1 Name: BIOS 703 MIDTERM EXAMINATIONS (5 marks per question, total = 100 marks) You will have 75 minutest to complete this examination. Some of the questions refer to Crizotinib in ROS1- Rearranged Non Small- Cell Lung Cancer by Shaw et al. You are allowed to bring in a copy of this paper WITH annotations; but otherwise this is a closed- book examination. THE DUKE COMMUNITY STANDARD Duke University is a community dedicated to scholarship, leadership, and service and to the principles of honesty, fairness, respect, and accountability. Citizens of this community commit to reflect upon and uphold these principles in all academic and nonacademic endeavors, and to protect and promote a culture of integrity. To uphold the Duke Community Standard: I will not lie, cheat, or steal in my academic endeavors; I will conduct myself responsibly in all my endeavors; and I will act if the Standard is compromised
2 Q1a. What does a phase 3 open- label trial assess? a. Primarily SAFETY, with patients being UNAWARE of the group to which they were assigned. b. Primarily SAFETY, with patients being AWARE of the group to which they were assigned. c. Primarily EFFICACY, with patients being UNAWARE of the group to which they were assigned. d. Primarily EFFICACY, with patients being AWARE of the group to which they were assigned. D. Phase 3 trials primarily study efficacy. Open- label means that patients are aware of the group to which they are assigned. Q1b. In plain English, what is the main danger associated with an open- label trial? Since the patients (and the investigators) know which group is which they can, either intentionally or not, report outcomes in a biased fashion. For example, someone who strongly believes that a drug works might report less severe symptoms. The impact of this problem can be reduced by using outcomes that are as objective as possible?
3 Q2a. In plain English, what is the difference between overall survival and progression- free survival? Overall survival is time until death; progression- free survival is time until disease progression (or death). Q2b. Which of the two was the primary endpoint for this trial? a. Overall survival b. Progression- free survival B. Progression- free survival Q2c. Which will be shorter: overall survival or progression- free survival? a. Overall survival b. Progression- free survival B. Progression- free survival the patient can have the disease progress before they die.
4 Q3a. Match the scale of measurement for the outcome variable with the appropriate visualization technique and statistical analysis. (In other words, draw lines to connect likes with likes.) Scales: continuous binary time- to- event Visualization: Kaplan- Meier curve box plot table of percentages Analytical method: chi- square test t- test log- rank test Continuous, box plot, t- test (and, for a model with multiple predictors: ANCOVA / multiple regression) Binary, table of percentages, chi- square test, (and logistic regression) Time- to- event, Kaplan- Meier curve, log- rank test, (and Cox regression) Q3b. From the paper, give an example of a continuous outcome, a binary outcome, and a time- to- event outcome. a. Continuous outcome quality of life b. Binary outcome presence/absence of a serious adverse event c. Time- to- event outcome overall or progression- free survival
5 Q4a. In plain English, why might stratified randomization be preferred to simple randomization? If a rare subpopulation is of interest, stratification allows you to collect a sufficiently large sample of that subpopulation Q4b. In the paper, what variables were used as stratification factors? This depends on the paper. Sorry about the confusion! Q4c. Suppose that an investigator finds the argument for stratification to be compelling, and proposes to stratify the randomization on 10 variables simultaneously. What is likely to go wrong? The strata will be tiny so tiny, in fact, that many of them will only contain a single patient (and thus not be distributed among the two groups). Even though not every trial that uses stratified randomization compares the groups within each stratum, if you do so there will be problems with small sample sizes and multiple comparisons.
6 Q5a. In plain English, explain the difference between an intention- to- treat analysis and an on- treatment analysis. ITT = analyze based on the groups to which patients were randomized OT = analyze based on the treatments that patients actually received Q5b. Match the types of analysis below. (In other words, draw lines to connect likes with likes.) Intention: safety analysis efficacy analysis Population: intention to treat on treatment ITT = randomization OT = safety
7 Q6a. True or false: table 1 suggests that the 2 study groups are relatively well- balanced. True (regardless of the paper) Q6b. If the 2 study groups are well- balanced, what would be the primary reason for performing an adjusted analysis? a. To account for bias. b. To improve precision. c. To account for bias and to improve precision. B. Bias only comes into play when the groups are imbalanced. Precision comes into play when some of the covariates are good predictors of the outcome (and, thus, reduce the noise in the statistical analysis). Q6c. True or false: from the perspective of using statistical software, performing an adjusted analysis simply means to add some covariates to the list of predictors within your statistical model. True. The unadjusted analysis uses study group as the only predictor. The adjusted analysis uses study group and the covariates.
8 Q7a. In plain English, explain how a power calculation works. A power calculation specifies 2 of (power, effect size, sample size) and derives the third. Critical to the understanding of a power calculation is the recognition that there is a probability that a study will be statistically significant. If the groups don t differ (i.e., effect size = 0, null hypothesis is true) this probability will be small (typically 0.05, the type 1 error rate). As the groups differ by an increasing amount (i.e., as the effect size increases) this probability (i.e., the inverse of power) rises. Q7b. What were the inputs to the power calculation in the paper? Type 1 error rate = One- sided or two- sided test = Power = Effect size = This depends on the paper. When people had trouble with this question it was because they couldn t identify the effect size. For a power calculation involving survival the effect size is the hazard ratio. It is sufficient to describe the inputs to the hazard ratio for example, 5- year survival for the 2 groups. Q7c. What was the output of the power calculation? When the desired power and the effect size are specified, the output of the power calculation is the sample size.
9 Q8a. In the paper, what is the median progression- free survival for the 2 groups? By consensus, 7.7 months crizotinib, 3.0 months chemotherapy Q8b. What are the numerical values for the point estimate and the confidence interval for the hazard ratio? a. Point estimate = 0.49 b. Confidence interval = (0.37 to 0.64) Q8c. If the minimum clinically important hazard ratio is 0.80, how should the results be interpreted? a. Statistically NON- SIGNIFICANT and clinically NON- SIGNIFICANT b. Statistically NON- SIGNIFICANT and clinically SIGNIFICANT c. Statistically SIGNIFICANT and clinically NON- SIGNIFICANT d. Statistically SIGNIFICANT and clinically SIGNIFICANT D. The null value of the hazard ratio is 1 since the confidence interval doesn t contain 1 the results are statistically significant. Since the confidence interval doesn t contain the MCID of 0.80 (or any points above the MCID) the results are clinically significant as well. Q8d. In plain English, explain why a confidence interval is helpful to report (i.e., above and beyond a point estimate). Anything that mentioned precision / quantification of variability was fine.
10 Q8e. If the investigators assumed that there would be a 20% improvement in progression- free survival (i.e., rather than a 56% improvement): would the required sample size increase, decrease, or stay the same? a. Increase b. Decrease c. Stay the same Increase. As the effect size decreases the required sample size will increase. (It takes a larger sample size to detect a subtle difference between the groups than it would take to detect a dramatic difference between the groups).
11 Q9a. In plain English, define interaction. The impact of the predictor A on the outcome B depends on the value of the covariate C. Q9b. Figure S2 of the supplementary materials (no problem if you didn t print these) is a forest plot of the hazard ratios by age (e.g., >=65 years, <65 years), gender (male, female) and various other characteristics. The point estimates for all these hazard ratios are similar. Moreover, all the confidence intervals overlap. True or false: Interaction is likely to be present. False. The definition of interaction implies some sort of inconsistency, but the premise of the question is that the results are consistent.
12 Q10a. In plain English, explain why table 3 of the paper presents any adverse events and grade 3 and 4 adverse events separately. The reason is that grade 3 and 4 events are much more serious than the others for example, they might induce dose modifications or changes of treatment. Physicians think of these events differently from the others, thus suggesting that they should be considered separately. Q10b. Suppose that the 2 study groups are compared on the 14 types of adverse events using a type 1 error rate of 0.05 for each test. Which is more likely: to declare as statistically non- significant adverse event rates that actually differ between the groups or to declare as statistically significant adverse event rates that don t actually differ between the groups? Declare as statistically significant adverse event rates that don t actually differ between the groups this is one version of the multiple comparisons problem. One way to think of this is that the expected number of false positive conclusions is (0.05)*(14) or, more generally, the type 1 error rate multiplied by the number of tests. If the type 1 error rate is held constant then the more tests the more false positive conclusions are expected.
13 Q11. What is the difference between an endemic, epidemic and pandemic infection? Give an example of each. Endemic disease that is regularly found in a region or among a group of people Epidemic widespread outbreak of a disease in a region or among a group of people Pandemic like an epidemic but on a global scale
14 Q12. Give an example of a disease that is representative of the following evolutionary causes - A) defense mechanism, (B) conflict, (C) novel environment, (D) trade- off and (E) historical constraint Defense Fever Conflict Infection Novel environment Obesity Trade- off Sickle cell anemia Historical constraint - Appendicitis
15 Q13. Define gene, locus, allele, mutation and polymorphism Gene region of DNA that codes for a protein Locus Position on chromosome where gene is found Allele a variant of a gene Mutation change in DNA sequence in the chromosome Polymorphism presence of 2 or more alleles of a gene in a populaiton
16 Q14. Draw a sketch of the stages in the cell cycle. Briefly describe what happens at each stage. Include checkpoints. G0 rest G1 gap phase 1 cell prepares for DNA duplication [G1 S checkpoint] S Duplication of DNA G2 Gap 2 cell increases in size [G2- M checkpoint] M mitosis [M checkpoint]
17 Q15. Describe possible mechanisms for how a cancer cell gains self- sufficiency in growth signals. Increased receptors Constitutionally active receptors Anticrime secretion of growth facors Heterotypic communication to increase release of growth factor by another cell Increased sensitivity by inactivating phosphatases Fusion protein downstream that is constitutionally active.
18 Q16. Sketch and label the parts of an antibody. Give 2 uses of monoclonal antibodies in medicine. Show Y shape and label Light chain Heavy chain Variable regions Constant region Complementarity determining regions (CDR) Uses: therapeutic agents (e.g. antibodies to CTLA- 4), passive immunization (e.g., anti- serum), laboratory uses (e.g. immunohistochemistry), many others
19 Q17. Explain how a cancer can evade immune destruction. Decrease MHC presentation Express FasL to kill cytotoxic T cells (Fas counterattack), Increase resistance to cytotoxic chemicals (e.g. via serine proteases that inactivate granzymes), Release of chemicals that inhibit lymphocytes such as TGF- b Recruit inhibitory immune cells such as Tregs and myeloid derived suppressor cells.
20 Q18. Give 5 examples of targeted therapy for cancer and the different hallmarks or enabling characteristics that are targeted.
21 Q19. With reference to the paper by Shaw et al, explain the role of chromosomal rearrangement in the pathogenesis of non- small lung cell cancer. Chromosomal rearrangements lead to the creation of fusion proteins from 2 genes that are otherwise separated in the absence of the chromosomal rearrangement. The general idea is that a fusion protein results in new properties not seen with either gene alone. For example, one of the 2 proteins in the fusion product may result in constitutive dimerization and constitutive cross- activation of he other product (a tyrosine kinases). Or some other oncogene is fused to be close to a strong promoter region (e.g. of an immunoglobulin gene) resulting in over- expression of the oncogene.
22 Q20. What is break-apart FISH and why do Shaw et al use it in this study? In the intact chromosome, two different colored probes are used that bind to different (but close) regions straddling the translocation breakpoint. In cells without the translocation, we therefore see the mixture of the 2 colors (e.g. RED + GREEN probes give YELLOW) on the same chromosome. When the translocation occurs, the RED and GREEN probes are now on separate chromosomes, so we see RED and GREEN light on separate chromosomes. This allows us to discriminate cells with and without the specific chromosomal translocation.
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