Reading Response Discussions

Specify a hypothesis regarding a particular exposure and outcome and a binary effect modifier including specific measures of association (specify the magnitudes of that association you anticipate: I suggest making everything cross- sectional). Using the software of your choice, generate a population with 1000 people under a causal structure consistent with this hypothesis. Draw a simple random sample 100 individuals from this population and estimate the population average exposure-outcome association and the association stratified by your modifier of interest within this subset. Repeat this 10 times and write the parameter estimates and CI each time.

Note: This week's response is optional.

For Kara Rudolph - Due before class: May 11, 2015

Read articles:

Rudolph, Kara E., et al. "Estimating Population Treatment Effects From a Survey Subsample." American journal of epidemiology (2014): Oct 1;180(7):737-48. doi: 10.1093/aje/kwu197

Gruber, Susan, and Mark J. van der Laan. "Targeted maximum likelihood estimation: A gentle introduction." (2009).

And install R: http://cran.r-project.org/

Answer the following questions:
(1) When running a regression model to do analysis, what should we be worried about?
(2) Why use TMLE instead of regression?
(3) Why use TMLE instead of inverse probability of treatment weighting?
(4) Are there instances when we wouldn't want to use TMLE?
(5) What are marginal effects? What are conditional effects? Which do we usually report? Which are the most useful?
(6) Can you think of applications in your field in which TMLE might be helpful?

Moving to Opportunity and a new related study from Harvard on KQUED - There was an interesting discussion with listeners after! 

http://www.kqed.org/a/forum/R201505050900

When is a quasi- or natural-experiment more appropriate than a randomized experiment? When is a quasi- or natural- experiment more informative than a conventional observational study? Give an example of a substantive question and a stakeholder (e.g., policymaker, patient, clinician) who would be more interested in an ITT effect estimate vs an IV effect estimate. Discuss how each (ITT and IV) correspond to effect estimates from conventional studies.

Due before class: May 4, 2015.

Here is the slide from the week 4 lecture from Epi 207, where Lydia talked about atomistic fallacy.

Raj

For a specific exposure-outcome combination of interest to you, specify which lifecourse model is likely most appropriate and why you think this is the case. Describe the regression models you could use to test your hypothesis. Are there any possible data sets in which this test could be conducted, and if so, what concerns would you have about interpreting your proposed test of the lifecourse model?

Due before class: April 27, 2015

Choose at least 3 distinct data sources (e.g., ARIC, HRS, death certificate data, NHS, etc), and give an example of a research question (e.g., a hypothesis about the effect of a specific exposure on a specific outcome) you consider the study exceptionally strong to address. For each, provide an example of a research question you consider the design very weak to address. Explain why the data source is strong or weak for each question. Do not just discuss the questions addressed in the readings, think of new questions, preferably things you might be interested in. This is not supposed to be a commentary related to the substantive questions in the readings: the goal is to focus on the pros and cons of various data sources. For hypotheses each study would not be well equipped to address, if possible describe another study that could address the hypothesis.

Please post your responses below this thread as a reply.