A two parameter ratio-product-ratio estimator using auxiliary information

We propose a two parameter ratio-product-ratio estimator for a finite population mean in a simple random sample without replacement following the methodology in Ray and Sahai (1980), Sahai and Ray (1980), Sahai and Sahai (1985) and Singh and Ruiz Espejo (2003). The bias and mean square error of our proposed estimator are obtained to the first degree of approximation. We derive conditions for the parameters under which the proposed estimator has smaller mean square error than the sample mean, ratio and product estimators. We carry out an application showing that the proposed estimator outperforms the traditional estimators using groundwater data taken from a geological site in the state of Florida.Comment: 13 pages, 2 figures, 4 table

A comparison of analytic approaches for individual patient data meta-analyses with binary outcomes

Abstract Background Individual patient data meta-analyses (IPD-MA) are often performed using a one-stage approach-- a form of generalized linear mixed model (GLMM) for binary outcomes. We compare (i) one-stage to two-stage approaches (ii) the performance of two estimation procedures (Penalized Quasi-likelihood-PQL and Adaptive Gaussian Hermite Quadrature-AGHQ) for GLMMs with binary outcomes within the one-stage approach and (iii) using stratified study-effect or random study-effects. Methods We compare the different approaches via a simulation study, in terms of bias, mean-squared error (MSE), coverage and numerical convergence, of the pooled treatment effect (β 1) and between-study heterogeneity of the treatment effect (τ 1 2 ). We varied the prevalence of the outcome, sample size, number of studies and variances and correlation of the random effects. Results The two-stage and one-stage methods produced approximately unbiased β 1 estimates. PQL performed better than AGHQ for estimating τ 1 2 with respect to MSE, but performed comparably with AGHQ in estimating the bias of β 1 and of τ 1 2 . The random study-effects model outperformed the stratified study-effects model in small size MA. Conclusion The one-stage approach is recommended over the two-stage method for small size MA. There was no meaningful difference between the PQL and AGHQ procedures. Though the random-intercept and stratified-intercept approaches can suffer from their underlining assumptions, fitting GLMM with a random-intercept are less prone to misfit and has good convergence rate

Statistical challenges in individual patient data meta-analyses of binary outcomes

Meta-analyses (MA) based on individual patient data (IPD) are regarded as the gold standard and are becoming increasingly common, having several advantages over MA of summary statistics. These analyses are being undertaken in an increasing diversity of settings, often having a binary outcome. Parameter estimation of generalized linear mixed models (GLMMs), which are frequently used to perform inference on binary outcomes, is convoluted by intractable integrals in the marginal likelihood. Penalized quasi-likelihood (PQL) and adaptive Gauss-Hermite quadrature (AGHQ) are estimation methods commonly used in practice. However, few comparisons for the assessment of the performances of these estimation methods have been reported in the context of IPD meta-analyses (IPD-MA) with binary outcomes. I considered as a first step to the thesis, a systematic review of the literature. In a previous systematic review of articles published between 1999-2001, the statistical approach was seldom reported in sufficient detail, and the outcome was binary in 32% of the studies considered. Here, we explore statistical methods used for IPD-MA of binary outcomes only, a decade later. 19 of the 26 MA used a one-step approach verses a two-step approach and random-effect logistic regression was the most common method for these binary outcomes, allowing the treatment effect to vary across studies. However, the estimation technique used in these studies (e.g. a GLMM estimated via PQL or AGHQ) was rarely reported.Afterwards, via simulation studies, whose design is realistic for conducting IPD-MA of binary outcomes, to compare techniques to estimate multilevel models, and to address the concern of including trial-membership as fixed or random? The parameters of the one-step models were estimated using PQL and AGHQ while that of the two-step model were estimated via restricted maximum likelihood (REML) at the second step. Size and number of study, total sample sizes, variances and correlation in the random effects distribution were varied. The comparison is done in terms of bias, root mean square error (RMSE), numerical convergence, and coverage of interval estimates. The two-step and one-step (via PQL, and AGHQ) methods produced approximately unbiased pooled treatment effect estimates, although the manner in which PQL achieves this is an advantage. The AGHQ methods for estimating the random treatment effect variance performed better with respect to bias and coverage, but RMSE performed relatively poor in comparison with PQL for all data sizes and model misspecification.Les méta-analyses (MA) de données individuelles de patient (IPD) sont considérées comme une approche de référence et deviennent de plus en plus communes puisqu’elles ont plusieurs avantages comparativement aux méta-analyses de statistiques sommaires. Le champ d’application de ces analyses est diversifié et relève souvent une réponse binaire. L’estimation des paramètres dans de modèles linéaires généralisés mixtes (GLMMs), qui sont souvent utilisés pour inférer sur des réponses binaires, est compliquée par l’évaluation d’intégrales insolubles dans la vraisemblance marginale. Les méthodes d’estimation quasi-vraisemblance pénalisée (PQL) et quadrature Gauss-Hermite adaptive (AGHQ) sont couramment utilisées dans la pratique. Cependant, peu de comparaisons sur l’évaluation de la performance de ces méthodes d’estimation ont été rapportées dans le contexte de méta-analyses de données individuelles de patient (IPD-MA) avec des réponses binaires.La première étape de la thèse est une revue systématique de la littérature. Dans une précédente revue systématique d’articles publiés entre 1999 et 2001, la méthode statistique était rarement rapportée avec suffisamment de détails et 32% des études considérées reportaient une réponse binaire. Une décennie plus tard, nous explorons les méthodes statistiques utilisées seulement pour les IPD-MA avec des réponses binaires. 19 des 26 MA identifiées utilisaient une approche en une étape au lieu d’une approche en deux étapes et la regression logistique avec effets aléatoires était la méthode la plus commune pour ces données binaires, permettant à l’effet du traitment de varier d’une étude à l’autre. Cependant, la technique d’estimation utilisée dans ces études (e.g. un GLMM estimé via PQL ou AGHQ) était rarement rapportée.Ensuite, des études de simulation dont la conception est réaliste pour appliquer une IPD-MA avec des réponses binaires sont réalisées pour comparer des techniques d’estimation de modèles à plusieurs niveaux et pour considérer l’inclusion de l’adhésion provisoire en tant qu’effet fixe ou aléatoire. Les paramètres de l’approche en une étape sont estimés avec PQL et AGHQ tandis que ceux de l’approche en deux étapes sont estimés via la vraisemblance maximale restreinte (REML) à la deuxième étape. La taille et le nombre d’études, les tailles d’échantillon totales, les variances et les corrélation de la distribution des effets aléatoires sont variés. La comparaison concerne le biais, l’erreur quadratique moyenne (RSME), la convergence numérique et la couverture des intervalles des estimés. Les approches en une et deux étapes (via PQL et AGHQ) produisaient des estimations combinées de l’effet du traitement approximativement non-biaisées, bien que la manière dont la méthode PQL produisait cette estimation soit avantageuse. La méthode AGHQ pour estimer la variance des effets aléatoires du traitement performaient mieux concernant le biais et la couverture, mais RSME performait relativement mal comparativement à PQL pour toutes les tailles de données et les mauvaises spécifications de modèle

Systematic review of methods for individual patient data meta- analysis with binary outcomes

Abstract Background Meta-analyses (MA) based on individual patient data (IPD) are regarded as the gold standard for meta-analyses and are becoming increasingly common, having several advantages over meta-analyses of summary statistics. These analyses are being undertaken in an increasing diversity of settings, often having a binary outcome. In a previous systematic review of articles published between 1999–2001, the statistical approach was seldom reported in sufficient detail, and the outcome was binary in 32% of the studies considered. Here, we explore statistical methods used for IPD-MA of binary outcomes only, a decade later. Methods We selected 56 articles, published in 2011 that presented results from an individual patient data meta-analysis. Of these, 26 considered a binary outcome. Here, we review 26 IPD-MA published during 2011 to consider: the goal of the study and reason for conducting an IPD-MA, whether they obtained all the data they sought, the approach used in their analysis, for instance, a two-stage or a one stage model, and the assumption of fixed or random effects. We also investigated how heterogeneity across studies was described and how studies investigated the effects of covariates. Results 19 of the 26 IPD-MA used a one-stage approach. 9 IPD-MA used a one-stage random treatment-effect logistic regression model, allowing the treatment effect to vary across studies. Twelve IPD-MA presented some form of statistic to measure heterogeneity across studies, though these were usually calculated using two-stage approach. Subgroup analyses were undertaken in all IPD-MA that aimed to estimate a treatment effect or safety of a treatment,. Sixteen meta-analyses obtained 90% or more of the patients sought. Conclusion Evidence from this systematic review shows that the use of binary outcomes in assessing the effects of health care problems has increased, with random effects logistic regression the most common method of analysis. Methods are still often not reported in enough detail. Results also show that heterogeneity of treatment effects is discussed in most applications

Additional file 5: of A comparison of analytic approaches for individual patient data meta-analyses with binary outcomes

Percent Coverage (percent convergence rate) for treatment effect, β1 for different approach, by number of studies, total average sample size, mixture of studies sizes and degree of random effects variances - data generated from random study- and treatment effect: Eq. 1 with 5% outcome rate. (DOC 62 kb

Data from: Effectiveness of continence promotion for older women via community organisations: a cluster randomised trial

Objectives: The primary objective of this cluster randomised controlled trial was to compare the effectiveness of the three experimental continence promotion interventions against a control intervention on urinary symptom improvement in older women with untreated incontinence recruited from community organisations. Setting: 71 community organisations across the United Kingdom Participants: 259 women aged 60 years and older with untreated incontinence entered the trial; 88% completed the 3-month follow-up. Interventions: The three active interventions consisted of a single 60-minute group workshop on 1) continence education (20 clusters, 64 women); 2) evidence-based self-management (17 clusters, 70 women); or 3) combined education and self-management (17 clusters, 61 women). The control intervention was a single 60-minute educational group workshop on memory loss, polypharmacy and osteoporosis (17 clusters, 64 women). Primary and secondary outcome measures: The primary outcome was self-reported improvement in incontinence 3 months post-intervention at the level of the individual. The secondary outcome was change in the International Consultation on Incontinence Questionnaire (ICIQ). Changes in incontinence-related knowledge and behaviours were also assessed. Results: The highest rate of urinary symptom improvement occurred in the combined intervention group (66% vs 11% of the control group, prevalence difference 55%, 95% CI 43%-67%, intracluster correlation 0). Thirty percent versus 6% of participants reported significant improvement respectively (prevalence difference 23%, 95% CI 10%-36%, intracluster correlation 0). The number-needed-to-treat was 2 to achieve any improvement in incontinence symptoms, and 5 to attain significant improvement. Compared to controls, the combined group reported an adjusted mean 2.05 point (95% CI 0.87-3.24) greater improvement on the ICIQ. Changes in knowledge and self-reported risk-reduction behaviours paralleled rates of improvement in all intervention arms. Conclusion: Continence education combined with evidence-based self-management improves symptoms of incontinence among untreated older women. Community organisations represent an untapped vector for delivering effective continence promotion interventions

Urinary incontinence dataset

Urinary incontinence datase

Feasibility of a Pilot Randomized Controlled Trial Examining a Multidimensional Intervention in Women with Gynecological Cancer at Risk of Lymphedema

There is limited knowledge on non-invasive lymphedema risk-reduction strategies for women with gynecological cancer. Understanding factors influencing the feasibility of randomized controlled trials (RCTs) can guide future research. Our objectives are to report on the design and feasibility of a pilot RCT examining a tailored multidimensional intervention in women treated for gynecological cancer at risk of lymphedema and to explore the preliminary effectiveness of the intervention on lymphedema incidence at 12 months. In this pilot single-blinded, parallel-group, multi-centre RCT, women with newly diagnosed gynecological cancer were randomized to receive post-operative compression stockings and individualized exercise education (intervention group: IG) or education on lymphedema risk-reduction alone (control group: CG). Rates of recruitment, retention and assessment completion were recorded. Intervention safety and feasibility were tracked by monitoring adverse events and adherence. Clinical outcomes were evaluated over 12 months: presence of lymphedema, circumferential and volume measures, body composition and quality of life. Fifty-one women were recruited and 36 received the assigned intervention. Rates of recruitment and 12-month retention were 47% and 78%, respectively. Two participants experienced post-operative cellulitis, prior to intervention delivery. At three and six months post-operatively, 67% and 63% of the IG used compression ≥42 h/week, while 56% engaged in ≥150 weekly minutes of moderate-vigorous exercise. The cumulative incidence of lymphedema at 12 months was 31% in the CG and 31.9% in the IG (p = 0.88). In affected participants, lymphedema developed after a median time of 3.2 months (range, 2.7–5.9) in the CG vs. 8.8 months (range, 2.9–11.8) in the IG. Conducting research trials exploring lymphedema risk-reduction strategies in gynecological cancer is feasible but challenging. A tailored intervention of compression and exercise is safe and feasible in this population and may delay the onset of lymphedema. Further research is warranted to establish the role of these strategies in reducing the risk of lymphedema for the gynecological cancer population