Lecture: Propensity Scoring
Assignments should be submitted in Blackboard. Include in the first page a summary page. In the summary page write statements comparing your work to answers given or videos. For example, "I got the same answers as the Teach One video for question 1."
Question 1: The following data were collected for residents in the Medical Foster Home and in Nursing Homes. The data is organized by quartiles of severity of illness. Each quartile shows increasing participation in Medical Foster Home program, indicating that sicker patients are more likely to participate in the medical foster home program. We want to remove the effect of participation in the program from our estimate of cost differences. Report whether the residents in the Medical Foster Home have lower cost to residents in the Nursing Homes with similar likelihood of participation in the Medical Foster Home program.
Question 2: Using the data, what is the inverse propensity weight (i.e. one ver the conditional probability of participating in the Medical Foster Home) for the 45 patients who fall in quintile 1.
Answer in chapter 13 Statistical Analysis of Electronic Health Records in Big Data in Healthcare, pages 337 to 338
Question 3: The following data provide the survival among cancer patients. The data provides 35 common comorbidities for patients who have or don't have stomach cancer. Use both logistic and ordinary regression to analyze these data and report the difference of the findings, in particular:
Question 4: The objective of this analysis is to find response to antidepressants. You can select one of the antidepressants. These data come from STAR*D experiment conducted by National Institute of Medicine. The data are report bi-weekly or monthly. There are 22,254 records for about 4,000 patients. Organize the data so there is one row for each patient.
Focus: The enclosed data report on bupropion. Please focus the analysis on only one of the antidepressants or a combination of two antidepressants taken simultaneously. For the time being ignore the dose of the medication.
Exclusions: Patients who did not receive bupropion are assumed to have received the alternative antidepressants. The unit of the analysis is antidepressant trials and not necessary unique person. So the ID that should be used is the combination of patient ID and Concat_Levels.
Treatment: If the patient has taken the antidepressant at any time during the study period, then mark it as 1, otherwise 0. Notice that some patients have taken the medication and others have not. Within the combination of ID and Concat_levels look for any occasion of use of bupropion.
Covariates: For the covariates, include gender, risk of suicide, heart, vascular, haematopoietic, eyes ears nose throat larynx, gastrointestinal, renal, genitourinary, musculoskeletal Integument, neurological, psychiatric illness, respiratory, liver, endocrine, alcohol, amphetamine, cannibis use, opioid use, panic, specific phobia, social phobia, OCD, PTSD, anxiety, borderline personality, dependent personality, antisocial personality, paranoid personality, personality disorder, anorexia, bulimia, and cocaine use. If the covariate is ever present assume that it is present. Exclude covariates that are not present for any of the patients.
Outcome: The medication is considered to have caused the remission, if while on the medication, the patient is discharged to follow-up portion of the study, then "Treatment_plan_equal_3" is set to 1. Use "Treatment_Plan_Equal_3" and not "Remission" variable as an indication of effectiveness of the antidepressant, since the remission variable does not indicate that the clinician was in agreement that the patients symptoms are well managed.
Balance the data to remove the effects of covariates. Show visually that you have successfully balanced the data. Use the following steps to accomplish this:
Question 5: The following provides the joint distribution of treatment and case mix severity for the patients in a hypothetical hospital. The data provided is the joint distribution of treatment and case mix severity, i.e., p(treatment, case mix severity). Calculate the propensity of participating in treatment, given that the case mix severity is low. This is the conditional probability of treatment given low severity, i.e. p(treated | low severity). Calculate the probability of case mix severity being low given that the patient is treated. This is the conditional probability of low severity given that the patient was treated, i.e. p(low severity | treated).
For patients in low severity, the treated patients should be weighted by which of the following?
Question 6: The following problem was first created by Morgan and Harding and we have adjusted it to fit within health care. In this example, the outcome are length of stay in the hospital, the treatment is the clinician/his peer group and the strata are a mix of medical history and demographic variables that account for the pattern of self-selection into treatment. This mix have been divided into 3 strata: low, medium and high risk. Notice that treated patients are likely to fall in the high strata. Untreated patients are more likely to fall in the low strata. What is the impact of clinician on length of stay, after removing confounding associated with this selection bias.
Question 7: The following data have been taken from nurses rounding in a facility. The time they spent with patients has been recorded. In addition, several characteristics of the patients have also been recorded and standardized. Do any of the nurses have a significant impact on overall satisfaction in the unit?
Question 8: In a nursing home, data were collected on residents' survival and disabilities. The data are listed in the following order: ID, age, gender (M for male, F for Female), number of assessments completed on the person, number of days followed, days since first assessment, days to last assessment, unable to eat, unable to transfer, unable to groom, unable to toilet, unable to bathe, unable to walk, unable to dress, unable to bowel, unable to urine, dead (1) or alive (0), and assessment number. Predict from the patient's assessments (i.e. their age and disabilities at time of assessment) if the patient is likely to die and should be admitted to the hospice program.
Question 9: What is the overlap between cases and controls and how does it affect study findings?
For more, see chapter 13 Statistical Analysis of Electronic Health Records in Big Data in Healthcare, page 343.
For additional information (not part of the required reading), please see the following links:/p>
This page is part of the course on Comparative Effectiveness by Farrokh Alemi PhD Home► Email►