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Chapter 3: Data Examples and Simulations

3.1 Introduction

3.2 The REFLECTIONS Study

3.3 The Lindner Study

3.4 Simulations

3.5 Analysis Data Set Examples

3.5.1 Simulated REFLECTIONS Data

3.5.2 Simulated PCI Data

3.6 Summary

References

3.1 Introduction

In this chapter, we present both the core data sets that are used as examples throughout the book and demonstrate how to simulate data to mimic an existing data set. Simulations are a common tool for examining and comparing the operating characteristics of different statistical methods. One must know the true value of the parameter of interest when assessing how well a particular method performs. In simulations, as opposed to a case study from actual data, the true parameter values are known, and one can test the performance of methods across various data scenarios specified by the research. However, real world data is very complex – with complex distributions and correlations amongst the many variables, missing data patterns, and so on. Often, published simulations are performed with a limited number of variables using known parametric functions to generate values along with simple or no correlations between covariates or missing data. Thus, simulations based on actual data that retain the complex correlations and missing data patterns, often called “plasmode simulations” (Gadbury et al. 2008, Franklin et al. 2014), can provide a superior test of how methods perform under real world data settings.

This chapter is structured as follows. Sections 2 and 3 present background information about two observational studies (REFLECTIONS and Lindner) that serve as the basis of analyses throughout the book. Section 4 discusses options for simulating real world data from an existing study data set. Section 5 presents the SAS code and the analysis data sets generated for use in the later chapters.

3.2 The REFLECTIONS Study

The Real World Examination of Fibromyalgia: Longitudinal Evaluation of Cost and Treatments (REFLECTIONS) study was a prospective observational study conducted between 2008 and 2011 at 58 clinical sites in the United States and Puerto Rico (Robinson et al. 2012). The primary objective of the study was to examine the burden of illness, treatment patterns, and outcomes for patients initiating new treatments for fibromyalgia. Data was collected via physician surveys, a clinical report form completed at the baseline office visit, and computer-assisted telephone patient interviews at five time points over the one-year study. The physician surveys collected information about the clinical site and lead physician, including physician demographics and practice characteristics. At the baseline visit, data from a thorough clinical summary of the patient was captured. This included demographics, medical history, socio-economic and work/disability status, and treatment. Phone surveys at baseline and throughout the study included information from the patient regarding changes in treatments and disease severity using multiple validated patient rating scales.

The study enrolled a total of 1700 patients and 1575 met criteria for the analysis dataset. A summary of the demographics and baseline patient characteristics is provided in Section 3.5. One analysis conducted from the REFLECTIONS data was an examination of outcomes from patients initiating opioid treatments. Peng et al. (2015) used propensity score matching to compare Brief Pain Inventory (BPI) scores and other outcomes over the one-year follow-up period for patients initiating opioids versus those initiating other treatments for fibromyalgia. We use this example to demonstrate the creation of two simulated data sets based on the REFLECTIONS data: a one observation per patient data set used to demonstrate various propensity score-based analyses in Chapters 4–10 and a longitudinal analysis data set used to demonstrate marginal structural model and replicates analysis methods in Chapters 11 and 12.

3.3 The Lindner Study

The Lindner study was also a prospective observational study (Kereiakes et al. 2000). It was conducted in 1997 at a single site, the Lindner Center for Research and Education, Christ Hospital, Cincinnati, Ohio. Lindner staff members used their research database system to store detailed patient data, including patient feedback and survival information from at least six consecutive months of telephone follow-up.

Lindner doctors were high-volume practitioners of interventional cardiology involving percutaneous coronary intervention (PCI). Specifically, all Lindner operators performed >200 PCIs/year, and their average was 280 PCIs per operator in 1997. The only viable alternative to some PCI procedures is open-heart surgery, such as a coronary artery bypass graft (CABG).

Follow-up analyses of 1472 consecutive PCIs performed at the Lindner Center in 1997 found that their research database contained the “initial” PCIs for 996 distinct patients. Of these patients, 698 (roughly 70% of the 996) had received usual PCI care augmented with planned or rescue use of a new “blood thinner” treatment and are considered the treated group in later analyses. On the other hand, 298 patients (roughly 30% of the 996) did not receive the blood thinner during their initial PCI at Lindner in 1997; these 298 patients constitute the “usual PCI care alone” treatment cohort (control group). Details of the variables included in the data set are provided in section 3.5.2. The simulated PCI15K data set is used in the example analyses of Chapter 7 (stratification), Chapter 14 (generalizability), and Chapter 15 (personalized medicine).

3.4 Simulations

The term “plasmode” has come to represent data that is based on real data (Gadbury et al. 2008). In our case, we wanted a data set that contained no actual patient data – in order that we could freely share and allow readers to implement the various approaches in this book without confidentiality or ownership issues. However, we also wanted data that was truly representative of real world health care research – maintaining the complex correlation structures and addressing common research interests. Thus, “plasmode” simulations based on the REFLECTIONS and Lindner studies were used to generate the data sets used in the remainder of this book. In particular, the method of rank transformations of Conover and Iman (1976) as implemented by Wicklin (2013) serves as the basis for the programs.

3.5 Analysis Data Set Examples

3.5.1 Simulated REFLECTIONS Data

The Peng et al. (2015) analysis from the REFLECTIONS study included 1575 patients in 3 treatment groups based on their treatment at initiation: opioid treatments (378), non-narcotic opioid like treatment (215), and all other treatments (982). Each patient had up to 5 visits including baseline. Tables 3.1 and 3.2 list the key variables in the original analysis data set from which the simulated data was formed.

Table 3.1: List of Patient-wise Variables

Variable NameVariable Label
SubjIDSubject Number
CohortCohort
GenderGender
AgeAge in years
BMI_BBMI at Baseline
RaceRace
InsuranceInsurance
DrSpecialtyDoctor Specialty
ExerciseExercise
InptHospInpatient hospitalization in last 12 months
MissWorkOthOther missed paid work to help your care in last 12 months
UnPdCaregiverHave you used an unpaid caregiver in last 12 months
PdCaregiverHave you hired a caregiver in last 12 months
DisabilityHave you received disability income in last 12 months
SymDurDuration (in years) of symptoms
DxDurTime (in years) since initial Dx
TrtDurTime (in years) since initial Trtmnt
PhysicalSymp_BPHQ 15 total score at Baseline
FIQ_BFIQ Total Score at Baseline
GAD7_BGAD7 total score at Baseline
MFIpf_BMFI Physical Fatigue at Baseline
MFImf_BMFI Mental Fatigue at Baseline
CPFQ_BCPFQ Total Score at Baseline
ISIX_BISIX total score at Baseline
SDS_BSDS total score at Baseline

Table 3.2: List of Visit-wise Variables

Variable NameVariable Label
VisitVisit
OPIynOpioids use continued/started at this visit
SatisfCareSatisfaction with Overall Fibro Treatment
SatisfMedSatisfaction with Prescribed Medication
PHQ8PHQ8 total score
BPIPainBPI Pain score
BPIInterfBPI Interference score

For the REFLECTIONS simulated data set, simulation was performed separately for each treatment cohort. First, the original dataset was transformed from a vertical (one observation per patient per time-point) into a horizontal format (one record per patient). Next, a cohort-specific data set was created by random sampling (with replacement) from each original variable. The size of sample was 240, 140, and 620 for opioid, non-narcotic opioid, and other treatment cohort, respectively. The SAS/IML programming language was used to implement the Iman-Conover method following the code of Wicklin (2013) as shown in Program 3.1 using the sampled data (A) and the desired between variables rank-correlations (C).

Program 3.1: Iman-Conover Method to Create a Simulated REFLECTIONS Data Set

/* Use Iman-Conover method to generate MV data with known marginals

and known rank correlation. */

start ImanConoverTransform(Y, C);

X = Y;

N = nrow(X);

R = J(N, ncol(X));

/* compute scores of each column */

do i = 1 to ncol(X);

h = quantile(“Normal”, rank(X[,i])/(N+1));

R[,i] = h;

end;

/* these matrices are transposes of those in Iman & Conover */

Q = root(corr(R));

P = root(C);

S = solve(Q,P);

M = R*S; /* M has rank correlation close to target C */

/* reorder columns of X to have same ranks as M.

In Iman-Conover (1982), the matrix is called R_B. */

do i = 1 to ncol(M);

rank = rank(M[,i]);

tmp = X[,i];

call sort(tmp);

X[,i] = tmp[rank];

end;

return( X );

finish;

X = ImanConoverTransform(A, C);

The three cohort-specific simulated matrices (X) were concatenated and then the dropout and missing data were imposed at random in order to reflect the amount of dropout/missingness observed in the actual REFLECTIONS data. Then the structure of the simulated data was converted from horizontal to back to vertical.

The distributions of variables were almost identical for real and simulated data as displayed in Tables 3.3 and 3.4. This can be expected because the Iman-Conover algorithm simply rearranges the elements of columns of the data matrix. The descriptive statistics for real and simulated data are presented below.

Table 3.3: Comparison of Actual and Simulated REFLECTIONS Data for One Observation per Patient Variables

realtype
realsimulated
AllN15751000
Cohort13.6514.00
NN opioidColPctN
opioidColPctN24.0024.00
otherColPctN62.3562.00
Gender94.5493.20
femaleColPctN
maleColPctN5.466.80
Race83.6282.30
CaucasianColPctN
OtherColPctN16.3817.70
Insurance78.1075.70
private/combinationColPctN
public/no insuranceColPctN21.9024.30
Doctor Specialty17.6517.60
Other SpecialtyColPctN
Primary CareColPctN15.8715.70
RheumatologyColPctN66.4866.70
Exercise10.0311.00
NoColPctN
YesColPctN89.9789.00
Inpatient hospitalization in last 12 months89.8490.70
NoColPctN
YesColPctN10.169.30
Other missed paid work to help your care in last 12 months77.7179.60
NoColPctN
YesColPctN22.2920.40
Have you used an unpaid caregiver in last 12 months62.8660.50
NoColPctN
YesColPctN37.1439.50
Have you hired a caregiver in last 12 months95.5695.70
NoColPctN
YesColPctN4.444.30
Have you received disability income in last 12 months70.8672.30
NoColPctN
YesColPctN29.1427.70
Age in yearsNMiss00
Mean50.4550.12
Std11.7111.56
BMI at BaselineNMiss00
Mean31.3031.36
Std7.347.01
Duration (in years) of symptomsNMiss216133
Mean10.2810.03
Std9.269.02
Time (in years) since initial DxNMiss216133
Mean5.735.29
Std6.276.05
Time (in years) since initial TrtmntNMiss216133
Mean5.225.26
Std6.026.18
PHQ 15 total score at BaselineNMiss00
Mean13.8114.03
Std4.644.79
FIQ Total Score at BaselineNMiss00
Mean54.5454.56
Std13.4313.47
GAD7 total score at BaselineNMiss00
Mean10.8110.64
Std5.775.67
MFI Physical Fatigue at BaselineNMiss00
Mean13.0913.00
Std2.282.17
MFI Mental Fatigue at BaselineNMiss00
Mean11.5111.52
Std2.382.49
CPFQ Total Score at BaselineNMiss00
Mean26.5126.62
Std6.446.43
ISIX total score at BaselineNMiss00
Mean17.6417.91
Std5.975.74
SDS total score at BaselineNMiss00
Mean18.2718.28
Std7.507.56

Table 3.4: Comparison of Actual and Simulated REFLECTIONS Data for Visit-wise Variables

realtype
realsimulated
Visit15751000
1N
Opioids use76.0076.00
NoColPctN
YesColPctN24.0024.00
Satisfaction with Overall Fibro Treatment5.336.10
.ColPctN
1ColPctN12.1312.10
2ColPctN20.9519.70
3ColPctN25.2724.20
4ColPctN22.8624.30
5ColPctN13.4613.60
Satisfaction with Prescribed Medication10.039.80
.ColPctN
1ColPctN7.436.80
2ColPctN15.8115.60
3ColPctN31.6831.90
4ColPctN23.7524.30
5ColPctN11.3011.60
PHQ8 total scoreNMiss00
Mean13.0713.14
Std6.046.02
BPI Pain scoreNMiss00
Mean5.515.54
Std1.741.76
BPI Interference scoreNMiss00
Mean6.086.00
Std2.172.15
realtype
realsimulated
Visit15751000
2N
Opioids use3.112.70
ColPctN
NoColPctN71.0570.10
YesColPctN25.8427.20
Satisfaction with Overall Fibro Treatment5.654.80
.ColPctN
1ColPctN16.1316.60
2ColPctN25.3326.50
3ColPctN27.3028.10
4ColPctN18.4817.00
5ColPctN7.117.00
Satisfaction with Prescribed Medication6.296.10
.ColPctN
1ColPctN11.3710.50
2ColPctN24.3824.00
3ColPctN30.4831.90
4ColPctN19.5620.50
5ColPctN7.947.00
PHQ8 total scoreNMiss5022
Mean11.8811.86
Std5.925.75
BPI Pain scoreNMiss6247
Mean5.335.34
Std1.921.94
BPI Interference scoreNMiss4936
Mean5.545.50
Std2.362.40
realtype
realsimulated
Visit1483950
3N
Opioids use4.995.05
ColPctN
NoColPctN68.3765.37
YesColPctN26.6429.58
Satisfaction with Overall Fibro Treatment8.506.63
.ColPctN
1ColPctN16.6616.74
2ColPctN25.6225.47
3ColPctN26.5026.84
4ColPctN16.4516.84
5ColPctN6.277.47
Satisfaction with Prescribed Medication8.029.47
.ColPctN
1ColPctN12.7413.47
2ColPctN23.4021.58
3ColPctN31.6331.89
4ColPctN17.8716.32
5ColPctN6.347.26
PHQ8 total scoreNMiss7444
Mean12.1812.31
Std6.226.30
BPI Pain scoreNMiss9552
Mean5.235.13
Std1.971.98
BPI Interference scoreNMiss7451
Mean5.475.64
Std2.432.36
realtype
realsimulated
Visit1378888
4N
Opioids use3.854.62
ColPctN
NoColPctN67.8566.10
YesColPctN28.3029.28
Satisfaction with Overall Fibro Treatment8.139.91
.ColPctN
1ColPctN18.8716.55
2ColPctN25.4725.23
3ColPctN27.0728.38
4ColPctN15.4615.20
5ColPctN5.014.73
Satisfaction with Prescribed Medication7.846.98
.ColPctN
1ColPctN13.1314.41
2ColPctN26.8525.34
3ColPctN31.2029.95
4ColPctN15.8917.23
5ColPctN5.086.08
PHQ8 total scoreNMiss5634
Mean11.4811.65
Std6.066.12
BPI Pain scoreNMiss7248
Mean5.205.15
Std2.002.05
BPI Interference scoreNMiss5340
Mean5.395.59
Std2.472.47
realtype
realsimulated
Visit1189773
5N
Opioids use0.250.13
ColPctN
NoColPctN68.2167.53
YesColPctN31.5432.34
Satisfaction with Overall Fibro Treatment3.033.36
.ColPctN
1ColPctN16.8214.62
2ColPctN27.7527.30
3ColPctN28.8530.53
4ColPctN16.0616.04
5ColPctN7.498.15
Satisfaction with Prescribed Medication4.794.79
.ColPctN
1ColPctN13.4612.42
2ColPctN27.3325.49
3ColPctN33.5635.58
4ColPctN14.8915.14
5ColPctN5.976.60
PHQ8 total scoreNMiss00
Mean11.9111.70
Std6.266.27
BPI Pain scoreNMiss1811
Mean5.165.10
Std2.062.08
BPI Interference scoreNMiss10
Mean5.315.34
Std2.472.53

Figure 3.1 presents the full distribution of a continuous variable (BPI Pain score) for the real and simulated data by visit.

Figure 3.1: Histograms of BPI Pain Scores by Visit for Actual and Simulated REFLECTIONS Data


Figures 3.2 and 3.3 present the correlation matrices for the actual and simulated data sets. The correlation patterns are well preserved in the simulated data though the strength of the associations is slightly less. Again, the Iman-Conover method approximates the desired rank correlations.

Figure 3.2: Rank-correlation Matrix for Actual REFLECTIONS Data


Figure 3.3: Rank-correlation Matrix for Simulated REFLECTIONS Data


In addition to the visit-wise simulated REFLECTIONS data described previously (used for Chapters 11 and 12), we created a one observation per patient version of the data set with variables as shown in Table 3.5. This is referred to as the REFL data set and is used in Chapters 4–6 and 8–10.

Table 3.5: REFL Data Set Variables

Variable NameVariable Label
SubjIDSubject Number
CohortCohort
GenderGender
AgeAge in years
BMI_BBMI at Baseline
RaceRace
InsuranceInsurance
DrSpecialtyDoctor Specialty
ExerciseExercise
InptHospInpatient hospitalization in last 12 months
MissWorkOthOther missed paid work to help your care in last 12 months
UnPdCaregiverHave you used an unpaid caregiver in last 12 months
PdCaregiverHave you hired a caregiver in last 12 months
DisabilityHave you received disability income in last 12 months
SymDurDuration (in years) of symptoms
DxDurTime (in years) since initial Dx
TrtDurTime (in years) since initial Trtmnt
SatisfCare_BSatisfaction with Overall Fibro Treatment over past month
BPIPain_BBPI Pain score at Baseline
BPIInterf_BBPI Interference score at Baseline
PHQ8_BPHQ8 total score at Baseline
PhysicalSymp_BPHQ 15 total score at Baseline
FIQ_BFIQ Total Score at Baseline
GAD7_BGAD7 total score at Baseline
MFIpf_BMFI Physical Fatigue at Baseline
MFImf_BMFI Mental Fatigue at Baseline
CPFQ_BCPFQ Total Score at Baseline
ISIX_BISIX total score at Baseline
SDS_BSDS total score at Baseline
BPIPain_LOCFBPI Pain score LOCF
BPIInterf_LOCFBPI Interference score LOCF

3.5.2 Simulated PCI Data

The objective in simulating a new PCI data set from the observational data was primarily to produce a larger data set allowing us to more effectively illustrate the unsupervised, nonparametric Local Control alternative to conventional propensity score stratification (Chapter 7) and machine learning methods (Chapter 15). Starting from the observational data on 996 patients who received their initial PCI at Ohio Heart Health, Lindner Center, Christ Hospital, Cincinnati (Kereiakes et al, 2000), we generated this much larger data set via plasmode simulation. The simulated data set contains 11 variables on 15,487 patients with no missing values and is referred to as the PCI15K simulated data set. The key variables in the data set are described in Table 3.6. The treatment cohort for later analyses is represented by the variable THIN and the outcomes by SURV6MO (binary) and CARDCOST (continuous). As details of a process for generating simulated data was described for the REFLECTIONS example, only a brief summary and listing of the final simulated dataset variables are provided for the PCK15K dataset.

Table 3.6: PCI Simulated Data Set Variables

Variable NameVariable Label
patidPatient ID number: 1 to 15487
surv6moBinary PCI Survival variable: 1 => survival for at least six months following PCI, 0 => survival for less than six months
cardcostCardiac related costs incurred within six months of patient’s initial PCI; numerical values in 1998 dollars; costs were truncated by death for the 404 patients with surv6mo = 0
thinNumeric treatment selection indicator: thin = 0 implies usual PCI care alone; thin = 1 implies usual PCI care augmented by either planned or rescue treatment with the new blood thinning agent
stentCoronary stent deployment; numeric, with 1 meaning YES and 0 meaning NO
heightHeight in centimeters; numeric integer from 133 to 198
femaleFemale gender; numeric, with 1 meaning YES and 0 meaning NO
diabeticDiabetes mellitus diagnosis; numeric, with 1 meaning YES and 0 meaning NO
acutemiAcute myocardial infarction within the previous 7 days; numeric, with 1 meaning YES and 0 meaning NO
ejfractLeft ejection fraction; numeric value from 17 percent to 77 percent
ves1procNumber of vessels involved in the patient’s initial PCI procedure; numeric integer from 0 to 5

Tables 3.7 and 3.8 summarize the outcome data from the original data and the simulated Lindner data. Data are similar with slightly narrower group differences in the simulated data. In Chapters 7, 14, and 15, the PCI simulated data set is used for analysis and is named PCI15K.

Table 3.7: Lindner STUDY (Kereiakes et al. 2000)

PatientsNumber Surviving Six MonthsPercent Surviving Six MonthsAverage Cardiac Related Cost
Trtm = 029828394.97%$14,614
Trtm = 169868798.42%$16,127

Table 3.8: PCI Blood Thinner Simulation

PatientsNumber Surviving Six MonthsPercent Surviving Six MonthsAverage Cardiac Related Cost
Thin = 08476815896.25%$15,343
Thin = 17011692598.77%$15,643

3.6 Summary

In this chapter, two observational studies were introduced: the REFLECTIONS one-year study of patients with fibromyalgia and the Lindner study of patients undergoing PCI. The concept of plasmode simulations, where one builds a simulated data set that retains the same variables and correlation structure as the original data, was introduced and applied to the REFLECTIONS and Lindner data sets. SAS IML code for the application to the REFLECTIONS data was provided and was demonstrated to retain the similarities of the original data. These two data sets (simulated REFLECTIONS and PCI15K) are used throughout the remainder of the book to demonstrate the various methods for real world data analyses demonstrated in each chapter.

References

Austin P (2008). Goodness-of-fit Diagnostics for the Propensity Score Model When Estimating Treatment Effects Using Covariate Adjustment With the Propensity Score. Pharmacoepi & Drug Safety 17: 1202-1217.

Conover WG and Iman RL (1976). Rank Transformations in Discriminant Analysis.

Franklin JM, Schneeweis S, Polinski JM, Rassen J (2014). Plasmode simulation for the evaluation of pharacoepidemiologic methods in complex healthcare databases. Comput Stat Data Anal 72:219-226.

Gadbury GL, Xiang Q, Yang L, Barnes S, Page GP, Allison DB (2008). Evaluating Statistical Methods Using Plasmode Data Sets in the Age of Massive Public Databases: An Illustration Using False Discovery Rates. PLoS Genet 4(6): e1000098.

Kereiakes DJ, Obenchain RL, Barber BL, Smith A, McDonald M, Broderick TM, Runyon JP, Shimshak TM, Schneider JF, Hattemer CH, Roth EM, Whang DD, Cocks DL, Abbottsmith CW (2000). Abciximab provides cost effective survival advantage in high volume interventional practice. American Heart J 140: 603-610.

Peng X, Robinson RL, Mease P, Kroenke K, Williams DA, Chen Y, Faries D, Wohlreich M, McCarberg B, Hann D (2015). Long-Term Evaluation of Opioid Treatment in Fibromyalgia. Clin J Pain 31: 7-13.

Robinson RL, Kroenke K, Mease P, Williams DA, Chen Y, D’Souza D, Wohlreich M, McCarberg B (2012). Burden of Illness and Treatment Patterns for Patients with Fibromyalgia. Pain Medicine 13:1366-1376.

Wicklin R (2013). Simulating Data with SAS®. Cary, NC: SAS Institute Inc.

Real World Health Care Data Analysis

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