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Published in: Trials 1/2020

Open Access 01-12-2020 | Antidepressant Drugs | Methodology

Estimating dose-response for time to remission with instrumental variable adjustment: the obscuring effects of drug titration in Genome Based Therapeutic Drugs for Depression Trial (GENDEP): clinical trial data

Authors: Jennifer Hellier, Richard Emsley, Andrew Pickles

Published in: Trials | Issue 1/2020

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Abstract

Background

Threshold regression, in which time to remission is modelled as a stochastic drift towards a boundary, is an alternative to the proportional hazards survival model and has a clear conceptual mechanism for examining the effects of drug dose. However, for both threshold regression and proportional hazard models, when dose titration occurs during treatment, the estimated causal effect of dose can be biased by confounding. An instrumental variable analysis can be used to minimise such bias.

Method

Weekly antidepressant dose was measured in 380 men and women with major depression treated with escitalopram or nortriptyline for 12 weeks as part of the Genome Based Therapeutic Drugs for Depression (GENDEP) study. The averaged dose relative to maximum prescribing dose was calculated from the 12 trial weeks and tested for association with time to depression remission. We combined the instrumental variable approach, utilising randomised treatment as an instrument, with threshold regression and proportional hazard survival models.

Results

The threshold model was constructed with two linear predictors. In the naïve models, averaged daily dose was not associated with reduced time to remission. By contrast, the instrumental variable analyses showed a clear and significant relationship between increased dose and faster time to remission, threshold regression (velocity estimate: 0.878, 95% confidence interval [CI]: 0.152–1.603) and proportional hazards (log hazards ratio: 3.012, 95% CI: 0.086–5.938).

Conclusions

We demonstrate, using the GENDEP trial, the benefits of these analyses to estimate causal parameters rather than those that estimate associations. The results for the trial dataset show the link between antidepressant dose and time to depression remission. The threshold regression model more clearly distinguishes the factors associated with initial severity from those influencing treatment effect. Additionally, applying the instrumental variable estimator provides a more plausible causal estimate of drug dose on treatment effect. This validity of these results is subject to meeting the assumptions of instrumental variable analyses.

Trial registration

EudraCT, 2004–001723-38; ISRCTN, 03693000. Registered on 27 September 2007.
Appendix
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Literature
1.
go back to reference Hernan MA, Robins JM. Instruments for causal inference - An epidemiologist's dream? Epidemiology. 2006;17(4):360–72.CrossRef Hernan MA, Robins JM. Instruments for causal inference - An epidemiologist's dream? Epidemiology. 2006;17(4):360–72.CrossRef
2.
go back to reference Terza JV, Basu A, Rathouz PJ. Two-stage residual inclusion estimation: addressing endogeneity in health econometric modeling. J Health Econ. 2008;27(3):531–43.CrossRef Terza JV, Basu A, Rathouz PJ. Two-stage residual inclusion estimation: addressing endogeneity in health econometric modeling. J Health Econ. 2008;27(3):531–43.CrossRef
3.
go back to reference Tchetgen Tchetgen EJ, Walter S, Vansteelandt S, Martinussen T, Glymour M. Instrumental variable estimation in a survival context. Epidemiology. 2015;26(3):402–10.CrossRef Tchetgen Tchetgen EJ, Walter S, Vansteelandt S, Martinussen T, Glymour M. Instrumental variable estimation in a survival context. Epidemiology. 2015;26(3):402–10.CrossRef
4.
go back to reference Angrist JD, Imbens GW, Rubin DB. Identification of causal effects using instrumental variables. J Am Stat Assoc. 1996;91(434):444–55.CrossRef Angrist JD, Imbens GW, Rubin DB. Identification of causal effects using instrumental variables. J Am Stat Assoc. 1996;91(434):444–55.CrossRef
5.
go back to reference Ballenger JC. Clinical guidelines for establishing remission in patients with depression and anxiety. J Clin Psychiatry. 1999;60:29–34.CrossRef Ballenger JC. Clinical guidelines for establishing remission in patients with depression and anxiety. J Clin Psychiatry. 1999;60:29–34.CrossRef
6.
go back to reference Rush AJ, Trivedi MH. Treating depression to remission. Psychiatr Ann. 1995;25(12):704–9.CrossRef Rush AJ, Trivedi MH. Treating depression to remission. Psychiatr Ann. 1995;25(12):704–9.CrossRef
7.
go back to reference Thase ME. Evaluating antidepressant therapies: Remission as the optimal outcome. J Clin Psychiatry. 2003;64:18–25.CrossRef Thase ME. Evaluating antidepressant therapies: Remission as the optimal outcome. J Clin Psychiatry. 2003;64:18–25.CrossRef
8.
go back to reference Lee ML, Whitmore GA. Proportional hazards and threshold regression: their theoretical and practical connections. Lifetime Data Anal. 2010;16(2):196–214.CrossRef Lee ML, Whitmore GA. Proportional hazards and threshold regression: their theoretical and practical connections. Lifetime Data Anal. 2010;16(2):196–214.CrossRef
9.
go back to reference Lee M-LT, Whitmore GA. Threshold regression for survival analysis: Modeling event times by a stochastic process reaching a boundary. Stat Sci. 2006;21(4):501–13.CrossRef Lee M-LT, Whitmore GA. Threshold regression for survival analysis: Modeling event times by a stochastic process reaching a boundary. Stat Sci. 2006;21(4):501–13.CrossRef
10.
go back to reference Kennedy SH, Law RW, Cohen NL, Ravindran AV, CANMAT Depression Work Group. Clinical guidelines for the treatment of depressive disorders IV. IV. Medications and other biological treatments. Can J Psychiatr. 2001;46:38S–58S. Kennedy SH, Law RW, Cohen NL, Ravindran AV, CANMAT Depression Work Group. Clinical guidelines for the treatment of depressive disorders IV. IV. Medications and other biological treatments. Can J Psychiatr. 2001;46:38S–58S.
11.
go back to reference Hernan MA, Robins JM. Instruments for causal inference: an epidemiologist's dream? Erratum. Epidemiology. 2014;25(1):164. Hernan MA, Robins JM. Instruments for causal inference: an epidemiologist's dream? Erratum. Epidemiology. 2014;25(1):164.
12.
go back to reference Sjolander A, Dahlqwist E, Zetterqvist J. A note on the noncollapsibility of rate differences and rate ratios. Epidemiology. 2016;27(3):356–9.CrossRef Sjolander A, Dahlqwist E, Zetterqvist J. A note on the noncollapsibility of rate differences and rate ratios. Epidemiology. 2016;27(3):356–9.CrossRef
13.
go back to reference Uher R, Maier W, Hauser J, Marusic A, Schmael C, Mors O, et al. Differential efficacy of escitalopram and nortriptyline on dimensional measures of depression. Br J Psychiatry. 2009;194(3):252–9.CrossRef Uher R, Maier W, Hauser J, Marusic A, Schmael C, Mors O, et al. Differential efficacy of escitalopram and nortriptyline on dimensional measures of depression. Br J Psychiatry. 2009;194(3):252–9.CrossRef
14.
go back to reference Schulz K, Altman D, Moher D, CONSORT Group. CONSORT 2010 Statement: updated guidelines for reporting parallel group randomised trials. BMJ. 2010;340:c332.CrossRef Schulz K, Altman D, Moher D, CONSORT Group. CONSORT 2010 Statement: updated guidelines for reporting parallel group randomised trials. BMJ. 2010;340:c332.CrossRef
15.
go back to reference Montgomery SA, Asberg M. New depression scale designed to be sensitive to change. Br J Psychiatry. 1979;134(APR):382–9.CrossRef Montgomery SA, Asberg M. New depression scale designed to be sensitive to change. Br J Psychiatry. 1979;134(APR):382–9.CrossRef
16.
go back to reference Sanchez C, Bergqvist PB, Brennum LT, Gupta S, Hogg S, Larsen A, et al. Escitalopram, the S-(+)-enantiomer of citalopram, is a selective serotonin reuptake inhibitor with potent effects in animal models predictive of antidepressant and anxiolytic activities. Psychopharmacology. 2003;167(4):353–62.CrossRef Sanchez C, Bergqvist PB, Brennum LT, Gupta S, Hogg S, Larsen A, et al. Escitalopram, the S-(+)-enantiomer of citalopram, is a selective serotonin reuptake inhibitor with potent effects in animal models predictive of antidepressant and anxiolytic activities. Psychopharmacology. 2003;167(4):353–62.CrossRef
17.
go back to reference Sanchez C, Hyttel J. Comparison of the effects of antidepressants and their metabolites on reuptake of biogenic amines and on receptor binding. Cell Mol Neurobiol. 1999;19(4):467–89.CrossRef Sanchez C, Hyttel J. Comparison of the effects of antidepressants and their metabolites on reuptake of biogenic amines and on receptor binding. Cell Mol Neurobiol. 1999;19(4):467–89.CrossRef
18.
go back to reference Aalen OO, Gjessing HK. Understanding the shape of the hazard rate: A process point of view. Stat Sci. 2001;16(1):1–14. Aalen OO, Gjessing HK. Understanding the shape of the hazard rate: A process point of view. Stat Sci. 2001;16(1):1–14.
19.
go back to reference Pickles A, and Crouchley R. Stochastic Process Models in Clinical Psychology and Psychiatry. Statistics in medicine: based on the proceedings of a conference on applications of statistics in medicine. USA: Oxford University Press; 1991. Pickles A, and Crouchley R. Stochastic Process Models in Clinical Psychology and Psychiatry. Statistics in medicine: based on the proceedings of a conference on applications of statistics in medicine. USA: Oxford University Press; 1991.
20.
go back to reference Cox DR. Regression models and life-tables. J R Stat Soc B Stat Meth. 1972;34(2):187–220. Cox DR. Regression models and life-tables. J R Stat Soc B Stat Meth. 1972;34(2):187–220.
21.
go back to reference Ford I, Norrie J, Ahmadi S. Model inconsistency, illustrated by the Cox proportional hazards model. Stat Med. 1995;14(8):735–46.CrossRef Ford I, Norrie J, Ahmadi S. Model inconsistency, illustrated by the Cox proportional hazards model. Stat Med. 1995;14(8):735–46.CrossRef
22.
go back to reference Burgess S. Commentary: Consistency and collapsibility: are they crucial for instrumental variable analysis with a survival outcome in mendelian randomization? Epidemiology. 2015;26(3):411–3.CrossRef Burgess S. Commentary: Consistency and collapsibility: are they crucial for instrumental variable analysis with a survival outcome in mendelian randomization? Epidemiology. 2015;26(3):411–3.CrossRef
23.
go back to reference Sommer A, Zeger SL. On estimating efficacy from clinical trials. Stat Med. 1991;10(1):45–52.CrossRef Sommer A, Zeger SL. On estimating efficacy from clinical trials. Stat Med. 1991;10(1):45–52.CrossRef
24.
go back to reference Staiger D, Stock JH. Instrumental variables regression with weak instruments. Econometrica. 1997;65(3):557–86.CrossRef Staiger D, Stock JH. Instrumental variables regression with weak instruments. Econometrica. 1997;65(3):557–86.CrossRef
25.
go back to reference Hausman J. Specification tests in econometrics. Econometrica. 1978;46(6):1251–71.CrossRef Hausman J. Specification tests in econometrics. Econometrica. 1978;46(6):1251–71.CrossRef
26.
go back to reference Davidson R, MacKinnon J. Estimation and Inference in Econmerics. New York: Oxford University Press; 1993. Davidson R, MacKinnon J. Estimation and Inference in Econmerics. New York: Oxford University Press; 1993.
27.
go back to reference Clogg CC, Petkova E, Shihadeh ES. Statistical methods for analyzing collapsibility in regression models. J Educ Stat. 1992;17(1):51–74.CrossRef Clogg CC, Petkova E, Shihadeh ES. Statistical methods for analyzing collapsibility in regression models. J Educ Stat. 1992;17(1):51–74.CrossRef
28.
go back to reference Greenland S, Robins JM. Conceptual problems in the definition and interpretation of attributable fractions. Am J Epidemiol. 1988;128(6):1185–97.CrossRef Greenland S, Robins JM. Conceptual problems in the definition and interpretation of attributable fractions. Am J Epidemiol. 1988;128(6):1185–97.CrossRef
29.
30.
go back to reference Maracy M, Dunn G. Estimating dose-response effects in psychological treatment trials: the role of instrumental variables. Stat Methods Med Res. 2011;20(3):191–215.CrossRef Maracy M, Dunn G. Estimating dose-response effects in psychological treatment trials: the role of instrumental variables. Stat Methods Med Res. 2011;20(3):191–215.CrossRef
31.
go back to reference Greenland S. An introduction to instrumental variables for epidemiologists (vol 29, pg 722, 2000). Int J Epidemiol. 2000;29(6):1102.CrossRef Greenland S. An introduction to instrumental variables for epidemiologists (vol 29, pg 722, 2000). Int J Epidemiol. 2000;29(6):1102.CrossRef
Metadata
Title
Estimating dose-response for time to remission with instrumental variable adjustment: the obscuring effects of drug titration in Genome Based Therapeutic Drugs for Depression Trial (GENDEP): clinical trial data
Authors
Jennifer Hellier
Richard Emsley
Andrew Pickles
Publication date
01-12-2020
Publisher
BioMed Central
Published in
Trials / Issue 1/2020
Electronic ISSN: 1745-6215
DOI
https://doi.org/10.1186/s13063-019-3810-9

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