Abstract
Objective
To evaluate the power of responder analyses in a randomized controlled trial.
Study design and setting
Simulations were based on the Chronic Kidney Disease Antidepressant Sertraline Trial
(CAST), which compared sertraline to placebo for the treatment of depression in kidney disease.
Baseline disease severity, placebo response, effect size, and the proportion of responders
were varied across 72 scenarios. Power was assessed using a t-test for change scores, and the chi-square test for dichotomized outcomes of the minimal
important difference (MID), improvement and remission in 10,000 datasets with a fixed
sample size of 193.
Results
The t-test had >80% power except for scenarios with the lowest sertraline effect size. The
chi-square test using the MID had <7% power in all scenarios while improvement and
remission of achieved >80% power only at higher effect sizes and/or when the proportion
of responders was highest at 0.5. The chi-square test for improvement had marginal
power increases compared to the t-test (4/72 scenarios = 5.6%) and that for remission did not outperform the t-test in any scenario.
Conclusions
The t-test outperforms the chi-square test for dichotomized outcomes regardless of baseline
disease severity, placebo response, effect size and the proportion of responders to
the intervention.
Keywords
To read this article in full you will need to make a payment
Purchase one-time access:
Academic & Personal: 24 hour online accessCorporate R&D Professionals: 24 hour online accessOne-time access price info
- For academic or personal research use, select 'Academic and Personal'
- For corporate R&D use, select 'Corporate R&D Professionals'
Subscribe:
Subscribe to Journal of Clinical EpidemiologyAlready a print subscriber? Claim online access
Already an online subscriber? Sign in
Register: Create an account
Institutional Access: Sign in to ScienceDirect
References
- On the practice of dichotomization of quantitative variables.Psychol Methods. 2002; 7: 19-40https://doi.org/10.1037/1082-989x.7.1.19
Services USDoHaH. Guidance for industry patient-reported outcome measures: use in medical product development to support labelling claims. 2009. Available at: https://www.fda.gov/regulatory-information/search-fda-guidance-documents/patient-reported-outcome-measures-use-medical-product-development-support-labeling-claims
- The cost of dichotomising continuous variables.BMJ. 2006; 332: 1080https://doi.org/10.1136/bmj.332.7549.1080
- Disappointing dichotomies.Pharm Stat. 2003; 2: 239-240
- The cost of dichotimization.Appl Psychol Meas. 1983; 7: 249-253
- Power considerations when a continuous outcome variable is dichotomized.J Biopharm Stat. 1998; 8: 337-352https://doi.org/10.1080/10543409808835243
- Power and sample size determination when assessing the clinical relevance of trial results by 'responder analyses.Stat Med. 2004; 23: 3287-3305https://doi.org/10.1002/sim.1910
- Dichotomizing a continuous outcome in cluster randomized trials: impact on power.Stat Med. 2012; 31: 2822-2832https://doi.org/10.1002/sim.5409
- Inflation of the type I error rate when a continuous confounding variable is categorized in logistic regression analyses.Stat Med. 2004; 23: 1159-1178https://doi.org/10.1002/sim.1687
- Effects of categorization method, regression type, and variable distribution on the inflation of Type-I error rate when categorizing a confounding variable.Stat Med. 2015; 34: 936-949https://doi.org/10.1002/sim.6387
- Vital signs should be maintained as continuous variables when predicting bacterial infections in febrile children.J Clin Epidemiol. 2013; 66: 453-457https://doi.org/10.1016/j.jclinepi.2012.09.014
- Responder analyses and the assessment of a clinically relevant treatment effect.Trials. 2007; 8: 31https://doi.org/10.1186/1745-6215-8-31
- Measurement of health status. Ascertaining the minimal clinically important difference.Control Clin Trials. 1989; 10: 407-415
- Consequences of dichotomization.Pharm Stat. 2009; 8: 50-61https://doi.org/10.1002/pst.331
- A conceptual and empirical examination of justifications for dichotomization.Psychol Methods. 2009; 14: 349-366https://doi.org/10.1037/a0016956
- The design of simulation studies in medical statistics.Stat Med. 2006; 25: 4279-4292https://doi.org/10.1002/sim.2673
- Effect of sertraline on depressive symptoms in patients with chronic kidney disease without dialysis dependence: the CAST randomized clinical trial.JAMA. 2017; 318: 1876-1890https://doi.org/10.1001/jama.2017.17131
- Validation of depression screening scales in patients with CKD.Am J Kidney Dis. 2009; 54 (doi[doi]): 433-439https://doi.org/10.1053/j.ajkd.2009.03.016
- Placebo response rates in antidepressant trials: a systematic review of published and unpublished double-blind randomised controlled studies.Lancet Psychiatry. 2016; 3: 1059-1066https://doi.org/10.1016/s2215-0366(16)30307-8
- Comparative efficacy and acceptability of 21 antidepressant drugs for the acute treatment of adults with major depressive disorder: a systematic review and network meta-analysis.Lancet. 2018; 391 (17)32802-7: 1357-1366https://doi.org/10.1016/s0140-6736
- The 16-Item Quick Inventory of Depressive Symptomatology (QIDS), clinician rating (QIDS-C), and self-report (QIDS-SR): a psychometric evaluation in patients with chronic major depression.Biol Psychiatry. 2003; 54: 573-583https://doi.org/10.1016/s0006-3223(02)01866-8
- Epidemiology, diagnosis, and management of depression in patients with CKD.Am J Kidney Dis. 2009; 54 (doi[doi]): 741-752https://doi.org/10.1053/j.ajkd.2009.05.003
- The prevalence of symptoms in end-stage renal disease: a systematic review.Adv Chronic Kidney Dis. 2007; 14 (S1548-5595(06)00163-7): 82-99
- Psychosocial intervention improves depression, quality of life, and fluid adherence in hemodialysis.J Am Soc Nephrol. 2014; 25 (doi[doi]): 196-206https://doi.org/10.1681/ASN.2012111134
- Comparison of Beck depression inventories -IA and -II in psychiatric outpatients.J Pers Assess. 1996; 67: 588-597https://doi.org/10.1207/s15327752jpa6703_13
- Sample size calculation: inaccurate a priori assumptions for nuisance parameters can greatly affect the power of a randomized controlled trial.PLoS One. 2015; 10e0132578https://doi.org/10.1371/journal.pone.0132578
- Distribution of total depressive symptoms scores and each depressive symptom item in a sample of Japanese employees.PLoS One. 2016; 11e0147577https://doi.org/10.1371/journal.pone.0147577
- Distribution of item responses and total item scores for the Center for Epidemiologic Studies Depression Scale (CES-D): data from the Irish Longitudinal Study on Ageing (TILDA).PLoS One. 2018; 13e0202607https://doi.org/10.1371/journal.pone.0202607
- Covariate adjustment in randomized controlled trials with dichotomous outcomes increases statistical power and reduces sample size requirements.J Clin Epidemiol. 2004; 57: 454-460https://doi.org/10.1016/j.jclinepi.2003.09.014
- On responder analyses when a continuous variable is dichotomized and measurement error is present.Biom J. 2011; 53: 137-155https://doi.org/10.1002/bimj.201000069
- Classifying responders and non-responders; does it help when there is evidence of differentially responding patient groups?.J Psychiatr Res. 2012; 46: 1169-1173https://doi.org/10.1016/j.jpsychires.2012.05.005
- Interpreting treatment effects in randomised trials.BMJ. 1998; 316: 690-693https://doi.org/10.1136/bmj.316.7132.690
- GRADE guidelines: preparing summary of findings tables and evidence profiles-continuous outcomes.J Clin Epidemiol. 2013; 66: 173-183https://doi.org/10.1016/j.jclinepi.2012.08.001
Article Info
Publication History
Published online: February 05, 2021
Accepted:
January 28,
2021
Footnotes
Conflict of interest: None.
Role of the funding source: This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.
Identification
Copyright
© 2021 Elsevier Inc. All rights reserved.
