Reading Response Discussions

This is a simulation exercise to illustrate how any specific sample and data analysis you conduct is just a single draw from a larger population and multiple possible iterations of the sample and analysis. 

Specify a hypothesis regarding a particular exposure and outcome and a binary effect modifier including specific measures of association (e.g., the relative risk or the odds ratio, specify the magnitudes of that association you anticipate: I suggest making everything cross-sectional for simplicity). 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.

Repeat the data set construction, setting the causal effect to the null.  Again repeat this 10 times and write the parameter estimate and CI each time (if you figure out how to automate it, run it 1000 times and post the histogram of the parameter estimates and p-values).

Please post your results from each of the 10 runs under the hypothesized effect and under the null and your code.

If you are flummoxed by this, note that example code is in Mayeda's simulation paper (which she posted last week) and in Glymour and Vittinghoff "Selection bias, just because it's possible doesn't mean it's plausible", published in Epidemiology in 2014.  But write the simulation in whatever language you prefer and don't worry too much about making beautiful code. 

Read recent issues of a journal in your field (e.g., Am J Epi; Epidemiology; Neurology; Obesity; Diabetes Care). Choose an original research article that addresses a research topic of interest to you using an observational longitudinal design.

1) Briefly summarize the study objective and design.

2) Consider selection into the analysis sample, including differential enrollment (from refusal to participate or differential survival up to the time of study initiation) and differential attrition of enrolled participants (from death or drop out). Did the authors describe potential selective participation/attrition? Did they describe predictors of participation/attrition? Do you think selection bias is a major potential source of bias in this study?

3) Now imagine a hypothetical trial to test the hypothesis of the observational study you selected (this is a thought experiment—time and money are no issue for your hypothetical trial). When would you enroll participants, randomize participants, and assess the outcome to minimize selection bias?

Discuss briefly;  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.

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?

Attached is the Sampson paper

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.

Assignment: Find any article using clustered data and describe: the unit of clustering; the hypothesized effects and the level at which the exposure is measured (is it a characteristic of the cluster or the observation within the cluster); and the statistical model used to estimate the effect.  Describe whether there are any other statistical models that might be appropriate and whether they would be preferable (e.g., GEE vs mixed).  It is helpful if you post the reference for the article or a link.