Although randomized controlled trials are the gold standard for establishing causation in clinical research, their aggregated results can be misleading when applied to individual patients. A treatment may be beneficial in some patients, but its harms may outweigh benefits in others. While conventional one-variable-at-a-time subgroup analyses have well-known limitations, multivariable risk-based analyses can help uncover clinically significant heterogeneity in treatment effects that may be otherwise obscured. Trials in kidney transplantation have yielded the finding that a reduction in acute rejection does not translate into a similar benefit in prolonging graft survival and improving graft function. This paradox might be explained by the variation in risk for acute rejection among included kidney transplant recipients varying the likelihood of benefit or harm from intense immunosuppressive regimens. Analyses that stratify patients by their immunological risk may resolve these otherwise puzzling results. Reliable risk models should be developed to investigate benefits and harms in rationally designed risk-based subgroups of patients in existing RCT datasets. These risk strata would need to be validated in future prospective clinical trials examining long term effects on patient and graft survival. This approach may allow better individualized treatment choices for kidney transplant recipients.
In randomised clinical trials involving time-to-event outcomes, the failures concerned may be events of an entirely different nature and as such define a classical competing risks framework. In designing and analysing clinical trials involving such endpoints, it is important to account for the competing events, and evaluate how each contributes to the overall failure. An appropriate choice of statistical model is important for adequate determination of sample size.
We describe how competing events may be summarised in such trials using cumulative incidence functions and Gray's test. The statistical modelling of competing events using proportional cause-specific and subdistribution hazard functions, and the corresponding procedures for sample size estimation are outlined. These are illustrated using data from a randomised clinical trial (SQNP01) of patients with advanced (non-metastatic) nasopharyngeal cancer.
In this trial, treatment has no effect on the competing event of loco-regional recurrence. Thus the effects of treatment on the hazard of distant metastasis were similar via both the cause-specific (unadjusted csHR = 0.43, 95% CI 0.25 - 0.72) and subdistribution (unadjusted subHR 0.43; 95% CI 0.25 - 0.76) hazard analyses, in favour of concurrent chemo-radiotherapy followed by adjuvant chemotherapy. Adjusting for nodal status and tumour size did not alter the results. The results of the logrank test (p = 0.002) comparing the cause-specific hazards and the Gray's test (p = 0.003) comparing the cumulative incidences also led to the same conclusion. However, the subdistribution hazard analysis requires many more subjects than the cause-specific hazard analysis to detect the same magnitude of effect.
The cause-specific hazard analysis is appropriate for analysing competing risks outcomes when treatment has no effect on the cause-specific hazard of the competing event. It requires fewer subjects than the subdistribution hazard analysis for a similar effect size. However, if the main and competing events are influenced in opposing directions by an intervention, a subdistribution hazard analysis may be warranted.
Mounting evidence suggests that there is frequently considerable variation in the risk of the outcome of interest in clinical trial populations. These differences in risk will often cause clinically important heterogeneity in treatment effects (HTE) across the trial population, such that the balance between treatment risks and benefits may differ substantially between large identifiable patient subgroups; the "average" benefit observed in the summary result may even be non-representative of the treatment effect for a typical patient in the trial. Conventional subgroup analyses, which examine whether specific patient characteristics modify the effects of treatment, are usually unable to detect even large variations in treatment benefit (and harm) across risk groups because they do not account for the fact that patients have multiple characteristics simultaneously that affect the likelihood of treatment benefit. Based upon recent evidence on optimal statistical approaches to assessing HTE, we propose a framework that prioritizes the analysis and reporting of multivariate risk-based HTE and suggests that other subgroup analyses should be explicitly labeled either as primary subgroup analyses (well-motivated by prior evidence and intended to produce clinically actionable results) or secondary (exploratory) subgroup analyses (performed to inform future research). A standardized and transparent approach to HTE assessment and reporting could substantially improve clinical trial utility and interpretability.
Schizophrenia elevates the risk for aggressive behavior and violent crime, and different approaches have been used to manage this problem. The results of such treatments vary. One reason for this variation is that aggressive behavior in schizophrenia is heterogeneous in origin. This heterogeneity has usually not been accounted for in treatment trials nor is it adequately appreciated in routine clinical treatment planning. Here, we review pathways that may lead to the development of aggressive behavior in patients with schizophrenia and discuss their impact on treatment. Elements in these pathways include predisposing factors such as genotype and prenatal toxic effects, development of psychotic symptoms and neurocognitive impairments, substance abuse, nonadherence to treatment, childhood maltreatment, conduct disorder, comorbid antisocial personality disorder/psychopathy, and stressful experiences in adult life. Clinicians’ knowledge of the patient’s historical trajectory along these pathways may inform the choice of optimal treatment of aggressive behavior. Clozapine has superior antiaggressive activity in comparison with other antipsychotics and with all other pharmacological treatments. It is usually effective when aggressive behavior is related to psychotic symptoms. However, in many patients, aggression is at least partly based on other factors such as comorbid substance use disorder, comorbid antisocial personality disorder/psychopathy, or current stress. These conditions which are sometimes underdiagnosed in clinical practice must be addressed by appropriate adjunctive psychosocial approaches or other treatments. Treatment adherence has a crucial role in the prevention of aggressive behavior in schizophrenia patients.
violence; antipsychotics; substance use; antisocial personality disorder; psychopathy; adherence
When subgroup analyses of a positive clinical trial are unrevealing, such findings are commonly used to argue that the treatment's benefits apply to the entire study population; however, such analyses are often limited by poor statistical power. Multivariable risk-stratified analysis has been proposed as an important advance in investigating heterogeneity in treatment benefits, yet no one has conducted a systematic statistical examination of circumstances influencing the relative merits of this approach vs. conventional subgroup analysis.
Using simulated clinical trials in which the probability of outcomes in individual patients was stochastically determined by the presence of risk factors and the effects of treatment, we examined the relative merits of a conventional vs. a "risk-stratified" subgroup analysis under a variety of circumstances in which there is a small amount of uniformly distributed treatment-related harm. The statistical power to detect treatment-effect heterogeneity was calculated for risk-stratified and conventional subgroup analysis while varying: 1) the number, prevalence and odds ratios of individual risk factors for risk in the absence of treatment, 2) the predictiveness of the multivariable risk model (including the accuracy of its weights), 3) the degree of treatment-related harm, and 5) the average untreated risk of the study population.
Conventional subgroup analysis (in which single patient attributes are evaluated "one-at-a-time") had at best moderate statistical power (30% to 45%) to detect variation in a treatment's net relative risk reduction resulting from treatment-related harm, even under optimal circumstances (overall statistical power of the study was good and treatment-effect heterogeneity was evaluated across a major risk factor [OR = 3]). In some instances a multi-variable risk-stratified approach also had low to moderate statistical power (especially when the multivariable risk prediction tool had low discrimination). However, a multivariable risk-stratified approach can have excellent statistical power to detect heterogeneity in net treatment benefit under a wide variety of circumstances, instances under which conventional subgroup analysis has poor statistical power.
These results suggest that under many likely scenarios, a multivariable risk-stratified approach will have substantially greater statistical power than conventional subgroup analysis for detecting heterogeneity in treatment benefits and safety related to previously unidentified treatment-related harm. Subgroup analyses must always be well-justified and interpreted with care, and conventional subgroup analyses can be useful under some circumstances; however, clinical trial reporting should include a multivariable risk-stratified analysis when an adequate externally-developed risk prediction tool is available.
For competing risks data, the Fine–Gray proportional hazards model for subdistribution has gained popularity for its convenience in directly assessing the effect of covariates on the cumulative incidence function. However, in many important applications, proportional hazards may not be satisfied, including multicenter clinical trials, where the baseline subdistribution hazards may not be common due to varying patient populations. In this article, we consider a stratified competing risks regression, to allow the baseline hazard to vary across levels of the stratification covariate. According to the relative size of the number of strata and strata sizes, two stratification regimes are considered. Using partial likelihood and weighting techniques, we obtain consistent estimators of regression parameters. The corresponding asymptotic properties and resulting inferences are provided for the two regimes separately. Data from a breast cancer clinical trial and from a bone marrow transplantation registry illustrate the potential utility of the stratified Fine–Gray model.
Clustering; Dependent censoring; Hazard of subdistribution; Inverse weighting; Martingale; Multicenter trials; Partial likelihood
Subjects with breast cancer enrolled in trials may experience multiple events such as local recurrence, distant recurrence or death. These events are not independent; the occurrence of one may increase the risk of another, or prevent another from occurring. The most commonly used Cox proportional hazards (Cox-PH) model ignores the relationships between events, resulting in a potential impact on the treatment effect and conclusions. The use of statistical methods to analyze multiple time-to-event events has mainly been focused on superiority trials. However, their application to non-inferiority trials is limited. We evaluate four statistical methods for multiple time-to-event endpoints in the context of a non-inferiority trial.
Three methods for analyzing multiple events data, namely, i) the competing risks (CR) model, ii) the marginal model, and iii) the frailty model were compared with the Cox-PH model using data from a previously-reported non-inferiority trial comparing hypofractionated radiotherapy with conventional radiotherapy for the prevention of local recurrence in patients with early stage breast cancer who had undergone breast conserving surgery. These methods were also compared using two simulated examples, scenario A where the hazards for distant recurrence and death were higher in the control group, and scenario B. where the hazards of distant recurrence and death were higher in the experimental group. Both scenarios were designed to have a non-inferiority margin of 1.50.
In the breast cancer trial, the methods produced primary outcome results similar to those using the Cox-PH model: namely, a local recurrence hazard ratio (HR) of 0.95 and a 95% confidence interval (CI) of 0.62 to 1.46. In Scenario A, non-inferiority was observed with the Cox-PH model (HR = 1.04; CI of 0.80 to 1.35), but not with the CR model (HR = 1.37; CI of 1.06 to 1.79), and the average marginal and frailty model showed a positive effect of the experimental treatment. The results in Scenario A contrasted with Scenario B with non-inferiority being observed with the CR model (HR = 1.10; CI of 0.87 to 1.39), but not with the Cox-PH model (HR = 1.46; CI of 1.15 to 1.85), and the marginal and frailty model showed a negative effect of the experimental treatment.
When subjects are at risk for multiple events in non-inferiority trials, researchers need to consider using the CR, marginal and frailty models in addition to the Cox-PH model in order to provide additional information in describing the disease process and to assess the robustness of the results. In the presence of competing risks, the Cox-PH model is appropriate for investigating the biologic effect of treatment, whereas the CR models yields the actual effect of treatment in the study.
Non-inferiority; Cox model; Correlation; Marginal model; Frailty model; Competing risks
Despite the use of standardized protocols in, multicentre, randomised clinical trials (RCTs), outcome may vary between centres. Such heterogeneity may alter the interpretation and reporting of the treatment effect. Below, we propose a general frailty modelling approach for investigating, inter alia, putative treatment-by-centre interactions in time-to-event data in multi-centre clinical trials. A correlated random effects model is used to model the baseline risk and the treatment effect across centres. It may be based on shared, individual or correlated random-effects. For inference we develop the hierarchical-likelihood (or h-likelihood) approach which facilitates computation of prediction intervals for the random effects with proper precision. We illustrate our methods using disease-free time-to-event data on bladder cancer patients participating in an European Organization for Research and Treatment of Cancer (EORTC) trial, and a simulation study. We also demonstrate model selection using h-likelihood criteria.
Correlated random effects; Focussed model selection; Frailty models; Hierarchical likelihood; Prediction interval; Random treatment-by-centre interaction
Studies centered on the detection of cognitive impairment and its relationship to cardiovascular risk factors in elderly people have gained special relevance in recent years. Knowledge of the cardiovascular risk factors that may be associated to cognitive impairment could be very useful for introducing treatments in early stages - thereby possibly contributing to improve patient quality of life.
The present study explores cognitive performance in people over 65 years of age in Salamanca (Spain), with special emphasis on the identification of early symptoms of cognitive impairment, with the purpose of detecting mild cognitive impairment and of studying the relationships between this clinical situation and cardiovascular risk factors.
A longitudinal study is contemplated. The reference population will consist of 420 people over 65 years of age enrolled through randomized sampling stratified by healthcare area, and who previously participated in another study. Measurement: a) Sociodemographic variables; b) Cardiovascular risk factors; c) Comorbidity; d) Functional level for daily life activities; and e) Study of higher cognitive functions based on a neuropsychological battery especially adapted to the evaluation of elderly people.
We hope that this study will afford objective information on the representative prevalence of cognitive impairment in the population over 65 years of age in Salamanca. We also hope to obtain data on the relationship between cognitive impairment and cardiovascular risk factors in this specific population group. Based on the results obtained, we also will be able to establish the usefulness of some of the screening tests applied during the study, such as the Mini-Mental State Examination and the 7 Minute Screen test.
Recent advances in Alzheimer's disease imply a need for adequate clinical trials of new treatments which require careful design. The disorder is progressive and shows clinical heterogeneity. While large-scale trials of elderly subjects are appropriate in relation to assessment of drugs or other treatments designed to prevent progression of the disorder, the outcome measurements in such biological treatment trials require careful planning. Studies of individual patients are relevant for answering certain specific questions. Relatively short cross-over trial designs may be appropriate to some pharmacological studies. The choice of neuropsychological instruments for measuring change is critically important, particularly in excluding test/retest artefact and in avoiding floor and ceiling effects. Test scales designed for assessment of specific neuropsychological deficits, or forming part of standard IQ assessments are unlikely to prove robust. Tests can be selected and developed for individual patients, but generalisation of the results of such experiments to the disease as a whole is not inevitable. There is a need to develop psychological instruments for measuring change that are robust and relevant to the clinical problem of progressive dementia.
Breast cancer is the most frequent malignant tumor in women worldwide and as breast cancer incidence increases with increasing age, over 40% of new cases are diagnosed in women older than 65 years of age. However, older patients are not treated to the same extent as younger patients and increasing age at diagnosis predicts deviation from guidelines for all treatment modalities. Evidence-based medicine in older patients is lacking as they are usually excluded from clinical trials often because of existing comorbidities and limited life expectancy. Accordingly, there is a higher competing risk of death from other causes than breast cancer compared with younger patients and this may have led to the false interpretation that prognosis of breast cancer in older patients is relatively good. However, every treatment modality should be evaluated during treatment decision making. Multimodal therapy should not be routinely withheld as data show that disease-specific mortality increases with age, probably due to undertreatment. Prognostic markers, fitness and comorbidities rather than chronological age should determine optimal, individualized therapy. It is recommended that treatment decisions should be discussed in a multidisciplinary setting, ideally in combination with any form of geriatric assessment, to improve breast cancer outcome in the older population.
breast cancer; evidence-based medicine; older people; prognosis; treatment
Meta-analysis handles randomized trials with no outcome events in both treatment and control arms inconsistently, including them when risk difference (RD) is the effect measure but excluding them when relative risk (RR) or odds ratio (OR) are used. This study examined the influence of such trials on pooled treatment effects.
Analysis with and without zero total event trials of three illustrative published meta-analyses with a range of proportions of zero total event trials, treatment effects, and heterogeneity using inverse variance weighting and random effects that incorporates between-study heterogeneity.
Including zero total event trials in meta-analyses moves the pooled estimate of treatment effect closer to nil, decreases its confidence interval and decreases between-study heterogeneity. For RR and OR, inclusion of such trials causes small changes, even when they comprise the large majority of included trials. For RD, the changes are more substantial, and in extreme cases can eliminate a statistically significant effect estimate.
To include all relevant data regardless of effect measure chosen, reviewers should also include zero total event trials when calculating pooled estimates using OR and RR.
Joint modeling of longitudinal and survival data has been increasingly considered in clinical trials, notably in cancer and AIDS. In critically ill patients admitted to an intensive care unit (ICU), such models also appear to be of interest in the investigation of the effect of treatment on severity scores due to the likely association between the longitudinal score and the dropout process, either caused by deaths or live discharges from the ICU. However, in this competing risk setting, only cause-specific hazard sub-models for the multiple failure types data have been used.
We propose a joint model that consists of a linear mixed effects submodel for the longitudinal outcome, and a proportional subdistribution hazards submodel for the competing risks survival data, linked together by latent random effects. We use Markov chain Monte Carlo technique of Gibbs sampling to estimate the joint posterior distribution of the unknown parameters of the model. The proposed method is studied and compared to joint model with cause-specific hazards submodel in simulations and applied to a data set that consisted of repeated measurements of severity score and time of discharge and death for 1,401 ICU patients.
Time by treatment interaction was observed on the evolution of the mean SOFA score when ignoring potentially informative dropouts due to ICU deaths and live discharges from the ICU. In contrast, this was no longer significant when modeling the cause-specific hazards of informative dropouts. Such a time by treatment interaction persisted together with an evidence of treatment effect on the hazard of death when modeling dropout processes through the use of the Fine and Gray model for sub-distribution hazards.
In the joint modeling of competing risks with longitudinal response, differences in the handling of competing risk outcomes appear to translate into the estimated difference in treatment effect on the longitudinal outcome. Such a modeling strategy should be carefully defined prior to analysis.
Acute myeloid leukemia (AML) is a heterogeneous disease with variable clinical outcomes. Cytogenetic analysis reveals which patients may have favorable risk disease, but 5-year survival in this category is only approximately 60%, with intermediate and poor risk groups faring far worse. Advances in our understanding of the biology of leukemia pathogenesis and prognosis have not been matched with clinical improvements. Unsatisfactory outcomes persist for the majority of patients with AML, particularly the elderly. Novel agents and treatment approaches are needed in the induction, post-remission and relapsed settings. The additions of clofarabine for relapsed or refractory disease and the hypomethylating agents represent recent advances. Clinical trials of FLT3 inhibitors have yielded disappointing results to date, with ongoing collaborations attempting to identify the optimal role for these agents. Potential leukemia stem cell targeted therapies and treatments in the setting of minimal residual disease are also under investigation. In this review, we will discuss recent advances in AML treatment and novel therapeutic strategies.
acute myeloid leukemia; clofarabine; FLT3; gemtuzumab ozogamicin; cancer stem cells
BACKGROUND: Somatisation is highly prevalent in primary care (present in 25% of visiting patients) but often goes unrecognised. Non-recognition may lead to ineffective treatment, risk of iatrogenic harm, and excessive use of healthcare services. AIM: To examine the effect of training on diagnosis of somatisation in routine clinical practice by general practitioners (GPs). DESIGN OF STUDY: Cluster randomised controlled trial, with practices as the randomisation unit. SETTING: Twenty-seven general practices (with a total of 43 GPs) in Vejle County, Denmark. METHOD: Intervention consisted of a multifaceted training programme (the TERM [The Extended Reattribution and Management] model). Patients were enrolled consecutively over a period of 13 working days. Psychiatric morbidity was assessed by means of a screening questionnaire. GPs categorised their diagnoses in another questionnaire. The primary outcome was GP diagnosis of somatisation and agreement with the screening questionnaire. RESULTS: GPs diagnosed somatisation less frequently than had previously been observed, but there was substantial variation between GPs. The difference between groups in the number of diagnoses of somatisation failed to reach the 5% significance (P = 0.094). However, the rate of diagnoses of medically unexplained physical symptoms was twice as high in the intervention group as in the control group (7.7% and 3.9%, respectively, P = 0.007). Examination of the agreement between GPs' diagnoses and the screening questionnaire revealed no significant difference between groups. CONCLUSION: Brief training increased GPs' awareness of medically unexplained physical symptoms. Diagnostic accuracy according to a screening questionnaire remained unaffected but was difficult to evaluate, as there is no agreement on a gold standard for somatisation in general practice.
Rationale: Benefits of identifying risk factors for bronchopulmonary dysplasia in extremely premature infants include providing prognostic information, identifying infants likely to benefit from preventive strategies, and stratifying infants for clinical trial enrollment.
Objectives: To identify risk factors for bronchopulmonary dysplasia, and the competing outcome of death, by postnatal day; to identify which risk factors improve prediction; and to develop a Web-based estimator using readily available clinical information to predict risk of bronchopulmonary dysplasia or death.
Methods: We assessed infants of 23–30 weeks' gestation born in 17 centers of the Eunice Kennedy Shriver National Institute of Child Health and Human Development Neonatal Research Network and enrolled in the Neonatal Research Network Benchmarking Trial from 2000–2004.
Measurements and Main Results: Bronchopulmonary dysplasia was defined as a categorical variable (none, mild, moderate, or severe). We developed and validated models for bronchopulmonary dysplasia risk at six postnatal ages using gestational age, birth weight, race and ethnicity, sex, respiratory support, and FiO2, and examined the models using a C statistic (area under the curve). A total of 3,636 infants were eligible for this study. Prediction improved with advancing postnatal age, increasing from a C statistic of 0.793 on Day 1 to a maximum of 0.854 on Day 28. On Postnatal Days 1 and 3, gestational age best improved outcome prediction; on Postnatal Days 7, 14, 21, and 28, type of respiratory support did so. A Web-based model providing predicted estimates for bronchopulmonary dysplasia by postnatal day is available at https://neonatal.rti.org.
Conclusions: The probability of bronchopulmonary dysplasia in extremely premature infants can be determined accurately using a limited amount of readily available clinical information.
bronchopulmonary dysplasia; prematurity; low-birth-weight infant
Many, if not most, patients with a suspected small choroidal melanoma are currently managed by observation until tumor enlargement is documented. Current evidence appears to be insufficient to determine the correctness of this approach. A randomized clinical trial that could resolve this issue is probably not feasible. In the absence of satisfactory evidence, the decision about how to manage such patients depends on a subjective benefit-risk analysis that takes into account two competing but indeterminate risks: the risk of inadequate treatment for those patients who have a true melanoma and the risk of excessive treatment for those who have a benign nevus. Technologic advances and development of effective treatment for metastatic disease may eliminate most of the concern that currently accompanies observation as management for such tumors in the future.
Competing risks observations, where patients are subject to a number of potential failure events, are a feature of most clinical cancer studies. With competing risks, several modeling approaches are available to evaluate the relationship of covariates to cause-specific failures. We discuss the use and interpretation of commonly used competing risks regression models.
For competing risks analysis, the influence of covariate can be evaluated in relation to cause-specific hazard or on the cumulative incidence of the failure types. We present simulation studies to illustrate how covariate effects differ between these approaches. We then show the implications of model choice in an example from a Radiation Therapy Oncology Group (RTOG) clinical trial for prostate cancer.
The simulation studies illustrate that, depending on the relationship of a covariate to both the failure type of principal interest and the competing failure type, different models can result in substantially different effects. For example, a covariate that has no direct influence on the hazard of a primary event can still be significantly associated with the cumulative probability of that event, if the covariate influences the hazard of a competing event. This is a logical consequence of a fundamental difference between the model formulations. The example from RTOG similarly shows differences in the influence of age and tumor grade depending on the endpoint and the model type used.
Competing risks regression modeling requires that one consider the specific question of interest and subsequent choice of the best model to address it.
competing risks; cause-specific hazard; cumulative incidence; prognostic covariate
Introduction: 5-alpha reductase inhibitors can reduce the risk of prostate cancer (PCa) but can be associated with significant side effects. A library of nomograms which predict the risk of clinical endpoints relevant to dutasteride treatment may help determine if chemoprevention is suited to the individual patient. Methods: Data from the REDUCE trial was used to identify predictive factors for 9 endpoints relevant to dutasteride treatment. Using the treatment and placebo groups from the biopsy cohort, Cox proportional hazards (PH) and competing risks regression (CRR) models were used to build 18 nomograms, whose predictive ability was measured by concordance index (CI) and calibration plots. Results: A total of 18 nomograms assessing the risks of cancer, high grade cancer, high grade prostatic intraepithelial neoplasia (HGPIN), atypical small acinar proliferation (ASAP), erectile dysfunction (ED), acute urinary retention (AUR), gynecomastia, urinary tract infection (UTI) and BPH-related surgery either on or off dutasteride were created. The nomograms for cancer, high grade cancer, ED, AUR, and BPH-related surgery demonstrated good discrimination and calibration while those for gynecomastia, UTI, HGPIN, and ASAP predicted no better than random chance. Conclusions: To aid patients in determining whether the benefits of dutasteride use outweigh the risks, we have developed a comprehensive metagram that can generate individualized risks of 9 outcomes relevant to men considering chemoprevention. Better models based on more predictive markers are needed for some of the endpoints but the current metagram demonstrates potential as a tool for patient counseling and decision-making that is accessible, intuitive, and clinically relevant.
prostatic neoplasms; nomogram; chemoprevention; prediction
Under-treatment of osteoporosis is common, even for high risk patients. Among the reasons for under-treatment may be a clinician’s perception of a lack of treatment benefit, particularly in light of patients’ expected future mortality. Among U.S. Medicare beneficiaries, we evaluated the risk for second fracture vs. death in the five years following a hip, clinical vertebral, and wrist/forearm fracture.
Using data from 1999–2006 for a random 5% sample of U.S. Medicare beneficiaries, we identified individuals who experienced an incident hip, clinical vertebral, or wrist/forearm fracture in 2000 or 2001. We evaluated the risk for a second incident fracture versus death in the following five years. Results were stratified by age, gender, race/ethnicity, and medical comorbidities. In light of the competing mortality risk, and assuming 30% efficacy of an osteoporosis medication to prevent a second fracture, we calculated the number of individuals needed to treat (NNT) for 5 years after first fracture to prevent one additional subsequent fracture.
We identified 18,853, 12,751, and 7,635 persons with an incident hip, clinical vertebral, and wrist/forearm fracture, respectively. While the 5-year risk of death usually exceeded the risk for second fracture across age, gender, racial groups, and primary fracture type (median ratio of death to second fracture = 1.4, inter-quartile range 0.9, 2.0), the 5-year risk for second fracture was high, varying from a low of 13% to a high of 43%. Across demographic groups, the NNT to prevent a second fracture was low, ranging from 8 to 46.
Among older persons with hip, clinical vertebral, or wrist/forearm fracture, while the risk for death was usually greater than the risk for a second fracture, both were high. The relatively low NNT to prevent one additional subsequent fracture fell within a range generally considered acceptable for secondary prevention strategies.
hip fracture; vertebral fracture; wrist fracture; epidemiology; mortality; osteoporosis
Purpose: To determine whether between-trial heterogeneity in relative risk of fertilisation for intracytoplasmic sperm injection (ICSI) compared to in vitro fertilisation (IVF) can be explained by learning or by between-trial variation in patient characteristics.
Methods: Systematic review and meta-analysis of trials comparing fertilisation outcomes for ICSI and IVF (without surgical sperm retrieval). Meta-regressions to identify associations between treatment effect and trial characteristics.
Results: Coefficients on individually significant covariates from the meta-regressions confirm that the ICSI versus IVF treatment effect is increased when patients are “unsuited for IVF” but reduced as semen quality improves and when IVF insemination concentrations are increased. However, the relative risk of fertilisation varies inversely with publication date; contrary to the hypothesised learning effect.
Conclusion: While it is recognised that publication date might proxy for unobserved covariates, the possibility of a learning effect in favour of ICSI is not supported by the meta-regression.
Health technology assessment; ICSI; IVF; meta-regression.
Intensive treatment of multiple cardiovascular risk factors can halve mortality among people with established type 2 diabetes. We investigated the effect of early multifactorial treatment after diagnosis by screening.
In a pragmatic, cluster-randomised, parallel-group trial done in Denmark, the Netherlands, and the UK, 343 general practices were randomly assigned screening of registered patients aged 40–69 years without known diabetes followed by routine care of diabetes or screening followed by intensive treatment of multiple risk factors. The primary endpoint was first cardiovascular event, including cardiovascular mortality and morbidity, revascularisation, and non-traumatic amputation within 5 years. Patients and staff assessing outcomes were unaware of the practice's study group assignment. Analysis was done by intention to treat. This study is registered with ClinicalTrials.gov, number NCT00237549.
Primary endpoint data were available for 3055 (99·9%) of 3057 screen-detected patients. The mean age was 60·3 (SD 6·9) years and the mean duration of follow-up was 5·3 (SD 1·6) years. Improvements in cardiovascular risk factors (HbA1c and cholesterol concentrations and blood pressure) were slightly but significantly better in the intensive treatment group. The incidence of first cardiovascular event was 7·2% (13·5 per 1000 person-years) in the intensive treatment group and 8·5% (15·9 per 1000 person-years) in the routine care group (hazard ratio 0·83, 95% CI 0·65–1·05), and of all-cause mortality 6·2% (11·6 per 1000 person-years) and 6·7% (12·5 per 1000 person-years; 0·91, 0·69–1·21), respectively.
An intervention to promote early intensive management of patients with type 2 diabetes was associated with a small, non-significant reduction in the incidence of cardiovascular events and death.
National Health Service Denmark, Danish Council for Strategic Research, Danish Research Foundation for General Practice, Danish Centre for Evaluation and Health Technology Assessment, Danish National Board of Health, Danish Medical Research Council, Aarhus University Research Foundation, Wellcome Trust, UK Medical Research Council, UK NIHR Health Technology Assessment Programme, UK National Health Service R&D, UK National Institute for Health Research, Julius Center for Health Sciences and Primary Care, University Medical Center, Utrecht, Novo Nordisk, Astra, Pfizer, GlaxoSmithKline, Servier, HemoCue, Merck.
Although research has consistently established that depression and elevated depressive symptoms are associated with an increased risk of acute coronary syndrome (ACS) recurrence and mortality, clinical trials have failed to show that conventional depression interventions offset this risk. As depression is a complex and heterogeneous syndrome, we believe that using simpler, or intermediary, phenotypes rather than one complex phenotype may allow better identification of those at particular risk of ACS recurrence and mortality and may contribute to the development of specific depression treatments that would improve medical outcomes. Although there are many possible intermediary phenotypes, specifiers, and dimensions of depression, we will focus on only two when considering the relation between depression and risk of ACS recurrence and mortality: Inflammation-Induced Incident Depression and Anhedonic Depression. Future research on intermediary phenotypes of depression is needed to clarify which are associated with the greatest risk for ACS recurrence and mortality and which, if any, are benign. Theoretical advances in depression phenotyping may also help elucidate the behavioral and biological mechanisms underlying the increased risk of ACS among patients with specific depression phenotypes. Finally, tests of depression interventions may be guided by this new theoretical approach.
cardiovascular diseases; depressive disorder; depression; acute coronary syndrome; myocardial infarction; phenotype
Despite the considerable amount of evidence from randomized controlled trials and meta-analyses, uncertainty remains regarding the efficacy and safety of high-frequency oscillatory ventilation as compared to conventional ventilation in the early treatment of respiratory distress syndrome in preterm infants. This results in a wide variation in the clinical use of high-frequency oscillatory ventilation for this indication throughout the world. The reasons are an unexplained heterogeneity between trial results and a number of unanswered, clinically important questions. Do infants with different risk profiles respond differently to high-frequency oscillatory ventilation? How does the ventilation strategy affect outcomes? Does the delay – either from birth or from the moment of intubation – to the start of high-frequency oscillation modify the effect of the intervention? Instead of doing new trials, those questions can be addressed by re-analyzing the individual patient data from the existing randomized controlled trials.
A systematic review with meta-analysis based on individual patient data. This involves the central collection, validation and re-analysis of the original individual data from each infant included in each randomized controlled trial addressing this question.
The study objective is to estimate the effect of high-frequency oscillatory ventilation on the risk for the combined outcome of death or bronchopulmonary dysplasia or a severe adverse neurological event. In addition, it will explore whether the effect of high-frequency oscillatory ventilation differs by the infant's risk profile, defined by gestational age, intrauterine growth restriction, severity of lung disease at birth and whether or not corticosteroids were given to the mother prior to delivery. Finally, it will explore the importance of effect modifying factors such as the ventilator device, ventilation strategy and the delay to the start of high-frequency ventilation.
An international collaborative group, the PreVILIG Collaboration (Prevention of Ventilator Induced Lung Injury Group), has been formed with the investigators of the original randomized trials to conduct this systematic review. In the field of neonatology, individual patient data meta-analysis has not been used previously. Final results are expected to be available by the end of 2009.
It is often difficult to synthesize information about the risks and benefits of recommended management strategies in older patients with end-stage renal disease since they may have more comorbidity and lower life expectancy than patients described in clinical trials or practice guidelines. In this review, we outline a framework for individualizing end-stage renal disease management decisions in older patients. The framework considers three factors: life expectancy, the risks and benefits of competing treatment strategies, and patient preferences. We illustrate the use of this framework by applying it to three key end-stage renal disease decisions in older patients with varying life expectancy: choice of dialysis modality, choice of vascular access for hemodialysis, and referral for kidney transplantation. In several instances, this approach might provide support for treatment decisions that directly contradict available practice guidelines, illustrating circumstances when strict application of guidelines may be inappropriate for certain patients. By combining quantitative estimates of benefits and harms with qualitative assessments of patient preferences, clinicians may be better able to tailor treatment recommendations to individual older patients, thereby improving the overall quality of end-stage renal disease care.
dialysis; elderly; end-stage renal disease; transplantation