1. PV-Trend: A JSL Application for Trending Topics for Pharmacovigilance
Peter Mroz and Geoff Gipson
Janssen Pharmaceutical Companies of Johnson & Johnson
Abstract
PV-Trend Workflow
Sample Output
Responsible for monitoring the safety of hundreds of medicinal products
Proactive monitoring critical for patient safety/regulatory expectations
PV-Trend allows medical reviewers to take control of trending analysis
Create/manage complex safety topics
JMP: user interfaces, analyses, graphs
Oracle: data storage, backend logic
Medical Reviewer uses PV-Trend to:
Define MedDRA categories
Define topics
Define intervals and exposure
Create trending dataset
Create trend graphs
The reporting fraction of Medication Error/Maladministration in MIRACURLOX was in the first PSUR period of investigation and in the latest PSUR period. The periodic interval analysis resulted in 3 prior PSUR periods found to be not statistically significant, 0 prior PSUR periods found to be significantly greater than the current period, and 2 prior PSUR periods found to be significantly less than the current period.
Features
User-friendly interfaces created with Application Builder
Error checking
Outputs saved to MS-Word
Statistical interpretations
Distributed analytics
Metadata stored in Oracle for easy re-use/sharing
Significant time savings
Improved consistency
Previous Trending Workflow
Statistician
Med Reviewer
Statistical interpretation
Topics, time intervals, exposure, search parameters
Med Reviewer
Help Desk
Q/C, create trend charts
Intake, create ticket
Send to programmers
Help Desk
Clarifications, corrections
Q/C output, send to Medical Reviewer
Programmers
The best fit model estimates of the reporting fraction of Medication Error/ Maladministration in MIRACURLOX was in the first interval of investigation and in the latest interval. The reporting fraction movement over time was found to be statistically significant (p < .0001).
Validate, program, Q/C, create output

2. PV-Trend: A JSL Application for Trending Topics for Pharmacovigilance
Peter Mroz and Geoff Gipson
Janssen Pharmaceutical Companies of Johnson & Johnson
Custom MedDRA Categories
Topic Definition
Interval, Exposure Definition
Set of terms defined by MedDRA level (SOC, HLGT, HLT, PT, SMQ)
Error checking
Stored in Oracle
Refine custom MedDRA categories
Where to search (events, medical history, indications)
Additional fatal criteria
Age and age group, region, country
Define intervals and date ranges
Enter exposure for each interval
Exposure Divisor: Per 100, Per 1,000, Per 10,000 etc.
Units: Patients, Capsules, etc.
Sample error checking output
Sample topic for Children

3. PV-Trend: A JSL Application for Trending Topics for Pharmacovigilance
Peter Mroz and Geoff Gipson
Janssen Pharmaceutical Companies of Johnson & Johnson
Create Trending Dataset
Users Specify
Drug(s) of interest
One or more data sources:
Aggregate report(s) (snapshot of cases)
Caselist(s) (snapshot of cases)
Spreadsheet
General database query
Additional selection criteria
Date range
Valid
Medically Confirmed
Associated to drug
Interval Definition
Topic Set

4. PV-Trend: A JSL Application for Trending Topics for Pharmacovigilance
Peter Mroz and Geoff Gipson
Janssen Pharmaceutical Companies of Johnson & Johnson
Trending Dataset
All search parameters are table variables
Topics are represented by separate columns with 0 meaning no match and 1 meaning match

5. PV-Trend: A JSL Application for Trending Topics for Pharmacovigilance
Peter Mroz and Geoff Gipson
Janssen Pharmaceutical Companies of Johnson & Johnson
Trending Analysis
Separate output for each topic
Click on a topic to see output displayed in the various tabs
Tabs for:
Tabulations
Reporting Fraction (graphs)
Reporting Rate (graphs)
Topic Details
General Search Parameters
Methods
Save to MSWord button copies all output for a topic to a Word document

6. PV-Trend: A JSL Application for Trending Topics for Pharmacovigilance
Peter Mroz and Geoff Gipson
Janssen Pharmaceutical Companies of Johnson & Johnson
Trending Analysis graphs
Periodic Interval Analysis: Compare current (latest) period with all other periods
Current-Cumulative Comparison: compare current (latest) period with cumulative value of all previous periods
Regression: for general trend
Reporting Fraction (RF) and Report Rate (RR) shown on separate tabs

7. PV-Trend: A JSL Application for Trending Topics for Pharmacovigilance
Peter Mroz and Geoff Gipson
Janssen Pharmaceutical Companies of Johnson & Johnson
Topic Details
General Search Parameters
Shows what was searched (Events, Indications, Medical History)
Shows the set of MedDRA terms used for this topic
Shows search parameters that apply to all topics
Drugs
Date Range
Data Sources
Valid (Y/N)
Medically Confirmed (Y/N)
Associated to drug (Y/N)
Overall Search Parameters
Parameter Value
Drugs MIRACURLOX
Start Date 01-Jul-2010
End Date Dec-2015
PSUR Period Definition MIRACURLOX PBRER (01-Jan-2015 to 31-Dec-2015)
PSUR Topics MIRACURLOX 5-Year Search
Aggregate Report Descriptions Final, MIRACURLOX PBRER/PSUR Dataset 01Jan15-31Dec15
Import Cases from Database Query Yes
Valid Valid
Med Confirmed Medically Confirmed
Associated Associated

8. PV-Trend: A JSL Application for Trending Topics for Pharmacovigilance
Peter Mroz and Geoff Gipson
Janssen Pharmaceutical Companies of Johnson & Johnson
Methods
Reporting Fraction
RF Periodic Interval Analysis
A periodic interval analysis was performed on reporting fractions for the most recent 6 intervals. A periodic interval analysis compares the confidence interval from the most recent interval with the confidence intervals from each preceding interval. The confidence interval for each reporting fraction is calculated using the Clopper-Pearson method. The confidence intervals are adjusted for multiple testing using the Bonferroni correction using an α = Since there are 5 comparisons of interest in this analysis (current period vs. each prior period), the confidence intervals are calculated using the lower 0.5% bound and upper 99.5% bound of the Beta distribution.
RF Cumulative Prior Interval Analysis
A cumulative prior interval analysis was performed on reporting fractions for the most recent 6 intervals. A cumulative prior interval analysis compares the confidence interval from the most recent interval with the confidence interval for the aggregated data from the preceding intervals. The confidence interval for each reporting fraction is calculated using the Clopper-Pearson method using an α = The confidence intervals are calculated using the lower and upper 95% bounds of the Beta distribution.
RF Regression Analysis
A regression analysis was performed on reporting fractions for the most recent 6 intervals. The regression analysis evaluates whether there is a trend in the reporting fractions over time. For the reporting fraction regression a Generalized Linear Model [family = Binomial, link = logit] model was utilized.
Reporting Rate
RR Periodic Interval Analysis
A periodic interval analysis was performed on reporting rates for the most recent 6 intervals. A periodic interval analysis compares the confidence interval from the most recent interval with the confidence intervals from each preceding interval. The confidence interval for each reporting rate is calculated using the Exact Gamma method. The confidence intervals are adjusted for multiple testing using the Bonferroni correction using an α = Since there are 5 comparisons of interest in this analysis (current period vs. each prior period), the confidence intervals are calculated using the lower 0.5% bound and upper 99.5% bound of the Gamma distribution.
RR Cumulative Prior Interval Analysis
A cumulative prior interval analysis was performed on reporting rates for the most recent 6 intervals. A cumulative prior interval analysis compares the confidence interval from the most recent interval with the confidence interval for the aggregated data from the preceding intervals. The confidence interval for each reporting rate is calculated using the Exact Gamma method using an α = The confidence intervals are calculated using the lower and upper 95% bounds of the Gamma distribution.
RR Regression Analysis
A regression analysis was performed on reporting rates for the most recent 6 intervals. The regression analysis evaluates whether there is a trend in the reporting rates over time. For the reporting rate regression, a Generalized Linear Model [family = Poisson, link = log, offset = log(Exposure)] model was utilized.

9. PV-Trend: A JSL Application for Trending Topics for Pharmacovigilance
Peter Mroz and Geoff Gipson
Janssen Pharmaceutical Companies of Johnson & Johnson
Trending Analysis graphs
Interval Analysis: Statistically significant when upper and lower confidence bounds are non-overlapping
Regression: Statistically significant when a horizontal trend line CANNOT be fit inside confidence bounds
Regression Plot R e p o r t i n g F a c
0.03
0.04
0.05
0.06
0.07 1 2 3 4 5 6 7
Interval
Statistically
significant
Not
statistically
significant
Not
statistically
significant

10. PV-Trend: A JSL Application for Trending Topics for Pharmacovigilance
Peter Mroz and Geoff Gipson
Janssen Pharmaceutical Companies of Johnson & Johnson
Trending Analysis Calculations
Calculate the total number of cases per interval
For each topic, calculate the number of cases per topic per interval
Create a cumulative table with two rows.
Row 1 is for the intervals 1 to (n-1)
Row 2 is for the nth interval
Calculate the Bonferroni Correction. Start with 95% and divide by the greater of (nperiods – 1) or 1.
bonferroni_factor = max(nperiods - 1, 1);
lcl_alpha = .025 / bonferroni_factor;
ucl_alpha = 1 - lcl_alpha;
5. Loop over each topic:
Calculate the reporting fraction (# topic cases per period / total cases per period)
Calculate the reporting fraction lower confidence limit using a beta distribution JSL function:
beta quantile(lcl_alpha, Topic_cases[i],
(Total_Cases[i] - Topic_cases[i] + 1));
c. Calculate the reporting fraction upper confidence limit using a beta distribution JSL function:
beta quantile(ucl_alpha, (topic_cases[i] + 1),
(:Total_Cases[i] - topic_cases[i]))
d. Calculate the reporting rate
topic_cases[i] * Exposure_Divisor / Exposure[i]
e. Calculate the reporting rate lower confidence limit using a gamma distribution JSL function:
Exposure_Divisor *
gamma quantile(lcl_alpha, topic_cases[i], 1 /
Exposure[i])
f. Calculate the reporting rate upper confidence limit using a gamma distribution JSL function:
Exposure_Divisor * gamma quantile(ucl_alpha, (topic_cases[i] + 1), 1 / Exposure[i])
g. Calculate the values for a – f for the cumulative comparison

11. PV-Trend: A JSL Application for Trending Topics for Pharmacovigilance
Peter Mroz and Geoff Gipson
Janssen Pharmaceutical Companies of Johnson & Johnson
Trending Analysis Regression Calculations
h. Calculate a regression line for reporting fraction by interval using JMP’s Fit Model platform. Parameters are:
Y values: Topic Cases, Total Cases
Effects: Interval
Personality: Generalized Linear Model
GLM Distribution: Binomial
Link Function: Logit
i. Save predicted values and mean confidence intervals from the reporting fraction regression to the dataset. This will add the following columns to the dataset:
Pred <topic>
Lower 95% Mean <topic>
Upper 95% Mean <topic>
j. Convert the predicted values and mean confidence intervals to a reporting fraction by dividing by the total number of cases per period.
k. Calculate a regression line for reporting rate by interval using JMP’s Fit Model platform. Parameters are:
Offset: Log(Exposure)
Y value: Topic Cases
Effects: Interval
Personality: Generalized Linear Model
GLM Distribution: Poisson
Link Function: Log
l. Save predicted values and mean confidence intervals from the reporting rate regression to the dataset. This will add the following columns to the dataset:
Pred <topic> 2
Lower 95% Mean <topic> 2
Upper 95% Mean <topic> 2
m. Convert the predicted values and mean confidence intervals to a reporting rate:
Pred <topic> 2[i] * (:Exposure Divisor / :Exposure)
Lower 95% Mean <topic> 2[i] * (:Exposure Divisor / :Exposure)
Upper 95% Mean <topic> 2[i] * (:Exposure Divisor / :Exposure)

12. PV-Trend: A JSL Application for Trending Topics for Pharmacovigilance
Peter Mroz and Geoff Gipson
Janssen Pharmaceutical Companies of Johnson & Johnson
Summary/Conclusions
Innovative tool
Distributed analytics
Metadata stored in Oracle for easy re-use/sharing
Time savings
Improved consistency
Improved analysis leads to improved patient safety
JMP crucial to the success of this system:
User interface design
Graphs
Analysis (tabulations, confidence intervals, regression)
Oracle access