Data Analysis- Part 1
数据分析代写 Part 1 is the descriptive part of this data analysis project. It includes the introduction and proposed methods of the analysis as well as ···
Instructions: 数据分析代写
Part 1 is the descriptive part of this data analysis project. It includes the introduction and proposed methods of the analysis as well as characteristics of the population presented in Table 1.
- Preparation:
- Develop a primary research question based on the variables available investigating the association between an exposure and an outcome. (We do not expect the research question or hypothesis to be novel, cutting-edge or thesis/dissertation quality. Keep in mind that this is a class assignment). This research question must include the following: exposure, outcome, and population.
- Develop a secondary research question involving effect modificationof your primary exposure-outcome association.
3- Search for information about your exposure-outcome association and previous studies examining this association. Also find information about the public health significance of this proposed study.
4- Search the codebook for information about the variables you are using in your analysis including the exposure, the outcome, potential confounders (at least 4 variables), and an effect modifier (one variable).
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Keep in mind the required minimum sample size for this analysis. Please use a reasonably common outcome and exposure so that there at least 150 people in your analysis and at least 30 people in the smallest cell of your exposure by outcome 2X2 table. These numbers are the absolute minimum.
- Submission:
Present the following in your data analysis-part 1 document:
(1) An introduction (2 points): A well-written paragraph that includes all of the following:
- Purpose of this study, including some scholarly background information about the problem you are attempting to address through this analysis
- Clearly-stated main research question in terms of an exposure and outcome.
iii. Clearly-stated secondary research question related to your effect modifier.
(2) Methods (2 points) 数据分析代写
- A one or two sentence description of your analysis population including any inclusion/exclusion criteria.
- Description of your exposure and outcome
iii. List of covariates you intend to adjust for.
- Description of your selected effect modifier (this must be a categorical variable). List the levels of your variable by which you are going to stratify your analysis.
(3) Table 1 (4 points): This is the initial summary table of your population. The columns of Table 1 can be defined either by levels of exposure or levels of outcome, whichever you think is more appropriate. The rows must include your chosen covariates and your effect modifier. If you defined your columns by outcome, you should also include exposure as a row variable and vice-versa.
– Please present only the actual numbers and ignore all sampling weights. Sampling weights are beyond the scope of the class.
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– The table must include both counts and percentages. Appropriate choice of which type of percentage to report in the table is very important. Incorrect usage of percentages will cost you 2 points.
Optional: Provide statistical testing and p-values for differences between your columns.
*An example of Table 1 is provided at the end of this document.
(4) SAS code (no points). Providing the SAS code will help us see any errors or issues with your code.
About the data: 数据分析代写
The data are from the 2012 National Health Interview Survey. The survey first asked a lot of questions about the family. Then they took a sample adult from the family and asked questions about that adult. Then they took a sample child from the family (if there was one) and surveyed that child. The three surveys (family, adult, child) were originally provided in separate files. For your analysis, you may choose to use the adult file or child file, but not both.
If for any reason, you would like to include as part of your analysis one or more of the variables in the family file, which include measures of socioeconomic measures, insurance and family size, it is easy to merge the family file with one of the other two files. Here is the code to merge.
data familyadult ;
merge in.nhis2012family in.nhis2012sampleadult (in=inxxadult);
by hhx fmx;
if inxxadult;
run;
data familychild ;
merge in.nhis2012family in.nhis2012samplechild (in=inxxchild);
by hhx fmx;
if inxxchild;
run; 数据分析代写
Again, using variables from the family file, either as exposure, potential confounder or effect modifier, is optional.
In addition, we are giving you six files (in pairs) for documentation. These are (1) the codebook and the questionnaire for the family; (2) the codebook and the questionnaire for the sample adult; (3) the codebook and the questionnaire for the sample child.
Note: if the name of the documentation contains -0002, this pertains to the family data
If the name of the documentation contains -0004, this pertains to the sample adult data
If the name of the documentation contains -0005, this pertains to the sample child data
Here are the 6 documentation files (links posted on Canvas):
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- 36146-0002-Codebook.pdf
- 36146-0002-Questionnaire-English_MULTI.pdf
- 36146-0004-Codebook.pdf
- 36146-0004-Questionnaire-English.pdf
- 36146-0005-Codebook.pdf
- 36146-0005-Questionnaire-English.pdf
- 36146-0005-Questionnaire-English.pdf
Here are the 3 datasets (links posted on Canvas):
- sas7bdat
- sas7bdat
- sas7bdat
An example of (a partial) Table 1 is presented below. You can also see examples in most published journal article related to epidemiology.
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