A systematic review of the role of bisphosphonates in metastatic disease

Objectives: To identify evidence for the role of bisphosphonates in malignancy for the treatment of hypercalcaemia, prevention of skeletal morbidity and use in the adjuvant setting. To perform an economic review of current literature and model the cost effectiveness of bisphosphonates in the treatment of hypercalcaemia and prevention of skeletal morbidity Data sources: Electronic databases (1966-June 2001). Cochrane register. Pharmaceutical companies. Experts in the field. Handsearching of abstracts and leading oncology journals (1999-2001). Review methods: Two independent reviewers assessed studies for inclusion, according to predetermined criteria, and extracted relevant data. Overall event rates were pooled in a meta-analysis, odds ratios ( OR) were given with 95% confidence intervals (CI). Where data could not be combined, studies were reported individually and proportions compared using chi- squared analysis. Cost and cost-effectiveness were assessed by a decision analytic model comparing different bisphosphonate regimens for the treatment of hypercalcaemia; Markov models were employed to evaluate the use of bisphosphonates to prevent skeletal-related events (SRE) in patients with breast cancer and multiple myeloma. Results: For acute hypercalcaemia of malignancy, bisphosphonates normalised serum calcium in >70% of patients within 2-6 days. Pamidronate was more effective than control, etidronate, mithramycin and low-dose clodronate, but equal to high dose clodronate, in achieving normocalcaemia. Pamidronate prolongs ( doubles) the median time to relapse compared with clodronate or etidronate. For prevention of skeletal morbidity, bisphosphonates compared with placebo, significantly reduced the OR for fractures (OR [95% CI], vertebral, 0.69 [0.57-0.84], non-vertebral, 0.65 [0.54-0.79], combined, 0.65 [0.55-0.78]) radiotherapy 0.67 [0.57-0.79] and hypercalcaemia 0.54 [0.36-0.81] but not orthopaedic surgery 0.70 [0.46-1.05] or spinal cord compression 0.71 [0.47-1.08]. However, reduction in orthopaedic surgery was significant in studies that lasted over a year 0.59 [0.39-0.88]. Bisphosphonates significantly increased the time to first SRE but did not affect survival. Subanalyses were performed for disease groups, drugs and route of administration. Most evidence supports the use of intravenous aminobisphosphonates. For adjuvant use of bisphosphonates, Clodronate, given to patients with primary operable breast cancer and no metastatic disease, significantly reduced the number of patients developing bone metastases. This benefit was not maintained once regular administration had been discontinued. Two trials reported significant survival advantages in the treated groups. Bisphosphonates reduce the number of bone metastases in patients with both early and advanced breast cancer. Bisphosphonates are well tolerated with a low incidence of side-effects. Economic modelling showed that for acute hypercalcaemia, drugs with the longest cumulative duration of normocalcaemia were most cost-effective. Zoledronate 4 mg was the most costly, but most cost-effective treatment. For skeletal morbidity, Markov models estimated that the overall cost of bisphosphonate therapy to prevent an SRE was pound250 and pound1500 per event for patients with breast cancer and multiple myeloma, respectively. Bisphosphonate treatment is sometimes cost-saving in breast cancer patients where fractures are prevented. Conclusions: High dose aminobisphosphonates are most effective for the treatment of acute hypercalcaemia and delay time to relapse. Bisphosphonates significantly reduce SREs and delay the time to first SRE in patients with bony metastatic disease but do not affect survival. Benefit is demonstrated after administration for at least 6-12 months. The greatest body of evidence supports the use of intravenous aminobisphosphonates. Further evidence is required to support use in the adjuvant setting

Small Area Estimation under Limited Auxiliary Population Data Dealing with Model Violations and their Economic Applications

For evidence-based policy-making, reliable information on socio-economic indicators are essential. Sample surveys have a long tradition of providing cost-efficient information on these indicators. Mostly, there is a demand for the quantity of interest not only at the level of the total population, but especially at the level of sub-populations (geographic areas or sociodemographic groups) called areas or domains. To gain insights into these sub-populations, disaggregated direct estimators can be used, which are calculated solely on area-specific survey data. An area is regarded as ’large’ if the sample size is large enough to enable reliable direct estimates. If the precision of the direct estimates is not sufficient or the sample size is even zero, the area is considered as ’small’. This is particularly common at high spatial or socio-demographic resolutions. Small area estimation (SAE) is promising to overcome this problem without the need for larger and thus more costly surveys. The essence of SAE techniques is that they ’borrow strength’ from other areas to improve their predictions. For this purpose, a model is built on survey data that links additional auxiliary data and exploits area-specific structures. Suitable auxiliary data sources are administrative and register data, such as the census. In many countries, such data are strictly protected by confidentiality agreements and access to population micro-data is a challenge even for gatekeeper organisations. Thus, users have an increased interest in SAE estimators that do not require population micro-data to serve as auxiliary data. In this thesis, new methods in the absence of population micro-data are presented and applications on socio-economic highly relevant indicators are demonstrated. Since different SAE models impose different data requirements, Part I bundles research combining unit-level survey data and limited auxiliary data, e.g., aggregated data such as means, which is a common data situation for users. To account for the unit-level survey information the use of the well-known nested error regression (NER) model is targeted. This model is a special case of a linear mixed model based on several assumptions. But how can users proceed if the model assumptions are not fulfilled? In Part I, this thesis provides two new approaches to deal with this issue. One promising approach is to transform the response. Since several socio-economically relevant variables, such as income, have a skewed distribution, the log-transformation of the response is an established way to meet the assumptions. However, the data-driven log-shift transformation is even more promising because it extends the log by an additional parameter and achieves more flexibility. Chapter 1 introduces both transformations in the absence of population micro-data. A particular challenge is the transformation of the small area means back to the original scale. Hence, the proposed approach introduces aggregate statistics (means and covariances) and kernel density estimation to resolve the issue of lacking population micro-data. Uncertainty estimation is developed, and all methods are evaluated in design- and model-based settings. The proposed method is applied to estimate regional income in Germany using the Socio-Economic Panel and census data. It achieves a clear improvement in reliability, and thus demonstrates the importance of the method. To conveniently enable further applications, this new methodology is implementedin the R package saeTrafo. Chapter 2 describes the various functionalities of the package using publicly available income data. To increase user-friendliness, established unit-level models under transformations and their uncertainty estimations are implemented and the most suitable method is automatically selected. For some applications, however, it is challenging to find a suitable transformation or, more generally, to specify a model, particularly in the presence of complex interactions. For this case, machine learning methods are valuable as a transformation is not necessarily required nor a model needs to be explicitly specified. The semi-parametric framework of mixed effects random forest (MERF) combines the advantages of random forests (robustness against outliers and implicit model-selection) with the ability to model hierarchical dependencies as present in SAE approaches. Chapter 3 introduces MERFs in the absence of population micro-data. As existing random forest algorithm require unit-level auxiliary population data, an alternative strategy is introduced. It adaptively incorporates aggregated auxiliary information through calibration-weights to circumvent unit-level auxiliary data. Applying the proposed method on opportunity costs of care work for Germany using the Socio-Economic Panel and census data demonstrates the gain in accuracy in comparison to both direct estimates and the classical NER model. In contrast to methods using a unit-level sample survey, Part II focuses on the well-known class of area-level SAE models requiring direct estimates from a survey while using (once again) only aggregated population auxiliary data. This thesis presents two particularly relevant applications of this model class. Chapter 4 examines regional consumer price indices (CPIs) in the United Kingdom (UK), contributing to the great interest in monitoring inflation at the spatial level. The SAE challenge is to construct model-based expenditure weights to generate the regional basket of goods and services for the twelve regions of the UK. They are estimated and constructed from the living cost and food survey. Furthermore, available price data are linked to the SAE estimated baskets to produce regional CPIs. The resulting CPI series are closely examined, and smoothing techniques are applied. As a result, the reliability improves, but the CPI series are still too volatile for policy use. However, our research serves as a valuable framework for the creation of a regional CPI in the future. The second application also explores the reliability of the disaggregated estimation of a politically and economically highly relevant indicator, in this case the unemployment rate. The regional target level are the functional urban areas in the German federal state North Rhine-Westphalia. In Chapter 5, two types of unemployment rates - the traditional one and an alternative definition taking commuting into account - are estimated and compared. Direct estimates from the labour force survey are linked with SAE methods to passively collected mobile network data. This alternative data source is real-time available, offers spatial flexible resolutions, and is dynamic. In compliance with data protection rules, we obtain aggregated auxiliary mobile network information from the data provider. The SAE methods improve the reliability, and the resulting predictions show that alternative unemployment rates in German city cores are lower than traditional estimated official unemployment rates indicate

Public health training in Europe. Development of European masters degrees in public health.

BACKGROUND: Changing political and economic relations in Europe mean that there are new challenges for public health and public health training. There have been several attempts to develop training at the master's level in public health which is focused on meeting the new needs. These have failed due to being too inflexible to allow participation by schools of public health. METHODS: A project funded by the European Union involving public health trainers has developed a new approach which allows participating schools to retain their national differences and work within local rules and traditions, but which aims to introduce the European dimension into public health training. This paper reports the conclusions of this project. CONCLUSIONS: A network of schools wishing to develop European Master's degrees is being established and other schools offering good quality programmes will be able to join

Statistical modelling and inference for financial auditing

The fundamental problem addressed in this thesis is the problem of constructing confidence limits for mean or totals in finite populations, when the underlying distribution is highly skewed and contains a substantial proportion of zero values. This situation is often encountered in statistical applications such as statistical auditing, reliability, insurance, meteorology and biostatistics. The motivating example underlying this research is that of auditing (see the report published by the National Academy Press entitled “Statistical Models and Analysis in Auditing”, Panel on Non-standard Mixtures of Distributions 1989), where interest is focused on computing the confidence bounds for the true total error amount. In such populations the use of the classical survey-sampling estimators such as the mean-per-unit, the difference, the ratio or regression, based on the normality assumption of the sampling distribution of the estimates, has been found unreliable, (e.g. Stringer 1963, Kaplan 1973, and Neter and Loebbecke 1975, 1977). Several alternative methods have been proposed, of which the Stringer bound (Stringer 1963), is the most widely used. This bound, while overcoming the unreliability problem of the classical estimators, has been found to be extremely conservative. In this research, we develop new methods for constructing confidence intervals for the mean of a bounded random variable. Further, we apply these new methods to data that are heavily skewed and marked by many zero values. Our proposed confidence intervals have a good coverage probability and precision. The first method is based on a novel use of the Edgeworth expansion for the studentised compound Poisson sum. In this work, we have reduced the problem of estimating the total error amount in auditing to the compound Poisson sum, and explored the asymptotic expansion for a compound Poisson distribution as a method of constructing confidence bounds on the total error amount. This method is less restrictive than the Stringer bound, and imposes no prior structure on the error distribution. We obtain a bound on the cumulative distribution function of the prorated errors, which we then use to give an alternative form of the Stringer bound. With this form of the Stringer bound, we were able to use Bolshev’s recursion to obtain a lower bound on its coverage probability, and showed that, for a sample size, n ≤ 2, this lower bound is greater than or equal to the stated coverage probability. We illustrate numerically that the Stringer Bound is reliable when (n, α) falls into a number of ranges; specifically n ≤ 11 and a significance level α ∈ (0, 0.05); n ≤ 10 and α ∈ (0, 0.1); n ≤ 9 and α ∈ (0, 0.20); n ≤ 8 and α ∈ (0, 0.40); and n ≤ 7 and α ∈ (0, .5). We also proposed an extension to the Stringer method based on Rom’s adjusted significance levels, and illustrated numerically the reliability of the extended Stringer bound for values of α in the range .05 to .5, and for n = 1 to n = 20. For the new bounds, we provide explicit expressions which make their computations straightforward. Monte Carlo simulations are carried out to evaluate the performance of the methods developed in this thesis when applied to accounting data, we investigate the performance of each method and assess whether or not it is affected by varying the distribution of accounting data, the effects of 100-percent overstatement error and the effects of error rates, using real and simulated populations. The method based on compound Poisson sum seems to reliable for large samples. However, for small samples the compound Poisson bound has the poorest results (in the sense of coverage probability), in particular, for populations containing a lower concentration of small error amounts. Although the extended Stringer bound, has a good coverage probability for all sample sizes and significance levels, it shares the extreme conservativeness of the Stringer bound

Techniques for Estimating the Variance of Specific Estimators within Complex Surveys

The objective of this thesis is to present the various procedures for estimating the variance of specific statistics obtained from different types of survey designs, leading up to more advanced designs such complex surveys. The thesis starts with defining the various sampling designs that are to be used for illustrations (Ch 2). Chapter two gives further descriptions of how various sampling designs are performed (from simple designs to not so simple) and shows the sophistication in calculating the estimates and variances. Chapter three cites the actual equations necessary for estimating the variances of the statistics for each design and demonstrates the potential difficulty especially in estimating the variance of the statistics, as the designs get more complex. Each design is illustrated with numerical examples. Chapter four defines current methods for estimating the variance and introduces the Bootstrap and Jackknife approaches. In Chapter 5 the ideas behind what is considered to be a complex survey are described and two nationally known complex surveys (NHANES and NHIS) currently being done in the U.S. are explained as examples. Chapter six reports the main statistical results, comparing the variances, etc., for all the designs and finally a summary conclusion is in chapter 7

Goal-directed versus outcome-based financial incentives for weight loss among low-income patients with obesity: rationale and design of the Financial Incentives foR Weight Reduction (FIReWoRk) randomised controlled trial.

IntroductionObesity is a major public health challenge and exacerbates economic disparities through employment discrimination and increased personal health expenditures. Financial incentives for weight management may intensify individuals' utilisation of evidence-based behavioural strategies while addressing obesity-related economic disparities in low-income populations. Trials have focused on testing incentives contingent on achieving weight loss outcomes. However, based on social cognitive and self-determination theories, providing incentives for achieving intermediate behavioural goals may be more sustainable than incentivising outcomes if they enhance an individual's skills and self-efficacy for maintaining long-term weight loss. The objective of this paper is to describe the rationale and design of the Financial Incentives foR Weight Reduction study, a randomised controlled trial to test the comparative effectiveness and cost-effectiveness of two financial incentive strategies for weight loss (goal directed vs outcome based) among low-income adults with obesity, as well as compared with the provision of health behaviour change resources alone.Methods and analysisWe are recruiting 795 adults, aged 18-70 years with a body mass index ≥30 kg/m2, from three primary care clinics serving residents of socioeconomically disadvantaged neighbourhoods in New York City and Los Angeles. All participants receive a 1-year commercial weight loss programme membership, self-monitoring tools (bathroom scale, food journal and Fitbit Alta HR), health education and monthly check-in visits. In addition to these resources, those in the two intervention groups can earn up to $750 over 6 months for: (1) participating in an intensive weight management programme, self-monitoring weight and diet and meeting physical activity guidelines (goal-directed arm); or (2) a ≥1.5% to ≥5% reduction in baseline weight (outcome-based arm). To maximise incentive efficacy, we incorporate concepts from behavioural economics, including immediacy of payments and framing feedback to elicit regret aversion. We will use generalised mixed effect models for repeated measures to examine intervention effects on weight at 6, 9 and 12 months.Ethics and disseminationHuman research protection committees at New York University School of Medicine, University of California Los Angeles (UCLA) David Geffen School of Medicine and Olive-View-UCLA Medical Center granted ethics approval. We will disseminate the results of this research via peer-reviewed publications, conference presentations and meetings with stakeholders.Trial registration numberNCT03157713

Multilevel modelling in the analysis of observational datasets in the health care setting

In health care-related research, many studies circle around the problem of identifying risk factors for clinical events of interest, with a potential for economic consequences, or risk factors for increased health care costs. Multivariate regression methods are typically used to analyse such studies and have become central for an efficient control of confounding and assessment of effect modification. However, most of the data used for this type of research are characterised by hierarchical (multilevel) data structures (e.g., patients are frequently nested within treating physicians or study centres). Standard multivariate regression methods tend to ignore this aspect and it has been shown that this may lead to a loss of statistical efficiency and, in some cases, to wrong conclusions. Multilevel regression modelling is an emerging statistical technique which claims to correctly address this type of data, and to make use of their full potential. The author conducted and/or analysed three observational studies of factors associated with clinical events or cost endpoints of interest. In all cases, conventional regression methods were primarily used. In a second step, multilevel re-analyses were performed and the results were compared. The first study addressed the effect of exacerbation status, disease severity and other covariates on the disease-specific health care costs of adult Swiss asthma patients. Among other factors, the occurrence of asthma exacerbations was confirmed to be independently associated with higher costs, and to interact with disease severity. The second study addressed the impact of gatekeeping, a technique widely used to manage the use of health care resources, on the health care costs accrued by a general Swiss population. In a situation characterised by ambiguous research findings, the author's study indicated substantial cost savings through gatekeeping as opposed to fee-for-service based health insurance. Finally, a combined dataset of six retrospective audits of breast cancer treatment from several Western European countries was used to estimate, for common chemotherapy regimen types, the frequency of chemotherapy-induced neutropenic events and to identify or confirm potential neutropenic event risk factors. Neutropenic events were shown to occur frequently in routine clinical practice. Several factors, including age, chemotherapy regimen type, planned chemotherapy dose intensity, and planned number of chemotherapy cycles, were shown to be potentially important elements of neutropenia risk models. Multilevel re-analysis showed higher level variation (i.e., variation at the level of the treating physicians or study centres) to be present in the asthma dataset and in the neutropenia dataset, but not in the gatekeeping dataset. In the first-mentioned cases, multilevel modelling allowed to quantify the amount of higher level variation; to identify its sources; to identify spurious findings by analysing influential higher level units; to achieve a gain in statistical precision; and to achieve a modest gain in predictive ability for out-of-sample observations whose corresponding higher level units contributed to model estimation. The main conclusions of the conventional analyses were confirmed. Based on these findings and in conjunction with published sources, it is concluded that multilevel modelling should be used systematically where hierarchical data structures are present, except if the higher level units must be regarded as distinct, unrelated entities or if their number is very small. Erroneous inferences will thus become more unlikely. Moreover, multilevel modelling is the only technique to date which allows to efficiently test hypotheses at different hierarchical levels, and hypotheses involving several levels, simultaneously. In the authors opinion, multilevel analysis is of particular interest where characteristics of health care providers, and clinical practice patterns in particular, may impact on health outcomes or health economic outcomes. It is only another facet of the same argument that multilevel modelling should also be used in multi-centre studies (including randomised clinical trials) to take into account study centre-specific characteristics and behaviours. In many instances, the use of the technique will be tentative and rule out the presence of substantial higher level variation. If so, simpler methods can again be used. Besides some technical issues, the main disadvantage of multilevel modelling is the complexity involved with the modelling process and with correctly interpreting the results. A careful approach is therefore needed. Multilevel modelling can be applied to datasets post hoc, as the author has done, but superior results can be expected from studies which are planned with the requirements of multilevel analysis (e.g., appropriate sample size, collection of relevant covariates at all hierarchical levels) in mind

Moderated Mediation Analysis: A Review and Application to School Climate Research

Moderated mediation analysis is a valuable technique for assessing whether an indirect effect is conditional on values of a moderating variable. We review the basis of moderation and mediation and their integration into a combined model of moderated mediation within a regression framework. Thereafter, an analytic and interpretive illustration of the technique is provided in the context of a substantive school climate research question. The illustration is based on a sample of 318 high schools that examines whether school-wide student engagement mediates the association between the prevalence of teasing and bullying (PTB) and academic achievement on a state-mandated reading exam; and whether this indirect effect was moderated by student perceptions of teacher support

Bayesian Predictive Inference for Three Topics in Survey Samples.

In this thesis, I study three problems in survey samples: inference for finite population quantities in unequal probability sampling, variable selection for multiply imputed data, and the application of the multiple imputation method to the problem of detection limits. In survey samples, design-based estimators are often used for inference about finite population quantities when sample sizes are large. However, design-based inference relies on asymptotic assumptions; mean square error can be very large and nominal confidence interval coverage relatively poor when the sample is small. When design information is available to modelers, it can be used to improve the efficiency of the estimators. In Chapters II and III, I provide Bayesian model-based estimators for finite population proportions and quantiles in unequal probability sampling settings by fitting the survey outcomes on the penalized splines of the selection probabilities. Simulation studies show that the robust Bayesian estimator for proportions is more efficient and its 95% CI provides better confidence coverage with shorter average width than the Hajek estimator or the generalized regression estimator. The Bayesian estimators for quantiles also outperform the design-based estimators, with smaller mean squared errors and shorter average width of 95% CIs. When sparse data are selected into samples, the Bayesian estimators yield better confidence coverage. The second part of the research is motivated by two statistical issues connected with the University of Michigan Dioxin Exposure Study which employs a complex survey design. In Chapter IV, I propose a “combine then select” variable selection method which calculates combined p-values using the multiple imputation combining rule and then selects variables based on the combined p-values in each step of the selection. I show through simulations and the dioxin study data that the “combine then select” method is less likely to incorrectly select variables into the model than competing methods currently used in epidemiological studies. In Chapter V, I employ a proper multiple imputation approach to impute the serum dioxin concentrations for those below the limit of detection. I then use the complete imputed data to predict the age- and sex- specific percentiles of serum dioxin concentrations among the U.S. population.Ph.D.BiostatisticsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/64741/1/qixuan_1.pd