Humanized Patient-derived Xenograft Models of Disseminated Ovarian Cancer Recapitulate Key Aspects of the Tumor Immune Environment within the Peritoneal Cavity.

The importance of the immune microenvironment in ovarian cancer progression, metastasis, and response to therapies has become increasingly clear, especially with the new emphasis on immunotherapies. To leverage the power of patient-derived xenograft (PDX) models within a humanized immune microenvironment, three ovarian cancer PDXs were grown in humanized NBSGW (huNBSGW) mice engrafted with human CD34+ cord blood-derived hematopoietic stem cells. Analysis of cytokine levels in the ascites fluid and identification of infiltrating immune cells in the tumors demonstrated that these humanized PDX (huPDX) established an immune tumor microenvironment similar to what has been reported for patients with ovarian cancer. The lack of human myeloid cell differentiation has been a major setback for humanized mouse models, but our analysis shows that PDX engraftment increases the human myeloid population in the peripheral blood. Analysis of cytokines within the ascites fluid of huPDX revealed high levels of human M-CSF, a key myeloid differentiation factor as well as other elevated cytokines that have previously been identified in ovarian cancer patient ascites fluid including those involved in immune cell differentiation and recruitment. Human tumor-associated macrophages and tumor-infiltrating lymphocytes were detected within the tumors of humanized mice, demonstrating immune cell recruitment to tumors. Comparison of the three huPDX revealed certain differences in cytokine signatures and in the extent of immune cell recruitment. Our studies show that huNBSGW PDX models reconstitute important aspects of the ovarian cancer immune tumor microenvironment, which may recommend these models for preclinical therapeutic trials. Significance: huPDX models are ideal preclinical models for testing novel therapies. They reflect the genetic heterogeneity of the patient population, enhance human myeloid differentiation, and recruit immune cells to the tumor microenvironment.

Moderators of the effect of therapeutic exercise for knee and hip osteoarthritis: a systematic review and individual participant data meta-analysis.

BACKGROUND: Many international clinical guidelines recommend therapeutic exercise as a core treatment for knee and hip osteoarthritis. We aimed to identify individual patient-level moderators of the effect of therapeutic exercise for reducing pain and improving physical function in people with knee osteoarthritis, hip osteoarthritis, or both. METHODS: We did a systematic review and individual participant data (IPD) meta-analysis of randomised controlled trials comparing therapeutic exercise with non-exercise controls in people with knee osteoathritis, hip osteoarthritis, or both. We searched ten databases from March 1, 2012, to Feb 25, 2019, for randomised controlled trials comparing the effects of exercise with non-exercise or other exercise controls on pain and physical function outcomes among people with knee osteoarthritis, hip osteoarthritis, or both. IPD were requested from leads of all eligible randomised controlled trials. 12 potential moderators of interest were explored to ascertain whether they were associated with short-term (12 weeks), medium-term (6 months), and long-term (12 months) effects of exercise on self-reported pain and physical function, in comparison with non-exercise controls. Overall intervention effects were also summarised. This study is prospectively registered on PROSPERO (CRD42017054049). FINDINGS: Of 91 eligible randomised controlled trials that compared exercise with non-exercise controls, IPD from 31 randomised controlled trials (n=4241 participants) were included in the meta-analysis. Randomised controlled trials included participants with knee osteoarthritis (18 [58%] of 31 trials), hip osteoarthritis (six [19%]), or both (seven [23%]) and tested heterogeneous exercise interventions versus heterogeneous non-exercise controls, with variable risk of bias. Summary meta-analysis results showed that, on average, compared with non-exercise controls, therapeutic exercise reduced pain on a standardised 0-100 scale (with 100 corresponding to worst pain), with a difference of -6·36 points (95% CI -8·45 to -4·27, borrowing of strength [BoS] 10·3%, between-study variance [τ2] 21·6) in the short term, -3·77 points (-5·97 to -1·57, BoS 30·0%, τ2 14·4) in the medium term, and -3·43 points (-5·18 to -1·69, BoS 31·7%, τ2 4·5) in the long term. Therapeutic exercise also improved physical function on a standardised 0-100 scale (with 100 corresponding to worst physical function), with a difference of -4·46 points in the short term (95% CI -5·95 to -2·98, BoS 10·5%, τ2 10·1), -2·71 points in the medium term (-4·63 to -0·78, BoS 33·6%, τ2 11·9), and -3·39 points in the long term (-4·97 to -1·81, BoS 34·1%, τ2 6·4). Baseline pain and physical function moderated the effect of exercise on pain and physical function outcomes. Those with higher self-reported pain and physical function scores at baseline (ie, poorer physical function) generally benefited more than those with lower self-reported pain and physical function scores at baseline, with the evidence most certain in the short term (12 weeks). INTERPRETATION: There was evidence of a small, positive overall effect of therapeutic exercise on pain and physical function compared with non-exercise controls. However, this effect is of questionable clinical importance, particularly in the medium and long term. As individuals with higher pain severity and poorer physical function at baseline benefited more than those with lower pain severity and better physical function at baseline, targeting individuals with higher levels of osteoarthritis-related pain and disability for therapeutic exercise might be of merit. FUNDING: Chartered Society of Physiotherapy Charitable Trust and the National Institute for Health and Care Research.

Multivariate meta-analysis of multiple outcomes: characteristics and predictors of borrowing of strength from Cochrane reviews.

OBJECTIVES: Multivariate meta-analysis allows the joint synthesis of multiple outcomes accounting for their correlation. This enables borrowing of strength (BoS) across outcomes, which may lead to greater efficiency and even different conclusions compared to separate univariate meta-analyses. However, multivariate meta-analysis is complex to apply, so guidance is needed to flag (in advance of analysis) when the approach is most useful. STUDY DESIGN AND SETTING: We use 43 Cochrane intervention reviews to empirically investigate the characteristics of meta-analysis datasets that are associated with a larger BoS statistic (from 0 to 100%) when applying a bivariate meta-analysis of binary outcomes. RESULTS: Four characteristics were identified as strongly associated with BoS: the total number of studies, the number of studies with the outcome of interest, the percentage of studies missing the outcome of interest, and the largest absolute within-study correlation. Using these characteristics, we then develop a model for predicting BoS in a new dataset, which is shown to have good performance (an adjusted R2 of 50%). Applied examples are used to illustrate the use of the BoS prediction model. CONCLUSIONS: Cochrane reviewers mainly use univariate meta-analysis methods, but the identified characteristics associated with BoS and our subsequent prediction model for BoS help to flag when a multivariate meta-analysis may also be beneficial in Cochrane reviews with multiple binary outcomes. Extension to non-Cochrane reviews and other outcome types is still required.

Determining the Prevalence and Incidence of SARS-CoV-2 Infection in Prisons in England: Protocol for a Repeated Panel Survey and Enhanced Outbreak Study.

BACKGROUND: There are over 80,000 people imprisoned in England and Wales in 117 prisons. The management of the COVID-19 pandemic presents particular challenges in this setting where confined, crowded, and poorly ventilated conditions facilitate the rapid spread of infectious diseases. OBJECTIVE: The COVID-19 in Prison Study aims to examine the epidemiology of SARS-CoV-2 in prisons in England in order to inform public health policy and practice during the pandemic and recovery. The primary objective is to estimate the proportion of positive tests of SARS-CoV-2 infection among residents and staff within selected prisons. The secondary objectives include estimating the incidence rate of SARS-CoV-2 infection and examining how the proportion of positive tests and the incidence rate vary among individual, institutional, and system level factors. METHODS: Phase 1 comprises a repeated panel survey of prison residents and staff in a representative sample of 28 prisons across England. All residents and staff in the study prisons are eligible for inclusion. Participants will be tested for SARS-CoV-2 using a nasopharyngeal swab twice (6 weeks apart). Staff will also be tested for antibodies to SARS-CoV-2. Phase 2 focuses on SARS-CoV-2 infection in prisons with recognized COVID-19 outbreaks. Any prison in England will be eligible to participate if an outbreak is declared. In 3 outbreak prisons, all participating staff and residents will be tested for SARS-CoV-2 antigens at the following 3 timepoints: as soon as possible after the outbreak is declared (day 0), 7 days later (day 7), and at day 28. They will be swabbed twice (a nasal swab for lateral flow device testing and a nasopharyngeal swab for polymerase chain reaction testing). Testing will be done by external contractors. Data will also be collected on individual, prison level, and community factors. Data will be stored and handled at the University of Southampton and Public Health England. Summary statistics will summarize the prison and participant characteristics. For the primary objective, simple proportions of individuals testing positive for SARS-CoV-2 and incidence rates will be calculated. Linear regression will examine the individual, institutional, system, and community factors associated with SARS-CoV-2 infection within prisons. RESULTS: The UK Government's Department for Health and Social Care funds the study. Data collection started on July 20, 2020, and will end on May 31, 2021. As of May 2021, we had enrolled 4192 staff members and 6496 imprisoned people in the study. Data analysis has started, and we expect to publish the initial findings in summer/autumn 2021. The main ethical consideration is the inclusion of prisoners, who are vulnerable participants. CONCLUSIONS: This study will provide unique data to inform the public health management of SARS-CoV-2 in prisons. Its findings will be of relevance to health policy makers and practitioners working in prisons. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/30749.

Community-based delivery of intermittent preventive treatment of malaria in pregnancy in Burkina Faso: a qualitative study.

BACKGROUND: Burkina Faso is among ten countries with the highest rates of malaria cases and deaths in the world. Delivery and coverage of intermittent preventive treatment of malaria in pregnancy (IPTp) is insufficient in Burkina Faso; In a 2016 survey, only 22% of eligible women had received their third dose of IPTp. It is also an extremely rural country and one with an established cadre of community healthcare workers (CHWs). To better meet the needs of pregnant women, an enhanced programme was established to facilitate distribution of IPTp at the community level by CHWs. METHODS: In order to assess the perceptions of CHWs and facility healthcare workers (HCWs) involved in this programme rollout, semi-structured interviews were conducted at three high malaria burden health districts in Burkina Faso. Interviews were conducted at baseline with 104 CHWs and 35 HCWs prior to the introduction of community based IPTp (c-IPTp) to assess capacity and any areas of concern. At endline, interviews were conducted with 29 CHWs and 21 HCWs to identify key facilitators and suggestions for further implementation of the c-IPTp programme. RESULTS: CHWs reported feeling capable of supporting c-IPTp delivery and facilitating linkage to antenatal care (ANC). They noted that the opportunity for enhanced training and close and ongoing connections with facility HCWs and supportive supervision were imperative. Both CHWs and HCWs perceived this approach as acceptable to community members and noted the importance of close community engagement, monthly meetings between CHWs and facility HCWs, and maintaining regular supplies of sulfadoxine-pyrimethamine (SP). Those interviewed noted that it was beneficial to have the involvement of both female and male CHWs. CONCLUSIONS: Community-based delivery of IPTp was feasible and acceptable to both facility HCWs and CHWs. This approach has the potential to strengthen delivery and uptake of IPTp and ANC both in Burkina Faso and across the region.

Factors associated with SARS-CoV-2 infection and outbreaks in long-term care facilities in England: a national cross-sectional survey.

BACKGROUND: Outbreaks of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection have occurred in long-term care facilities (LTCFs) worldwide, but the reasons why some facilities are particularly vulnerable to outbreaks are poorly understood. We aimed to identify factors associated with SARS-CoV-2 infection and outbreaks among staff and residents in LTCFs. METHODS: We did a national cross-sectional survey of all LTCFs providing dementia care or care to adults aged 65 years or older in England between May 26 and June 19, 2020. The survey collected data from managers of eligible LTCFs on LTCF characteristics, staffing factors, the use of disease control measures, and the number of confirmed cases of infection among staff and residents in each LTCF. Survey responses were linked to individual-level SARS-CoV-2 RT-PCR test results obtained through the national testing programme in England between April 30 and June 13, 2020. The primary outcome was the weighted period prevalence of confirmed SARS-CoV-2 infections in residents and staff reported via the survey. Multivariable logistic regression models were fitted to identify factors associated with infection in staff and residents, an outbreak (defined as at least one case of SARS-CoV-2 infection in a resident or staff member), and a large outbreak (defined as LTCFs with more than a third of the total number of residents and staff combined testing positive, or with >20 residents and staff combined testing positive) using data from the survey and from the linked survey-test dataset. FINDINGS: 9081 eligible wLTCFs were identified, of which 5126 (56·4%) participated in the survey, providing data on 160 033 residents and 248 594 staff members. The weighted period prevalence of infection was 10·5% (95% CI 9·9-11·1) in residents and 3·8% (3·4-4·2) in staff members. 2724 (53·1%) LTCFs reported outbreaks, and 469 (9·1%) LTCFs reported large outbreaks. The odds of SARS-CoV-2 infection in residents (adjusted odds ratio [aOR] 0·80 [95% CI 0·75-0·86], p<0·0001) and staff (0·70 [0·65-0·77], p<0·0001), and of large outbreaks (0·59 [0·38-0·93], p=0·024) were significantly lower in LTCFs that paid staff statutory sick pay compared with those that did not. Each one unit increase in the staff-to-bed ratio was associated with a reduced odds of infection in residents (0·82 [0·78-0·87], p<0·0001) and staff (0·63 [0·59-0·68], p<0·0001. The odds of infection in residents (1·30 [1·23-1·37], p<0·0001) and staff (1·20 [1·13-1·29], p<0·0001), and of outbreaks (2·56 [1·94-3·49], p<0·0001) were significantly higher in LTCFs in which staff often or always cared for both infected or uninfected residents compared with those that cohorted staff with either infected or uninfected residents. Significantly increased odds of infection in residents (1·01 [1·01-1·01], p<0·0001) and staff (1·00 [1·00-1·01], p=0·0005), and of outbreaks (1·08 [1·05-1·10], p<0·0001) were associated with each one unit increase in the number of new admissions to the LTCF relative to baseline (March 1, 2020). The odds of infection in residents (1·19 [1·12-1·26], p<0·0001) and staff (1·19 [1·10-1·29], p<0·0001), and of large outbreaks (1·65 [1·07-2·54], p=0·024) were significantly higher in LTCFs that were for profit versus those that were not for profit. Frequent employment of agency nurses or carers was associated with a significantly increased odds of infection in residents (aOR 1·65 [1·56-1·74], p<0·0001) and staff (1·85 [1·72-1·98], p<0·0001), and of outbreaks (2·33 [1·72-3·16], p<0·0001) and large outbreaks (2·42 [1·67-3·51], p<0·0001) compared with no employment of agency nurses or carers. Compared with LTCFs that did not report difficulties in isolating residents, those that did had significantly higher odds of infection in residents (1·33 [1·28-1·38], p<0·0001) and staff (1·48 [1·41-1·56], p<0·0001), and of outbreaks (1·84 [1·48-2·30], p<0·0001) and large outbreaks (1·62 [1·24-2·11], p=0·0004). INTERPRETATION: Half of LTCFs had no cases of SARS-CoV-2 infection in the first wave of the pandemic. Reduced transmission from staff is associated with adequate sick pay, minimal use of agency staff, an increased staff-to-bed ratio, and staff cohorting with either infected or uninfected residents. Increased transmission from residents is associated with an increased number of new admissions to the facility and poor compliance with isolation procedures. FUNDING: UK Government Department of Health and Social Care.

A cluster randomized trial of delivery of intermittent preventive treatment of malaria in pregnancy at the community level in Burkina Faso.

BACKGROUND: Malaria in pregnancy is responsible for 8-14% of low birth weight and 20% of stillbirths in sub-Saharan Africa. To prevent these adverse consequences, the World Health Organization recommends intermittent preventive treatment of pregnant women (IPTp) with sulfadoxine-pyrimethamine be administered at each ANC visit starting as early as possible in the second trimester. Global IPTp coverage in targeted countries remains unacceptably low. Community delivery of IPTp was explored as a means to improve coverage. METHODS: A cluster randomized, controlled trial was conducted in 12 health facilities in a 1:1 ratio to either an intervention group (IPTp delivered by CHWs) or a control group (standard practice, with IPTp delivered at HFs) in three districts of Burkina Faso to assess the effect of IPTp administration by community health workers (CHWs) on the coverage of IPTp and antenatal care (ANC). The districts and facilities were purposively selected taking into account malaria epidemiology, IPTp coverage, and the presence of active CHWs. Pre- and post-intervention surveys were carried out in March 2017 and July-August 2018, respectively. A difference in differences (DiD) analysis was conducted to assess the change in coverage of IPTp and ANC over time, accounting for clustering at the health facility level. RESULTS: Altogether 374 and 360 women were included in the baseline and endline surveys, respectively. At baseline, women received a median of 2.1 doses; by endline, women received a median of 1.8 doses in the control group and 2.8 doses in the intervention group (p-value < 0.0001). There was a non-statistically significant increase in the proportion of women attending four ANC visits in the intervention compared to control group (DiD = 12.6%, p-value = 0.16). By the endline, administration of IPTp was higher in the intervention than control, with a DiD of 17.6% for IPTp3 (95% confidence interval (CI) - 16.3, 51.5; p-value 0.31) and 20.0% for IPTp4 (95% CI - 7.2, 47.3; p-value = 0.15). CONCLUSIONS: Community delivery of IPTp could potentially lead to a greater number of IPTp doses delivered, with no apparent decrease in ANC coverage.

One-stage individual participant data meta-analysis models for continuous and binary outcomes: Comparison of treatment coding options and estimation methods.

A one-stage individual participant data (IPD) meta-analysis synthesizes IPD from multiple studies using a general or generalized linear mixed model. This produces summary results (eg, about treatment effect) in a single step, whilst accounting for clustering of participants within studies (via a stratified study intercept, or random study intercepts) and between-study heterogeneity (via random treatment effects). We use simulation to evaluate the performance of restricted maximum likelihood (REML) and maximum likelihood (ML) estimation of one-stage IPD meta-analysis models for synthesizing randomized trials with continuous or binary outcomes. Three key findings are identified. First, for ML or REML estimation of stratified intercept or random intercepts models, a t-distribution based approach generally improves coverage of confidence intervals for the summary treatment effect, compared with a z-based approach. Second, when using ML estimation of a one-stage model with a stratified intercept, the treatment variable should be coded using "study-specific centering" (ie, 1/0 minus the study-specific proportion of participants in the treatment group), as this reduces the bias in the between-study variance estimate (compared with 1/0 and other coding options). Third, REML estimation reduces downward bias in between-study variance estimates compared with ML estimation, and does not depend on the treatment variable coding; for binary outcomes, this requires REML estimation of the pseudo-likelihood, although this may not be stable in some situations (eg, when data are sparse). Two applied examples are used to illustrate the findings.

Pharmacological interventions for prevention of depression in high risk conditions: Systematic review and meta-analysis.

BACKGROUND: Background Depressive disorders account for almost half of all Disability Adjusted Life Years caused by psychiatric disorders but efficacy of pharmacological interventions to prevent depressive disorders is not known. We aimed to assess efficacy of pharmacological treatments in prevention of depression. METHODS: We searched PubMed, Psych Info, EMBASE, and CINHAL from 1980 to January 2020 and bibliographies of relevant systematic reviews. We selected randomised controlled trials (RCTs) that used a pharmacological intervention to prevent the onset of the new depressive episode in adult population. Study selection, data extraction and reporting was done following PRISMA guidelines. Data were pooled using random-effects meta-analysis. RESULTS: 28 trials (2745 participants) were included in meta-analysis. Antidepressants (22 studies), Selenium, Hormone Replacement Therapy Omega-3 fatty acids and Melatonin were used to prevent depression, mostly in physical conditions associated with high risk of depression. All pharmacological interventions [pooled Odds Ratios (OR) 0.37 CI (0.25-0.54)], and antidepressants (OR 0.29, 95% CI: 0.18, 0.46) were significantly more effective than placebo in preventing depression. Antidepressants were significantly better than placebo in trials that had low risk of bias (n = 16; OR 0.43 [0.30, 0.60]), in preventing post stroke depression (OR = 0.16, 95% CI: 0.05, 0.55) and depression associated with Hepatitis C (OR = 0.56, 95% CI: 0.31, 1.02). Limitations include small number of studies focussed only on high risk conditions and short follow up in most studies. CONCLUSIONS: Prevention of depression may be possible in patients who have high-risk conditions such as stroke but the strategy requires complete risk and benefits analysis before it can be considered for clinical practice.

Do comorbidities predict pain and function in knee osteoarthritis following an exercise intervention, and do they moderate the effect of exercise? Analyses of data from three randomized controlled trials.

BACKGROUND: Although exercise is a core treatment for people with knee osteoarthritis (OA), it is currently unknown whether those with additional comorbidities respond differently to exercise than those without. We explored whether comorbidities predict pain and function following an exercise intervention in people with knee OA, and whether they moderate response to: exercise versus no exercise; and enhanced exercise versus usual exercise-based care. METHODS: We undertook analyses of existing data from three randomized controlled trials (RCTs): TOPIK (n = 217), APEX (n = 352) and Benefits of Effective Exercise for knee Pain (BEEP) (n = 514). All three RCTs included: adults with knee pain attributable to OA; physiotherapy-led exercise; data on six comorbidities (overweight/obesity, pain elsewhere, anxiety/depression, cardiac problems, diabetes mellitus and respiratory conditions); the outcomes of interest (six-month Western Ontario and McMaster Universities Arthritis Index knee pain and function). Adjusted mixed models were fitted where data was available; otherwise linear regression models were used. RESULTS: Obesity compared with underweight/normal body mass index was significantly associated with knee pain following exercise, as was the presence compared with absence of anxiety/depression. The presence of cardiac problems was significantly associated with the effect of enhanced versus usual exercise-based care for knee function, indicating that enhanced exercise may be less effective for improving knee function in people with cardiac problems. Associations for all other potential prognostic factors and moderators were weak and not statistically significant. CONCLUSIONS: Obesity and anxiety/depression predicted pain and function outcomes in people offered an exercise intervention, but only the presence of cardiac problems might moderate the effect of exercise for knee OA. Further confirmatory investigations are required.

Predictors of the effects of treatment for shoulder pain: protocol of an individual participant data meta-analysis.

BACKGROUND: Shoulder pain is one of the most common presentations of musculoskeletal pain with a 1-month population prevalence of between 7 and 26%. The overall prognosis of shoulder pain is highly variable with 40% of patients reporting persistent pain 1 year after consulting their primary care clinician. Despite evidence for prognostic value of a range of patient and disease characteristics, it is not clear whether these factors also predict (moderate) the effect of specific treatments (such as corticosteroid injection, exercise, or surgery). OBJECTIVES: This study aims to identify predictors of treatment effect (i.e. treatment moderators or effect modifiers) by investigating the association between a number of pre-defined individual-level factors and the effects of commonly used treatments on shoulder pain and disability outcomes. METHODS: This will be a meta-analysis using individual participant data (IPD). Eligible trials investigating the effectiveness of advice and analgesics, corticosteroid injection, physiotherapy-led exercise, psychological interventions, and/or surgical treatment in patients with shoulder conditions will be identified from systematic reviews and an updated systematic search for trials, and risk of bias will be assessed. Authors of all eligible trials will be approached for data sharing. Outcomes measured will be shoulder pain and disability, and our previous work has identified candidate predictors. The main analysis will be conducted using hierarchical one-stage IPD meta-analysis models, examining the effect of treatment-predictor interaction on outcome for each of the candidate predictors and describing relevant subgroup effects where significant interaction effects are detected. Random effects will be used to account for clustering and heterogeneity. Sensitivity analyses will be based on (i) exclusion of trials at high risk of bias, (ii) use of restricted cubic splines to model potential non-linear associations for candidate predictors measured on a continuous scale, and (iii) the use of a two-stage IPD meta-analysis framework. DISCUSSION: Our study will collate, appraise, and synthesise IPD from multiple studies to examine potential predictors of treatment effect in order to assess the potential for better and more efficient targeting of specific treatments for individuals with shoulder pain. SYSTEMATIC REVIEW REGISTRATION: PROSPERO CRD42018088298.

Minimum sample size for developing a multivariable prediction model: PART II - binary and time-to-event outcomes.

When designing a study to develop a new prediction model with binary or time-to-event outcomes, researchers should ensure their sample size is adequate in terms of the number of participants (n) and outcome events (E) relative to the number of predictor parameters (p) considered for inclusion. We propose that the minimum values of n and E (and subsequently the minimum number of events per predictor parameter, EPP) should be calculated to meet the following three criteria: (i) small optimism in predictor effect estimates as defined by a global shrinkage factor of ≥0.9, (ii) small absolute difference of ≤ 0.05 in the model's apparent and adjusted Nagelkerke's R2 , and (iii) precise estimation of the overall risk in the population. Criteria (i) and (ii) aim to reduce overfitting conditional on a chosen p, and require prespecification of the model's anticipated Cox-Snell R2 , which we show can be obtained from previous studies. The values of n and E that meet all three criteria provides the minimum sample size required for model development. Upon application of our approach, a new diagnostic model for Chagas disease requires an EPP of at least 4.8 and a new prognostic model for recurrent venous thromboembolism requires an EPP of at least 23. This reinforces why rules of thumb (eg, 10 EPP) should be avoided. Researchers might additionally ensure the sample size gives precise estimates of key predictor effects; this is especially important when key categorical predictors have few events in some categories, as this may substantially increase the numbers required.

Minimum sample size for developing a multivariable prediction model: Part I - Continuous outcomes.

In the medical literature, hundreds of prediction models are being developed to predict health outcomes in individuals. For continuous outcomes, typically a linear regression model is developed to predict an individual's outcome value conditional on values of multiple predictors (covariates). To improve model development and reduce the potential for overfitting, a suitable sample size is required in terms of the number of subjects (n) relative to the number of predictor parameters (p) for potential inclusion. We propose that the minimum value of n should meet the following four key criteria: (i) small optimism in predictor effect estimates as defined by a global shrinkage factor of ≥0.9; (ii) small absolute difference of ≤ 0.05 in the apparent and adjusted R2 ; (iii) precise estimation (a margin of error ≤ 10% of the true value) of the model's residual standard deviation; and similarly, (iv) precise estimation of the mean predicted outcome value (model intercept). The criteria require prespecification of the user's chosen p and the model's anticipated R2 as informed by previous studies. The value of n that meets all four criteria provides the minimum sample size required for model development. In an applied example, a new model to predict lung function in African-American women using 25 predictor parameters requires at least 918 subjects to meet all criteria, corresponding to at least 36.7 subjects per predictor parameter. Even larger sample sizes may be needed to additionally ensure precise estimates of key predictor effects, especially when important categorical predictors have low prevalence in certain categories.

Persistent sex disparities in clinical outcomes with percutaneous coronary intervention: Insights from 6.6 million PCI procedures in the United States.

BACKGROUND: Prior studies have reported inconsistencies in the baseline risk profile, comorbidity burden and their association with clinical outcomes in women compared to men. More importantly, there is limited data around the sex differences and how these have changed over time in contemporary percutaneous coronary intervention (PCI) practice. METHODS AND RESULTS: We used the Nationwide Inpatient Sample to identify all PCI procedures based on ICD-9 procedure codes in the United States between 2004-2014 in adult patients. Descriptive statistics were used to describe sex-based differences in baseline characteristics and comorbidity burden of patients. Multivariable logistic regressions were used to investigate the association between these differences and in-hospital mortality, complications, length of stay and total hospital charges. Among 6,601,526 patients, 66% were men and 33% were women. Women were more likely to be admitted with diagnosis of NSTEMI (non-ST elevation acute myocardial infarction), were on average 5 years older (median age 68 compared to 63) and had higher burden of comorbidity defined by Charlson score ≥3. Women also had higher in-hospital crude mortality (2.0% vs 1.4%) and any complications compared to men (11.1% vs 7.0%). These trends persisted in our adjusted analyses where women had a significant increase in the odds of in-hospital mortality men (OR 1.20 (95% CI 1.16,1.23) and major bleeding (OR 1.81 (95% CI 1.77,1.86). CONCLUSION: In this national unselected contemporary PCI cohort, there are significant sex-based differences in presentation, baseline characteristics and comorbidity burden. These differences do not fully account for the higher in-hospital mortality and procedural complications observed in women.

Individual participant data meta-analysis of continuous outcomes: A comparison of approaches for specifying and estimating one-stage models.

One-stage individual participant data meta-analysis models should account for within-trial clustering, but it is currently debated how to do this. For continuous outcomes modeled using a linear regression framework, two competing approaches are a stratified intercept or a random intercept. The stratified approach involves estimating a separate intercept term for each trial, whereas the random intercept approach assumes that trial intercepts are drawn from a normal distribution. Here, through an extensive simulation study for continuous outcomes, we evaluate the impact of using the stratified and random intercept approaches on statistical properties of the summary treatment effect estimate. Further aims are to compare (i) competing estimation options for the one-stage models, including maximum likelihood and restricted maximum likelihood, and (ii) competing options for deriving confidence intervals (CI) for the summary treatment effect, including the standard normal-based 95% CI, and more conservative approaches of Kenward-Roger and Satterthwaite, which inflate CIs to account for uncertainty in variance estimates. The findings reveal that, for an individual participant data meta-analysis of randomized trials with a 1:1 treatment:control allocation ratio and heterogeneity in the treatment effect, (i) bias and coverage of the summary treatment effect estimate are very similar when using stratified or random intercept models with restricted maximum likelihood, and thus either approach could be taken in practice, (ii) CIs are generally best derived using either a Kenward-Roger or Satterthwaite correction, although occasionally overly conservative, and (iii) if maximum likelihood is required, a random intercept performs better than a stratified intercept model. An illustrative example is provided.

Simulation-based power calculations for planning a two-stage individual participant data meta-analysis.

BACKGROUND: Researchers and funders should consider the statistical power of planned Individual Participant Data (IPD) meta-analysis projects, as they are often time-consuming and costly. We propose simulation-based power calculations utilising a two-stage framework, and illustrate the approach for a planned IPD meta-analysis of randomised trials with continuous outcomes where the aim is to identify treatment-covariate interactions. METHODS: The simulation approach has four steps: (i) specify an underlying (data generating) statistical model for trials in the IPD meta-analysis; (ii) use readily available information (e.g. from publications) and prior knowledge (e.g. number of studies promising IPD) to specify model parameter values (e.g. control group mean, intervention effect, treatment-covariate interaction); (iii) simulate an IPD meta-analysis dataset of a particular size from the model, and apply a two-stage IPD meta-analysis to obtain the summary estimate of interest (e.g. interaction effect) and its associated p-value; (iv) repeat the previous step (e.g. thousands of times), then estimate the power to detect a genuine effect by the proportion of summary estimates with a significant p-value. RESULTS: In a planned IPD meta-analysis of lifestyle interventions to reduce weight gain in pregnancy, 14 trials (1183 patients) promised their IPD to examine a treatment-BMI interaction (i.e. whether baseline BMI modifies intervention effect on weight gain). Using our simulation-based approach, a two-stage IPD meta-analysis has < 60% power to detect a reduction of 1 kg weight gain for a 10-unit increase in BMI. Additional IPD from ten other published trials (containing 1761 patients) would improve power to over 80%, but only if a fixed-effect meta-analysis was appropriate. Pre-specified adjustment for prognostic factors would increase power further. Incorrect dichotomisation of BMI would reduce power by over 20%, similar to immediately throwing away IPD from ten trials. CONCLUSIONS: Simulation-based power calculations could inform the planning and funding of IPD projects, and should be used routinely.

Deriving percentage study weights in multi-parameter meta-analysis models: with application to meta-regression, network meta-analysis and one-stage individual participant data models.

Many meta-analysis models contain multiple parameters, for example due to multiple outcomes, multiple treatments or multiple regression coefficients. In particular, meta-regression models may contain multiple study-level covariates, and one-stage individual participant data meta-analysis models may contain multiple patient-level covariates and interactions. Here, we propose how to derive percentage study weights for such situations, in order to reveal the (otherwise hidden) contribution of each study toward the parameter estimates of interest. We assume that studies are independent, and utilise a decomposition of Fisher's information matrix to decompose the total variance matrix of parameter estimates into study-specific contributions, from which percentage weights are derived. This approach generalises how percentage weights are calculated in a traditional, single parameter meta-analysis model. Application is made to one- and two-stage individual participant data meta-analyses, meta-regression and network (multivariate) meta-analysis of multiple treatments. These reveal percentage study weights toward clinically important estimates, such as summary treatment effects and treatment-covariate interactions, and are especially useful when some studies are potential outliers or at high risk of bias. We also derive percentage study weights toward methodologically interesting measures, such as the magnitude of ecological bias (difference between within-study and across-study associations) and the amount of inconsistency (difference between direct and indirect evidence in a network meta-analysis).

Bayesian bivariate meta-analysis of correlated effects: Impact of the prior distributions on the between-study correlation, borrowing of strength, and joint inferences.

Multivariate random-effects meta-analysis allows the joint synthesis of correlated results from multiple studies, for example, for multiple outcomes or multiple treatment groups. In a Bayesian univariate meta-analysis of one endpoint, the importance of specifying a sensible prior distribution for the between-study variance is well understood. However, in multivariate meta-analysis, there is little guidance about the choice of prior distributions for the variances or, crucially, the between-study correlation, ρB; for the latter, researchers often use a Uniform(-1,1) distribution assuming it is vague. In this paper, an extensive simulation study and a real illustrative example is used to examine the impact of various (realistically) vague prior distributions for ρB and the between-study variances within a Bayesian bivariate random-effects meta-analysis of two correlated treatment effects. A range of diverse scenarios are considered, including complete and missing data, to examine the impact of the prior distributions on posterior results (for treatment effect and between-study correlation), amount of borrowing of strength, and joint predictive distributions of treatment effectiveness in new studies. Two key recommendations are identified to improve the robustness of multivariate meta-analysis results. First, the routine use of a Uniform(-1,1) prior distribution for ρB should be avoided, if possible, as it is not necessarily vague. Instead, researchers should identify a sensible prior distribution, for example, by restricting values to be positive or negative as indicated by prior knowledge. Second, it remains critical to use sensible (e.g. empirically based) prior distributions for the between-study variances, as an inappropriate choice can adversely impact the posterior distribution for ρB, which may then adversely affect inferences such as joint predictive probabilities. These recommendations are especially important with a small number of studies and missing data.

Perioperative supplementation with a fruit and vegetable juice powder concentrate and postsurgical morbidity: A double-blind, randomised, placebo-controlled clinical trial.

BACKGROUND & AIMS: Surgical trauma leads to an inflammatory response that causes surgical morbidity. Reduced antioxidant micronutrient (AM)a levels and/or excessive levels of Reactive Oxygen Species (ROS)b have previously been linked to delayed wound healing and presence of chronic wounds. We aimed to evaluate the effect of pre-operative supplementation with encapsulated fruit and vegetable juice powder concentrate (JuicePlus+®) on postoperative morbidity and Quality of Life (QoL)c. METHODS: We conducted a randomised, double-blind, placebo-controlled two-arm parallel clinical trial evaluating postoperative morbidity following lower third molar surgery. Patients aged between 18 and 65 years were randomised to take verum or placebo for 10 weeks prior to surgery and during the first postoperative week. The primary endpoint was the between-group difference in QoL over the first postoperative week, with secondary endpoints being related to other measures of postoperative morbidity (pain and trismus). RESULTS: One-hundred and eighty-three out of 238 randomised patients received surgery (Intention-To-Treat population). Postoperative QoL tended to be higher in the active compared to the placebo group. Furthermore, reduction in mouth opening 2 days after surgery was 3.1 mm smaller (95% CI 0.1, 6.1), the mean pain score over the postoperative week was 8.5 mm lower (95% CI 1.8, 15.2) and patients were less likely to experience moderate to severe pain on postoperative day 2 (RR 0.58, 95% CI 0.35, 0.95), comparing verum to placebo groups. CONCLUSION: Pre-operative supplementation with a fruit and vegetable supplement rich in AM may improve postoperative QoL and reduce surgical morbidity and post-operative complications after surgery. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT01145820; Registered June 16, 2010.