A patient’s length of follow-up is the amount of time after diagnosis for which they were under observation (with records in the data). What was the median length of follow-up time? What percentage of the patients had at least 1 year (365 days) of records?
For patients with at least 1 year (365 days) of follow-up, their one-year adherence to a medication is the proportion of days in the first year after diagnosis during which the medication was possessed. For each medication, what was the average one-year adherence of the patients? Use only the patients with at least 1 year of follow-up records.
For ace inhibitors, beta blockers, and statins, we are interested in the number of these medications that patients take at the same time. During the first year (365 days), how frequently are the patients taking 0, 1, 2, or all 3 medications?
Only perform this calculation among patients with at least one year of follow-up. Create a table that shows the percentage for each number of medications taken.
What is the impact of diabetes, age, gender, region, and baseline condition on the one-year adherence to each medication? Use only the patients with at least 1 year (365 days) of follow-up records. Fit separate linear regression models for each medicine. Then briefly comment on the results.
Summary of Significant Results (where p-value < 0.05):
Older patients, males, and those with moderate health conditions are less likely to adhere to their ACE inhibitor regimen.
Patients with diabetes, and those living in the Northeast or West regions show higher adherence rates.
Summary of Significant Results (where p-value < 0.05):
Older patients, males, and those with moderate health conditions are less likely to adhere to their beta blocker regimen.
Patients with diabetes, and those living in the Northeast or West regions show higher adherence rates.
Summary of Significant Results (where p-value < 0.05):
Older patients, males, and those with moderate health conditions are less likely to adhere to their statin regimen.
Patients with diabetes, and those living in the Northeast or West regions show higher adherence rates.
For each medicine, what percentage of the patients filled a prescription in the first two weeks (before day t1 = 14) after their initial diagnoses? Use data from all of the patients for this analysis, classifying each one as filling or not filling the prescription during this period.
Now let’s compare those who filled a prescription for a statin in the first two weeks (before day t1 = 14) after diagnosis to those who did not. Do these two groups have different baseline covariates? Compare the groups based on their ages. Then compare the distribution of baseline conditions in the two groups. For continuous variables, compare their means using a t-test. For the categorical variables, compare their distributions using a chi-squared test of independence.
The t-test was conducted to compare the ages of two groups:
Interpretation:
Statistical significance: The p-value is very small (p < 0.01), meaning the difference in the average ages of the two groups is statistically significant, which is further confirmed by the 95% confidence interval for the difference in means is between -0.75938 to -0.498907 and does not include 0.
Direction of the difference: The negative t-value (-9.4683898) indicates that the mean age of Group 1 (early initiators) is lower than the mean age of Group 2 (non-initiators). Specifically, the mean age for early initiators is about 64.8714451 years, while the mean age for non-initiators is 65.5005885 years.
Conclusion:
There is a small but significant difference in age between the two groups. Patients who filled a statin prescription within the first two weeks are, on average, slightly younger than those who did not.
The Chi-squared test was used to compare the distribution of baseline conditions between the two groups.
Interpretation:
Statistical significance: The p-value is extremely small (p < 0.01), which means the difference in the distribution of baseline conditions between the two groups is statistically significant.
Chi-squared statistic: A large chi-squared value (126.34) indicates that the difference between the expected and observed frequencies for the baseline conditions is substantial.
Conclusion: There is a statistically significant difference in the baseline conditions between patients who filled a statin prescription within the first two weeks and those who did not. This suggests that baseline health conditions play a role in whether a patient initiates their medication early or not.
How do the variables of age, gender, region, diabetes, and baseline condition impact the likelihood of initiating a medication within 14 days (before day t1 = 14)? For each medicine, fit a logistic regression model and comment on the odds ratios. Use data from all of the patients for this analysis.
Interpretation:
The odds of initiating the medication within 14 days are higher for patients with diabetes and those living in the Northeast, South, and West regions,
Older patients and males are slightly less likely to initiate within two weeks.
Patients with moderate symptoms or light procedures are much less likely to initiate within 14 days.
The odds of initiating the medication within 14 days are higher for patients with diabetes and those living in the Northeast, South, and West regions,
Older patients and males are slightly less likely to initiate within two weeks.
Patients with moderate symptoms or light procedures are much less likely to initiate within 14 days.
The odds of initiating the medication within 14 days are higher for patients with diabetes and those living in the Northeast, South, and West regions,
Older patients and males are slightly less likely to initiate within two weeks.
Patients with moderate symptoms or light procedures are much less likely to initiate within 14 days.
For patients who did fill their prescriptions within 2 weeks (before day t1 = 14), how long does it typically take to fill that first prescription after the initial diagnosis? For each medicine, provide the mean, median, and standard deviation in units of days.
How does filling a prescription in the first two weeks impact adherence? If we want to see that a medicine is working, we need to start the observation after the patient has had a chance to fill the prescription. To answer this question, we will follow a number of steps:
Identify which patients filled a prescription in the first two weeks. You can call this variable initiated with binary values (1 for yes, 0 for no).
Then, for each patient with at least 379 days of followup, measure the one-year adherence rate (see Question 2) starting at two weeks after the initial diagnosis. This interval will begin at day 14 and last for 365 days.
Fit a linear regression model of this one-year adherence including the baseline covariates (age, gender, region, diabetes, baseline condition) and an indicator of whether this patient filled a prescription for the medicine in the first two weeks.
Perform this analysis for each medicine and comment on the results.
Interpretation:
Interpretation:
Interpretation:
Once a patient starts a medication, how long do they continuously have a filled prescription? For each patient who filled a medication, start with the first filled prescription and count the duration of days until a gap occurs or follow-up ends. (Hint: The first duration begins the first time ace = 1. The first duration ends at the first time ace = 0 after this point. Each patient will have 1 calculated duration if they filled a prescription and 0 otherwise.) Then provide the mean, median, and standard deviation for these durations. Do this separately for each medicine.