppose mean age 61 74

  7. Review of research designs RCT cohort case-control cross-sectional Questions 1. most time efficient and least financially costly? 2. yields the highest quality of data? 3. not likely to raise ethical issues? 4. medical records can lead to poor data quality? 5. most suitable for studying uncommon diseases? 6. best for avoiding confounding?

  8. Review of research designs RCT cohort case-control cross-sectional Questions 7. recall bias can be a problem? 8. best for cause-effect relationship? 9. “switching” groups can occur?

  9. Amoxicillin High-dose, short courseStandard course Randomized Day 0 345 346 Completed regimen/ specimen obtained 335 333 Did not complete regimen/ specimen obtained 10 13 Specimen obtained 345 346 Do you compare 335 to 333? OR Do you compare 345 to 346? Why? What is the analysis of the 345 and 346 called?

  10. Random Assignment Random Sampling (Random Allocation) (Random Selection) in RCT Is confounding avoided? Is sample representative of population?

  11. Internal Validity External Validity (Generalizability) Random Assignment Random Sampling (Random Allocation) (Random Selection) in RCT Is confounding avoided? Is sample representative of population? bias: not affected by Is sample representative random assignment of my practice?

  12. Measurement •  Independent variable (characteristic) •  Dependent variable (outcome) • Types of data •  Nominal (categorical, i.e. counts) •  Ordinal •  Numerical: continuous, discrete • Data distributions • Mode, Median, Mean • Standard deviation, Standard Error, Confidence Intervals

  13. Types of Variables Nominal Sex, Race, Blood Type, ______________ Ordinal Education Level, Stage of Cancer, ____________ Numerical (continuous, discrete) age, heart rate, blood pressure, ______________

  14. When data are normally distributed, the mode, median, and mean are identical and are located at the center of the distribution.

  15. Normal distribution Positively skewed distribution Negatively skewed distribution

  16. Central Tendency Serum cholesterol values for adults (age 40-60) n = 85 182 229 188 165 218  306 197  mean = sum of values 85 median = point at which 1/2 of values are above and below (i.e., 43rd highest value) mode = value that occurred most frequently

  17. Variability standard deviation

  18. Tronvik E et al. Prophylactic treatment of migraine with an angiotensin II receptor blocker. JAMA 2003; 289: 65-69. Migraine experience during 12-week period Candesartan (n = 57) Placebo (n = 57) Migraine hours 59.4±66.6 92.2±76.8 ± standard deviation Is there a small or large amount of variation among subjects regarding migraine hours?

  19. For a normal distribution: approximately 68% of the observations in a set of data lie between the mean and  1 standard deviation ~95% lie between the mean and  2 standard deviations ~99.7% lie between the mean and  3 standard deviations

  20. Establishing a normal range (in medicine) 1. locate a disease-free population (reference population) 2. perform test (e.g., glucose) 3. plot distribution 4. mark off central 95% of reference population -------------------------------------------------------------------------- serumnormal range fasting glucose 70-110 mg/dL sodium 135-146 mEq/L triglycerides 35-160 mg/dL

  21. standard error of the mean (SEM) How precisely does the sample statistic (e.g., the mean) estimate the population parameter (e.g., mean)? SEM = SD

  22. Confidence Intervals (CIs) The Standard Error (SE): The 95% CI for sample means, proportions, and differences between means or proportions are equal to: Point Estimate +/- 2(SE) These CIs are symmetric around the point estimate, AND: For differences between means or proportions: If the 95% CI includes the value zero, the differences are not statistically significant at alpha = 0.05.

  23. Heart rates: healthy college students n = 400 = 69.88 SD = 5.90 SD 5.90 SEM = = = .30 95% CI = ± 2 x SEM 69.88 ± 2 x .30 69.88 ± .60 95% CI = 69.28 to 70.48

  24. WSU 95% CI = 69.28 to 70.48 OSU 95% CI = 72.50 to 73.98

  25. A physician would like to estimate the average heart rate (bpm) in a particular population. In a random sample of 100 individuals from the population, he obtains a mean heart rate of 70 bpm and a standard deviation of 16 bpm. The 95% confidence interval is?

  26. Inferential Statistics Steps of statistical hypothesis testing: 1. Formulate null and research hypotheses 2. Set alpha error (Type I error) and beta error (Type II error) 3. Compute statistical test and determine statistical significance 4. Draw conclusion

  27. 1. Formulate null and research hypotheses Null Hypothesis (H0):There is no difference between groups; there is no relationship between the independent and dependent variable(s). Research Hypothesis (HR): There is a difference between groups; there is a relationship between the independent and dependent variable(s).

  28. 2. Set alpha error (Type I error) and beta error (Type II error) Acceptable error in hypothesis testing: Alpha (): 0.05 conventional Beta (): 0.10 to 0.20 Thus, power (1 - ) = 0.80 - 0.90 Consequences of  error: A drug or treatment is judged effective when it may not be Consequences of  error: A possibly effective drug or treatment is judged ineffective

  29. Hypothesis testing The possible outcomes in statistical hypothesis testing

  30. Patients with in-hospital procedures and events Stanford (n = 233), %McGill (n = 285),%p Invasive procedures angiography 55 34 <.0001 angioplasty 30 13 <.0001 bypass surgery 10 4 <.0001 Noninvasive procedures exercise test 20 56 <.0001 left ventricular function test 59 86 <.0001 Events reinfarction 1 1 >.05 mortality 12 11 >.05

  31. Power of an inferential statistical test is determined by: • alpha • sample size • effect size (clinically meaningful difference) • Examples of effect size: • 1. New therapy should reduce CHD mortality 10% compared to usual care. • 2. Drug A should reduce SBP more than Drug B by 7 mmHg.

  32. Therapy ATherapy B mortality 60% 30% actual difference? clin signif diff? stat signif diff? sample size

  33. Mr. Statistician: How many subjects (patients) do I need? • In planning a study, the researcher specifies • effect size (i.e., clinicaly meaningful difference) • alpha • beta or power (1 - beta); power = prob that a specified • effect size will be statistically significant • Plug alpha, beta, and ES into appropriate formula to determine sample size needed.

  34. H0: WSU = OSU Hr: WSU  OSU WSU 95% CI = 69.28 to 70.48 OSU 95% CI = 72.50 to 73.98 p is ?

  35. Relative Risk and Odds Ratio • RCT or cohort study  incidence  relative risk • case-control study  prevalence  odds ratio

  36. RCT or cohort study  incidence  relative risk JAMA Sept 25, 2002: losartan vs. atenolol for patients with isolated systolic HTN and LV hypertrophy pts randomly assigned to losartan (n = 660) or atenolol (n = 666) mean = 4.7 years Outcome: cardiovascular death, stroke, or MI

  37. Outcome yes no total % w/outcome losartan 75 585 660 11.4% atenolol 104 562 666 15.6% RCT  incidence  relative risk RR = 75/660 ÷ 104/666 = 0.71 (95% CI = 0.53 to 0.92)

  38. RR = % of the losartan group with outcome ÷ % of atenolol group with outcome = 11.4% ÷ 15.6% = 0.71 (95% CI = 0.53 to 0.92) Relative Risk

  39. 10-year stroke follow-up stroke no stroke diabetes 20 80 no diabetes 10 90 cohort study  incidence  relative risk RR = 20/100 ÷ 10/100 = 2.00 (95% CI = 1.20 to 2.70)

  40. case-control study  prevalence  odds ratioJAMA June 19, 2002: vasectomy and risk of prostate cancer 923 cases vs. 1224 controls • matched in 5-year age groups • all were white New Zealanders

  41. Outcome Cases (Prostate Ca) Controls (No Ca) Vas 216 333 No Vas 707 891 case-control  prevalence  odds ratio OR = 216/333 ÷ 707/891= 0.82 (95% CI = 0.67 to 0.99)

  42. Odds Ratio cases (prostate ca) with risk factor (vasectomy) / controls (no prostate ca) with risk factor÷ cases without the risk factor / controls without the risk factorOR = 216/333 ÷ 707/891= 0.82 (95% CI = 0.67 to 0.99)

  43. Diagnostic Test Gold Standard e.g.: sign symptom lab test safer/less invasive less costly simpler less painful exercise angiography EKG CAD

  44. The first step toward wisdom is knowing what the words mean. Aristotle EBM Concepts EBM CategoryResearch DesignConcepts to Know diagnosis cross-sectional true positive (TP) false positive (FP) true negative (TN) false negative (FN) sensitivity (sens) specificity (spec) positive predictive value (PPV) negative predictive value (NPV) rule-in/-out diagnosis ROC curve

  45. Clinical Diagnostic Testing status (gold standard) sick healthy diagnostic test positive TP FP results negative FN TN test characteristics sensitivity = TP TP + FN specificity = TN TN + FP test performance positive predictive value = TP TP + FP negative predictive value = TN TN + FN

  46. Prostate Cancer No Prostate Cancer Total Pos: PSA > 4.0 ng/ml Neg: PSA < 4.0 ng/ml Prevalence = No. Prostate Ca = 200 = 2% Positive 160 (TP) 6,860 (FP) 7,020 Negative 40 (FN) 2,940 (TN) 2,980 200 9,800 10,000 Total 10,000

  47. Prostate Cancer No Prostate Cancer Total Pos: PSA > 4.0 ng/ml Neg: PSA < 4.0 ng/ml Sensitivity = TP = 160 = 80% Positive 160 (TP) 6,860 (FP) 7,020 Negative 40 (FN) 2,940 (TN) 2,980 200 9,800 10,000 TP + FN 200

  48. Prostate Cancer No Prostate Cancer Total Pos: PSA > 4.0 ng/ml Neg: PSA < 4.0 ng/ml Specificity = TN = 2,940 = 30% Positive 160 (TP) 6,860 (FP) 7,020 Negative 40 (FN) 2,940 (TN) 2,980 200 9,800 10,000 TN + FP 9,800

  49. Prostate Cancer No Prostate Cancer Total Pos: PSA > 4.0 ng/ml Neg: PSA < 4.0 ng/ml Positive Predictive Value = TP = 160 = 2.3% Positive 160 (TP) 6,860 (FP) 7,020 Negative 40 (FN) 2,940 (TN) 2,980 200 9,800 10,000 TP + FP 7,020

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