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Efficacy of prescribed injectable diacetylmorphine in the Andalusian trial: Bayesian analysis of responders and non-responders according to a multi domain outcome index
© Perea-Milla et al; licensee BioMed Central Ltd. 2009
Received: 15 April 2009
Accepted: 14 August 2009
Published: 14 August 2009
The objective of this research was to evaluate data from a randomized clinical trial that tested injectable diacetylmorphine (DAM) and oral methadone (MMT) for substitution treatment, using a multi-domain dichotomous index, with a Bayesian approach.
Sixty two long-term, socially-excluded heroin injectors, not benefiting from available treatments were randomized to receive either DAM or MMT for 9 months in Granada, Spain. Completers were 44 and data at the end of the study period was obtained for 50. Participants were determined to be responders or non responders using a multi-domain outcome index accounting for their physical and mental health and psychosocial integration, used in a previous trial. Data was analyzed with Bayesian methods, using information from a similar study conducted in The Netherlands to select a priori distributions. On adding the data from the present study to update the a priori information, the distribution of the difference in response rates were obtained and used to build credibility intervals and relevant probability computations.
In the experimental group (n = 27), the rate of responders to treatment was 70.4% (95% CI 53.2-87.6), and in the control group (n = 23), it was 34.8% (95% CI 15.3-54.3). The probability of success in the experimental group using the a posteriori distributions was higher after a proper sensitivity analysis. Almost the whole distribution of the rates difference (the one for diacetylmorphine minus methadone) was located to the right of the zero, indicating the superiority of the experimental treatment.
The present analysis suggests a clinical superiority of injectable diacetylmorphine compared to oral methadone in the treatment of severely affected heroin injectors not benefiting sufficiently from the available treatments.
Current Controlled Trials ISRCTN52023186
Opioid addiction is a chronic relapsing disease that affects the lives of sufferers in very different ways . Opioid-dependent people continue using these drugs despite the consequences for their health, legal situation, social integration and personal relations . Opioid substitution therapies (such as methadone, buprenorphine or diacetylmorphine) are intended to reduce illicit opioid use, deaths, disease and crime, as well as to improve patients' health, quality of life and psychosocial integration. Therefore, the effectiveness of a treatment may be reflected in different areas of patients' lives and as a consequence a treatment can be evaluated in different ways.
Various studies have provided evidence of the effectiveness, safety, viability and cost-effectiveness of prescribing diacetylmorphine (DAM) for the treatment of long term opioid-dependent persons who have not benefited from other treatments [3–11]. DAM is currently prescribed, as a regular programme or in the context of a clinical trial, in six countries: the UK, Switzerland, the Netherlands, Germany, Spain and Canada .
In the Dutch trial testing co-prescribed diacetylmorphine vs. methadone for long-term opioid dependence, treatment effectiveness was evaluated by means of a multi-domain outcome index (MDO) in order to obtain an overall measure of treatment success or failure [10, 13]. The goal of the MDO is to assess response by means of a dichotomous variable addressing, as a combined measure, different aspects involved in the process of stabilizing drug-dependent patients: their physical and mental health and psychosocial integration.
It has been remarked that although a MDO allows to capture the complexity of drug-dependence and summarizes various measures by means of a single index, it does not enable the weighting of each dimension making difficult to evaluate in which particular aspects the patient has improved; moreover, a MDO makes it more complicated to perform comparisons with other studies [14, 15]. The first of these problems may be addressed by separating the dimensions constituting the MDO, in order to determine their individual performance, as we have done in a previous analysis . The goal of the present study is to overcome the second obstacle: we seek to evaluate the results of the DAM prescription trial carried out in Andalusia (Spain) with the multi-domain dichotomous index proposed in the Dutch study . Here we analyze data from the Andalusian study by formally applying prior empirical evidence reported on the evidence of this treatment. In addition, we discuss the contribution of the results to the state of the art.
We analyzed data from a randomized controlled trial comparing injectable DAM vs. oral MMT conducted in Andalusia, Spain, from February 2003 to December 2004. Study design, methods and results have been published elsewhere . Briefly, 62 long-term, opioid dependent individuals with severe health and other drug related problems were randomized to receive either injectable DAM (plus oral methadone) or oral methadone alone. A total of 44 participants completed the 9 month treatment period and 50 completed the follow-up evaluations.
For the present study we analyse data from the Andalusian trial using a multi-domain outcome measure reported in a previous study conducted in The Netherlands, also comparing injectable DAM and oral MMT . The MDO is a dichotomous index, imputing success when the patient shows at least 40% improvement at 9 months, compared to the baseline values, in physical health (MAP-H) , or mental status (SCL-90), or social functioning (illegal activities and/or contact with non drug users), without a deterioration superior to 40% in any of these dimensions and no substantial increase (20%) in cocaine use. More details about this MDO can be found elsewhere .
Statistical analyses were performed using a Bayesian approach in order to take advantage of previous information, a strategy highly appropriated when working with small sample sizes (small samples are very common in trials aimed at treating conditions with low-incidence in the community). Previous information in big samples would have virtually no impact in the results. We calculated success rates, the relative risk (RR) and the respective 95% confidence intervals (CI). Using data derived from the Dutch study, a priori information was obtained for analysis of the Andalusian study data using Bayesian methods [18–21]. Analyses were performed by intention to treat, with no imputation for missing values. We denote by θ 1 the percentage of patients who responded to the experimental treatment (DAM), while θ 2 represents the percentage of those responding to the conventional treatment (methadone). Bayesian analysis enables us to calculate the probability of θ 1 being greater than θ 2 by a specified magnitude, based on the data from our trial and prior information from the Dutch trial. Upon clinical judgment and based on the target populations (i.e. treatment-refractory opioid-dependent individuals) and outcome expectations (i.e. stabilization, long-term treatment), we assumed as clinically relevant a minimal difference of 15% between the rates of responders in each group, and assessed the probability of this being fulfilled under different assumptions.
For each of the parameters θ 1 and θ 2 we selected three a priori distributions from the family of beta distributions with parameters a and b which approximately represent the implicit number of responders and non-responders in the prior distribution. These three scenarios represent different degrees of incorporation of prior evidence. In the first scenario ('No use' of historical data) Jeffreys' priors were used, which are non-informative prior beta distributions with parameters a = b = 0.5 for both, θ 1 and θ 2. The remaining two pair of priors were set on the basis of the knowledge derived from a previous clinical trial using injected DAM.  The respective CI associated with these prior data were calculated, and parameters were chosen (a and b in the beta distribution) such that the maximum density intervals of these distributions coincided approximately with the CI obtained previously. The second scenario ('Partial use') down-weighted the Dutch study by dividing a and b by 5. Finally, we repeated the process using the values a and b without modification ('Full use'), essentially equivalent to a full pooling of the trial results in a meta-analysis.
In order to perform a sensitivity analysis, several scenarios need to be imagined. The one considered when we do a 'partial use' of previous data is placed between two extreme situations: no use of previous data (meaning there are no similarities between contexts) and full use of them (meaning both contexts are equal). These extreme positions are extreme, since we cannot assume the Dutch and Andalusian context are the same, or that they have nothing in common either. The chosen halfway scenario takes into account this argument. A division by 5 of the parameters derived from the Beta-distribution was chosen in order to substantially increase the distribution dispersion attributed to previous data, allowing an adequate sensitivity analysis.
For each one of these prior choices, we obtained the conjugate beta distributions for the response rate in each arm of the trial using our binomial data. A total of 20.000 simulations were made from these a posteriori distributions, and the corresponding 20.000 differences θ 1 - θ 2 were calculated providing an a posteriori distribution of the difference between the proportions: Δ = θ 1 - θ 2. This was used to derive simulation-based estimates of the probability of relevant magnitudes concerning Δ: P(Δ larger than 0), P(Δ larger than 0.15) and a maximum density interval (probability interval for Δ) at 95%. EPIDAT 3.1 was used for all computations .
a and b values for each parameter θ 1 and θ 2 among the three groups of the a priori distributions used.
Among the patients in the experimental group (n = 27), the rate treatment responders was 70.4% (95% CI 53.2-87.6), while for those in the control group (n = 23) it was 34.8% (95% CI 15.3-54.3). The difference in response rates between the two groups was 36.6% in favour of the experimental group. The probability of a positive response to treatment by participants allocated to experimental group (RR) was 2.2 times greater than for those of the control group (95% CI 1.2-4.3; p = 0.012). The number needed to treat was 2.8 (IC 95% 1.6-10.0).
Probability values of the difference in success rates between the experimental and control groups being bigger than 0 and 0.15, and probability intervals (95%) for the possible three scenarios: without using the Dutch data in order to determine the priors, partial use, and total use).
Probability Interval (95%)
8.0 - 57.6
9.9 - 50.4
13.9 - 39.3
Our analysis of the Andalusian trial data using a multi-domain outcome measure as a treatment response criterion shows that the group receiving injectable diacetylmorphine had a greater probability of responding to treatment than the group that receive only oral methadone, both in clinical and in statistical terms.
The results obtained with this MDO are remarkable given that this indicator has a high level of exigency, as much by its complex definition, the magnitude of the demanded change (40%) and by the inclusion of the criterion of the cocaine consumption. Also, the MDO is a dichotomous variable, being less sensitive to change than the dimensional measures. For a fixed sample size a binary outcome measure would be able to detect a change of a 10% of the variance, whereas a dimensional measurement could detect changes of 1% .
It is important to note that the results come from a small sample and this limits their generalizability; other limitations derived from the design of the study have been discussed elsewhere . When comparing the present study with the one conducted in the Netherlands , it should be taken into account that the control group in the Andalusian trial received larger average doses of methadone, and also they received an optimized version of MMT (involving greater psychosocial resources than the treatment that is normally provided). Also, the intervention lasted 12 months in the Dutch RCT, and 9 months in the Andalusian one. Nevertheless, the differences between the groups in the Dutch RCT stabilized after approximately 10 months.
In the present study the Bayesian analysis reveals a clear superiority of the diacetylmorphine-based treatment over methadone. The fact that the probability of the experimental treatment surpassing the conventional one by at least 15% gives such a high result (over 0.9 in the different scenarios) is important, especially considering the case in which this value is derived from the formal integration of earlier data with those from the present study. Our findings fit in with the a priori probability of the superiority of injectable DAM versus oral methadone in the case of treatment-refractory patients, and show how even partial use of the historical data reinforce the confidence in a clinically relevant difference.
The results obtained using Bayesian analyses are similar to those derived from the classical statistical approach when large sample sizes are used. The Bayesian method used in this analysis, however, was especially well suited because of the small sample size in our trial; in addition, it allowed to integrate previously obtained results into the current study to a partial or full extent. Moreover, this method is in agreement with recommendations of paying special attention to calculating the magnitude of the effect of the treatment being studied, and not so much on its statistical power [24, 25].
National and European data shows a stabilization in the use of heroin. However, a sub-group of heroin users with high health and social needs are not properly served by the health care system. Pharmacological alternatives are needed to attract and engage these individuals in treatment. The evidence for the greater efficacy of injectable DAM, in comparison with oral methadone, in the case of long-term, treatment-refractory opioid-dependent patients is supported by the present study and by others [4–6, 10, 26, 27]. The next step would be to design a study evaluating the provision of DAM in standard clinical practice, i.e. in more ecological settings. However, the delay in the approval of those programs still depends more on the political and moral contexts than on the scientific conclusions reached over recent years [12, 28].
The authors gratefully acknowledge the advice of David Spiegelhalter. We want to thank Dr. María Victoria Zunzunegui for her priceless contribution at the beginning of the study. We also want to acknowledge the contribution of the members of the PEPSA team: Andrés Estrada Moreno, José Manuel Rodríguez, Salvador Rodríguez Rus, Francisco Carrasco Limón, Rosario Ballesta, Araceli Plaza. Furthermore, we thank the study participants for their time and effort. Funded by the Drug Commission, Council for Equality and Social Welfare, Andalusian Government.
- Johnson RE, Chutuape MA, Strain EC, Walsh SL, Stitzer ML, Bigelow GE: A comparison of levomethadyl acetate, buprenorphine, and methadone for opioid dependence. N Engl J Med. 2000, 343: 1290-1297. 10.1056/NEJM200011023431802.View ArticlePubMedGoogle Scholar
- Ward J, Hall W, Mattick RP: Role of maintenance treatment in opioid dependence. Lancet. 1999, 353: 221-226. 10.1016/S0140-6736(98)05356-2.View ArticlePubMedGoogle Scholar
- Güttinger F, Gschwend P, Schulte B, Rehm J, Uchtenhagen A: Evaluating long-term effects of heroin-assisted treatment: the results of a 6-year follow-up. Eur Addict Res. 2003, 9: 73-79. 10.1159/000068811.View ArticlePubMedGoogle Scholar
- Haasen C, Verthein U, Degkwitz P, Berger J, Krausz M, Naber D: Heroin-assisted treatment for opioid dependence: Randomised controlled trial. Br J Psychiatry. 2007, 191: 55-62. 10.1192/bjp.bp.106.026112.View ArticlePubMedGoogle Scholar
- Rehm J, Gschwend P, Steffen T, Gutzwiller F, Dobler-Mikola A, Uchtenhagen A: Feasibility, safety, and efficacy of injectable heroin prescription for refractory opioid addicts: a follow-up study. Lancet. 2001, 358: 1417-1423. 10.1016/S0140-6736(01)06529-1.View ArticlePubMedGoogle Scholar
- Oviedo-Joekes E, Brissette S, Marsh DC, Lauzon P, Guh D, Anis A, Schechter MT: Diacetylmorphine vs. methadone for the treatment of opioid addiction. N Eng J Med. 2009,Google Scholar
- Killias M, Uchtenhagen A: Does medical heroin prescription reduce delinquency among drug-addicts? On the evaluation of the Swiss heroin prescription projects and its methodology. Studies on Crime and Crime Prevention. 1996, 5: 245-256.Google Scholar
- Gutzwiller F, Steffen T: Cost-benefit analysis of heroin maintenance treatment. Medical prescription of narcotics. 2000, Basle: KargerView ArticleGoogle Scholar
- Dijkgraaf MG, Zanden van der BP, de Borgie CA, Blanken P, van Ree JM, Brink van den W: Cost utility analysis of co-prescribed heroin compared with methadone maintenance treatment in heroin addicts in two randomised trials. BMJ. 2005, 330: 1297-10.1136/bmj.330.7503.1297.View ArticlePubMedPubMed CentralGoogle Scholar
- Brink van den W, Hendriks VM, Blanken P, Koeter MW, van Zwieten BJ, van Ree JM: Medical prescription of heroin to treatment resistant heroin addicts: two randomised controlled trials. BMJ. 2003, 327: 310-10.1136/bmj.327.7410.310.View ArticlePubMedPubMed CentralGoogle Scholar
- March JC, Oviedo-Joekes E, Perea-Milla E, Carrasco F, PEPSA team: Controlled trial of prescribed heroin in the treatment of opioid addiction. J Subst Abuse Treat. 2006, 31: 203-211. 10.1016/j.jsat.2006.04.007.View ArticlePubMedGoogle Scholar
- Fischer B, Oviedo-Joekes E, Blanken P, Haasen C, Rehm J, Schechter MT, Strang J, Brink van den W: Heroin-assisted Treatment (HAT) a Decade Later: A Brief Update on Science and Politics. J Urban Health. 2007, 84: 552-562. 10.1007/s11524-007-9198-y.View ArticlePubMedPubMed CentralGoogle Scholar
- Brink van den W, Hendriks VM, Blanken P, Huijsman IA, van Ree JM: Medical co-prescription of heroin: Two randomized controlled trials. Utrecht: Central Committee on the Treatment of Heroin Addicts. 2002Google Scholar
- Rehm J: Scientific evaluations of opioid-assisted substitution treatment. Basic and clinical science of opioid addiction. Edited by: Kuntze MF, Müller-Spahn F, Ladewig D, Bullinger AH. 2003, Basel: KargerGoogle Scholar
- Ferri M, Davoli M, Perucci CA: Heroin maintenance for chronic heroin dependents. Cochrane Database Syst Rev. 2005, CD003410-Google Scholar
- Marsden J, Gossop M, Stewart D, Best D, Farrell M, Lehmann P, Edwards C, Strang J: The Maudsley Addiction Profile (MAP): a brief instrument for assessing treatment outcome. Addiction. 1998, 93: 1857-1867. 10.1046/j.1360-0443.1998.9312185711.x.View ArticlePubMedGoogle Scholar
- Derogatis LR, Cleary PA: Factorial invariance across gender for the primary symptom dimensions of the SCL-90. Br J Soc Clin Psychol. 1977, 16: 347-356.View ArticlePubMedGoogle Scholar
- Bland JM, Altman DG: Bayesians and frequentists. BMJ. 1998, 317: 1151-1160.View ArticlePubMedPubMed CentralGoogle Scholar
- Spiegelhalter DJ, Myles JP, Jones DR, Abrams KR: Methods in health service research. An introduction to bayesian methods in health technology assessment. BMJ. 1999, 319: 508-512.View ArticlePubMedPubMed CentralGoogle Scholar
- Brophy JM, Joseph L: Placing trials in context using Bayesian analysis. GUSTO revisited by Reverend Bayes. JAMA. 1995, 73: 871-875. 10.1001/jama.273.11.871.View ArticleGoogle Scholar
- Joseph L, Reinhold C: Statistical inference for proportions. AJR Am J Roentgenol. 2005, 184: 1057-1064.View ArticlePubMedGoogle Scholar
- EPIDAT 3.1: Software for analysis of tabulated data. 2006, Pan American Health Organization. Xunta de GaliciaGoogle Scholar
- Dennis ML, Lennox RI, Foss M: Practical power analysis for substance abuse health services research. The Science of Prevention: methodological advances from alcohol and substance abuse research. Edited by: Bryant KJ, Windle M, West SG. 1997, Washington, DC, American Psychological Association, 367-405.View ArticleGoogle Scholar
- Schulz KF, Grimes DA: Sample size calculations in randomised trials: mandatory and mystical. Lancet. 2005, 365: 1348-1353. 10.1016/S0140-6736(05)61034-3.View ArticlePubMedGoogle Scholar
- ICMJE: Uniform requirements for manuscripts submitted to biomedical journals. International Committee of Medical Journal Editors. 2005, http://www.icmje.org/ Google Scholar
- Hartnoll RL, Mitcheson MC, Battersby A, Brown G, Ellis M, Fleming P, Hedley N: Evaluation of heroin maintenance in controlled trial. Arch Gen Psychiatry. 1980, 37: 877-884.View ArticlePubMedGoogle Scholar
- Perneger TV, Giner F, del Rio M, Mino A: Randomised trial of heroin maintenance programme for addicts who fail in conventional drug treatments. BMJ. 1998, 317: 13-18. 10.1186/1477-7517-3-16.View ArticlePubMedPubMed CentralGoogle Scholar
- Small DR, Drucker E: Policy Makers Ignoring Science Scientists Ignoring Policy: The Medical Ethical Challenges of Heroin Treatment. Harm Reduct J. 2006, 3: 16-View ArticlePubMedPubMed CentralGoogle Scholar
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