Evaluating Interventions on Drug Utilization: Analysis Methods

Transcription

1 Evaluating Interventions on Drug Utilization: Analysis Methods Nicole Pratt Quality Use of Medicines and Pharmacy Research Centre University of South Australia For: Colin Dormuth, ScD Associate Professor, Department of Anesthesiology, Pharmacology and Therapeutics, Faculty of Medicine, University of British Columbia, Canada

2 Nothing to declare Disclosures

3 Drug Policy Evaluation Methods available to evaluate drug policy changes and other interventions affecting drug utilization: Suboptimal study designs Uncontrolled time series Controlled time series Randomized trials Worse Better

4 Observational Design: Simple pre-post comparisons Quantity Prescriptions for omeprazole in British Columbia Intervention Preferential listing policy for rabeprazole 302, , / /04 Assumptions for causal inference: Time 1. The pre experience represents the post experience had there been no intervention

5 Observational Design: Simple pre-post comparisons Quantity Intervention Preferential listing policy for rabeprazole 302,000 Prescriptions for omeprazole in British Columbia?? 161, / /04 Assumptions for causal inference: Time 1. The pre experience represents the post experience had there been no intervention

6 Observational Design: Simple pre-post comparisons Quantity Prescriptions for omeprazole in British Columbia 274, , ,000 Intervention Preferential listing policy for rabeprazole 161, ,000 Threat to causal inference: 2002/ /04 Single pre-post estimates are averages of an underlying trend independent of the Intervention Time

7 Observational Design: Simple pre-post comparisons Quantity Prescriptions for omeprazole in British Columbia Intervention Preferential listing policy for rabeprazole 302, ,000 Threat to causal inference: 2002/ /04 Single pre-post estimates are averages of an underlying trend independent of the Intervention Time

8 Time Series Analysis and Segmented Regression

9 Quantity Prescriptions for omeprazole in British Columbia Intervention Preferential listing policy for rabeprazole 302, , / /04 Time

10 Monthly Prescriptions for Omeprazole in British Columbia, Canada 2002/04 to 2004/

11 Monthly Prescriptions for Omeprazole in British Columbia, Canada 2002/04 to 2004/ Best Fitting Line Across All 24 Data Points

12 Summarising trends over time Regression: estimate 2 parameters of interest 1. Level: value of the series at the beginning of a given time interval (intercept) 2. Trend: rate of change of the measure over time (slope) Y = intercept + β *Time Interpretation: 1. Intercept: mean value of Y at time 0 2. Slope (β) : change in they for every 1 month increment in time

13 Intervention *SAS CODE; PROC AUTOREG DATA=UTILIZATION_DATA; MODEL QUANTITY=MONTH; RUN; TIME (t) QUANTITY (y) D1 D

14 Monthly Prescriptions for Omeprazole in British Columbia, Canada 2002/04 to 2004/ Best Fitting Line Across All 24 Data Points

15 Monthly Prescriptions for Omeprazole in British Columbia, Canada 2002/04 to 2004/

16 Monthly Prescriptions for Omeprazole in British Columbia, Canada 2002/04 to 2004/ Assumed Counterfactual

17 Monthly Prescriptions for Omeprazole in British Columbia, Canada 2002/04 to 2004/ Assumed Counterfactual Significant?

18 Segmented Regression Segmented regression is a method in regression analysis in which the independent variable (time) is partitioned into intervals and a separate line segment is fit to each interval Allows us to assess, in statistical terms, how much and intervention changed an outcome both immediately and over time

19 Segmented Regression Two parameters for each segment of time Level: value of the series at the beginning of a given time interval (intercept) Trend: rate of change of the series over time (slope) In segmented regression each segment is allowed to have its own level and trend Formally assess whether there is a change in level after the intervention and/or a change in trend after the intervention Wagner AK et al Segmented regression analysis of interrupted time series studies in medication research. JClinPharmTherap 2002

20 Segmented regression Statistical model for estimating intervention effects in time series trends Evaluate changes over time in medicine utilisation due to interventions, changes in policy, introduction of a new medicine Fit a least squares regression line to each segment, ie before an intervention and after an intervention Compare the parameter estimates of intercept and slope before the intervention with those after the intervention and assess if the pattern has changed

21 At t0: a + b*t0 + intervention = c + d*t0 c = a + (b-d) * t0 + intervention Y2 = a + (b-d)*t0 + intervention + d*time = a + b*t0 + intervention + d*(time t0) Y1=a+b*time Y2=c+d*time Intervention time t=t0 time

22 y = b o + b 1 *t + b 2 *int +b 3 *tafter +e baseline level of the series, mean number of prescriptions per patient per month, at time zero

23 y = b o + b 1 *t + b 2 *int +b 3 *tafter +e change in the mean number of prescriptions per patient that occurs with each month before the intervention

24 y = b o + b 1 *t + b 2 *int +b 3 *tafter +e level change in the mean monthly number of prescriptions per patient immediately after the intervention compared to just before

25 y = b o + b 1 *t + b 2 *int +b 3 *tafter +e change in the trend in the mean monthly number of prescriptions per patient after the cap, compared with the monthly trend before the cap OR Change in the trend after the intervention compared to before Note that the sum of b and d is the post-intervention slope

26 Intervention *SAS CODE; PROC AUTOREG DATA=UTILIZATION_DATA; MODEL QUANTITY=MONTH D1 D2; RUN; TIME (t) QUANTITY (y) D1 D

27 *SAS OUTPUT; Standard Approx Variable Variable DF Estimate Error t Value Pr > t Label Intercept <.0001 Month Month D <.0001 D1 D D

28 Expressing intervention effects Now that we have a regression equation we can compare the estimated post-intervention values of the outcome to the values estimated at that time based on the pre-intervention pattern

29 Monthly Prescriptions for Omeprazole in British Columbia, Canada 2002/04 to 2004/ Assumed Counterfactual

30 Example Consider time t=26 (6 months after the intervention) Y = *time 13655*intervention *timeafterint Calculate 1. Y13 (with policy) 2. Y13 (without policy conubterfactual)

31 Example Consider time t=15 (2 months after the intervention) Y = *time 13655*intervention *timeafterint Calculate 1. Y13 (with policy) = * * *2 = Y13 (without policy conuterfactual) = * * *0 =

32 Multiple interventions Sometimes multiple interventions occur in a series and each impacts on the trend over time

33 Non-steroidal Anti-inflammatory medicine use in Australia Percent

34 Non-steroidal Anti-inflammatory medicine use in Australia Percent Rofecoxib first subsidised Aug Celecoxib first subsidised Aug 2000 Rofecoxib withdrawn Oct 2004

35 Segmented regression Assume that distinct linear relationships exist with-in different time periods Period1: Prior Celecoxib subsidised, Period2:After Celecoxib/Rofecoxib subsidised, Period3: After Rofecoxib Withdrawal

36 y = b o + b 1 *t + b 2 *int1 +b 3 *tafterint1 + b 4 *int2 +b 5 *tafterint2 +e

37 Baseline level : 19.5% % -0.2% Percent 0.5% Rofecoxib first subsidised Aug 2000 Celecoxib first subsidised Aug % Rofecoxib withdrawn Oct %

38 Other explanations In all the examples so far, one takes on faith that the change in use after the intervention was indeed due to the intervention In reality, the researcher must always be on the lookout for other non-policy-related variables that could have produced the observed change. Any such variable is known as a cointervention

39 Cointerventions British Columbia introduced a new drug copayment policy

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41 Cointerventions

42 Percent Baseline level : 19.5% % 42% -0.2% Samples are not captured in the administrative data This period was excluded to avoid overestimating uptake -25% % Celecoxib first subsidised Aug 2000

43 Thank You