Detecting heterogeneity of intervention effects using analysis and meta-analysis of differences in variance between arms of a trial

Mills, Harriet L.; Higgins, Julian P T; Morris, Richard W; Keßler, David; Heron, Jon; Wiles, Nicola; Smith, George Davey; Tilling, Kate

doi:10.1101/2020.03.07.20032516

Cited by 17 publications

(26 citation statements)

References 51 publications

(83 reference statements)

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“…Different requirements and assumptions apply to the VR and the CVR. Both assume normality of the data, although for the CVR, adjustments to the calculation can make the method suitable for non-normally distributed data [48]. The CVR, additionally, requires the data to be measured on a ratio scale [48].…”

Section: Discussionmentioning

confidence: 99%

“…Both assume normality of the data, although for the CVR, adjustments to the calculation can make the method suitable for non-normally distributed data [48]. The CVR, additionally, requires the data to be measured on a ratio scale [48]. However, given the psychometric properties of the HAMD and the MADRS, and likely other depression severity rating scales, they should not be considered ratio scales.…”

Section: Discussionmentioning

confidence: 99%

“…As both the HAMD [49,50] and the MADRS [50] are multidimensional the interpretation of a single summed score is unclear [49] and changes over time because of a lack of longitudinal measurement invariance of the scales [51]. Therefore, investigating variance using the CVR as effect size in studies using the HAMD and the MADRS, as done in previous studies [37,38], does not comply with the requirements for such analysis, regardless of whether endpoint-or change scores are analysed [48]. Another issue related to the CVR is the necessity of addressing the relationship between the mean and the standard deviation in the analysis, as spurious findings may otherwise be observed [38].…”

Section: Discussionmentioning

confidence: 99%

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Individual response to antidepressants for depression in adults-a meta-analysis and simulation study

2020

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Background The observation that some patients appear to respond better to antidepressants for depression than others encourages the assumption that the effect of antidepressants differs between individuals and that treatment can be personalized. Objective To compare the outcome variance in patients receiving antidepressants with the outcome variance in patients receiving placebo in randomized controlled trials (RCTs) of adults with major depressive disorder (MDD) and to illustrate, using simulated data, components of variation of RCTs. Methods From a dataset comprising 522 RCTs of antidepressants for adult MDD, we selected the placebo-controlled RCTs reporting outcomes on the 17 or 21 item Hamilton Depression Rating Scale or the Montgomery-Asberg Depression Rating Scale and extracted the means and SDs of raw endpoint scores or baseline to endpoint changes scores on eligible depression symptom rating scales. We conducted inverse variance random-effects meta-analysis with the variability ratio (VR), the ratio between the outcome variance in the group of patients receiving antidepressants and the outcome variance in the group receiving placebo, as the primary outcome. An increased variance in the antidepressant group would indicate individual differences in response to antidepressants. Results We analysed 222 RCTs that investigated 19 different antidepressants compared with placebo in 345 comparisons, comprising a total of 61144 adults with an MDD diagnosis. Across all comparisons, the VR for raw endpoint scores was 0.98 (95% CI 0.96 to 1.00, I 2 = 0%) and 1.00 (95% CI 0.99 to 1.02, I 2 = 0%) for baseline-to-endpoint change scores.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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Individual response to antidepressants for depression in adults-a meta-analysis and simulation study

2020

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“…10 By comparing the variances between treatment and control groups of RCTs, 11 greater variability in treatment would indicate that there is a component of variation, the side effect-by-patient or side effect-by-subgroup interaction, that indicates variability of side effects. 10 Note that this method 12 has recently been applied for antipsychotics, 6 antidepressants, 7,13,14 and brain stimulation. 15 It is worth noting that these studies found little evidence for treatment effect variation.…”

Section: Introductionmentioning

confidence: 99%

“…6,7,13,14 Importantly, in the case of pre-post differences used as input for a meta-analysis of variance it is crucial to think carefully about the way the variability ratio is expressed, 11,14,16 as the use of the coefficient of variation ratio (CVR) that has been proposed as an alternative of the variability ratio (VR) 11 may lead to unreliable results. 12,16…”

Section: Introductionmentioning

confidence: 99%

Examining side effect variation in antipsychotic treatment and schizophrenia spectrum disorders

Neumeier

Homan

Vetter

et al. 2020

Preprint

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Background: Side effects of antipsychotic drugs play a key role in non-adherence and discontinuation of treatment in schizophrenia spectrum disorders (SSD). Precision medicine aims to minimize such side effects by selecting the right treatment for the right patient. However, to determine the extent of precision medicine that is required, we need to (1) show that there is indeed variation in side effects and (2) estimate the amount of variation in those side effects between patients. While clinical observations suggest that such variation may be considerable, a statistical comparison of side effect variation between active and control treatments is required to confirm this. Here, we hypothesized to find larger side effect variation in treatment compared with control in patients treated with first and second generation antipsychotics. Methods: We included double-blind, placebo-controlled, randomized controlled trials (RCTs) of adults with a diagnosis of SSD and prescription for licensed antipsychotic drugs. Standard deviations of the pre-post treatment differences of weight gain, prolactin levels,and corrected QT (QTc) times were extracted. Data quality and validity were ensured by following the PRISMA guidelines. The outcome measure was the overall variability ratio of treatment to control across RCTs. Individual variability ratios were weighted by the inverse-variance method and entered into a random-effects model. Results: We included N = 16578 patients for weight gain, N = 16633 patients for prolactin levels, and N = 10384 patients for QTc time. Variability ratios (VR) were significantly increased for weight gain (VR = 1.08; 95% CI: 1.02 - 1.14; P = 0.004) and prolactin levels (VR = 1.38; 95% CI: 1.17 - 1.62; P < 0.001) but did not reach significance for QTc time (VR = 1.05; 95% CI: 0.98 - 1.12; P = 0.135). Conclusion: We found increased variability in major side effects in patients with SSD under treatment with second generation antipsychotics, suggesting that subgroups of patients or even individual patients may benefit from improved treatment allocation through stratified or personalized medicine, respectively.

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Individual Differences in Response to Antidepressants

et al. 2021

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IMPORTANCE Antidepressants are commonly used to treat major depressive disorder (MDD). Antidepressant outcomes can vary based on individual differences; however, it is unclear whether specific factors determine this variability or whether it is at random. OBJECTIVE To investigate the assumption of systematic variability in symptomatic response to antidepressants and to assess whether variability is associated with MDD severity, antidepressant class, or study publication year.DATA SOURCES Data used were updated from a network meta-analysis of treatment with licensed antidepressants in adults with MDD. The Cochrane Central Register of Controlled Trials, CINAHL, Embase, LILACS database, MEDLINE, MEDLINE In-Process, and PsycInfo were searched from inception to March 21, 2019. Additional sources were international trial registries and sponsors, drug companies and regulatory agencies' websites, and reference lists of published articles. Data were analyzed between June 8, 2020, and June 13, 2020.STUDY SELECTION Analysis was restricted to double-blind, randomized placebo-controlled trials with depression scores available at the study's end point. DATA EXTRACTION AND SYNTHESISBaseline means, number of participants, end point means and SDs of total depression scores, antidepressant type, and publication year were extracted.MAIN OUTCOMES AND MEASURES Log SDs (b ln σ ˆ) were derived for treatment groups (ie, antidepressant and placebo). A random-slope mixed-effects model was conducted to estimate the difference in b ln σ ˆbetween treatment groups while controlling for end point mean. Secondary models determined whether differences in variability between groups were associated with baseline MDD severity; antidepressant class (selective serotonin reuptake inhibitors and other related drugs; serotonin and norepinephrine reuptake inhibitors; norepinephrine-dopamine reuptake inhibitors; noradrenergic agents; or other antidepressants); and publication year. RESULTSIn the 91 eligible trials (18 965 participants), variability in response did not differ significantly between antidepressants and placebo (b ln σ ˆ, 1.02; 95% CI, 0.99-1.05; P = .19). This finding is consistent with a range of treatment effect SDs (up to 16.10), depending on the association between the antidepressant and placebo effects. Variability was not associated with baseline MDD severity or publication year. Responses to noradrenergic agents were 11% more variable than responses to selective serotonin reuptake inhibitors (b ln σ ˆ, 1.11; 95% CI, 1.01-1.21; P = .02).CONCLUSIONS AND RELEVANCE Although this study cannot rule out the possibility of treatment effect heterogeneity, it does not provide empirical support for personalizing antidepressant treatment based solely on total depression scores. Future studies should explore whether individual symptom scores or biomarkers are associated with variability in response to antidepressants.

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Detecting heterogeneity of intervention effects using analysis and meta-analysis of differences in variance between arms of a trial

Cited by 17 publications

References 51 publications

Individual response to antidepressants for depression in adults-a meta-analysis and simulation study

Individual response to antidepressants for depression in adults-a meta-analysis and simulation study

Examining side effect variation in antipsychotic treatment and schizophrenia spectrum disorders

Individual Differences in Response to Antidepressants

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