The HIVPOINT dataset, available on the course website, includes information on 5,680 HIV-positive individuals in Europe and the United States. You will use these data to estimate the effect of initiating antiretroviral treatment A (1: yes, 0: no) at baseline on CD4 cell count after 3 years of follow-up Y. CD4 count, measured in cells/μl, is a marker of immunosuppression; the higher the CD4 cell count, the better. No individuals were lost to follow-up. The goal is to estimate the average causal effect,
E[Ya=1] − E[Ya=0], of treatment A on outcome Y. A code book for the HIVPOINT dataset can be found on the final project page.
Consider the following list of variables measured at baseline: age, sex, geographic origin, calendar year, mode of HIV transmission, CD4 cell count, and viral load (concentration of viral RNA in blood). Remember, you have an uncle who is a famous novelist with interest, but no formal training in epidemiology. In each of the questions below, explain your answers to him in a way that he can easily understand (i.e., plain and unambiguous English).
Assume the variables listed in the prompt above are sufficient and necessary to control for confounding. Explain what this assumption means to your uncle.
a. The group that received ART and the group that did not receive ART are unconditionally exchangeable or comparable. That is, the group that received ART would have the same average CD4 cell count after 3 years of follow-up as the group that did not receive ART had they actually been treated and vice versa (i.e. if treatment assignment had been flipped).
b. The group that received ART and the group that did not receive ART are exchangeable or comparable conditional on age, sex, geographic origin, calendar year, mode of HIV transmission, CD4 cell count and viral load. That is, within levels of age, sex, geographic origin, calendar year, mode of HIV transmission, CD4 cell count and viral load, the group that received ART would have the same average CD4 cell count after 3 years of follow-up as the group that did not receive ART had they actually been treated and vice versa (i.e., if treatment assignment had been flipped).
c. The group that received ART and the group that did not receive ART are unconditionally exchangeable or comparable. That is, the average CD4 cell count after 3 years of follow-up had everyone received ART will be the same as the average CD4 cell count after 3 years of follow-up had nobody received ART.
d. The group that received ART and the group that did not receive ART are exchangeable or comparable conditional on age, sex, geographic origin, calendar year, mode of HIV transmission, CD4 cell count and viral load. That is, the average CD4 cell count after 3 years of follow-up had everyone received ART will be the same as the average CD4 cell count after 3 years of follow-up had nobody received ART, within levels of age, sex, geographic origin, calendar year, mode of HIV transmission, CD4 cell count and viral load.
Based on your subject-matter knowledge, which of the following are reasons why adjusting for the variable “baseline CD4 cell count” is advisable in this study?
a. Baseline CD4 cell count is a clinical measure of immunosuppression and can be used to determine initiation of ART.
b. Baseline CD4 cell count mediates the effect of ART on CD4 cell count at 3 years.
c. Immunologic status (an unmeasured U) is a common cause of baseline CD4 count and CD4 count at 3 years follow-up (outcome). E.g. people with worse immunologic status may have lower CD4 cell count at baseline and this will also affect CD4 count over time.
d. The effect of ART on CD4 cell count at 3 years of follow-up differs across different thresholds of the baseline CD4 cell count.
Which of the following is the average causal effect of interest given in the prompt?
a. The difference in the mean CD4 cell count after 3 years of follow-up among those in the study that were treated with ART compared to the mean CD4 cell count after 3 years of follow-up among those in the study that were untreated with ART.
b. The difference in the mean CD4 cell count after 3 years of follow-up among those in the study that were treated with ART compared to the mean CD4 cell count after 3 years of follow-up among those in the study that were untreated with ART, within levels of levels of age, sex, geographic origin, calendar year, mode of HIV transmission, CD4 cell count and viral load
c. The difference in the mean CD4 cell count after 3 years of follow-up that would have been observed had everyone in the study been treated with ART compared to what would have been observed had everyone in the study been untreated with ART.
d. The difference in the mean CD4 cell count after 3 years of follow-up that would have been observed had everyone in the study been treated with ART compared to what would have been observed had everyone in the study been untreated with ART, within levels of age, sex, geographic origin, calendar year, mode of HIV transmission, CD4 cell count and viral load.
Which of the following is a true statement about whether or not the definition of the average causal effect defined in question 3 depends on the variables that are being adjusted for?
a. The definition of this effect is marginal, meaning that it does not depend on the variables for which we are adjusting. The adjustment variables are sufficient to achieve conditional exchangeability, which is an assumption we need to make in order to estimate the (marginal) average causal effect in the population using our data.
b. The definition of this effect is conditional, meaning that it does depend on the variables for which we are adjusting. The adjustment variables are sufficient to achieve conditional exchangeability, which is an assumption we need to make in order to estimate the (conditional) causal effect in the population using our data.
Using the HIVPOINT dataset for questions 5-28, model all baseline continuous variables categorically. That is, use indicator variables as you’ve done throughout the course based on the cut-points given in the codebook (note: these categories have already been created for you in the data). Do not include any product terms in any models. Always round your answer to the nearest hundredth.
Using outcome regression, provide a point estimate of the causal effect of interest in this study. Round your answer to the nearest hundredth.
Using outcome regression, provide a valid 95% confidence interval for the causal effect of interest you estimated in question 5. Round your answer to the nearest hundredth.
Upper bound=
Lower bound=
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