Interrupted Time Series

30
Interrupted Time Series: What, Why and How Karen Smith

description

Presentation by Karen Smith

Transcript of Interrupted Time Series

Page 1: Interrupted Time Series

Interrupted Time Series:What, Why and How

Karen Smith

Page 2: Interrupted Time Series

Acknowledgement

• Motivated by consultancy work with the Centre for Suicide Research

• All analyses and graphs produced by Helen Bergen, Centre for Suicide Research

Page 3: Interrupted Time Series

• Motivating example• What is Interrupted Time Series?• Why use it?• Design issues• Analysis issues• Guidelines on use

Page 4: Interrupted Time Series

Motivating Example

• Between 1997 and 1999 the analgesic co-proxamol was the single drug used most frequently for suicide by self-poisoning in England and Wales, with 766 over the 3 year period

• There is a relatively narrow margin between therapeutic and potentially lethal levels

• Death occurs largely because of the toxic effects of dextropropoxyphene on respiration and cardiac conduction

• MHRA conducted a review of the efficacy/safety profile

• Committee on Safety of Medicines advised withdrawal from use in the UK, the final date being 31 December 2007

• Patients who find it difficult to move to an alternative drug can still be prescribed co-proxamol

Page 5: Interrupted Time Series

The Problem

• How to evaluate the impact of the announcement to withdraw co-proxamol on– Prescribing of analgesics– Mortality involving co-proxamol– Mortality involving other analgesics (substitution

of method is of concern)

Page 6: Interrupted Time Series

Available Data

• Quarterly data on prescriptions of co-proxamol, cocodamol, codeine, codydramol, dihydrocodeine, NSAIDs, paracetamol and tramadol (from Prescription Statistics department of the Information Centre for Health and Social Care, England, and Prescribing Service Unit, Health Solutions Wales)

• Quarterly data on drug poisoning deaths (suicides, open verdicts and accidental poisonings) involving co-proxamol alone, cocodamol, codeine, codydramol, dihydrocodeine, NSAIDs, paracetamol and tramadol, based on death registrations in England and Wales (from ONS) – single drug, with and without alcohol

• Quarterly data for overall drug poisoning deaths and for all deaths receiving suicide and undetermined verdicts

Page 7: Interrupted Time Series

Simple Analysis

• Compare the proportion of deaths involving co-proxamol prior to the legislation with proportion following legislation

• Compare total number of poisoning deaths before and after legislation

• Time series plots of prescriptions and deaths• Co-proxamol withdrawal has reduced suicide from

drugs in Scotland, E. A. Sandilands & D. N. Bateman, British Journal of Clinical Pharmacology, 2008.

Page 8: Interrupted Time Series

What’s Wrong With This?

• Ignores any trends, both before and after change in legislation (or intervention in a more general setting)

• Ignores any possible cyclical effects• Doesn’t pick up on any discontinuity• Variances around the means before and after the

intervention may be different• Effects may drift back toward the pre-

intervention level and/or slope over time if the effect wears off

• Effects may be immediate or delayed• Doesn’t take account of any possible

autocorrelation

Page 9: Interrupted Time Series

A Solution – Interrupted Time Series

• A special kind of time series in which we know the specific point in the series at which an intervention occurred

• Causal hypothesis is that observations after treatment will have a different level or slope from those before intervention – the interruption

• Strong quasi-experimental alternative to randomised design if this is not feasible

Page 10: Interrupted Time Series

Ramsay et al, 2003

Page 11: Interrupted Time Series

The ModelUse segmented regression analysis (Wagner et al, 2002):

Ŷt = β0 + β1 x timet + β2 x interventiont + β3 x time_after_interventiont + et

Yt is the outcome time indicates the number of quarters from the start of the

series intervention is a dummy variable taking the values 0 in the pre-

intervention segment and 1 in the post-intervention segment

time_after_intervention is 0 in the pre-intervention segment and counts the quarters in the post-intervention segment at time t

β0 estimates the base level of the outcome at the beginning of the series

β1 estimates the base trend, i.e. the change in outcome per quarter in the pre-intervention segment

β2 estimates the change in level in the post-intervention segment

β3 estimates the change in trend in the post-intervention segment

et estimates the error

Page 12: Interrupted Time Series

Threats to Validity

• Forces other than the intervention under investigation influenced the dependent variable– Could add a no-treatment time series from a control

group– Use qualitative or quantitative means to examine

plausible effect-causing events• Instrumentation – how was data

collected/recorded• Selection – did the composition of the

experimental group change at the time of intervention?

• Poorly specified intervention point; diffusion• Choice of outcome – usually have only routinely

collected data• Power, violated test assumptions, unreliability of

measurements, reactivity etc.

Page 13: Interrupted Time Series

Design Considerations

• Add a non-equivalent no-treatment control group

• Add non-equivalent dependent variables– Intervention should not affect but would respond

in the same way as primary variable to validity threat

• Remove intervention at a known time• Add multiple replications• Add switching replications

Page 14: Interrupted Time Series

Problems

• Interventions implemented slowly and diffuse• Effects may occur with unpredictable time delays• Many data series much shorter than the 100

observations recommended for analysis• Difficult to locate or retrieve data• Time intervals between each data point in archive

may be longer than needed• Missing data• Undocumented definitional shifts

Page 15: Interrupted Time Series

Applied to the Co-Proxamol Data

• 28 quarters in the pre-intervention period and 12 in post-intervention

• Examined a number of common analgesics– Prescriptions– Deaths

• Examined overall suicides• Some evidence of autocorrelation in the

data, hence Cochrane-Orcutt autoregression used (Durbin Watson statistic of final models close to 2)

Page 16: Interrupted Time Series

Prescriptions* for analgesics dispensed in England and Wales, 1998-2007

0

1000

2000

3000

4000

5000

6000

1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4

Year (quarters)

Pre

sc

rip

tio

n it

em

s d

isp

en

se

d p

er

qu

art

er

(th

ou

sa

nd

s)

co-proxamol

NSAIDs

paracetamol

co-codamol

tramadol

co-dydramol

codeine

dihydrocodeine

Co-proxamol w ithdrawal announced

1998 20001999 200620052004200320022001 2007

* excluding liquids, suppositories, granules, powders and effervescent preparations

Page 17: Interrupted Time Series

Mortality in England and Wales from analgesic poisoning (suicide and open verdicts), 1998-2007, for persons aged 10 years and over (substances taken alone, +/- alcohol)

0

10

20

30

40

50

60

70

80

90

1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4

Year (quarters)

Nu

mb

er

of

de

ath

s

co-proxamol

otheranalgesics

co-proxamolbest fit withoutannouncement

co-proxamolbest fit withannouncement

Co-proxamol withdrawal announced

1998 20001999 20052004200320022001 2006 2007

Page 18: Interrupted Time Series

Prescriptions

Pre-intervention Post-intervention

Base level, β0 (SE)

p Base trend,β1 (SE)

p Change inlevel, β2 (SE)

p Change intrend, β3

(SE)

p

Co-proxamol 3050.1 (139.9) <0.001 -45.9 (7.7) <0.001 -554.8 (74.9) <0.001 -46.8 (16.8) 0.01

Cocodamol 1349 (12.4) <0.001 34.1 (0.8) <0.001 300.5 (53.6) <0.001 30.7 (6.4) <0.001

Codeine 204.8 (4.2) 0.007 9.6 (0.2) <0.001 20.5 (11.6) 0.089 3.5 (1.2) 0.007

Codrydamol 1055.2 (6.1) <0.001 -1.1 (0.3) 0.004 148.7 (35.8) <0.001 -4.2 (4.1) 0.316

Dihydrocodeine 686.4 (31.1) <0.001 -1.5 (1.4) 0.291 -29.7 (2.4) <0.001 -0.8 (2.4) 0.731

NSAIDs 4652.4 (47.2) <0.001 28.4 (3) <0.001 -622.9 (70.2) <0.001 -66.2 (8.5) <0.001

Paracetamol 1493.4 (56.1) <0.001 42.1 (3.0) <0.001 232 (66.6) 0.001 23 (8.2) 0.01

Tramadol 47.2 (51.4) 0.365 31.4 (2.7) <0.001 -41.9 (6.8) <0.001 16.3 (5.2) 0.004

Page 19: Interrupted Time Series

Suicides

Pre-intervention Post-intervention

Base level, β0 (SE)

p Base trend,β1 (SE)

p Change inlevel, β2 (SE)

p Change intrend, β3 (SE)

p

Co-proxamol 81.0 (4.5) <0.001 -1.194 (0.3) <0.001 -28.3 (4.9) <0.001 0.6 (0.6) 0.355

Other analgesics

51.3 (2.8) <0.001 -0.3 (0.2) 0.095 6.4 (6.0) 0.297 -0.3 (0.6) 0.724

All drugs except co-proxamol and other analgesics

221.2 (7.3) <0.001 -0.5 (0.5) 0.299 21.6 (12.8) 0.100 -5.4 (1.4) <0.001

All drugs 353.7 (10.2) <0.001 -2.0 (0.7) 0.008 0.004 (18.1) 1.000 -4.9 (1.7) 0.007

All causes 1319.0 (22.5) <0.001 -4.8 (1.4) 0.002 12.8 (34.8) 0.716 -5.4 (4.1) 0.192

Page 20: Interrupted Time Series

Estimating Absolute Effect

• The model may be used to estimate the absolute effect of the intervention. This is the difference between the estimated outcome at a certain time after the intervention and the outcome at that time if the intervention not taken place.

• For example, to estimate the effect of the intervention at the midpoint of the post-intervention period (when time = 34.5 and time_after_intervention = 6.5), we have

Ŷ34.5 = β0 + β1 x 34.5 without intervention

Ŷ34.5 = β0 + β1 x 34.5 + β2 + β3 x 6.5 with intervention

• Thus, the absolute effect of the intervention isβ2 + β3 x 6.5

• Standard errors calculated using method of Zhang et al σ2

2 + 6.52 x σ32 + 2 x 6.5 x σ23

• Non-significant terms included due to correlation between slope and level terms

Page 21: Interrupted Time Series

Results

Estimates of absolute effect during 2005 to 2007

Mean quarterly estimated number pre announcement

Mean quarterly number post announcement

Mean quarterly change (95% CI)

Prescriptions (x1000)

Co-proxamol 1465.1 605.7 -859 (-1065 to -653)

Cocodamol 2524.7 3024.6 500 (459 to 540)

Codeine 534.6 578 43 (31 to 55)

Codrydamol 1018.2 1140.0 122 (99 to 145)

Dihydrocodeine 634.6 600.0 -35 (-68 to -2)

NSAIDs 5633.8 4581.0 -1053 (-1186 to -920)

Paracetamol 2947.0 3330.0 382 (268 to 497)

Tramadol 1130.1 1193.9 64 (-5 to 133)

Suicides, Open

Co-proxamol 39 15 -24 (-37 to -12)

Other analgesics 39 44 5 (-5 to 15)

All drugs except co-proxamol and other analgesics

204 191 -13 (-34 to 8)

All drugs 283 252 -31 (-66 to 3)

All causes 1152 1130 -22 (-89 to 45)

Page 22: Interrupted Time Series

Prescriptions

• Prescription data for England and Wales showed a steep reduction in prescribing of co-proxamol in the first two quarters of 2005, with further reductions thereafter.

• Regression analyses indicated a significant decrease in both level and slope in prescribing of co-proxamol - the number of prescriptions decreased by an average of 859 (95% confidence interval (CI) = 653 to 1065) thousand per quarter in the post-intervention period.

• This equates to an overall decrease of approximately 59% in the three year post-intervention period, 2005 to 2007.

• There were also significant decreases in prescribing of NSAIDS of an average of 1053 (95% CI = 920 to 1186) thousand per quarter, equating to an approximate 19% decrease overall for 2005 to 2007; and for dihydrocodeine of an average of 35 (95% CI = 2 to 68) thousand per quarter, or approximately 6% overall for 2005 to 2007.

• Prescriptions for the other analgesics increased significantly in the post-intervention period, apart from tramadol. Based on mean quarterly estimates this equated to percentage increases over the 2005 to 2007 period of approximately 20% for cocodamol, 13% for paracetamol, 12% for codydramol, and 8% for codeine.

Page 23: Interrupted Time Series

Deaths• Marked reduction in suicide and open verdicts involving co-proxamol in the

first quarter of 2005, which persisted until the end of 2007. • Prior to 2005 deaths due to co-proxamol alone were 19.5% (95% CI = 16.9

to 22.2) of all drug poisoning suicides, whereas between 2005 and 2007 they constituted just 6.4% (95% CI = 5.2 to 7.5).

• Regression analyses indicated a significant decrease in both level and slope for deaths involving co-proxamol which received a suicide or open verdict - decreased by on average 24 (95% CI = 12 to 37) per quarter in the post-intervention period.

• This equates to an estimated overall decrease of 295 (95% CI = 251 to 338) deaths, approximately 62%, in the three year post-intervention period 2005 to 2007.

• When accidental poisoning deaths involving co-proxamol were included, there was a mean quarterly decrease of 29 (95% CI = 17 to 42) deaths, equating to an overall decrease of 349 (95% CI = 306 to 392) deaths, approximately 61%, in the three year post-intervention period 2005 to 2007.

• There were no statistically significant changes in level or slope in the post-intervention period for deaths involving other analgesics (cocodamol, codeine, codydramol, dihydrocodeine, NSAIDs, paracetamol and tramadol) which received a suicide or open verdict (both including and excluding accidental deaths).

• There was a substantial though not statistically significant reduction during the post-intervention period in deaths (suicide and open verdicts) involving all drugs (including co-proxamol and other analgesics), with the mean quarterly change between 2005 and 2007 being -31 (95% CI = -66 to 3) deaths.

• The overall suicide rate (including open verdicts) during this period also decreased, though to a lesser extent, and the mean quarterly change of -22 (95% CI = -89 to 45) deaths was not statistically significant.

Page 24: Interrupted Time Series

Interpretation• Following the announcement of the withdrawal of co-proxamol in

January 2005 there was an immediate large reduction in prescriptions. This was associated with a 62% reduction in suicide deaths (including open verdicts), or an estimated 295 fewer deaths.

• Inclusion of accidental deaths, some of which were likely to have been suicides increased the estimated reduction in number of deaths to approximately 349 over 3 years.

• Possible substitution of method must be considered in estimating the effect of changing availability of a specific method of suicide.

• Withdrawal of co-proxamol was associated with changes in prescribing of other analgesics.

• Significant increases in prescribing of co-codamol, paracetamol, and codydramol occurred during 2005-2007.

• Analyses of suicide and open verdict deaths involving other analgesics combined indicated little evidence of substitution.

• An abrupt reduction in prescribing of NSAIDs occurred shortly before the announcement of the withdrawal of co-proxamol due to concerns about Cox 2 inhibitors. However, NSAIDs are rarely a direct acute cause of death, especially by suicide.

• Overall suicide and open verdict deaths decreased in England and Wales during 2005 to 2007. Thus underlying downward trends in suicide cannot explain the full extent of the decrease in co-proxamol related deaths following the MHRA announcement to withdraw co-proxamol.

Page 25: Interrupted Time Series

Limitations

• Interrupted time series autoregression controls for baseline level and trend when estimating expected changes in the number of prescriptions (or deaths) due to the intervention.

• The estimates of the overall effect on prescriptions and mortality involved extrapolation, which is inevitably associated with uncertainty.

• The regression method assumes linear trends over time, and the co-proxamol prescribing data, in particular, had a poor fit, resulting in large standard errors in the post-intervention period.

• Estimates of the standard errors for absolute mean quarterly changes in number of prescriptions or deaths were determined exactly, including the covariance of level and slope terms.

• Estimates of percentage changes over the three year post-estimation period are point estimates and were not determined with standard error calculations.

Page 26: Interrupted Time Series

Guidelines on Use

• Ramsay et al, 2003– Quality criteria

• Intervention occurred independently of other changes over time

• Intervention was unlikely to affect data collection• The primary outcome was assessed blindly or was

measured objectively• The primary outcome was reliable or was measured

objectively• The composition of the data set at each time point

covered at least 80% of the total number of participants in the study

• The shape of the intervention effect was prespecified• A rationale for the number and spacing of data points

was described• The study was analyzed appropriately using time series

techniques

Page 27: Interrupted Time Series

Findings of the Systematic Reviews

• Mass media review of 20 studies, Guideline dissemination and implementation review of 38 studies– Most studies had short time series

• Standard errors increased• Reduced power• Type I error increased• Failure to detect autocorrelation or secular trends

– Over 65% analysed inappropriately• Of the 37 re-analysed, 8 had significant pre-

intervention trends– Most were underpowered

• Rule of thumb: with 10 pre- and 10 post-intervention time points the study would have at least 80% power to detect a change in level of five standard deviations of the pre-data if the autocorrelation >0.4

• Long pre-intervention phase increases power to detect secular trends

Page 28: Interrupted Time Series

References

• Shadish, Cook and Campbell, 2002, Experimental and quasi-experimental designs for generalised causal inference, Houghton Mifflin.

• Ramsay CR, Matowe L, Grilli R, Grimshaw JM, Thomas RE. Interrupted time series designs in health technology assessment: Lessons from two systematic reviews of behavior change strategies. Int.J.Technol.Assess.Health Care 2003;19:613-23

• Wagner AK, Soumerai SB, Zhang F, Ross-Degnan D. Segmented regression analysis of interrupted time series studies in medication use research. J.Clin.Pharm.Ther. 2002;27:299-309

• Zhang, F, Wagner, A, Soumerai, S. B., and Ross-Degnan, D. Estimating confidence intervals around relative changes in outcomes in segmented regression analyses of time series data. 15th Annual NESUG (NorthEast SAS Users Group Inc) Conference Last update 2002. http://www.nesug.info/Proceedings/nesug02/st/st005.pdf. Accessed 22 October 2008.

Page 29: Interrupted Time Series

Examples of Use

• Matowe, L, Ramsay, C. R., Grimshaw, J. M., Gilbert F. J., MacLeod, M.-J. and Needham, G. Effects of mailed dissemination of the Royal College of Radiologists’ Guidelines on general practitioner referrals for radiography: a time series analysis. Clinical Radiology 2002, 57, 575-578

• Neustrom, M. W. and Norton, W. M. The impact of drunk driving legislation in Louisiana. Journal of Safety Research, 1993, 24, 107-121

• Ansari, F, Gray, K, Nathwani, D, Phillips, G, Ogston, S, Ramsay, C and Davey, P. Outcomes of an intervention to improve hospital antibiotic prescribing: interrupted time series with segmented regression analysis. Journal of Antimicrobial Chemotherapy, 2003, 52, 842-848

• Morgan, O. W., Griffiths, C and Majeed, A. Interrupted time-series analysis of regulations to reduce paracetamol (acetaminophen) poisoning. PLoS Medicine, 2007, 4, 0654-0659

Page 30: Interrupted Time Series

• K. Hawton, H. Bergen, S. Simkin, A. Brock, C. Griffiths, E. Romeri, K. L. Smith, N. Kapur, D. Gunnell (2009). Effect of withdrawal of co-proxamol on prescribing and deaths from drug poisoning in England and Wales: time series analysis. BMJ, 338:b2270