Use,option and externality values: are contingent valuation studies in health care mis‐specified?

A general population sample of Australian respondents completed a contingent valuation (CV) survey that asked them to value six scenarios. These varied according to whether the scenario was seeking to elicit: (i) use value; (ii) externality value; (iii) option value; or (iv) a combination. Results indicate that use plus externality and/or option value was significantly greater than use value alone. As CV studies in health (care) overwhelmingly focus on use value alone - often implicitly through study design rather than explicitly - this raises the possibility of mis-specification in CV research in health (care). The implications for CV in health (care) are considered.     

Robust versus consistent variance estimators in marginal structural Cox models

In survival analyses, inverse‐probability‐of‐treatment (IPT) and inverse‐probability‐of‐censoring (IPC) weighted estimators of parameters in marginal structural Cox models are often used to estimate treatment effects in the presence of time‐dependent confounding and censoring. In most applications, a robust variance estimator of the IPT and IPC weighted estimator is calculated leading to conservative confidence intervals. This estimator assumes that the weights are known rather than estimated from the data. Although a consistent estimator of the asymptotic variance of the IPT and IPC weighted estimator is generally available, applications and thus information on the performance of the consistent estimator are lacking. Reasons might be a cumbersome implementation in statistical software, which is further complicated by missing details on the variance formula. In this paper, we therefore provide a detailed derivation of the variance of the asymptotic distribution of the IPT and IPC weighted estimator and explicitly state the necessary terms to calculate a consistent estimator of this variance. We compare the performance of the robust and consistent variance estimators in an application based on routine health care data and in a simulation study. The simulation reveals no substantial differences between the 2 estimators in medium and large data sets with no unmeasured confounding, but the consistent variance estimator performs poorly in small samples or under unmeasured confounding, if the number of confounders is large. We thus conclude that the robust estimator is more appropriate for all practical purposes.     

A comparison of the results of intent‐to‐treat,per‐protocol,and g‐estimation in the presence of non‐random treatment changes in a time‐to‐event non‐inferiority trial

While intent‐to‐treat (ITT) analysis is widely accepted for superiority trials, there remains debate about its role in non‐inferiority trials. It has often been said that ITT analysis tends to be anti‐conservative in demonstrating non‐inferiority, suggesting that per‐protocol (PP) analysis may be preferable for non‐inferiority trials, despite the inherent bias of such analyses. We propose using randomization‐based g‐estimation analyses that more effectively preserve the integrity of randomization than do the more widely used PP analyses. Simulation studies were conducted to investigate the impacts of different types of treatment changes on the conservatism or anti‐conservatism of analyses using the ITT, PP, and g‐estimation methods in a time‐to‐event outcome. The ITT results were anti‐conservative for all simulations. Anti‐conservativeness increased with the percentage of treatment change and was more pronounced for outcome‐dependent treatment changes. PP analysis, in which treatment‐switching cases were censored at the time of treatment change, maintained type I error near the nominal level for independent treatment changes, whereas for outcome‐dependent cases, PP analysis was either conservative or anti‐conservative depending on the mechanism underlying the percentage of treatment changes. G‐estimation analysis maintained type I error near the nominal level even for outcome‐dependent treatment changes, although information on unmeasured covariates is not used in the analysis. Thus, randomization‐based g‐estimation analyses should be used to supplement the more conventional ITT and PP analyses, especially for non‐inferiority trials. Copyright © 2010 John Wiley & Sons, Ltd.     

THE COST‐EFFECTIVENESS OF USING FINANCIAL INCENTIVES TO IMPROVE PROVIDER QUALITY: A FRAMEWORK AND APPLICATION

Despite growing adoption of pay‐for‐performance (P4P) programmes in health care, there is remarkably little evidence on the cost‐effectiveness of such schemes. We review the limited number of previous studies and critique the frameworks adopted and the narrow range of costs and outcomes considered, before proposing a new more comprehensive framework, which we apply to the first P4P scheme introduced for hospitals in England. We emphasise that evaluations of cost‐effectiveness need to consider who the residual claimant is on any cost savings, the possibility of positive and negative spillovers, and whether performance improvement is a transitory or investment activity. Our application to the Advancing Quality initiative demonstrates that the incentive payments represented less than half of the £13m total programme costs. By generating approximately 5200 quality‐adjusted life years and £4.4m of savings in reduced length of stay, we find that the programme was a cost‐effective use of resources in its first 18 months. Copyright © 2013 John Wiley & Sons, Ltd.     

3‐carboxy‐4‐methyl‐5‐propyl‐2‐furanpropanoic acid (CMPF): A metabolite identified after consumption of fish oil and fish

3‐carboxy‐4‐methyl‐5‐propyl‐2‐furanpropanoic acid (CMPF) is a known metabolite of furan fatty acids and was first referred to as a urofuran fatty acid, as it was found in urine of humans and other species after consumption of furan fatty acids or foods containing furan fatty acids. More recently, CMPF has been identified as a highly prominent metabolite following the consumption of fish oil, fish oil fractions and diets rich in fish, and can be regarded as biomarker of oil‐rich fish or fish oil intakes. As furan fatty acids are known to occur in fish and fish oil (at a low level), it is possible that the CMPF in plasma arises from these furan fatty acids. On a structural basis, this is a likely explanation rather than the CMPF being an actual metabolite of long‐chain marine omega‐3 fatty acids. Recent studies in high fat‐fed mice given purified CMPF suggest that CMPF might contribute to the improved metabolic effects observed following consumption of long‐chain marine omega‐3 fatty acids but much is still to be known about the relationships between CMPF and health.     

Some insight on censored cost estimators

Censored survival data analysis has been studied for many years. Yet, the analysis of censored mark variables, such as medical cost, quality-adjusted lifetime, and repeated events, faces a unique challenge that makes standard survival analysis techniques invalid. Because of the 'informative' censorship imbedded in censored mark variables, the use of the Kaplan-Meier (Journal of the American Statistical Association 1958; 53:457-481) estimator, as an example, will produce biased estimates. Innovative estimators have been developed in the past decade in order to handle this issue. Even though consistent estimators have been proposed, the formulations and interpretations of some estimators are less intuitive to practitioners. On the other hand, more intuitive estimators have been proposed, but their mathematical properties have not been established. In this paper, we prove the analytic identity between some estimators (a statistically motivated estimator and an intuitive estimator) for censored cost data. Efron (1967) made similar investigation for censored survival data (between the Kaplan-Meier estimator and the redistribute-to-the-right algorithm). Therefore, we view our study as an extension of Efron's work to informatively censored data so that our findings could be applied to other marked variables.     

Stratification and weighting via the propensity score in estimation of causal treatment effects: a comparative study

Estimation of treatment effects with causal interpretation from observational data is complicated because exposure to treatment may be confounded with subject characteristics. The propensity score, the probability of treatment exposure conditional on covariates, is the basis for two approaches to adjusting for confounding: methods based on stratification of observations by quantiles of estimated propensity scores and methods based on weighting observations by the inverse of estimated propensity scores. We review popular versions of these approaches and related methods offering improved precision, describe theoretical properties and highlight their implications for practice, and present extensive comparisons of performance that provide guidance for practical use.     

Analysis of the short form-36 (SF-36): the beta-binomial distribution approach

Health-related quality of life (HRQoL) is an important indicator of health status and the Short Form-36 (SF-36) is a generic instrument to measure it. Multiple linear regression (MLR) is often used to study the relationship of HRQoL with patients' characteristics, though HRQoL outcomes tend to be not normally distributed, skewed and bounded (e.g. between 0 and 100). A sample of 193 patients with eating disorders has been analysed to assess the performance of the MLR under non-normality conditions. Normal distribution was rejected for seven out of the eight domains. A beta-binomial distribution is suggested to fit the SF-36 scores. The beta-binomial distribution is not rejected for five out of the eight domains. Thus, a beta-binomial regression (BBR) is suggested to analyse the SF-36 scores. Results using MLR and BBR have been compared for real and simulated data. Performance of the BBR is shown to be better than MLR in the HRQoL domains with few ordered categories and very similar to MLR in the more continuous domains. Moreover, the interpretation of the estimates obtained with BBR is clinically more meaningful. A common technique of statistical analysis is preferable for all the HRQoL dimensions. Therefore, the BBR approach is recommended not only to detect significant predictors of HRQoL when SF-36 is used, but also to analyse and interpret the effect of several explanatory variables on HRQoL. Further work is required to test the better performance of BBR against standard methods for other HRQoL outcomes, populations or interventions.     

Multipoint affected sibpair linkage methods for localizing susceptibility genes of complex diseases

Recently, Liang et al. ([2001] Hum. Hered. 51:64-78) proposed a general multipoint linkage method for estimating the chromosomal position of a putative susceptibility locus. Their technique is computationally simple and does not require specification of penetrance or a mode of inheritance. In complex genetic diseases, covariate data may be available which reflect etiologic or locus heterogeneity. We developed approaches to incorporating covariates into the method of Liang et al. ([2001] Hum. Hered. 51:64-78) with particular attention to exploiting age-at-onset information. The results of simulation studies, and a worked data example using a family data set ascertained through probands with schizophrenia, suggest that utilizing covariate information can yield substantial efficiency gains in localizing susceptibility genes.     

Meta‐analysis of Gaussian individual patient data: Two‐stage or not two‐stage?

Quantitative evidence synthesis through meta‐analysis is central to evidence‐based medicine. For well‐documented reasons, the meta‐analysis of individual patient data is held in higher regard than aggregate data. With access to individual patient data, the analysis is not restricted to a “two‐stage” approach (combining estimates and standard errors) but can estimate parameters of interest by fitting a single model to all of the data, a so‐called “one‐stage” analysis. There has been debate about the merits of one‐ and two‐stage analysis. Arguments for one‐stage analysis have typically noted that a wider range of models can be fitted and overall estimates may be more precise. The two‐stage side has emphasised that the models that can be fitted in two stages are sufficient to answer the relevant questions, with less scope for mistakes because there are fewer modelling choices to be made in the two‐stage approach. For Gaussian data, we consider the statistical arguments for flexibility and precision in small‐sample settings. Regarding flexibility, several of the models that can be fitted only in one stage may not be of serious interest to most meta‐analysis practitioners. Regarding precision, we consider fixed‐ and random‐effects meta‐analysis and see that, for a model making certain assumptions, the number of stages used to fit this model is irrelevant; the precision will be approximately equal. Meta‐analysts should choose modelling assumptions carefully. Sometimes relevant models can only be fitted in one stage. Otherwise, meta‐analysts are free to use whichever procedure is most convenient to fit the identified model.     

Time‐varying effect moderation using the structural nested mean model: estimation using inverse‐weighted regression with residuals

This article considers the problem of examining time‐varying causal effect moderation using observational, longitudinal data in which treatment, candidate moderators, and possible confounders are time varying. The structural nested mean model (SNMM) is used to specify the moderated time‐varying causal effects of interest in a conditional mean model for a continuous response given time‐varying treatments and moderators. We present an easy‐to‐use estimator of the SNMM that combines an existing regression‐with‐residuals (RR) approach with an inverse‐probability‐of‐treatment weighting (IPTW) strategy. The RR approach has been shown to identify the moderated time‐varying causal effects if the time‐varying moderators are also the sole time‐varying confounders. The proposed IPTW+RR approach provides estimators of the moderated time‐varying causal effects in the SNMM in the presence of an additional, auxiliary set of known and measured time‐varying confounders. We use a small simulation experiment to compare IPTW+RR versus the traditional regression approach and to compare small and large sample properties of asymptotic versus bootstrap estimators of the standard errors for the IPTW+RR approach. This article clarifies the distinction between time‐varying moderators and time‐varying confounders. We illustrate the methodology in a case study to assess if time‐varying substance use moderates treatment effects on future substance use. Copyright © 2013 John Wiley & Sons, Ltd.     

Utilization of the iPad application: Aid for Decision-making in Occupation Choice

The purpose of the study was to develop and evaluate an iPad (Apple) application, Aid for Decision-making in Occupation Choice (ADOC), so as to promote shared decision-making in an occupation-based goal setting. This application involves the client choosing from 94 illustrations describing daily activities related to the category of "activities and participation". One hundred occupational therapy clients evaluated the ADOC for goal setting; the clients and 37 occupational therapists underwent a survey to determine their perceptions of decision-making in the goal setting. More than 90% of the clients felt that they could give their opinions using the ADOC regarding goal setting. The majority of the occupational therapists (>90%) felt that ADOC would be useful in their clinical practice for setting client goals. The results indicated that ADOC is a useful and acceptable tool for both clients and occupational therapists in shared decision-making in occupation-based goal setting.     

Disentangling WTP per QALY data: different analytical approaches, different answers

A large random sample of the Danish general population was asked to value health improvements by way of both the time trade-off elicitation technique and willingness-to-pay (WTP) using contingent valuation methods. The data demonstrate a high degree of heterogeneity across respondents in their relative valuations on the two scales. This has implications for data analysis. We show that the estimates of WTP per QALY are highly sensitive to the analytical strategy. For both open-ended and dichotomous choice data we demonstrate that choice of aggregated approach (ratios of means) or disaggregated approach (means of ratios) affects estimates markedly as does the interpretation of the constant term (which allows for disproportionality across the two scales) in the regression analyses. We propose that future research should focus on why some respondents are unwilling to trade on the time trade-off scale, on how to interpret the constant value in the regression analyses, and on how best to capture the heterogeneity in preference structures when applying mixed multinomial logit.     

Lack of modulating influence of GSTM1 and GSTT1 polymorphisms on urinary biomonitoring markers in coke-oven workers

BACKGROUND: Coke-oven workers (COWs) are occupationally exposed to high concentrations of polycyclic aromatic hydrocarbons (PAHs). Urinary 8-hydroxy-2-deoxyguanosine (8-OH-dG) and 1-hydroxypyrene (1-OHP) are biological markers of oxidative DNA damage and PAHs metabolism, respectively. In this study, we investigated whether polymorphisms of glutathione S-transferase (GSTM1 and GSTT1) can modulate the relationship between urinary 8-OH-dG and 1-OHP concentrations among the COWs. METHODS: This was a cross-sectional study. Between February and November of 2001, 53 topside-oven and 130 side-oven workers with the presence of GSTM1 and GSTT1 genotypes were investigated. RESULTS: Urinary 1-OHP and 8-OH-dG concentrations (mean +/- SD) in the topside-oven workers with the presence of GSTM1 were 107.2 +/- 107.9 and 15.3 +/- 9.7 ng/ml, respectively, which were not significantly different from those in the absence of GSTM1 (84.1 +/- 104.5 and 12.8 +/- 14.1 ng/ml). The similar insignificant results were also noted in the sideoven workers. For GSTT1 polymorphism, the results remained insignificant. In contrast, individual excretion of urinary 8-OH-dG and 1-OHP concentrations were still highly correlated (Spearman correlation coefficients: r = 0.43, P < 0.0001, n = 183). CONCLUSIONS: GST may not play a role in the regulation of metabolism of urinary biological markers in COWs.     

The effects of non-compliance on intent-to-treat analysis of equivalence trials

The standard approach for analysing a randomized clinical trial is based on intent-to-treat (ITT) where subjects are analysed according to their assigned treatment group regardless of actual adherence to the treatment protocol. For therapeutic equivalence trials, it is a common concern that an ITT analysis increases the chance of erroneously concluding equivalence. In this paper, we formally investigate the impact of non-compliance on an ITT analysis of equivalence trials with a binary outcome. We assume 'all-or-none' compliance and independence between compliance and the outcome. Our results indicate that non-compliance does not always make it easier to demonstrate equivalence. The direction and magnitude of changes in the type I error rate and power of the study depend on the patterns of non-compliance, event probabilities, the margin of equivalence and other factors.     

The value of informal care–a further investigation of the feasibility of contingent valuation in informal caregivers

Including informal care in economic evaluations is increasingly advocated but problematic. We investigated three well‐known concerns regarding contingent valuation (CV): (1) the item non‐response of CV values, (2) the sensitivity of CV values to the individual circumstances of caring, and (3) the choice of valuation method by comparing willingness‐to‐pay (WTP) and willingness‐to‐accept (WTA) values for a hypothetical marginal change in hours of informal care currently provided. The study sample consisted of 1453 caregivers and 787 care recipients. Of the caregivers, 603 caregivers (41.5%) provided both WTP and WTA values, 983 (67.7%) provided at least one. Determinants of non‐response were dependent on the valuation method; primary determinants were education and satisfaction with amount of informal care provided. Caregivers' mean WTP (WTA) for reducing (increasing) informal care by 1 h was €9.13 (10.52). Care recipients' mean WTA (WTP) for reducing (increasing) informal care by 1 h was €8.88 (€6.85). Values were associated with a variety of characteristics of the caregiving situation; explanatory variables differed between WTP and WTA valuations. The differences between WTP and WTA valuations were small. Based on sensitivity CV appears to be a useful method to value informal care for use in economic evalations, non‐response, however, remains a matter of concern. Copyright © 2009 John Wiley & Sons, Ltd.     

Mixed models,linear dependency,and identification in age‐period‐cohort models

This paper examines the identification problem in age‐period‐cohort models that use either linear or categorically coded ages, periods, and cohorts or combinations of these parameterizations. These models are not identified using the traditional fixed effect regression model approach because of a linear dependency between the ages, periods, and cohorts. However, these models can be identified if the researcher introduces a single just identifying constraint on the model coefficients. The problem with such constraints is that the results can differ substantially depending on the constraint chosen. Somewhat surprisingly, age‐period‐cohort models that specify one or more of ages and/or periods and/or cohorts as random effects are identified. This is the case without introducing an additional constraint. I label this identification as statistical model identification and show how statistical model identification comes about in mixed models and why which effects are treated as fixed and which are treated as random can substantially change the estimates of the age, period, and cohort effects. Copyright © 2017 John Wiley & Sons, Ltd.     

Correcting for non‐compliance in randomized non‐inferiority trials with active and placebo control using structural models

The three‐arm clinical trial design, which includes a test treatment, an active reference, and placebo control, is the gold standard for the assessment of non‐inferiority. In the presence of non‐compliance, one common concern is that an intent‐to‐treat (ITT) analysis (which is the standard approach to non‐inferiority trials), tends to increase the chances of erroneously concluding non‐inferiority, suggesting that the per‐protocol (PP) analysis may be preferable for non‐inferiority trials despite its inherent bias. The objective of this paper was to develop statistical methodology for dealing with non‐compliance in three‐arm non‐inferiority trials for censored, time‐to‐event data. Changes in treatment were here considered the only form of non‐compliance. An approach using a three‐arm rank preserving structural failure time model and G‐estimation analysis is here presented. Using simulations, the impact of non‐compliance on non‐inferiority trials was investigated in detail using ITT, PP analyses, and the present proposed method. Results indicate that the proposed method shows good characteristics, and that neither ITT nor PP analyses can always guarantee the validity of the non‐inferiority conclusion. A Statistical Analysis System program for the implementation of the proposed test procedure is available from the authors upon request. Copyright © 2014 John Wiley & Sons, Ltd.