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      Reanalysis of morphine consumption from two randomized controlled trials of gabapentin using longitudinal statistical methods

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          Abstract

          Background

          Postoperative pain management in total joint replacement surgery remains ineffective in up to 50% of patients and has an overwhelming impact in terms of patient well-being and health care burden. We present here an empirical analysis of two randomized controlled trials assessing whether addition of gabapentin to a multimodal perioperative analgesia regimen can reduce morphine consumption or improve analgesia for patients following total joint arthroplasty (the MOBILE trials).

          Methods

          Morphine consumption, measured for four time periods in patients undergoing total hip or total knee arthroplasty, was analyzed using a linear mixed-effects model to provide a longitudinal estimate of the treatment effect. Repeated-measures analysis of variance and generalized estimating equations were used in a sensitivity analysis to compare the robustness of the methods.

          Results

          There was no statistically significant difference in morphine consumption between the treatment group and a control group (mean effect size estimate 1.0, 95% confidence interval −4.7, 6.7, P=0.73). The results remained robust across different longitudinal methods.

          Conclusion

          The results of the current reanalysis of morphine consumption align with those of the MOBILE trials. Gabapentin did not significantly reduce morphine consumption in patients undergoing major replacement surgeries. The results remain consistent across longitudinal methods. More work in the area of postoperative pain is required to provide adequate management for this patient population.

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          Most cited references 13

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          Modelling covariance structure in the analysis of repeated measures data.

          The term 'repeated measures' refers to data with multiple observations on the same sampling unit. In most cases, the multiple observations are taken over time, but they could be over space. It is usually plausible to assume that observations on the same unit are correlated. Hence, statistical analysis of repeated measures data must address the issue of covariation between measures on the same unit. Until recently, analysis techniques available in computer software only offered the user limited and inadequate choices. One choice was to ignore covariance structure and make invalid assumptions. Another was to avoid the covariance structure issue by analysing transformed data or making adjustments to otherwise inadequate analyses. Ignoring covariance structure may result in erroneous inference, and avoiding it may result in inefficient inference. Recently available mixed model methodology permits the covariance structure to be incorporated into the statistical model. The MIXED procedure of the SAS((R)) System provides a rich selection of covariance structures through the RANDOM and REPEATED statements. Modelling the covariance structure is a major hurdle in the use of PROC MIXED. However, once the covariance structure is modelled, inference about fixed effects proceeds essentially as when using PROC GLM. An example from the pharmaceutical industry is used to illustrate how to choose a covariance structure. The example also illustrates the effects of choice of covariance structure on tests and estimates of fixed effects. In many situations, estimates of linear combinations are invariant with respect to covariance structure, yet standard errors of the estimates may still depend on the covariance structure. Copyright 2000 John Wiley & Sons, Ltd.
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            Multimodal analgesia for controlling acute postoperative pain.

            Multimodal analgesia is needed for acute postoperative pain management due to adverse effects of opioid analgesics, which can impede recovery; a problem that is of increasing concern with the rapid increase in the number of ambulatory surgeries. Yet, the literature on multimodal analgesia often shows variable degrees of success, even with studies utilizing the same adjuvant medication. Nonsteroidal anti-inflammatory drugs and selective cyclooxygenase-2 inhibitors consistently reduce postoperative opioid consumption. The N-methyl-D-aspartate antagonists have produced variable results in studies, which may be due to the dose and timing of drug administration. Alpha-2 adrenergic agonists have been useful as adjuvant for regional analgesia but not when administered orally. The alpha-2-delta receptor modulators such as gabapentin have shown early promising results in multimodal analgesia. Local anesthetic injection at the surgical site, though not as a preemptive analgesic, has recently been demonstrated to be beneficial in multimodal analgesia. No new adjuvants have appeared in the last year, which robustly reduce opioid consumption and opioid-related adverse effects. There is a continuing need to explore new drug combinations to achieve all of the purported goals of multimodal anesthesia.
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              An overview of practical approaches for handling missing data in clinical trials.

              For a variety of reasons including poorly designed case report forms (CRFs), incomplete or invalid CRF data entries, and premature treatment or study discontinuations, missing data is a common phenomenon in controlled clinical trials. With the widely accepted use of the intent-to-treat (ITT) analysis dataset as the primary analysis dataset for the analysis of controlled clinical trial data, the presence of missing data could lead to complicated data analysis strategies and subsequently to controversy in the interpretation of trial results. In this article, we review the mechanisms of missing data and some common approaches to analyzing missing data with an emphasis on study dropouts. We discuss the importance of understanding the reasons for study dropouts with ways to assess the mechanisms of missingness. Finally, we discuss the results of a comparative Monte Carlo investigation of the performance characteristics of commonly utilized statistical methods for the analysis of clinical trial data with dropouts. The methods investigated include the mixed effects model for repeated measurements (MMRM), weighted and unweighted generalized estimating equations (GEE) method for the available case data, multiple-imputation-based GEE (MI-GEE), complete case (CC) analysis of covariance (ANCOVA), and last observation carried forward (LOCF) ANCOVA. Simulation experiments for the repeated measures model with missing at random (MAR) dropout, under varying dropout rates and intrasubject correlation, show that the LOCF, ANCOVA, and weighted GEE methods perform poorly in terms of percent relative bias for estimating a difference in means effect, while the MI-GEE and weighted GEE methods both have less power for rejecting a zero difference in means hypothesis.
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                Author and article information

                Journal
                J Pain Res
                J Pain Res
                Journal of Pain Research
                Journal of Pain Research
                Dove Medical Press
                1178-7090
                2015
                09 February 2015
                : 8
                : 79-85
                Affiliations
                [1 ]Department of Clinical Epidemiology and Biostatistics, McMaster University, Hamilton, ON, Canada
                [2 ]Department of Anesthesia, McMaster University, Hamilton, ON, Canada
                [3 ]Biostatistics Unit/Centre for Evaluation of Medicines, St Joseph’s Healthcare-Hamilton, Hamilton, ON, Canada
                [4 ]Population Health Research Institute, Hamilton Health Science/McMaster University, Hamilton, ON, Canada
                [5 ]Department of Surgery, Division of Orthopaedics, McMaster University, Hamilton, ON, Canada
                Author notes
                Correspondence: Lehana Thabane, Department of Clinical Epidemiology and Biostatistics, McMaster University, 1280 Main Street West, ON L8S 4K1, Hamilton, ON, Canada, Email thabanl@ 123456mcmaster.ca
                Article
                jpr-8-079
                10.2147/JPR.S56558
                4332293
                © 2015 Zhang et al. This work is published by Dove Medical Press Limited, and licensed under Creative Commons Attribution – Non Commercial (unported, v3.0) License

                The full terms of the License are available at http://creativecommons.org/licenses/by-nc/3.0/. Non-commercial uses of the work are permitted without any further permission from Dove Medical Press Limited, provided the work is properly attributed.

                Categories
                Original Research

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