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      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

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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.

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          Most cited references19

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          Two-stage residual inclusion estimation: addressing endogeneity in health econometric modeling.

          The paper focuses on two estimation methods that have been widely used to address endogeneity in empirical research in health economics and health services research-two-stage predictor substitution (2SPS) and two-stage residual inclusion (2SRI). 2SPS is the rote extension (to nonlinear models) of the popular linear two-stage least squares estimator. The 2SRI estimator is similar except that in the second-stage regression, the endogenous variables are not replaced by first-stage predictors. Instead, first-stage residuals are included as additional regressors. In a generic parametric framework, we show that 2SRI is consistent and 2SPS is not. Results from a simulation study and an illustrative example also recommend against 2SPS and favor 2SRI. Our findings are important given that there are many prominent examples of the application of inconsistent 2SPS in the recent literature. This study can be used as a guide by future researchers in health economics who are confronted with endogeneity in their empirical work.
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            An introduction To instrumental variables for epidemiologists

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              Differential efficacy of escitalopram and nortriptyline on dimensional measures of depression.

              Tricyclic antidepressants and serotonin reuptake inhibitors are considered to be equally effective, but differences may have been obscured by internally inconsistent measurement scales and inefficient statistical analyses. To test the hypothesis that escitalopram and nortriptyline differ in their effects on observed mood, cognitive and neurovegetative symptoms of depression. In a multicentre part-randomised open-label design (the Genome Based Therapeutic Drugs for Depression (GENDEP) study) 811 adults with moderate to severe unipolar depression were allocated to flexible dosage escitalopram or nortriptyline for 12 weeks. The weekly Montgomery-Asberg Depression Rating Scale, Hamilton Rating Scale for Depression, and Beck Depression Inventory were scored both conventionally and in a more novel way according to dimensions of observed mood, cognitive symptoms and neurovegetative symptoms. Mixed-effect linear regression showed no difference between escitalopram and nortriptyline on the three original scales, but symptom dimensions revealed drug-specific advantages. Observed mood and cognitive symptoms improved more with escitalopram than with nortriptyline. Neurovegetative symptoms improved more with nortriptyline than with escitalopram. The three symptom dimensions provided sensitive descriptors of differential antidepressant response and enabled identification of drug-specific effects.
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                Author and article information

                Contributors
                jennifer.hellier@kcl.ac.uk
                richard.emsley@kcl.ac.uk
                andrew.pickles@kcl.ac.uk
                Journal
                Trials
                Trials
                Trials
                BioMed Central (London )
                1745-6215
                3 January 2020
                3 January 2020
                2020
                : 21
                : 10
                Affiliations
                ISNI 0000 0001 2322 6764, GRID grid.13097.3c, Biostatistics and Health Informatics Department, , Institute of Psychiatry, Psychology & Neuroscience, King’s College London, ; De Crespigny Park, London, SE5 8AF UK
                Author information
                http://orcid.org/0000-0003-1760-3708
                Article
                3810
                10.1186/s13063-019-3810-9
                6942263
                31900198
                bc92035d-23ea-4b22-8031-4cacab81ee63
                © The Author(s). 2020

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

                History
                : 24 October 2018
                : 22 October 2019
                Funding
                Funded by: National Institute for Health Research (GB)
                Award ID: DRF-2015-08-012
                Categories
                Methodology
                Custom metadata
                © The Author(s) 2020

                Medicine
                depression,dose response,instrumental variables,survival analysis,threshold regression,time to remission

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