Longitudinal Methods for Pharmaceutical Policy Evaluation

Common Analytic Approaches

Michael Law

The Centre for Health Services and Policy Research The University of British Columbia Vancouver, Canada

Objectives Discuss two longitudinal methods

– Interrupted Time Series – Survival analysis

For each, I will briefly cover

– The data required – Modeling techniques and software

The key messages

• • • If you plan in advance, you can collect the right data There are multiple data sources that work – includes sales data, insurance claims data, hospital data and sample-based data Statistical methods are more sophisticated, but not impossible

90% 80% 70% 60% 50% 40% 30% 20% 10% 0% 2010 Higher Drug Co-payment 2011

90% 80% 70% 60% 50% 40% 30% 20% 10% 0% Higher Drug Co-payment

90% 80% 70% 60% 50% 40% 30% 20% 10% 0% Higher Drug Co-payment 2010 Region 1 Region 2 2011

90% 80% 70% 60% 50% 40% 30% 20% 10% 0% Higher Drug Co-payment 2010 Region 1 Region 2 2011

Interrupted Time Series Basic Design

– Compare longitudinal trends before and after the policy change – Good for sharply-defined interventions

Major Assumption

– The trend in the outcome among those exposed to the policy would have been the same absent the policy

Intervention Slope Change Level Change

Observed

Pre-intervention Post-intervention Time Adapted from Schneeweiss et al, Health Policy 2001

Source: Tamblyn et al. JAMA 2001;285:421-429.

ITS with Control Series

• • Estimate of counterfactual becomes the observation of what happened in the control group • Control group adds further legitimacy by limiting effect of possible history threats Can be an unaffected group, another jurisdiction, etc.

60% 50% 40% 30% 20% 10% 0% 20 01 , 2 20 01 , 3 20 01 , 4 20 02 , 1 20 02 , 2 20 02 , 3 20 02 , 4 20 03 , 1 20 03 , 2 20 03 , 3 20 03 , 4 20 04 , 1 20 04 , 2 20 04 , 3 20 04 , 4 20 05 , 1 20 05 , 2 20 05 , 3 20 05 , 4

Quarter

West Virginia Control States (n=38) Source: Law et al. Psychiatric Services. 2008

Strengths of Time Series

• • • Easy to show results graphically More robust to secular trends Less difficult to estimate and communicate than other methods – E.g. propensity score matching, instrumental variables estimates • Null results are more convincing

Problems with Time Series

• • • • Requires reasonably stable data Can be biased by co-interventions Need longer-term data Linear trend might not be realistic

Data setup for ITS

• Need: Time, population-level outcomes 7 8 4 5 6 2 3

Observation

1 7 8 4 5 6 2 3

Time

1

Outcome

45.3

54.2

47.5

56.3

52.3

48.6

50.2

46.2

1 1 0 1 1 0 0

Post

0 3 4 0 1 2 0 0

Post_Time

0

Statistical Modeling

• Statistical Model: segmented regression Outcome t =β 1 +β 1 time t + β 1 policy t +β 1 time t  policy t +ε • Should Account for autocorrelation – The tendency for subsequent values to be related

Individual-level ITS

• You can use data at the individual level – Means collecting each outcome for each person at each time • Requires using more sophisticated mixed model (e.g. logistic or poisson type GEE) • Provides more power, requires more statistical skill

Survival Analysis

• • • Method of studying longitudinal data on the occurrence of events Also known as “time to event” studies For example: – time until discontinuation – time until drug dispensing

When to use SA

• • • Time to event outcomes Data at the patient-level Time to anything (death, expenditure threshold, etc.)

Who to compare to?

• Two basic options: – Pre-post analysis of the same population • For example, people who initiate a particular class of medication – Concurrent analysis of those subject to and not subject to a policy • For example, individuals in another jurisdiction • Be wary of potential biases

Data setup

• You need the following variables to perform a survival analysis: – Censoring: 0 if event did not occur, 1 if the event did occur – Time to event: the number of time periods (e.g. days) before the event or censoring took place – Any control variables

2 1 4 3 0 X 1 2 3 2

Person

4 1 3 4 5 4

Survival Time

3 6 5 0 1

Event

1 1 6 X 7 X 8 O

Kaplan-Meier Analysis

• • • Non-parametric estimate of survival function Commonly used as descriptive statistic and for figures in manuscripts Requires categorical variables for including other variables

Cox Proportional Hazards

• • Method for fitting a survival model Compares hazard rates (the instantaneous probability of failure) between different groups • Assumes hazard functions are proportional to one another

Key Points

• Longitudinal designs make your study – More convincing – More publishable – You can do this based on your data • However, you need to plan for data collection from the start to ensure you get the necessary data

Thank you

Questions?

Michael R. Law

[email protected]

@Michael_R_Law

•

Further Reading

Interrupted Time Series – A.K. Wagner et al., “Segmented regression analysis of interrupted time series studies in medication use research,”

Journal of Clinical Pharmacy and Therapeutics

27, no. 4 (2002): 299-309.

• Survival Analysis – Paul Alison.

Survival Analysis Using SAS: A Practical Guide

. 2010. Cary, NC: The SAS Institute.

– John Fox. Introduction to Survival Analysis. http://socserv.mcmaster.ca/jfox/Courses/soc761/survi val-analysis.pdf

Time Series Example Code R

library (nlme) itsmodel <- gls(model=outcome ~ trend + post + post_time, data=timeseries, correlation=corARMA(p=1, form=~trend), method=“ML”)

SAS

proc autoreg data=timeseries; model outcome = time post post_time / method=ml nlag=(1 2 3) backstep; run;

Example code: Kaplan-Meier R

fit<-survfit(formula = Surv(time, censor)~variable, data = survivaldata) plot(fit, xlab="Time", ylab="Survival Probability", col = c("blue","red"))

SAS

Proc lifetest data=survivaldata plots=(s); time time*censor; run; strata variable;

Example code: Cox P-H R

library(survival) survmodel <- coxph(Surv(time,censor) ~ variable, data=survivaldata) summary(survmodel)

SAS

Proc phreg data=survivaldata; model time*censor(0) = variable run; /rl ties=exact;