Psychosocial factors, health behaviors and risk of cancer incidence: Testing interaction and effect modification in an individual participant data meta-analysis.

Depression, anxiety and other psychosocial factors are hypothesized to be involved in cancer development. We examined whether psychosocial factors interact with or modify the effects of health behaviors, such as smoking and alcohol use, in relation to cancer incidence. Two-stage individual participant data meta-analyses were performed based on 22 cohorts of the PSYchosocial factors and CAncer (PSY-CA) study. We examined nine psychosocial factors (depression diagnosis, depression symptoms, anxiety diagnosis, anxiety symptoms, perceived social support, loss events, general distress, neuroticism, relationship status), seven health behaviors/behavior-related factors (smoking, alcohol use, physical activity, body mass index, sedentary behavior, sleep quality, sleep duration) and seven cancer outcomes (overall cancer, smoking-related, alcohol-related, breast, lung, prostate, colorectal). Effects of the psychosocial factor, health behavior and their product term on cancer incidence were estimated using Cox regression. We pooled cohort-specific estimates using multivariate random-effects meta-analyses. Additive and multiplicative interaction/effect modification was examined. This study involved 437,827 participants, 36,961 incident cancer diagnoses, and 4,749,481 person years of follow-up. Out of 744 combinations of psychosocial factors, health behaviors, and cancer outcomes, we found no evidence of interaction. Effect modification was found for some combinations, but there were no clear patterns for any particular factors or outcomes involved. In this first large study to systematically examine potential interaction and effect modification, we found no evidence for psychosocial factors to interact with or modify health behaviors in relation to cancer incidence. The behavioral risk profile for cancer incidence is similar in people with and without psychosocial stress.

Who benefits from indirect prevention and treatment of depression using an online intervention for insomnia? Results from an individual-participant data meta-analysis.

BACKGROUND: Major depressive disorder (MDD) is highly prevalent and burdensome for individuals and society. While there are psychological interventions able to prevent and treat MDD, uptake remains low. To overcome structural and attitudinal barriers, an indirect approach of using online insomnia interventions seems promising because insomnia is less stigmatized, predicts MDD onset, is often comorbid and can outlast MDD treatment. This individual-participant-data meta-analysis evaluated the potential of the online insomnia intervention GET.ON Recovery as an indirect treatment to reduce depressive symptom severity (DSS) and potential MDD onset across a range of participant characteristics. METHODS: Efficacy on depressive symptom outcomes was evaluated using multilevel regression models controlling for baseline severity. To identify potential effect moderators, clinical, sociodemographic, and work-related variables were investigated using univariable moderation and random-forest methodology before developing a multivariable decision tree. RESULTS: IPD were obtained from four of seven eligible studies (N = 561); concentrating on workers with high work-stress. DSS was significantly lower in the intervention group both at post-assessment (d = -0.71 [95% CI-0.92 to -0.51]) and at follow-up (d = -0.84 [95% CI -1.11 to -0.57]). In the subsample (n = 121) without potential MDD at baseline, there were no significant group differences in onset of potential MDD. Moderation analyses revealed that effects on DSS differed significantly across baseline severity groups with effect sizes between d = -0.48 and -0.87 (post) and d = - 0.66 to -0.99 (follow-up), while no other sociodemographic, clinical, or work-related characteristics were significant moderators. CONCLUSIONS: An online insomnia intervention is a promising approach to effectively reduce DSS in a preventive and treatment setting.

EMDR v. other psychological therapies for PTSD: a systematic review and individual participant data meta-analysis.

BACKGROUND: This systematic review and individual participant data meta-analysis (IPDMA) examined the overall effectiveness of eye movement desensitization and reprocessing (EMDR) in reducing posttraumatic stress disorder (PTSD) symptoms, achieving response and remission, and reducing treatment dropout among adults with PTSD compared to other psychological treatments. Additionally, we examined available participant-level moderators of the efficacy of EMDR. METHODS: This study included randomized controlled trials. Eligible studies were identified by a systematic search in PubMed, Embase, PsyclNFO, PTSDpubs, and CENTRAL. The target population was adults with above-threshold baseline PTSD symptoms. Trials were eligible if at least 70% of study participants had been diagnosed with PTSD using a structured clinical interview. Primary outcomes included PTSD symptom severity, treatment response, and PTSD remission. Treatment dropout was a secondary outcome. The systematic search retrieved 15 eligible randomized controlled trials (RCTs); 8 of these 15 were able to be included in this IPDMA (346 patients). Comparator treatments included relaxation therapy, emotional freedom technique, trauma-focused cognitive behavioral psychotherapies, and REM-desensitization. RESULTS: One-stage IPDMA found no significant difference between EMDR and other psychological treatments in reducing PTSD symptom severity (ß = -0.24), achieving response (ß = 0.86), attaining remission (ß = 1.05), or reducing treatment dropout rates (ß = -0.25). Moderator analyses found unemployed participants receiving EMDR had higher PTSD symptom severity at the post-test, and males were more likely to drop out of EMDR treatment than females. CONCLUSION: The current study found no significant difference between EMDR and other psychological treatments. We found some indication of the moderating effects of gender and employment status.

Association between expectations and clinical outcomes in online v. face-to-face therapy - an individual participant data meta-analysis.

BACKGROUND: Online treatments are increasing in number and are currently available for a wide range of clinical problems. To date little is known about the role of treatment expectations and other placebo-like mechanisms in online settings compared to traditional face-to-face treatment. To address this knowledge gap, we analyzed individual participant data from randomized clinical trials that compared online and face-to-face psychological interventions. METHODS: MEDLINE (Ovid) and PsycINFO (Ovid) were last searched on 2 February 2021. Randomized clinical trials of therapist guided online v. face-to-face psychological interventions for psychiatric or somatic conditions using a randomized controlled design were included. Titles, abstracts, and full texts of studies were independently screened by multiple observers. The Preferred Reporting Items for Systematic Reviews and Meta-analyses guideline was followed. Authors of the matching trials were contacted for individual participant data. Ratings from the Credibility and Expectancy Questionnaire and the primary outcome measure from each trial were used to estimate the association between expectation ratings and treatment outcomes in online v. face-to-face interventions, using a mixed-effects model. RESULTS: Of 7045 screened studies, 62 full-text articles were retrieved whereof six studies fulfilled the criteria and provided individual participant data (n = 491). Overall, CEQ ratings predicted clinical outcomes (ß = 0.27) at end of treatment with no moderating effect of treatment modality (online v. face-to-face). CONCLUSIONS: Online treatment appears to be equally susceptible to expectancy effects as face-to-face therapy. This furthers our understanding of the importance of placebo-like factors in online treatment and may aid the improvement of healthcare in online settings.

Application of causal inference methods in individual-participant data meta-analyses in medicine: addressing data handling and reporting gaps with new proposed reporting guidelines.

Observational data provide invaluable real-world information in medicine, but certain methodological considerations are required to derive causal estimates. In this systematic review, we evaluated the methodology and reporting quality of individual-level patient data meta-analyses (IPD-MAs) conducted with non-randomized exposures, published in 2009, 2014, and 2019 that sought to estimate a causal relationship in medicine. We screened over 16,000 titles and abstracts, reviewed 45 full-text articles out of the 167 deemed potentially eligible, and included 29 into the analysis. Unfortunately, we found that causal methodologies were rarely implemented, and reporting was generally poor across studies. Specifically, only three of the 29 articles used quasi-experimental methods, and no study used G-methods to adjust for time-varying confounding. To address these issues, we propose stronger collaborations between physicians and methodologists to ensure that causal methodologies are properly implemented in IPD-MAs. In addition, we put forward a suggested checklist of reporting guidelines for IPD-MAs that utilize causal methods. This checklist could improve reporting thereby potentially enhancing the quality and trustworthiness of IPD-MAs, which can be considered one of the most valuable sources of evidence for health policy.

An assessment of the informative value of data sharing statements in clinical trial registries.

BACKGROUND: The provision of data sharing statements (DSS) for clinical trials has been made mandatory by different stakeholders. DSS are a device to clarify whether there is intention to share individual participant data (IPD). What is missing is a detailed assessment of whether DSS are providing clear and understandable information about the conditions for data sharing of IPD for secondary use. METHODS: A random sample of 200 COVID-19 clinical trials with explicit DSS was drawn from the ECRIN clinical research metadata repository. The DSS were assessed and classified, by two experienced experts and one assessor with less experience in data sharing (DS), into different categories (unclear, no sharing, no plans, yes but vague, yes on request, yes with specified storage location, yes but with complex conditions). RESULTS: Between the two experts the agreement was moderate to substantial (kappa=0.62, 95% CI [0.55, 0.70]). Agreement considerably decreased when these experts were compared with a third person who was less experienced and trained in data sharing ("assessor") (kappa=0.33, 95% CI [0.25, 0.41]; 0.35, 95% CI [0.27, 0.43]). Between the two experts and under supervision of an independent moderator, a consensus was achieved for those cases, where both experts had disagreed, and the result was used as "gold standard" for further analysis. At least some degree of willingness of DS (data sharing) was expressed in 63.5% (127/200) cases. Of these cases, around one quarter (31/127) were vague statements of support for data sharing but without useful detail. In around half of the cases (60/127) it was stated that IPD could be obtained by request. Only in in slightly more than 10% of the cases (15/127) it was stated that the IPD would be transferred to a specific data repository. In the remaining cases (21/127), a more complex regime was described or referenced, which could not be allocated to one of the three previous groups. As a result of the consensus meetings, the classification system was updated. CONCLUSION: The study showed that the current DSS that imply possible data sharing are often not easy to interpret, even by relatively experienced staff. Machine based interpretation, which would be necessary for any practical application, is currently not possible. Machine learning and / or natural language processing techniques might improve machine actionability, but would represent a very substantial investment of research effort. The cheaper and easier option would be for data providers, data requestors, funders and platforms to adopt a clearer, more structured and more standardised approach to specifying, providing and collecting DSS. TRIAL REGISTRATION: The protocol for the study was pre-registered on ZENODO ( https://zenodo.org/record/7064624#.Y4DIAHbMJD8 ).

An empirical comparison of statistical methods for multiple cut-off diagnostic test accuracy meta-analysis of the Edinburgh postnatal depression scale (EPDS) depression screening tool using published results vs individual participant data.

BACKGROUND: Selective reporting of results from only well-performing cut-offs leads to biased estimates of accuracy in primary studies of questionnaire-based screening tools and in meta-analyses that synthesize results. Individual participant data meta-analysis (IPDMA) of sensitivity and specificity at each cut-off via bivariate random-effects models (BREMs) can overcome this problem. However, IPDMA is laborious and depends on the ability to successfully obtain primary datasets, and BREMs ignore the correlation between cut-offs within primary studies. METHODS: We compared the performance of three recent multiple cut-off models developed by Steinhauser et al., Jones et al., and Hoyer and Kuss, that account for missing cut-offs when meta-analyzing diagnostic accuracy studies with multiple cut-offs, to BREMs fitted at each cut-off. We used data from 22 studies of the accuracy of the Edinburgh Postnatal Depression Scale (EPDS; 4475 participants, 758 major depression cases). We fitted each of the three multiple cut-off models and BREMs to a dataset with results from only published cut-offs from each study (published data) and an IPD dataset with results for all cut-offs (full IPD data). We estimated pooled sensitivity and specificity with 95% confidence intervals (CIs) for each cut-off and the area under the curve. RESULTS: Compared to the BREMs fitted to the full IPD data, the Steinhauser et al., Jones et al., and Hoyer and Kuss models fitted to the published data produced similar receiver operating characteristic curves; though, the Hoyer and Kuss model had lower area under the curve, mainly due to estimating slightly lower sensitivity at lower cut-offs. When fitting the three multiple cut-off models to the full IPD data, a similar pattern of results was observed. Importantly, all models had similar 95% CIs for sensitivity and specificity, and the CI width increased with cut-off levels for sensitivity and decreased with an increasing cut-off for specificity, even the BREMs which treat each cut-off separately. CONCLUSIONS: Multiple cut-off models appear to be the favorable methods when only published data are available. While collecting IPD is expensive and time consuming, IPD can facilitate subgroup analyses that cannot be conducted with published data only.

Vaginal misoprostol versus vaginal dinoprostone for cervical ripening and induction of labour: An individual participant data meta-analysis of randomised controlled trials.

BACKGROUND: Induction of labour (IOL) is common practice and different methods carry different effectiveness and safety profiles. OBJECTIVES: To compare the effectiveness, and maternal and perinatal safety outcomes of IOL with vaginal misoprostol versus vaginal dinoprostone using individual participant data from randomised clinical trials. SEARCH STRATEGY: The following databases were searched from inception to March 2023: CINAHL Plus, ClinicalTrials.gov, Cochrane Pregnancy and Childbirth Group Trial Register, Ovid Embase, Ovid Emcare, Ovid MEDLINE, Scopus and the World Health Organization (WHO) International Clinical Trials Registry Platform (ICTRP). SELECTION CRITERIA: Randomised controlled trials (RCTs), with viable singleton gestation, no language restrictions, and all published and unpublished data. DATA COLLECTION AND ANALYSIS: An individual participant data meta-analysis was carried out. MAIN RESULTS: Ten of 52 eligible trials provided individual participant data, of which two were excluded after checking data integrity. The remaining eight trials compared low-dose vaginal misoprostol versus dinoprostone, including 4180 women undergoing IOL, which represents 32.8% of all participants in the published RCTs. Of these, 2077 were assigned to low-dose vaginal misoprostol and 2103 were assigned to vaginal dinoprostone. Compared with vaginal dinoprostone, low-dose vaginal misoprostol had a comparable rate of vaginal birth. Composite adverse perinatal outcomes did not differ between the groups. Compared with vaginal dinoprostone, composite adverse maternal outcomes were significantly lower with low-dose vaginal misoprostol (aOR 0.80, 95% CI 0.65-0.98, P = 0.03, I2 = 0%). CONCLUSIONS: Low-dose vaginal misoprostol and vaginal dinoprostone for IOL are comparable in terms of effectiveness and perinatal safety. However, low-dose vaginal misoprostol is likely to lead to a lower rate of composite adverse maternal outcomes than vaginal dinoprostone.

Minimal Clinically Important Difference in the 6-minute-walk Distance for Patients with Pulmonary Arterial Hypertension.

Rationale: The 6-minute-walk distance (6MWD) is an important clinical and research metric in pulmonary arterial hypertension (PAH); however, there is no consensus about what minimal change in 6MWD is clinically significant. Objectives: We aimed to determine the minimal clinically important difference in the 6MWD. Methods: We performed a meta-analysis using individual participant data from eight randomized clinical trials of therapy for PAH submitted to the U.S. Food and Drug Administration to derive minimal clinically important differences in the 6MWD. The estimates were externally validated using the Pulmonary Hypertension Association Registry. We anchored the change in 6MWD to the change in the Medical Outcomes Survey Short Form physical component score. Measurements and Main Results: The derivation (clinical trial) and validation (Pulmonary Hypertension Association Registry) samples were comprised of 2,404 and 537 adult patients with PAH, respectively. The mean ± standard deviation age of the derivation sample was 50.5 ± 15.2 years, and 1,849 (77%) were female, similar to the validation sample. The minimal clinically important difference in the derivation sample was 33 meters (95% confidence interval, 27-38), which was almost identical to that in the validation sample (36 m [95% confidence interval, 29-43]). The minimal clinically important difference did not differ by age, sex, race, pulmonary hypertension etiology, body mass index, use of background therapy, or World Health Organization functional class. Conclusions: We estimated a 6MWD minimal clinically important difference of approximately 33 meters for adults with PAH. Our findings can be applied to the design of clinical trials of therapies for PAH.

Lifestyle and incident dementia: A COSMIC individual participant data meta­analysis.

INTRODUCTION: The LIfestyle for BRAin Health (LIBRA) index yields a dementia risk score based on modifiable lifestyle factors and is validated in Western samples. We investigated whether the association between LIBRA scores and incident dementia is moderated by geographical location or sociodemographic characteristics. METHODS: We combined data from 21 prospective cohorts across six continents (N = 31,680) and conducted cohort-specific Cox proportional hazard regression analyses in a two-step individual participant data meta-analysis. RESULTS: A one-standard-deviation increase in LIBRA score was associated with a 21% higher risk for dementia. The association was stronger for Asian cohorts compared to European cohorts, and for individuals aged ≤75 years (vs older), though only within the first 5 years of follow-up. No interactions with sex, education, or socioeconomic position were observed. DISCUSSION: Modifiable risk and protective factors appear relevant for dementia risk reduction across diverse geographical and sociodemographic groups. HIGHLIGHTS: A two-step individual participant data meta-analysis was conducted. This was done at a global scale using data from 21 ethno-regionally diverse cohorts. The association between a modifiable dementia risk score and dementia was examined. The association was modified by geographical region and age at baseline. Yet, modifiable dementia risk and protective factors appear relevant in all investigated groups and regions.

Personality predictors of dementia diagnosis and neuropathological burden: An individual participant data meta-analysis.

INTRODUCTION: The extent to which the Big Five personality traits and subjective well-being (SWB) are discriminatory predictors of clinical manifestation of dementia versus dementia-related neuropathology is unclear. METHODS: Using data from eight independent studies (Ntotal = 44,531; Ndementia = 1703; baseline Mage = 49 to 81 years, 26 to 61% female; Mfollow-up range = 3.53 to 21.00 years), Bayesian multilevel models tested whether personality traits and SWB differentially predicted neuropsychological and neuropathological characteristics of dementia. RESULTS: Synthesized and individual study results indicate that high neuroticism and negative affect and low conscientiousness, extraversion, and positive affect were associated with increased risk of long-term dementia diagnosis. There were no consistent associations with neuropathology. DISCUSSION: This multistudy project provides robust, conceptually replicated and extended evidence that psychosocial factors are strong predictors of dementia diagnosis but not consistently associated with neuropathology at autopsy. HIGHLIGHTS: N(+), C(-), E(-), PA(-), and NA(+) were associated with incident diagnosis. Results were consistent despite self-report versus clinical diagnosis of dementia. Psychological factors were not associated with neuropathology at autopsy. Individuals with higher conscientiousness and no diagnosis had less neuropathology. High C individuals may withstand neuropathology for longer before death.

Individual participant data meta-analysis with mixed-effects transformation models.

One-stage meta-analysis of individual participant data (IPD) poses several statistical and computational challenges. For time-to-event outcomes, the approach requires the estimation of complicated nonlinear mixed-effects models that are flexible enough to realistically capture the most important characteristics of the IPD. We present a model class that incorporates general normally distributed random effects into linear transformation models. We discuss extensions to model between-study heterogeneity in baseline risks and covariate effects and also relax the assumption of proportional hazards. Within the proposed framework, data with arbitrary random censoring patterns can be handled. The accompanying $\textsf{R}$ package tramME utilizes the Laplace approximation and automatic differentiation to perform efficient maximum likelihood estimation and inference in mixed-effects transformation models. We compare several variants of our model to predict the survival of patients with chronic obstructive pulmonary disease using a large data set of prognostic studies. Finally, a simulation study is presented that verifies the correctness of the implementation and highlights its efficiency compared to an alternative approach.

Arthroscopic partial meniscectomy vs non-surgical or sham treatment in patients with MRI-confirmed degenerative meniscus tears: a systematic review and meta-analysis with individual participant data from 605 randomised patients.

OBJECTIVE: To identify subgroups of patients with magnetic resonance imaging (MRI)-confirmed degenerative meniscus tears who may benefit from arthroscopic partial meniscectomy (APM) in comparison with non-surgical or sham treatment. METHODS: Individual participant data (IPD) from four RCTs were pooled (605 patients, mean age: 55 (SD: 7.5), 52.4% female) as to investigate the effectiveness of APM in patients with MRI-confirmed degenerative meniscus tears compared to non-surgical or sham treatment. Primary outcomes were knee pain, overall knee function, and health-related quality of life, at 24 months follow-up (0-100). The IPD were analysed in a one- and two-stage meta-analyses. Identification of potential subgroups was performed by testing interaction effects of predefined patient characteristics (e.g., age, gender, mechanical symptoms) and APM for each outcome. Additionally, generalized linear mixed-model trees were used for subgroup detection. RESULTS: The APM group showed a small improvement over the non-surgical or sham group on knee pain at 24 months follow-up (2.5 points (95% CI: 0.8-4.2) and 2.2 points (95% CI: 0.9-3.6), one- and two-stage analysis, respectively). Overall knee function and health-related quality of life did not differ between the two groups. Across all outcomes, no relevant subgroup of patients who benefitted from APM was detected. The generalized linear mixed-model trees did also not identify a subgroup. CONCLUSIONS: No relevant subgroup of patients was identified that benefitted from APM compared to non-surgical or sham treatment. Since we were not able to identify any subgroup that benefitted from APM, we recommend a restrained policy regarding meniscectomy in patients with degenerative meniscus tears.

Which patients benefit from adding short-term psychodynamic psychotherapy to antidepressants in the treatment of depression? A systematic review and meta-analysis of individual participant data.

BACKGROUND: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.

Multi-trial, aggregated, individual participant data mega-analysis of short-term antidepressant versus mood stabilizer monotherapy of bipolar type II major depressive episode.

BACKGROUND: Few studies have systematically examined the safety and effectiveness of antidepressant versus mood stabilizer monotherapy of bipolar II depression. To date, there are no aggregated or mega-analyses of prospective trials of individual participant-level data (IPD) to inform future treatment guidelines on the relative safety and effectiveness of antidepressant or lithium monotherapy. METHODS: Data from a series of four independent, similarly designed trials of antidepressant or lithium monotherapy (where longitudinal IPD were available) (n = 393) were aggregated into an IPD dataset (i.e., mega-analysis). Hierarchical log-linear growth models were used to analyze primary outcome of change over time in Hamilton Rating Scale for Depression (HRSD) scores; while secondary outcomes examined Clinical Global Impressions severity (CGI/S) and change (CGI/C) scores, and change over time in Young Mania Rating (YMR) scores. RESULTS: Relative to lithium monotherapy, antidepressant monotherapy demonstrated significantly greater symptom reduction on HRSD scores across time (b = -2.33, t = -6.68, p < 0.0001), significantly greater symptom reduction on the CGI/S across time (b = -0.414, t = -6.32, p < 0.001), and a significant improvement in CGI/C across time (b = -0.47, t = -7.43, p < 0.0001). No differences were observed in change over time for YMR scores between antidepressant and lithium monotherapy (b = 0.06, t = 0.49, p = 0.62). CONCLUSION: Findings from this IPD mega-analysis of bipolar II depression trials suggest a divergence from current evidence-based guidelines recommending combined mood stabilizer plus antidepressant therapy. The current mega-analysis suggests that antidepressant monotherapy may provide superior short-term effectiveness without clinically meaningful increase in treatment-emergent hypomanic symptoms compared to lithium monotherapy.

Sequential regression and simulation: a method for estimating causal effects from heterogeneous clinical trials without a common control group.

BACKGROUND: The advent of clinical trial data sharing platforms has created opportunities for making new discoveries and answering important questions using already collected data. However, existing methods for meta-analyzing these data require the presence of shared control groups across studies, significantly limiting the number of questions that can be confidently addressed. We sought to develop a method for meta-analyzing potentially heterogeneous clinical trials even in the absence of a common control group. METHODS: This work was conducted within the context of a broader effort to study comparative efficacy in Crohn's disease. Following a search of clnicaltrials.gov we obtained access to the individual participant data from nine trials of FDA-approved treatments in Crohn's Disease (N = 3392). We developed a method involving sequences of regression and simulation to separately model the placebo- and drug-attributable effects, and to simulate head-to-head trials against an appropriately normalized background. We validated this method by comparing the outcome of a simulated trial comparing the efficacies of adalimumab and ustekinumab against the recently published results of SEAVUE, an actual head-to-head trial of these drugs. This study was pre-registered on PROSPERO (#157,827) prior to the completion of SEAVUE. RESULTS: Using our method of sequential regression and simulation, we compared the week eight outcomes of two virtual cohorts subject to the same patient selection criteria as SEAVUE and treated with adalimumab or ustekinumab. Our primary analysis replicated the corresponding published results from SEAVUE (p = 0.9). This finding proved stable under multiple sensitivity analyses. CONCLUSIONS: This new method may help reduce the bias of individual participant data meta-analyses, expand the scope of what can be learned from these already-collected data, and reduce the costs of obtaining high-quality evidence to guide patient care.

Antibiotic treatment of bacterial vaginosis to prevent preterm delivery: Systematic review and individual participant data meta-analysis.

BACKGROUND: Bacterial vaginosis (BV) increases preterm delivery (PTD) risk, but treatment trials showed mixed results in preventing PTD. OBJECTIVES: Determine, using individual participant data (IPD), whether BV treatment during pregnancy reduced PTD or prolonged time-to-delivery. DATA SOURCES: Cochrane Systematic Review (2013), MEDLINE, EMBASE, journal searches, and searches (January 2013-September 2022) ("bacterial vaginosis AND pregnancy") of (i) clinicaltrials.gov; (ii) Cochrane Central Register of Controlled Trials; (iii) World Health Organization International Clinical Trials Registry Platform Portal; and (iv) Web of Science ("bacterial vaginosis"). STUDY SELECTION AND DATA EXTRACTION: Studies randomising asymptomatic pregnant individuals with BV to antibiotics or control, measuring delivery gestation. Extraction was from original data files. Bias risk was assessed using the Cochrane tool. Analysis used "one-step" logistic and Cox random effect models, adjusting gestation at randomisation and PTD history; heterogeneity by I2 . Subgroup analysis tested interactions with treatment. In sensitivity analyses, studies not providing IPD were incorporated by "multiple random-donor hot-deck" imputation, using IPD studies as donors. RESULTS: There were 121 references (96 studies) with 23 eligible trials (11,979 participants); 13 studies (6915 participants) provided IPD; 12 (6115) were incorporated. Results from 9 (4887 participants) not providing IPD were imputed. Odds ratios for PTD for metronidazole and clindamycin versus placebo were 1.00 (95% CI 0.84, 1.17), I2  = 62%, and 0.59 (95% CI 0.42, 0.82), I2  = 0 before; and 0.95 (95% CI 0.81, 1.11), I2  = 59%, and 0.90 (95% CI: 0.72, 1.12), I2  = 0, after imputation. Time-to-delivery did not differ from null with either treatment. Including imputed IPD, there was no evidence that either drug was more effective when administered earlier, or among those with a PTD history. CONCLUSIONS: Clindamycin, but not metronidazole, was beneficial in studies providing IPD, but after imputing data from missing IPD studies, treatment of BV during pregnancy did not reduce PTD, nor prolong pregnancy, in any subgroup or when started earlier in gestation.

Risk factors for late preterm and term stillbirth: A secondary analysis of an individual participant data meta-analysis.

OBJECTIVE: Identify independent and novel risk factors for late-preterm (28-36 weeks) and term (≥37 weeks) stillbirth and explore development of a risk-prediction model. DESIGN: Secondary analysis of an Individual Participant Data (IPD) meta-analysis investigating modifiable stillbirth risk factors. SETTING: An IPD database from five case-control studies in New Zealand, Australia, the UK and an international online study. POPULATION: Women with late-stillbirth (cases, n = 851), and ongoing singleton pregnancies from 28 weeks' gestation (controls, n = 2257). METHODS: Established and novel risk factors for late-preterm and term stillbirth underwent univariable and multivariable logistic regression modelling with multiple sensitivity analyses. Variables included maternal age, body mass index (BMI), parity, mental health, cigarette smoking, second-hand smoking, antenatal-care utilisation, and detailed fetal movement and sleep variables. MAIN OUTCOME MEASURES: Independent risk factors with adjusted odds ratios (aOR) for late-preterm and term stillbirth. RESULTS: After model building, 575 late-stillbirth cases and 1541 controls from three contributing case-control studies were included. Risk factor estimates from separate multivariable models of late-preterm and term stillbirth were compared. As these were similar, the final model combined all late-stillbirths. The single multivariable model confirmed established demographic risk factors, but additionally showed that fetal movement changes had both increased (decreased frequency) and reduced (hiccoughs, increasing strength, frequency or vigorous fetal movements) aOR of stillbirth. Poor antenatal-care utilisation increased risk while more-than-adequate care was protective. The area-under-the-curve was 0.84 (95% CI 0.82-0.86). CONCLUSIONS: Similarities in risk factors for late-preterm and term stillbirth suggest the same approach for risk-assessment can be applied. Detailed fetal movement assessment and inclusion of antenatal-care utilisation could be valuable in late-stillbirth risk assessment.

Identifying relapse predictors in individual participant data with decision trees.

BACKGROUND: Depression is a highly common and recurrent condition. Predicting who is at most risk of relapse or recurrence can inform clinical practice. Applying machine-learning methods to Individual Participant Data (IPD) can be promising to improve the accuracy of risk predictions. METHODS: Individual data of four Randomized Controlled Trials (RCTs) evaluating antidepressant treatment compared to psychological interventions with tapering ([Formula: see text]) were used to identify predictors of relapse and/or recurrence. Ten baseline predictors were assessed. Decision trees with and without gradient boosting were applied. To study the robustness of decision-tree classifications, we also performed a complementary logistic regression analysis. RESULTS: The combination of age, age of onset of depression, and depression severity significantly enhances the prediction of relapse risk when compared to classifiers solely based on depression severity. The studied decision trees can (i) identify relapse patients at intake with an accuracy, specificity, and sensitivity of about 55% (without gradient boosting) and 58% (with gradient boosting), and (ii) slightly outperform classifiers that are based on logistic regression. CONCLUSIONS: Decision tree classifiers based on multiple-rather than single-risk indicators may be useful for developing treatment stratification strategies. These classification models have the potential to contribute to the development of methods aimed at effectively prioritizing treatment for those individuals who require it the most. Our results also underline the existing gaps in understanding how to accurately predict depressive relapse.

Double-vs single-balloon catheter for induction of labor: Systematic review and individual participant data meta-analysis.

INTRODUCTION: Evidence comparing double-balloon vs single-balloon catheter for induction of labor is divided. We aim to compare the efficacy and safety of double-vs single-balloon catheters using individual participant data. MATERIAL AND METHODS: A search of Ovid MEDLINE, Embase, Ovid Emcare, CINAHL Plus, Scopus, and clinicaltrials.gov was conducted for randomized controlled trials published from March 2019 until April 13, 2021. Earlier trials were identified from the Cochrane Review on Mechanical Methods for Induction of Labour. Randomized controlled trials that compared double-balloon with single-balloon catheters for induction of labor in singleton gestations were eligible. Participant-level data were sought from trial investigators and an individual participant data meta-analysis was performed. The primary outcomes were rates of vaginal birth achieved, a composite measure of adverse maternal outcomes and a composite measure of adverse perinatal outcomes. We used a two-stage random-effects model. Data were analyzed from the intention-to-treat perspective. RESULTS: Of the eight eligible randomized controlled trials, three shared individual-level data with a total of 689 participants, 344 women in the double-balloon catheter group and 345 women in the single-balloon catheter group. The difference in the rate of vaginal birth between double-balloon catheter and single-balloon catheter was not statistically significant (relative risk [RR] 0.93, 95% confidence interval [CI] 0.86-1.00, p = 0.050; I2 0%; moderate-certainty evidence). Both perinatal outcomes (RR 0.81, 95% CI 0.54-1.21, p = 0.691; I2 0%; moderate-certainty evidence) and maternal composite outcomes (RR 0.65, 95% CI 0.15-2.87, p = 0.571; I2 55.46%; low-certainty evidence) were not significantly different between the two groups. CONCLUSIONS: Single-balloon catheter is at least comparable to double-balloon catheter in terms of vaginal birth rate and maternal and perinatal safety outcomes.