437 research outputs found
Investigating treatment-effect modification by a continuous covariate in IPD meta-analysis: an approach using fractional polynomials
Background: In clinical trials, there is considerable interest in investigating whether a treatment effect is similar in all patients, or that one or more prognostic variables indicate a differential response to treatment. To examine this, a continuous predictor is usually categorised into groups according to one or more cutpoints. Several weaknesses of categorization are well known. To avoid the disadvantages of cutpoints and to retain full information, it is preferable to keep continuous variables continuous in the analysis. To handle this issue, the Subpopulation Treatment Effect Pattern Plot (STEPP) was proposed about two decades ago, followed by the multivariable fractional polynomial interaction (MFPI) approach. Provided individual patient data (IPD) from several studies are available, it is possible to investigate for treatment heterogeneity with meta-analysis techniques. Meta-STEPP was recently proposed and in patients with primary breast cancer an interaction of estrogen receptors with chemotherapy was investigated in eight randomized controlled trials (RCTs). Methods: We use data from eight randomized controlled trials in breast cancer to illustrate issues from two main tasks. The first task is to derive a treatment effect function (TEF), that is, a measure of the treatment effect on the continuous scale of the covariate in the individual studies. The second is to conduct a meta-analysis of the continuous TEFs from the eight studies by applying pointwise averaging to obtain a mean function. We denote the method metaTEF. To improve reporting of available data and all steps of the analysis we introduce a three-part profile called MethProf-MA. Results: Although there are considerable differences between the studies (populations with large differences in prognosis, sample size, effective sample size, length of follow up, proportion of patients with very low estrogen receptor values) our results provide clear evidence of an interaction, irrespective of the choice of the FP function and random or fixed effect models. Conclusions: In contrast to cutpoint-based analyses, metaTEF retains the full information from continuous covariates and avoids several critical issues when performing IPD meta-analyses of continuous effect modifiers in randomised trials. Early experience suggests it is a promising approach. Trial registration: Not applicable
Reporting of prognostic studies of tumour markers: a review of published articles in relation to REMARK guidelines
Background: Poor reporting compromises the reliability and clinical value of prognostic tumour marker studies. We review articles to assess the reporting of patients and events using REMARK guidelines, at the time of guideline publication. Methods: We sampled 50 prognostic tumour marker studies from higher impact cancer journals between 2006 and 2007. The inclusion criteria were cancer; focus on single biological tumour marker; survival analysis; multivariable analysis; and not gene array or proteomic data. Articles were assessed for the REMARK profile and other REMARK guideline items. We propose a reporting aid, the REMARK profile, motivated by the CONSORT flowchart. Results: In 50 studies assessed for the REMARK profile, the number of eligible patients (56% of articles), excluded patients (54%) and patients in analyses (98%) was reported. Only 50% of articles reported the number of outcome events. In multivariable analyses, 54% and 30% of articles reported patient and event numbers for all variables. Of the studies, 66% used archival samples, indicating a potentially biased patient selection. Only 36% of studies reported clearly defined outcomes. Conclusions: Good reporting is critical for the interpretability and clinical applicability of prognostic studies. Current reporting of key information, such as the number of outcome events in all patients and subgroups, is poor. Use of the REMARK profile would greatly improve reporting and enhance prognostic research
Exploration of the variability of variable selection based on distances between bootstrap sample results
It is well known that variable selection in multiple regression can be unstable and that the model uncertainty can be considerable. The model uncertainty can be quantified and explored by bootstrap resampling, see Sauerbrei et al. (Biom J 57:531–555, 2015). Here approaches are introduced that use the results of bootstrap replications of the variable selection process to obtain more detailed information about the data. Analyses will be based on dissimilarities between the results of the analyses of different bootstrap samples. Dissimilarities are computed between the vector of predictions, and between the sets of selected variables. The dissimilarities are used to map the models by multidimensional scaling, to cluster them, and to construct heatplots. Clusters can point to different interpretations of the data that could arise from different selections of variables supported by different bootstrap samples. A new measure of variable selection instability is also defined. The methodology can be applied to various regression models, estimators, and variable selection methods. It will be illustrated by three real data examples, using linear regression and a Cox proportional hazards model, and model selection by AIC and BIC
Improving the Prognostic Ability through Better Use of Standard Clinical Data - The Nottingham Prognostic Index as an Example
Background Prognostic factors and prognostic models play a key role in medical
research and patient management. The Nottingham Prognostic Index (NPI) is a
well-established prognostic classification scheme for patients with breast
cancer. In a very simple way, it combines the information from tumor size,
lymph node stage and tumor grade. For the resulting index cutpoints are
proposed to classify it into three to six groups with different prognosis. As
not all prognostic information from the three and other standard factors is
used, we will consider improvement of the prognostic ability using suitable
analysis approaches. Methods and Findings Reanalyzing overall survival data of
1560 patients from a clinical database by using multivariable fractional
polynomials and further modern statistical methods we illustrate suitable
multivariable modelling and methods to derive and assess the prognostic
ability of an index. Using a REMARK type profile we summarize relevant steps
of the analysis. Adding the information from hormonal receptor status and
using the full information from the three NPI components, specifically
concerning the number of positive lymph nodes, an extended NPI with improved
prognostic ability is derived. Conclusions The prognostic ability of even one
of the best established prognostic index in medicine can be improved by using
suitable statistical methodology to extract the full information from standard
clinical data. This extended version of the NPI can serve as a benchmark to
assess the added value of new information, ranging from a new single clinical
marker to a derived index from omics data. An established benchmark would also
help to harmonize the statistical analyses of such studies and protect against
the propagation of many false promises concerning the prognostic value of new
measurements. Statistical methods used are generally available and can be used
for similar analyses in other diseases
Magnetic order in GdBiPt studied by x-ray resonant magnetic scattering
Rare earth (R) half-Heusler compounds, RBiPt, exhibit a wide spectrum of
novel ground states. Recently, GdBiPt has been proposed as a potential
antiferromagnetic topological insulator (AFTI). We have employed x-ray resonant
magnetic scattering to elucidate the microscopic details of the magnetic
structure in GdBiPt below T_N = 8.5 K. Experiments at the Gd L_2 absorption
edge show that the Gd moments order in an antiferromagnetic stacking along the
cubic diagonal [1 1 1] direction satisfying the requirement for an AFTI, where
both time-reversal symmetry and lattice translational symmetry are broken, but
their product is conserved.Comment: 4 pages, 4 figure
Meta‐analysis of non‐linear exposure‐outcome relationships using individual participant data: A comparison of two methods
Non‐linear exposure‐outcome relationships such as between body mass index (BMI) and mortality are common. They are best explored as continuous functions using individual participant data from multiple studies. We explore two two‐stage methods for meta‐analysis of such relationships, where the confounder‐adjusted relationship is first estimated in a non‐linear regression model in each study, then combined across studies. The “metacurve” approach combines the estimated curves using multiple meta‐analyses of the relative effect between a given exposure level and a reference level. The “mvmeta” approach combines the estimated model parameters in a single multivariate meta‐analysis. Both methods allow the exposure‐outcome relationship to differ across studies. Using theoretical arguments, we show that the methods differ most when covariate distributions differ across studies; using simulated data, we show that mvmeta gains precision but metacurve is more robust to model mis‐specification. We then compare the two methods using data from the Emerging Risk Factors Collaboration on BMI, coronary heart disease events, and all‐cause mortality (>80 cohorts, >18 000 events). For each outcome, we model BMI using fractional polynomials of degree 2 in each study, with adjustment for confounders. For metacurve, the powers defining the fractional polynomials may be study‐specific or common across studies. For coronary heart disease, metacurve with common powers and mvmeta correctly identify a small increase in risk in the lowest levels of BMI, but metacurve with study‐specific powers does not. For all‐cause mortality, all methods identify a steep U‐shape. The metacurve and mvmeta methods perform well in combining complex exposure‐disease relationships across studies
Supply driven mortgage choice
Variable mortgage contracts dominate the UK mortgage market (Miles, 2004). The dominance of the variable rate mortgage contracts has important consequences for the transmission mechanism of monetary policy decisions and systemic risks (Khandani et al., 2012; Fuster and Vickery, 2013). This raises an obvious concern that a mortgage market such as that in the UK, where the major proportion of mortgage debt is either at a variable or fixed for less than two years rate (Badarinza, et al., 2013; CML, 2012), is vulnerable to alterations in the interest rate regime. Theoretically, mortgage choice is determined by demand and supply factors. So far, most of the existing literature has focused on the demand side perspective, and what is limited is consideration of supply side factors in empirical investigation on mortgage choice decisions. This paper uniquely explores whether supply side factors may partially explain observed/ex-post mortgage type decisions. Empirical results detect that lenders’ profit motives and mortgage funding/pricing issues may have assisted in preferences toward variable rate contracts. Securitisation is found to positively impact upon gross mortgage lending volumes while negatively impacting upon the share of variable lending flows. This shows that an increase in securitisation not only improves liquidity in the supply of mortgage funds, but also has the potential to shift mortgage choices toward fixed mortgage debt. The policy implications may involve a number of measures, including reconsideration of the capital requirements for the fixed, as opposed to the variable rate mortgage debt, growing securitisation and optimisation of the mortgage pricing policies
Modelling the effects of standard prognostic factors in node-positive breast cancer
Prognostic models that predict the clinical course of a breast cancer patient are important in oncology. We propose an approach to constructing such models based on fractional polynomials in which useful transformations of the continuous factors are determined. The idea may be applied with all types of regression model, including Cox regression, the method of choice for survival-time data. We analyse a prospective study of node-positive breast cancer. Seven standard prognostic factors – age, menopausal status, tumour size, tumour grade, number of positive lymph nodes, progesterone and oestrogen receptor concentrations – were investigated in 686 patients, of whom 299 had an event for recurrence-free survival and 171 died. We determine a final model with transformations of prognostic factors and compare it with the more traditional approaches using categorized variables or assuming a straight line relationship. We conclude that analysis using fractional polynomials can extract important prognostic information which the traditional approaches may miss. © 1999 Cancer Research Campaig
Multivariable fractional polynomial interaction to investigate continuous effect modifiers in a meta-analysis on higher versus lower PEEP for patients with ARDS.
OBJECTIVES: A recent individual patient data (IPD) meta-analysis suggested that patients with moderate or severe acute respiratory distress syndrome (ARDS) benefit from higher positive end-expiratory pressure (PEEP) ventilation strategies. However, thresholds for continuous variables (eg, hypoxaemia) are often arbitrary and linearity assumptions in regression approaches may not hold; the multivariable fractional polynomial interaction (MFPI) approach can address both problems. The objective of this study was to apply the MFPI approach to investigate interactions between four continuous patient baseline variables and higher versus lower PEEP on clinical outcomes. SETTING: Pooled data from three randomised trials in intensive care identified by a systematic review. PARTICIPANTS: 2299 patients with acute lung injury requiring mechanical ventilation. INTERVENTIONS: Higher (N=1136) versus lower PEEP (N=1163) ventilation strategy. OUTCOME MEASURES: Prespecified outcomes included mortality, time to death and time-to-unassisted breathing. We examined the following continuous baseline characteristics as potential effect modifiers using MFPI: PaO2/FiO2 (arterial partial oxygen pressure/ fraction of inspired oxygen), oxygenation index, respiratory system compliance (tidal volume/(inspiratory plateau pressure-PEEP)) and body mass index (BMI). RESULTS: We found that for patients with PaO2/FiO2 below 150 mm Hg, but above 100 mm Hg or an oxygenation index above 12 (moderate ARDS), higher PEEP reduces hospital mortality, but the beneficial effect appears to level off for patients with very severe ARDS. Patients with mild ARDS (PaO2/FiO2 above 200 mm Hg or an oxygenation index below 10) do not seem to benefit from higher PEEP and might even be harmed. For patients with a respiratory system compliance above 40 mL/cm H2O or patients with a BMI above 35 kg/m(2), we found a trend towards reduced mortality with higher PEEP, but there is very weak statistical confidence in these findings. CONCLUSIONS: MFPI analyses suggest a nonlinear effect modification of higher PEEP ventilation by PaO2/FiO2 and oxygenation index with reduced mortality for some patients suffering from moderate ARDS. STUDY REGISTRATION NUMBER: CRD42012003129
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