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Essential for non-statisticians and researchers working with longitudinal data from medical studies, this updated new edition discusses the most important techniques available for analysing data of this type. Using non-technical language, the book explores simple methods such as the paired t-test and summary statistics as well as more sophisticated regression-based methods, including mixed model analysis. The emphasis of the discussion lies in the interpretation of the results of these different methods, covering data analysis with continuous, dichotomous, categorical and other outcome variables. Datasets used throughout the book are provided, enabling readers to re-analyse the examples as they make their way through chapters and improve their understanding of the material. Finally, an extensive and practical overview of, and comparison between, different software packages is provided. Readers will be able to use this book as a practical manual in their everyday work without needing a strong background in statistics.
For the analysis of clinical effects, multiple imputation (MI) of missing data was shown to be unnecessary when using longitudinal linear mixed-models (LLM). It remains unclear whether this also applies to cost estimates from trial-based economic evaluations, that are generally right-skewed. Therefore, this study aimed to assess whether MI is required prior to LLM when analyzing longitudinal cost-effectiveness data.
Two-thousand complete datasets were simulated containing five time points. Incomplete datasets were generated with 10 percent, 25 percent, and 50 percent missing data in costs and effects, assuming a Missing At Random (MAR) mechanism. Statistical performance of six different methodological strategies was compared in terms of empirical bias (EB), root-mean-squared error (RMSE), and coverage rate (CR). Six strategies were compared: (i) LLM (LLM), (ii) MI prior to LLM (MI-LLM), (iii) mean imputation prior to LLM (M-LLM), (iv) complete-case analysis prior to seemingly unrelated regression (CCA-SUR), (v) MI prior to SUR (MI-SUR), and (vi) mean imputation prior to SUR (M-SUR). To evaluate the impact on the probability of cost-effectiveness at different willingness-to-pay [WTPs] thresholds, cost-effectiveness analyses were performed by applying the six strategies to two empirical datasets with 9% and 50% of missing data, respectively.
For costs and effects, LLM, MI-LLM, and MI-SUR performed better than M-LLM, CCA-SUR, and M-SUR, as indicated by smaller EBs and RMSEs, as well as CRs closer to the nominal levels of 0.95. However, even though LLM, MI-LLM, and MI-SUR performed equally well for effects, MI-LLM and MI-SUR were found to perform better than LLM for costs at 10 percent and 25 percent missing data. At 50 percent missing data, all strategies resulted in relatively high EBs and RMSEs for costs. In both empirical datasets, LLM, MI-LLM, and MI-SUR all resulted in similar probabilities of cost-effectiveness at different WTPs.
When opting for using LLM for analyzing trial-based economic evaluation data, researchers are advised to multiply impute missing values first. Otherwise, MI-SUR may also be used.
Adding short-term psychodynamic psychotherapy (STPP) to antidepressants increases treatment efficacy, but it is unclear which patients benefit specifically. This study examined efficacy moderators of combined treatment (STPP + antidepressants) v. antidepressants for adults with depression.
For this systematic review and meta-analysis (PROSPERO registration number: CRD42017056029), we searched PubMed, PsycINFO, Embase.com, and the Cochrane Library from inception to 1 January 2022. We included randomized clinical trials comparing combined treatment (antidepressants + individual outpatient STPP) v. antidepressants in the acute-phase treatment of depression in adults. Individual participant data were requested and analyzed combinedly using mixed-effects models (adding Cochrane risk of bias items as covariates) and an exploratory machine learning technique. The primary outcome was post-treatment depression symptom level.
Data were obtained for all seven trials identified (100%, n = 482, combined: n = 238, antidepressants: n = 244). Adding STPP to antidepressants was more efficacious for patients with high rather than low baseline depression levels [B = −0.49, 95% confidence interval (CI) −0.61 to −0.37, p < 0.0001] and for patients with a depressive episode duration of >2 years rather than <1 year (B = −0.68, 95% CI −1.31 to −0.05, p = 0.03) and than 1–2 years (B = −0.86, 95% CI −1.66 to −0.06, p = 0.04). Heterogeneity was low. Effects were replicated in analyses controlling for risk of bias.
To our knowledge, this is the first study that examines moderators across trials assessing the addition of STPP to antidepressants. These findings need validation but suggest that depression severity and episode duration are factors to consider when adding STPP to antidepressants and might contribute to personalizing treatment selection for depression.
Cognitive therapy and behavioural activation are both widely applied and effective psychotherapies for depression, but it is unclear which works best for whom. Individual participant data (IPD) meta-analysis allows for examining moderators at the participant level and can provide more precise effect estimates than conventional meta-analysis, which is based on study-level data.
This article describes the protocol for a systematic review and IPD meta-analysis that aims to compare the efficacy of cognitive therapy and behavioural activation for adults with depression, and to explore moderators of treatment effect. (PROSPERO: CRD42022341602)
Systematic literature searches will be conducted in PubMed, PsycINFO, EMBASE and the Cochrane Library, to identify randomised clinical trials comparing cognitive therapy and behavioural activation for adult acute-phase depression. Investigators of these trials will be invited to share their participant-level data. One-stage IPD meta-analyses will be conducted with mixed-effects models to assess treatment effects and to examine various available demographic, clinical and psychological participant characteristics as potential moderators. The primary outcome measure will be depressive symptom level at treatment completion. Secondary outcomes will include post-treatment anxiety, interpersonal functioning and quality of life, as well as follow-up outcomes.
To the best of our knowledge, this will be the first IPD meta-analysis concerning cognitive therapy versus behavioural activation for adult depression. This study has the potential to enhance our knowledge of depression treatment by using state-of-the-art statistical techniques to compare the efficacy of two widely used psychotherapies, and by shedding more light on which of these treatments might work best for whom.
Antidepressant medication and interpersonal psychotherapy (IPT) are both recommended interventions in depression treatment guidelines based on literature reviews and meta-analyses. However, ‘conventional’ meta-analyses comparing their efficacy are limited by their reliance on reported study-level information and a narrow focus on depression outcome measures assessed at treatment completion. Individual participant data (IPD) meta-analysis, considered the gold standard in evidence synthesis, can improve the quality of the analyses when compared with conventional meta-analysis.
We describe the protocol for a systematic review and IPD meta-analysis comparing the efficacy of antidepressants and IPT for adult acute-phase depression across a range of outcome measures, including depressive symptom severity as well as functioning and well-being, at both post-treatment and follow-up (PROSPERO: CRD42020219891).
We will conduct a systematic literature search in PubMed, PsycINFO, Embase and the Cochrane Library to identify randomised clinical trials comparing antidepressants and IPT in the acute-phase treatment of adults with depression. We will invite the authors of these studies to share the participant-level data of their trials. One-stage IPD meta-analyses will be conducted using mixed-effects models to assess treatment effects at post-treatment and follow-up for all outcome measures that are assessed in at least two studies.
This will be the first IPD meta-analysis examining antidepressants versus IPT efficacy. This study has the potential to enhance our knowledge of depression treatment by comparing the short- and long-term effects of two widely used interventions across a range of outcome measures using state-of-the-art statistical techniques.
Since food banks have a strong influence on recipients’ diets, and seem to have difficulties in supporting healthy diets, improving the dietary quality of food parcels is important. Therefore, we aimed to assess whether improving the dietary quality of food parcels, using different strategies, can positively impact the actual dietary intake of Dutch food bank recipients.
Materials and methods:
This randomized cross-over controlled trial with four intervention conditions [1) Control (standard food parcel), 2) Snacks– (standard food parcel with replacement of snacks by staple foods), 3) FV + (standard food parcel plus the recommended daily amount of fruit and vegetables), 4) Snacks– + FV (standard food parcel with replacement of snacks by staple foods plus the recommended daily amount of fruit and vegetables)] included food bank recipients from three food banks. In total, 199 recipients were randomly allocated. At baseline, participants filled in a questionnaire and underwent anthropometric measurements. Dietary intake data were collected through 24-hour recalls after both intervention conditions at 4 and 8 weeks follow-up. Primary outcome was fruit and vegetable intake, secondary outcomes were dietary intakes of food groups and nutrients.
Multi-level linear regression analysis, using a two-level model showed a higher mean fruit intake in participants in the FV + condition than in participants in the Control condition (δ: 74 [40.3;107.6] g). Both mean fruit and mean vegetable intake were higher in participants in the Snacks– + FV + condition than in participants in the Control condition (fruit δ: 81.3 [56.5;106.2] g; vegetable: δ: 46.2 [17.5;74.9] g), as well as in the Snacks– condition (fruit: δ: 70.0 [38.8;101.1] g; vegetable δ: 62.2 [26.2; 98.2] g).
This study shows that improving the dietary content of food parcels can positively impact the dietary intake of Dutch food bank recipients. With this we can further develop effective strategies to improve dietary intake of food bank recipients.
It is unclear what session frequency is most effective in cognitive–behavioural therapy (CBT) and interpersonal psychotherapy (IPT) for depression.
Compare the effects of once weekly and twice weekly sessions of CBT and IPT for depression.
We conducted a multicentre randomised trial from November 2014 through December 2017. We recruited 200 adults with depression across nine specialised mental health centres in the Netherlands. This study used a 2 × 2 factorial design, randomising patients to once or twice weekly sessions of CBT or IPT over 16–24 weeks, up to a maximum of 20 sessions. Main outcome measures were depression severity, measured with the Beck Depression Inventory-II at baseline, before session 1, and 2 weeks, 1, 2, 3, 4, 5 and 6 months after start of the intervention. Intention-to-treat analyses were conducted.
Compared with patients who received weekly sessions, patients who received twice weekly sessions showed a statistically significant decrease in depressive symptoms (estimated mean difference between weekly and twice weekly sessions at month 6: 3.85 points, difference in effect size d = 0.55), lower attrition rates (n = 16 compared with n = 32) and an increased rate of response (hazard ratio 1.48, 95% CI 1.00–2.18).
In clinical practice settings, delivery of twice weekly sessions of CBT and IPT for depression is a way to improve depression treatment outcomes.