Laparoscopic Versus Open Hemihepatectomy: The ORANGE II PLUS Multicenter Randomized Controlled Trial

PURPOSE

To compare outcomes after laparoscopic versus open major liver resection (hemihepatectomy) mainly for primary or metastatic cancer. The primary outcome measure was time to functional recovery. Secondary outcomes included morbidity, quality of life (QoL), and for those with cancer, resection margin status and time to adjuvant systemic therapy.

PATIENTS AND METHODS

This was a multicenter, randomized controlled, patient-blinded, superiority trial on adult patients undergoing hemihepatectomy. Patients were recruited from 16 hospitals in Europe between November 2013 and December 2018.

RESULTS

Of the 352 randomly assigned patients, 332 patients (94.3%) underwent surgery (laparoscopic, n = 166 and open, n = 166) and comprised the analysis population. The median time to functional recovery was 4 days (IQR, 3-5; range, 1-30) for laparoscopic hemihepatectomy versus 5 days (IQR, 4-6; range, 1-33) for open hemihepatectomy (difference, -17.5% [96% CI, -25.6 to -8.4]; P < .001). There was no difference in major complications (laparoscopic 24/166 [14.5%] v open 28/166 [16.9%]; odds ratio [OR], 0.84; P = .58). Regarding QoL, both global health status (difference, 3.2 points; P < .001) and body image (difference, 0.9 points; P < .001) scored significantly higher in the laparoscopic group. For the 281 (84.6%) patients with cancer, R0 resection margin status was similar (laparoscopic 106 [77.9%] v open 122 patients [84.1%], OR, 0.60; P = .14) with a shorter time to adjuvant systemic therapy in the laparoscopic group (46.5 days v 62.8 days, hazard ratio, 2.20; P = .009).

CONCLUSION

Among patients undergoing hemihepatectomy, the laparoscopic approach resulted in a shorter time to functional recovery compared with open surgery. In addition, it was associated with a better QoL, and in patients with cancer, a shorter time to adjuvant systemic therapy with no adverse impact on cancer outcomes observed.

Best (but oft forgotten) practices: Efficient sample sizes for commonly used trial designs

Designing studies such that they have a high level of power to detect an effect or association of interest is an important tool to improve the quality and reproducibility of findings from such studies. Since resources (research subjects, time, and money) are scarce, it is important to obtain sufficient power with minimum use of such resources. For commonly used randomized trials of the treatment effect on a continuous outcome, designs are presented that minimize the number of subjects or the amount of research budget when aiming for a desired power level. This concerns the optimal allocation of subjects to treatments and, in case of nested designs such as cluster-randomized trials and multicenter trials, also the optimal number of centers versus the number of persons per center. Since such optimal designs require knowledge of parameters of the analysis model that are not known in the design stage, in particular outcome variances, maximin designs are presented. These designs guarantee a prespecified power level for plausible ranges of the unknown parameters and minimize research costs for the worst-case values of these parameters. The focus is on a 2-group parallel design, the AB/BA crossover design, and cluster-randomized and multicenter trials with a continuous outcome. How to calculate sample sizes for maximin designs is illustrated for examples from nutrition. Several computer programs that are helpful in calculating sample sizes for optimal and maximin designs are discussed as well as some results on optimal designs for other types of outcomes.

Laparoscopic versus open resections in the posterosuperior liver segments within an enhanced recovery programme (ORANGE Segments): study protocol for a multicentre randomised controlled trial

BACKGROUND

A shift towards parenchymal-sparing liver resections in open and laparoscopic surgery emerged in the last few years. Laparoscopic liver resection is technically feasible and safe, and consensus guidelines acknowledge the laparoscopic approach in the posterosuperior segments. Lesions situated in these segments are considered the most challenging for the laparoscopic approach. The aim of this trial is to compare the postoperative time to functional recovery, complications, oncological safety, quality of life, survival and costs after laparoscopic versus open parenchymal-sparing liver resections in the posterosuperior liver segments within an enhanced recovery setting.

METHODS

The ORANGE Segments trial is an international multicentre randomised controlled superiority trial conducted in centres experienced in laparoscopic liver resection. Eligible patients for minor resections in the posterosuperior segments will be randomised in a 1:1 ratio to undergo laparoscopic or open resections in an enhanced recovery setting. Patients and ward personnel are blinded to the treatment allocation until postoperative day 4 using a large abdominal dressing. The primary endpoint is time to functional recovery. Secondary endpoints include intraoperative outcomes, length of stay, resection margin, postoperative complications, 90-day mortality, time to adjuvant chemotherapy initiation, quality of life and overall survival. Laparoscopic liver surgery of the posterosuperior segments is hypothesised to reduce time to functional recovery by 2 days in comparison with open surgery. With a power of 80% and alpha of 0.04 to adjust for interim analysis halfway the trial, a total of 250 patients are required to be randomised.

DISCUSSION

The ORANGE Segments trial is the first multicentre international randomised controlled study to compare short- and long-term surgical and oncological outcomes of laparoscopic and open resections in the posterosuperior segments within an enhanced recovery programme.

TRIAL REGISTRATION

ClinicalTrials.gov NCT03270917 . Registered on September 1, 2017. Before start of inclusion.

PROTOCOL VERSION

version 12, May 9, 2017.

Imputation of Missing Covariates in Randomized Controlled Trials with Continuous Outcomes: Simple, Unbiased and Efficient Methods

The literature on dealing with missing covariates in nonrandomized studies advocates the use of sophisticated methods like multiple imputation (MI) and maximum likelihood (ML)-based approaches over simple methods. However, these methods are not necessarily optimal in terms of bias and efficiency of treatment effect estimation in randomized studies, where the covariate of interest (treatment group) is independent of all baseline (pre-randomization) covariates due to randomization. This has been shown in the literature, but only for missingness on a single baseline covariate. Here, we extend the situation to multiple baseline covariates with missingness and evaluate the performance of MI and ML compared with simple alternative methods under various missingness scenarios in RCTs with a quantitative outcome. We first derive asymptotic relative efficiencies of the simple methods under the missing completely at random (MCAR) scenario and then perform a simulation study for non-MCAR scenarios. Finally, a trial on chronic low back pain is used to illustrate the implementation of the methods. The results show that all simple methods give unbiased treatment effect estimation but with increased mean squared residual. It also turns out that mean imputation and the missing-indicator method are most efficient under all covariate missingness scenarios and perform at least as well as MI and LM in each scenario.

Maximin design of cluster randomized trials with heterogeneous costs and variances

Cluster randomized trials evaluate the effect of a treatment on persons nested within clusters, with clusters being randomly assigned to treatment. The optimal sample size at the cluster and person level depends on the study cost per cluster and per person, and the outcome variance at the cluster and the person level. The variances are unknown in the design stage and can differ between treatment arms. As a solution, this paper presents a Maximin design that maximizes the minimum relative efficiency (relative to the optimal design) over the variance parameter space, for trials with two treatment arms and a quantitative outcome. This maximin relative efficiency design (MMRED) is compared with a published Maximin design which maximizes the minimum efficiency (MMED). Both designs are also compared with the optimal designs for homogeneous costs and variances (balanced design) and heterogeneous costs and homogeneous variances (cost-conscious design), for a range of variances based upon three published trials. Whereas the MMED is balanced under high uncertainty about the treatment-to-control variance ratio, the MMRED then tends towards a balanced budget allocation between arms, leading to an unbalanced sample size allocation if costs are heterogeneous, similar to the cost-conscious design. Further, the MMRED corresponds to an optimal design for an intraclass correlation (ICC) in the lower half of the assumed ICC range (optimistic), whereas the MMED is the optimal design for the maximum ICC within the ICC range (pessimistic). Attention is given to the effect of the Welch-Satterthwaite degrees of freedom for treatment effect testing on the design efficiencies.

Sample size calculation and optimal design for regression-based norming of tests and questionnaires

To prevent mistakes in psychological assessment, the precision of test norms is important. This can be achieved by drawing a large normative sample and using regression-based norming. Based on that norming method, a procedure for sample size planning to make inference on Z-scores and percentile rank scores is proposed. Sampling variance formulas for these norm statistics are derived and used to obtain the optimal design, that is, the optimal predictor distribution, for the normative sample, thereby maximizing precision of estimation. This is done under five regression models with a quantitative and a categorical predictor, differing in whether they allow for interaction and nonlinearity. Efficient robust designs are given in case of uncertainty about the regression model. Furthermore, formulas are provided to compute the normative sample size such that individuals' positions relative to the derived norms can be assessed with prespecified power and precision. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

The perceived behavior and barriers of community care professionals in encouraging functional activities of older adults: the development and validation of the MAINtAIN-C questionnaire

BACKGROUND

Community care professionals need to encourage older adults in performing functional activities to maintain independence. However, professionals often perform functional activities on behalf of older adults. To change this, insights into the behavior and barriers of professionals in encouraging activities are required. In the current study, the MAINtAIN questionnaire, which was developed for nursing homes, was adopted. The objective was to create a modified version that is suitable for measuring behavior and barriers of community care professionals in encouraging functional activities of clients in the community care setting. The overall aims were to assess the content validity, construct validity, and internal consistency of the modified version.

METHODS

Data was collected by qualitative and quantitative methods in two phases. During phase one, the MAINtAIN was assessed on appropriateness and feasibility by community nurses (N = 7), and the adapted questionnaire was assessed on content validity by research experts (N = 9) and community care professionals (N = 18). During phase two, the psychometric properties of the adapted MAINtAIN-C were assessed in community care professionals (N = 80). Construct validity was evaluated by an Exploratory Factor Analysis (EFA), and internal consistency was determined by calculating Cronbach's alpha coefficients.

RESULTS

The formulation, verbs, and wording of the MAINtAIN were adapted; some items were excluded and relevant items were added, resulting in the MAINtAIN-C with two scales, showing good content validity. The Behaviors scale (20 items) measures perceived behavior in encouraging functional activities, expressing good internal consistency (Cronbach's alpha: .92). The Barriers scale measures barriers in encouraging functional activities related to two dimensions: 1) the clients' context (7 items), with good internal consistency (.78); and 2) the professional, social, and organizational contexts (21 items), showing good internal consistency (.83).

CONCLUSIONS

The MAINtAIN-C seems promising to assess the behavior and barriers of community care professionals in encouraging functional activities. It can be used to display a possible difference between perceived and actual behavior, to develop strategies for removing barriers in encouraging activities to foster behavioral change. The results also provide guidance for further research in a larger sample to obtain more insight into the psychometric properties.

Imputation of missing covariate in randomized controlled trials with a continuous outcome: Scoping review and new results

In this article, we first review the literature on dealing with missing values on a covariate in randomized studies and summarize what has been done and what is lacking to date. We then investigate the situation with a continuous outcome and a missing binary covariate in more details through simulations, comparing the performance of multiple imputation (MI) with various simple alternative methods. This is finally extended to the case of time‐to‐event outcome. The simulations consider five different missingness scenarios: missing completely at random (MCAR), at random (MAR) with missingness depending only on the treatment, and missing not at random (MNAR) with missingness depending on the covariate itself (MNAR1), missingness depending on both the treatment and covariate (MNAR2), and missingness depending on the treatment, covariate and their interaction (MNAR3). Here, we distinguish two different cases: (1) when the covariate is measured before randomization (best practice), where only MCAR and MNAR1 are plausible, and (2) when it is measured after randomization but before treatment (which sometimes occurs in nonpharmaceutical research), where the other three missingness mechanisms can also occur. The proposed methods are compared based on the treatment effect estimate and its standard error. The simulation results suggest that the patterns of results are very similar for all missingness scenarios in case (1) and also in case (2) except for MNAR3. Furthermore, in each scenario for continuous outcome, there is at least one simple method that performs at least as well as MI, while for time‐to‐event outcome MI is best.

An overview of mixture modelling for latent evolutions in longitudinal data: Modelling approaches, fit statistics and software

The use of finite mixture modelling (FMM) is becoming increasingly popular for the analysis of longitudinal repeated measures data. FMMs assist in identifying latent classes following similar paths of temporal development. This paper aims to address the confusion experienced by practitioners new to these methods by introducing the various available techniques, which includes an overview of their interrelatedness and applicability. Our focus will be on the commonly used model-based approaches which comprise latent class growth analysis (LCGA), group-based trajectory models (GBTM), and growth mixture modelling (GMM). We discuss criteria for model selection, highlight often encountered challenges and unresolved issues in model fitting, showcase model availability in software, and illustrate a model selection strategy using an applied example.

Relative efficiencies of two-stage sampling schemes for mean estimation in multilevel populations when cluster size is informative

In multilevel populations, there are two types of population means of an outcome variable ie, the average of all individual outcomes ignoring cluster membership and the average of cluster-specific means. To estimate the first mean, individuals can be sampled directly with simple random sampling or with two-stage sampling (TSS), that is, sampling clusters first, and then individuals within the sampled clusters. When cluster size varies in the population, three TSS schemes can be considered, ie, sampling clusters with probability proportional to cluster size and then sampling the same number of individuals per cluster; sampling clusters with equal probability and then sampling the same percentage of individuals per cluster; and sampling clusters with equal probability and then sampling the same number of individuals per cluster. Unbiased estimation of the average of all individual outcomes is discussed under each sampling scheme assuming cluster size to be informative. Furthermore, the three TSS schemes are compared in terms of efficiency with each other and with simple random sampling under the constraint of a fixed total sample size. The relative efficiency of the sampling schemes is shown to vary across different cluster size distributions. However, sampling clusters with probability proportional to size is the most efficient TSS scheme for many cluster size distributions. Model-based and design-based inference are compared and are shown to give similar results. The results are applied to the distribution of high school size in Italy and the distribution of patient list size for general practices in England.

Effects, costs and feasibility of the 'Stay Active at Home' Reablement training programme for home care professionals: study protocol of a cluster randomised controlled trial

BACKGROUND

According to the principles of Reablement, home care services are meant to be goal-oriented, holistic and person-centred taking into account the capabilities and opportunities of older adults. However, home care services traditionally focus on doing things for older adults rather than with them. To implement Reablement in practice, the 'Stay Active at Home' programme was developed. It is assumed that the programme leads to a reduction in sedentary behaviour in older adults and consequently more cost-effective outcomes in terms of their health and wellbeing. However, this has yet to be proven.

METHODS/ DESIGN

A two-group cluster randomised controlled trial with 12 months follow-up will be conducted. Ten nursing teams will be selected, pre-stratified on working area and randomised into an intervention group ('Stay Active at Home') or control group (no training). All nurses of the participating teams are eligible to participate in the study. Older adults and, if applicable, their domestic support workers (DSWs) will be allocated to the intervention or control group as well, based on the allocation of the nursing team. Older adults are eligible to participate, if they: 1) receive homecare services by the selected teams; and 2) are 65 years or older. Older adults will be excluded if they: 1) are terminally ill or bedbound; 2) have serious cognitive or psychological problems; or 3) are unable to communicate in Dutch. DSWs are eligible to participate if they provide services to clients who fulfil the eligibility criteria for older adults. The study consists of an effect evaluation (primary outcome: sedentary behaviour in older adults), an economic evaluation and a process evaluation. Data for the effect and economic evaluation will be collected at baseline and 6 and/or 12 months after baseline using performance-based and self-reported measures. In addition, data from client records will be extracted. A mixed-methods design will be applied for the process evaluation, collecting data of older adults and professionals throughout the study period.

DISCUSSION

This study will result in evidence about the effectiveness, cost-effectiveness and feasibility of the 'Stay Active at Home' programme.

TRIAL REGISTRATION

ClinicalTrials.gov: NCT03293303 , registered on 20 September 2017.

Efficient design of cluster randomized trials with treatment-dependent costs and treatment-dependent unknown variances

Cluster randomized trials evaluate the effect of a treatment on persons nested within clusters, where treatment is randomly assigned to clusters. Current equations for the optimal sample size at the cluster and person level assume that the outcome variances and/or the study costs are known and homogeneous between treatment arms. This paper presents efficient yet robust designs for cluster randomized trials with treatment‐dependent costs and treatment‐dependent unknown variances, and compares these with 2 practical designs. First, the maximin design (MMD) is derived, which maximizes the minimum efficiency (minimizes the maximum sampling variance) of the treatment effect estimator over a range of treatment‐to‐control variance ratios. The MMD is then compared with the optimal design for homogeneous variances and costs (balanced design), and with that for homogeneous variances and treatment‐dependent costs (cost‐considered design). The results show that the balanced design is the MMD if the treatment‐to control cost ratio is the same at both design levels (cluster, person) and within the range for the treatment‐to‐control variance ratio. It still is highly efficient and better than the cost‐considered design if the cost ratio is within the range for the squared variance ratio. Outside that range, the cost‐considered design is better and highly efficient, but it is not the MMD. An example shows sample size calculation for the MMD, and the computer code (SPSS and R) is provided as supplementary material. The MMD is recommended for trial planning if the study costs are treatment‐dependent and homogeneity of variances cannot be assumed.

Efficient treatment allocation in 2 × 2 multicenter trials when costs and variances are heterogeneous

At the design stage of a study, it is crucial to compute the sample size needed for treatment effect estimation with maximum precision and power. The optimal design depends on the costs, which may be known at the design stage, and on the outcome variances, which are unknown. A balanced design, optimal for homogeneous costs and variances, is typically used. An alternative to the balanced design is a design optimal for the known and possibly heterogeneous costs, and homogeneous variances, called costs considering design. Both designs suffer from loss of efficiency, compared with optimal designs for heterogeneous costs and variances. For 2 × 2 multicenter trials, we compute the relative efficiency of the balanced and the costs considering designs, relative to the optimal designs. We consider 2 heterogeneous costs and variance scenarios (in 1 scenario, 2 treatment conditions have small and 2 have large costs and variances; in the other scenario, 1 treatment condition has small, 2 have intermediate, and 1 has large costs and variances). Within these scenarios, we examine the relative efficiency of the balanced design and of the costs considering design as a function of the extents of heterogeneity of the costs and of the variances and of their congruence (congruent when the cheapest treatment has the smallest variance, incongruent when the cheapest treatment has the largest variance). We find that the costs considering design is generally more efficient than the balanced design, and we illustrate this theory on a 2 × 2 multicenter trial on lifestyle improvement of patients in general practices.

Efficient treatment allocation in 2 × 2 cluster randomized trials, when costs and variances are heterogeneous

Typically, clusters and individuals in cluster randomized trials are allocated across treatment conditions in a balanced fashion. This is optimal under homogeneous costs and outcome variances. However, both the costs and the variances may be heterogeneous. Then, an unbalanced allocation is more efficient but impractical as the outcome variance is unknown in the design stage of a study. A practical alternative to the balanced design could be a design optimal for known and possibly heterogeneous costs and homogeneous variances. However, when costs and variances are heterogeneous, both designs suffer from loss of efficiency, compared with the optimal design. Focusing on cluster randomized trials with a 2 × 2 design, the relative efficiency of the balanced design and of the design optimal for heterogeneous costs and homogeneous variances is evaluated, relative to the optimal design. We consider two heterogeneous scenarios (two treatment arms with small, and two with large, costs or variances, or one small, two intermediate, and one large costs or variances) at each design level (cluster, individual, and both). Within these scenarios, we compute the relative efficiency of the two designs as a function of the extents of heterogeneity of the costs and variances, and the congruence (the cheapest treatment has the smallest variance) and incongruence (the cheapest treatment has the largest variance) between costs and variances. We find that the design optimal for heterogeneous costs and homogeneous variances is generally more efficient than the balanced design and we illustrate this theory on a trial that examines methods to reduce radiological referrals from general practices. Copyright © 2016 John Wiley & Sons, Ltd.

The effect of heterogeneous variance on efficiency and power of cluster randomized trials with a balanced 2 × 2 factorial design

Sample size calculation for cluster randomized trials (CRTs) with a [Formula: see text] factorial design is complicated due to the combination of nesting (of individuals within clusters) with crossing (of two treatments). Typically, clusters and individuals are allocated across treatment conditions in a balanced fashion, which is optimal under homogeneity of variance. However, the variance is likely to be heterogeneous if there is a treatment effect. An unbalanced allocation is then more efficient, but impractical because the optimal allocation depends on the unknown variances. Focusing on CRTs with a [Formula: see text] design, this paper addresses two questions: How much efficiency is lost by having a balanced design when the outcome variance is heterogeneous? How large must the sample size be for a balanced allocation to have sufficient power under heterogeneity of variance? We consider different scenarios of heterogeneous variance. Within each scenario, we determine the relative efficiency of a balanced design, as a function of the level (cluster, individual, both) and amount of heterogeneity of the variance. We then provide a simple correction of the sample size for the loss of power due to heterogeneity of variance when a balanced allocation is used. The theory is illustrated with an example of a published 2 x2 CRT.

ANCOVA Versus CHANGE From Baseline in Nonrandomized Studies: The Difference

The pretest-posttest control group design can be analyzed with the posttest as dependent variable and the pretest as covariate (ANCOVA) or with the difference between posttest and pretest as dependent variable (CHANGE). These 2 methods can give contradictory results if groups differ at pretest, a phenomenon that is known as Lord's paradox. Literature claims that ANCOVA is preferable if treatment assignment is based on randomization or on the pretest and questionable for preexisting groups. Some literature suggests that Lord's paradox has to do with measurement error in the pretest. This article shows two new things: First, the claims are confirmed by proving the mathematical equivalence of ANCOVA to a repeated measures model without group effect at pretest. Second, correction for measurement error in the pretest is shown to lead back to ANCOVA or to CHANGE, depending on the assumed absence or presence of a true group difference at pretest. These two new theoretical results are illustrated with multilevel (mixed) regression and structural equation modeling of data from two studies.

Bayesian design for dichotomous repeated measurements with autocorrelation

In medicine and health sciences, binary outcomes are often measured repeatedly to study their change over time. A problem for such studies is that designs with an optimal efficiency for some parameter values may not be efficient for other values. To handle this problem, we propose Bayesian designs which formally account for the uncertainty in the parameter values for a mixed logistic model which allows quadratic changes over time. Bayesian D-optimal allocations of time points are computed for different priors, costs, covariance structures and values of the autocorrelation. Our results show that the optimal number of time points increases with the subject-to-measurement cost ratio, and that neither the optimal number of time points nor the optimal allocations of time points appear to depend strongly on the prior, the covariance structure or on the size of the autocorrelation. It also appears that for subject-to-measurement cost ratios up to five, four equidistant time points, and for larger cost ratios, five or six equidistant time points are highly efficient. Our results are compared with the actual design of a respiratory infection study in Indonesia and it is shown that, selection of a Bayesian optimal design will increase efficiency, especially for small cost ratios.

Efficient treatment allocation in two-way nested designs

Cluster randomized and multicenter trials sometimes combine two treatments A and B in a factorial design, with conditions such as A, B, A and B, or none. This results in a two-way nested design. The usual issue of sample size and power now arises for various clinically relevant contrast hypotheses. Assuming a fixed total sample size at each level (number of clusters or centers, number of patients), we derive the optimal proportion of the total sample to be allocated to each treatment arm. We consider treatment assignment first at the highest level (cluster randomized trial) and then at the lowest level (multicenter trial). We derive the optimal allocation ratio for various sets of clinically relevant hypotheses. We then evaluate the efficiency of each allocation and show that the popular balanced design is optimal or highly efficient for a range of research questions except for contrasting one treatment arm with all other treatment arms. We finally present simple equations for the total sample size needed to test each effect of interest in a balanced design, as a function of effect size, power and type I error α. All results are illustrated on a cluster-randomized trial on smoking prevention in primary schools and on a multicenter trial on lifestyle improvement in general practices.

Optimal Experimental Design With Nesting of Persons in Organizations

This paper introduces optimal design of randomized experiments where individuals are nested within organizations, such as schools, health centers, or companies. The focus is on nested designs with two levels (organization, individual) and two treatment conditions (treated, control), with treatment assignment to organizations, or to individuals within organizations. For each type of assignment, a multilevel model is first presented for the analysis of a quantitative dependent variable or outcome. Simple equations are then given for the optimal sample size per level (number of organizations, number of individuals) as a function of the sampling cost and outcome variance at each level, with realistic examples. Next, it is explained how the equations can be applied if the dependent variable is dichotomous, or if there are covariates in the model, or if the effects of two treatment factors are studied in a factorial nested design, or if the dependent variable is repeatedly measured. Designs with three levels of nesting and the optimal number of repeated measures are briefly discussed, and the paper ends with a short discussion of robust design.

Optimal Design for Functional Magnetic Resonance Imaging Experiments

This paper provides an overview of optimal design for functional magnetic resonance imaging (fMRI) studies. We present the main types of fMRI designs, namely blocked and event-related designs, and common objectives of fMRI experiments, for example, localization of task-related activity in the human brain. Furthermore, we present an introduction into the methodology for analysis and optimization of fMRI experiments, for instance common analysis models and applied optimality criteria. We outline some of the problems encountered when optimizing fMRI experiments, for example, the temporal autocorrelation between measurements in fMRI data. The most important results for optimization of blocked and event-related designs with regard to the different design objectives are presented and explained. Finally, we conclude with future perspectives and challenges for optimization of fMRI experiments.

Calculating sample sizes for cluster randomized trials: we can keep it simple and efficient!

OBJECTIVE

Simple guidelines for calculating efficient sample sizes in cluster randomized trials with unknown intraclass correlation (ICC) and varying cluster sizes.

METHODS

A simple equation is given for the optimal number of clusters and sample size per cluster. Here, optimal means maximizing power for a given budget or minimizing total cost for a given power. The problems of cluster size variation and specification of the ICC of the outcome are solved in a simple yet efficient way.

RESULTS

The optimal number of clusters goes up, and the optimal sample size per cluster goes down as the ICC goes up or as the cluster-to-person cost ratio goes down. The available budget, desired power, and effect size only affect the number of clusters and not the sample size per cluster, which is between 7 and 70 for a wide range of cost ratios and ICCs. Power loss because of cluster size variation is compensated by sampling 10% more clusters. The optimal design for the ICC halfway the range of realistic ICC values is a good choice for the first stage of a two-stage design. The second stage is needed only if the first stage shows the ICC to be higher than assumed.

CONCLUSION

Efficient sample sizes for cluster randomized trials are easily computed, provided the cost per cluster and cost per person are specified.

Specialised treatment based on cognitive behaviour therapy versus usual care for tinnitus: a randomised controlled trial

BACKGROUND

Up to 21% of adults will develop tinnitus, which is one of the most distressing and debilitating audiological problems. The absence of medical cures and standardised practice can lead to costly and prolonged treatment. We aimed to assess effectiveness of a stepped-care approach, based on cognitive behaviour therapy, compared with usual care in patients with varying tinnitus severity.

METHODS

In this randomised controlled trial, undertaken at the Adelante Department of Audiology and Communication (Hoensbroek, Netherlands), we enrolled previously untreated Dutch speakers (aged >18 years) who had a primary complaint of tinnitus but no health issues precluding participation. An independent research assistant randomly allocated patients by use of a computer-generated allocation sequence in a 1:1 ratio, stratified by tinnitus severity and hearing ability, in block sizes of four to receive specialised care of cognitive behaviour therapy with sound-focused tinnitus retraining therapy or usual care. Patients and assessors were masked to treatment assignment. Primary outcomes were health-related quality of life (assessed by the health utilities index score), tinnitus severity (tinnitus questionnaire score), and tinnitus impairment (tinnitus handicap inventory score), which were assessed before treatment and at 3 months, 8 months, and 12 months after randomisation. We used multilevel mixed regression analyses to assess outcomes in the intention-to-treat population. This study is registered with ClinicalTrials.gov, number NCT00733044.

FINDINGS

Between September, 2007 and January, 2011, we enrolled and treated 492 (66%) of 741 screened patients. Compared with 247 patients assigned to usual care, 245 patients assigned to specialised care improved in health-related quality of life during a period of 12 months (between-group difference 0·059, 95% CI 0·025 to 0·094; effect size of Cohen's d=0·24; p=0·0009), and had decreased tinnitus severity (-8·062, -10·829 to -5·295; d=0·43; p<0·0001) and tinnitus impairment (-7·506, -10·661 to -4·352; d=0·45; p<0·0001). Treatment seemed effective irrespective of initial tinnitus severity, and we noted no adverse events in this trial.

INTERPRETATION

Specialised treatment of tinnitus based on cognitive behaviour therapy could be suitable for widespread implementation for patients with tinnitus of varying severity.

FUNDING

Netherlands Organisation for Health Research and Development (ZonMW).

Open versus laparoscopic left lateral hepatic sectionectomy within an enhanced recovery ERAS® programme (ORANGE II-trial): study protocol for a randomised controlled trial

Background

The use of lLaparoscopic liver resection in terms of time to functional recovery, length of hospital stay (LOS), long-term abdominal wall hernias, costs and quality of life (QOL) has never been studied in a randomised controlled trial. Therefore, this is the subject of the international multicentre randomised controlled ORANGE II trial.

Methods

Patients eligible for left lateral sectionectomy (LLS) of the liver will be recruited and randomised at the outpatient clinic. All randomised patients will undergo surgery in the setting of an ERAS programme. The experimental design produces two randomised arms (open and laparoscopic LLS) and a prospective registry. The prospective registry will be based on patients that cannot be randomised because of the explicit treatment preference of the patient or surgeon, or because of ineligibility (not meeting the in- and exclusion criteria) for randomisation in this trial. Therefore, all non-randomised patients undergoing LLS will be approached to participate in the prospective registry, thereby allowing acquisition of an uninterrupted prospective series of patients. The primary endpoint of the ORANGE II trial is time to functional recovery. Secondary endpoints are postoperative LOS, percentage readmission, (liver-specific) morbidity, QOL, body image and cosmetic result, hospital and societal costs over 1 year, and long-term incidence of incisional hernias. It will be assumed that in patients undergoing laparoscopic LLS, length of hospital stay can be reduced by two days. A sample size of 55 patients in each randomisation arm has been calculated to detect a 2-day reduction in LOS (90% power and α = 0.05 (two-tailed)).

The ORANGE II trial is a multicenter randomised controlled trial that will provide evidence on the merits of laparoscopic surgery in patients undergoing LLS within an enhanced recovery ERAS programme.

Trial registration

ClinicalTrials.gov NCT00874224.

Self-regulatory processes mediate the intention-behavior relation for adherence and exercise behaviors

OBJECTIVES

Understanding the gap between people's intentions and actual health behavior is an important issue in health psychology. Our aim in this study was to investigate whether self-regulatory processes (monitoring goal progress and responding to discrepancies) mediate the intention-behavior relation in relation to HIV medication adherence (Study 1) and intensive exercise behavior (Study 2).

METHOD

In Study 1, questionnaire and electronically monitored adherence data were collected at baseline and 3 months later from patients in the control arm of an HIV-adherence intervention study. In Study 2, questionnaire data was collected at 3 time points 6-weeks apart in a cohort study of physical activity.

RESULTS

Complete data at all time points were obtained from 51 HIV-infected patients and 499 intensive exercise participants. Intentions were good predictors of behavior and explained 25 to 30% of the variance. Self-regulatory processes explained an additional 11% (Study 1) and 6% (Study 2) of variance in behavior on top of intentions. Regression and bootstrap analyses revealed at least partial, and possibly full, mediation of the intention-behavior relation by self-regulatory processes.

CONCLUSIONS

The present studies indicate that self-regulatory processes may explain how intentions drive behavior. Future tests, using different health behaviors and experimental designs, could firmly establish whether self-regulatory processes complement current health behavior theories and should become routine targets for intervention. (PsycINFO Database Record (c) 2012 APA, all rights reserved).

Optimal design for nonlinear estimation of the hemodynamic response function

Subject-specific hemodynamic response functions (HRFs) have been recommended to capture variation in the form of the hemodynamic response between subjects (Aguirre et al., [ 1998]: Neuroimage 8:360-369). The purpose of this article is to find optimal designs for estimation of subject-specific parameters for the double gamma HRF. As the double gamma function is a nonlinear function of its parameters, optimal design theory for nonlinear models is employed in this article. The double gamma function is linearized by a Taylor approximation and the maximin criterion is used to handle dependency of the D-optimal design on the expansion point of the Taylor approximation. A realistic range of double gamma HRF parameters is used for the expansion point of the Taylor approximation. Furthermore, a genetic algorithm (GA) (Kao et al., [ 2009]: Neuroimage 44:849-856) is applied to find locally optimal designs for the different expansion points and the maximin design chosen from the locally optimal designs is compared to maximin designs obtained by m-sequences, blocked designs, designs with constant interstimulus interval (ISI) and random event-related designs. The maximin design obtained by the GA is most efficient. Random event-related designs chosen from several generated designs and m-sequences have a high efficiency, while blocked designs and designs with a constant ISI have a low efficiency compared to the maximin GA design.

Optimal design of multi-subject blocked fMRI experiments

The design of a multi-subject fMRI experiment needs specification of the number of subjects and scanning time per subject. For example, for a blocked design with conditions A or B, fixed block length and block order ABN, where N denotes a null block, the optimal number of cycles of ABN and the optimal number of subjects have to be determined. This paper presents a method to determine the optimal number of subjects and optimal number of cycles for a blocked design based on the A-optimality criterion and a linear cost function by which the number of cycles and the number of subjects are restricted. Estimation of individual stimulus effects and estimation of contrasts between stimulus effects are both considered. The mixed-effects model is applied and analytical results for the A-optimal number of subjects and A-optimal number of cycles are obtained under the assumption of uncorrelated errors. For correlated errors with a first-order autoregressive (AR1) error structure, numerical results are presented. Our results show how the optimal number of cycles and subjects depend on the within- to between-subject variance ratio. Our method is a new approach to determine the optimal scanning time and optimal number of subjects for a multi-subject fMRI experiment. In contrast to previous results based on power analyses, the optimal number of cycles and subjects can be described analytically and costs are considered.

Association Between Event-Related Potentials and Pain Ratings

The association between event-related potentials (ERP) and ratings of pain has frequently been demonstrated, usually through bivariate correlations. However, the use of bivariate correlations precludes studying or correcting for confounding factors. The current study reinvestigated the association between ERP and the subjective experience of pain, using the more extensive statistical approach of multilevel analysis. Using this technique, it was possible to investigate and correct for effects of intensity and habituation. Eighty-five healthy subjects received intracutaneous electrical pain stimuli with simultaneous EEG registration. Each subject was asked to rate the intensity of each stimulus on a numeric rating scale (NRS). The multilevel analyses revealed a within-subject association between the ERP measures (especially P1 and P3) and the NRS score. Furthermore, this association was moderated by intensity of the stimulus and habituation. These results suggest that a direct translation from the pain ERP to subjective experience is delicate and that factors such as stimulus intensity and habituation must be taken into account.

Electronic monitoring-based counseling to enhance adherence among HIV-infected patients: a randomized controlled trial

OBJECTIVE

To investigated the effectiveness of an adherence intervention (AIMS) designed to fit HIV-clinics' routine care procedures.

DESIGN

Through block randomization, patients were allocated to the intervention or control group. The study included 2 months baseline measurement, 3 months intervention, and 4 months follow-up. HIV-nurses delivered a minimal intervention ("adherence sustaining") to patients scoring >95% adherence at baseline, and an intensive intervention ("adherence improving") to patients with <95% adherence. Control participants received high-quality usual care.

MAIN OUTCOME MEASURES

Electronically monitored adherence and viral load.

RESULTS

133 patients were included (67 control, 66 intervention), 60% had <95% adherence at baseline, and 87% (116/133) completed the trial. Intent-to-treat analyses showed that adherence improved significantly in the complete intervention sample. Subgroup analyses showed that this effect was caused by participants scoring <95% at baseline (mean difference = 15.20%; p < .001). These effects remained stable during follow-up. The number of patients with an undetectable viral load increased in the intervention group compared to the control group (OR = 2.96, p < .05). Treatments effects on viral load were mediated by the improvements in adherence.

CONCLUSIONS

The AIMS-intervention was effective and can be integrated in routine clinical care for HIV-infected patients. Future research should study its (cost)effectiveness among more heterogeneous samples and in settings with variable levels of standard care.

Robustness of optimal design of fMRI experiments with application of a genetic algorithm

In this paper we apply the genetic algorithm developed by Kao et al. (2009) to find designs which are robust against misspecification of the error autocorrelation. Two common optimality criteria, the A-optimality criterion and the D-optimality criterion, based upon a general linear model are employed to obtain locally optimal designs for a given value of the autocorrelation. The maximin criterion is then used to obtain designs which are robust against misspecification of the autocorrelation. Furthermore, robustness depending on the choice of optimality criterion is evaluated. We show analytically and empirically that the A- and D-optimality criterion will result in different optimal designs, e.g. with different stimulus frequencies. Optimal stimulus frequency for the A-optimality criterion has been derived by Liu et al. (2004) whereas we derive here the optimal stimulus frequency for the D-optimality criterion. Conclusions about the robustness of an optimal design against misspecification of model parameters and choice of optimality criterion are drawn based upon our results.

Optimal designs for clinical trials with second-order polynomial treatment effects

The effect of adding intermediate measures on the efficiency of treatment effect estimation is considered for a second-order polynomial treatment effect, equidistant time-points, different covariance structures and two optimality criteria, assuming either a fixed sample size or a fixed budget. The benefit of adding intermediate measures (at the expense of subjects) depends strongly on the assumed covariance structure and is hardly affected by the two used optimality criteria (Ds or c). For a fixed sample size, the increase in efficiency by adding intermediate measures is large for a compound symmetric structure and small for a first-order auto-regressive structure. For a first-order auto-regressive structure with measurement error, the results depend on the covariance parameter values. For a fixed budget and linear cost function, the design with only three measures per subject is often highly efficient. If the structure resembles compound symmetry and the cost per subject is eight or more times larger than the cost per repeated measure, however, more than three measures are required to obtain highly efficient treatment effect estimators. If the covariance structure is unknown, the optimal design based on a first-order auto-regressive structure with measurement error is preferable in terms of robustness against misspecification of the covariance structure. Given a design with three repeated measures and a second-order polynomial treatment effect, equidistant time-points are either optimal (Ds-) or highly efficient (c-optimality criterion). The results are illustrated by a practical example.

Relative efficiency of unequal versus equal cluster sizes in cluster randomized and multicentre trials

Cluster randomized and multicentre trials evaluate the effect of a treatment on persons nested within clusters, for instance, patients within clinics or pupils within schools. Optimal sample sizes at the cluster (centre) and person level have been derived under the restrictive assumption of equal sample sizes per cluster. This paper addresses the relative efficiency of unequal versus equal cluster sizes in case of cluster randomization and person randomization within clusters. Starting from maximum likelihood parameter estimation, the relative efficiency is investigated numerically for a range of cluster size distributions. An approximate formula is presented for computing the relative efficiency as a function of the mean and variance of cluster size and the intraclass correlation, which can be used for adjusting the sample size. The accuracy of this formula is checked against the numerical results and found to be quite good. It is concluded that the loss of efficiency due to variation of cluster sizes rarely exceeds 10 per cent and can be compensated by sampling 11 per cent more clusters.

Measuring perceived harmfulness of physical activities in patients with chronic low back pain: the Photograph Series of Daily Activities--short electronic version

Cognitive-behavioral models of chronic low back pain (CLBP) predict that dysfunctional assumptions about the harmfulness of activities may maintain pain-related fear and disability levels. The Photograph Series of Daily Activities (PHODA) is an instrument to determine the perceived harmfulness of daily activities in patients with CLBP. This study examined the psychometric properties of a short electronic version of the PHODA (PHODA-SeV). The results show that the PHODA-SeV measures a single factor and has a high internal consistency. The test-retest reliability and stability of the PHODA-SeV over a 2-week time interval are good, with discrepancies between 2 measurements over 20 points suggesting true change. The construct validity is supported by the finding that both self-reported pain severity and fear of movement/(re)injury were uniquely related to the PHODA-SeV. Validity is further corroborated by the finding that patients who have received exposure in vivo, that aimed to systematically reduce the perceived harmfulness of activities, had significantly lower PHODA-SeV scores after treatment than patients receiving graded activity that did not address these assumptions. The findings support the PHODA-SeV as a valid and reliable measure of the perceived harmfulness of activities in patients with CLBP. Preliminary normative data of the PHODA-SeV are presented.

PERSPECTIVE

This article describes a pictorial measurement tool (PHODA-SeV) for the assessment of the perceived harmfulness of activities in patients with chronic low back pain. The PHODA-SeV has good psychometric properties and can be used to elaborate on the contribution of beliefs about harmful consequences of activities to pain and disability.

Polymorphisms of the dopamine D4 receptor gene (DRD4 VNTR) and cannabinoid CB1 receptor gene (CNR1) are not strongly related to cue-reactivity after alcohol exposure

Polymorphisms in the D4 dopamine receptor gene (DRD4) and the CB1 cannabinoid receptor gene (CNR1) have been associated with a differential response to alcohol after consumption. The goal of the present study was to investigate whether heavy drinkers with these polymorphisms would respond with enhanced cue-reactivity after alcohol exposure. Eighty-eight male heavy drinkers were genotyped for the DRD4 variable number of tandem repeats (VNTR) [either DRD4 long (L) or short (S)] and the CNR1 rs2023239 polymorphism (either CT/CC or TT). Participants were exposed to water and beer in 3-minute trials. Dependent variables of main interest were subjective craving for alcohol, subjective arousal and salivary reactivity. Overall, no strong evidence was found for stronger cue-reactivity (= outcome difference between beer and water trial) in the DRD4 L and CNR1 C allele groups. The DRD4 VNTR polymorphism tended to moderate salivary reactivity such that DRD4 L participants showed a larger beverage effect than the DRD4 S participants. Unexpectedly, the DRD4 L participants reported, on average, less craving for alcohol and more subjective arousal during cue exposure, compared with the DRD4 S participants. As weekly alcohol consumption increased, the CNR1 C allele group tended to report more craving for alcohol during the alcohol exposure than the T allele group. The DRD4 and CNR1 polymorphisms do not appear to strongly moderate cue-reactivity after alcohol cue exposure, in male heavy drinkers.

Randomized clinical trials with a pre- and a post-treatment measurement: repeated measures versus ANCOVA models

Repeated measures (RM) and ANCOVA models are compared with respect to treatment effect estimation in randomized clinical trials with a pre- and a post-treatment measure. The covariance matrices of repeated measures are assumed to be I) homogeneous or II) heterogeneous across groups. In situation I, ANCOVA is preferred to RM, because the estimated variance of the treatment effect estimator is unbiased for ANCOVA and biased downwards for RM. In situation II, RM with Kenward and Roger's adjustment is preferred to ANCOVA, because the ANCOVA variance estimator does not correct for unknown pre-treatment expectation. The results are illustrated with an example.

A functional polymorphism of the mu-opioid receptor gene (OPRM1) influences cue-induced craving for alcohol in male heavy drinkers

BACKGROUND

The mu-opioid receptor gene (OPRM1) codes for the mu-opioid receptor, which binds beta-endorphin. The A118G polymorphism in this gene affects beta-endorphin binding such that the Asp40 variant (G allele) binds beta-endorphin 3 times more tightly than the more common Asn40 variant (A allele). This study investigated the influence of the A118G polymorphism on cue reactivity after exposure to an alcoholic beverage in male heavy drinkers.

METHODS

Participants were either homozygous for the A allele (n=84) or carrying at least 1 copy of the G allele (n=24). All participants took part in a cue-reactivity paradigm where they were exposed to water and beer in 3-minute trials. The dependent variables of main interest were subjective craving for alcohol, subjective arousal, and saliva production.

RESULTS

G allele carriers reported significantly more craving for alcohol than the A allele participants (as indicated by the within-subject difference in craving after beer vs after water exposure). No differences were found for subjective arousal and saliva. Both groups did not differ in family history of alcoholism. Participants with the G allele reported a significantly higher lifetime prevalence of drug use than participants homozygous for the A allele.

CONCLUSIONS

A stronger urge to drink alcohol after exposure to an alcoholic beverage might contribute to a heightened risk for developing alcohol-related problems in individuals with a copy of the G allele. The G allele might also predispose to drug use in general.

The Letter Digit Substitution Test: Normative Data for 1,858 Healthy Participants Aged 24–81 from the Maastricht Aging Study (MAAS): Influence of Age, Education, and Sex

The Letter Digit Substitution Test (LDST) is based on earlier developed substitution tests (e.g., Digit Symbol Substitution Test) but uses over-learned signs instead of the symbols used in other substitution tests. The written and oral versions of the LDST were administered to a large, cognitively screened sample (N = 1,858) of adults aged 24 to 81 years. Age was the most important predictor of LDST performance, and females outperformed males. A low level of education profoundly influenced LDST performance: the effect of a low versus high level of education on LDST performance was comparable to about 20 years of aging. Regression-based normative data were prepared for both the written and oral versions of the LDST.

Varieties of attention in neutral trials: linking RT to ERPs and EEG frequencies

It is often assumed that when a neutral cue is presented in a spatial cueing task, attention remains at fixation until target onset. We hypothesized that variance in nonspatial attention and switches of attention toward target locations can account for variance in reaction times of neutral trials. Lateralized event-related potentials (ERPs) and changes in electroencephalogram (EEG) frequency bands served as predictor variables in a single-trial logistic regression analysis to predict the direction of spatial attention in cued and neutral trials. The contingent negative variation (CNV) and non-lateralized changes in the alpha band served as markers of nonspatial attention. The direction of attention in cued trials was reliably predicted from single-trial lateralized ERP components. In neutral trials, only evidence for nonspatial attention was found, indicated by increases in the CNV and decreases in alpha preceding targets to which responses were relatively fast.

Optimal number of repeated measures and group sizes in clinical trials with linearly divergent treatment effects

The effect of number of repeated measures on the variance of the generalized least squares (GLS) treatment effect estimator is considered assuming a linearly divergent treatment effect, equidistant time-points and either a fixed number of subjects or a fixed study budget. The optimal combination of group sizes and number of repeated measures is calculated by minimizing this variance subject to a linear cost function. For a fixed number of subjects, the variance of the GLS treatment effect estimator can be decreased by adding intermediate measures per subject. This decrease is relatively large if a) the covariance structure is compound symmetric or b) the structure approaches compound symmetry and the correlation between two repeated measures does not exceed 0.80, or c) the correlation between two repeated measures does not exceed 0.60 if the time-lag goes to zero. In case the sample sizes and number of repeated measures are limited by budget constraints and the covariance structure includes a first-order auto-regression part, two repeated measures per subject yield highly efficient treatment effect estimators. Otherwise, it is more efficient to have more than two repeated measures. If the covariance structure is unknown, the optimal design based on a first-order auto-regressive structure with measurement error is preferable in terms of robustness against misspecification of the covariance structure. The numerical results are illustrated by three examples.

Rey's verbal learning test: normative data for 1855 healthy participants aged 24-81 years and the influence of age, sex, education, and mode of presentation

The Verbal Learning Test (VLT; Rey, 1958) evaluates the declarative memory. Despite its extensive use, it has been difficult to establish normative data because test administration has not been uniform. The purpose of the present study was to gather normative data for the VLT for a large number (N = 1855) of healthy participants aged 24-81 years, using a procedure in which the words to be learned were presented either verbally or visually. The results showed that VLT performance decreased in an age-dependent manner from an early age. The learning capacity of younger versus older adults differed quantitatively rather than qualitatively. Females and higher educated participants outperformed males and lower educated participants over the entire age range tested. Presentation mode affected VLT performance differently: auditory presentation resulted in a better recall on Trial 1 (a short-term or working memory measure), whereas visual presentation yielded a better performance on Trial 3, Trial 4, and Delta (a learning measure).

Optimal time-points in clinical trials with linearly divergent treatment effects

In repeated measures studies, equidistant time-points do not always yield efficient treatment effect estimators. In the present paper, the optimal allocation of time-points is calculated for a small number of repeated measures, different covariance structures and linearly divergent treatment effects. The gain in efficiency of the treatment effect estimator by using optimally allocated time-points instead of equidistant time-points or by adding optimally spaced measures (at the expense of patients) is then computed. The assumed covariance structure is crucial for the results. For a compound symmetric covariance structure, a large gain in efficiency is obtained by adding repeated measures at the end of the study. For a first-order auto-regressive covariance structure, highly efficient treatment effect estimators are obtained with only two repeated measures, i.e. at the start and at the end of the study. For a first-order auto-regressive covariance structure including measurement error, the gain in efficiency by adding optimally spaced measures depends on the covariance parameter values. The gain in efficiency is similar with or without a random intercept. For a fixed study budget, the commonly used design with more than two equally spaced measures was never optimal for the linear cost function and covariance structures that were used. If the covariance structure is unknown, the optimal design based on a first-order auto-regressive covariance structure with measurement error is preferable in terms of robustness against misspecification of the covariance structure. The numerical results are illustrated by two examples.

A comparison between traditional methods and multilevel regression for the analysis of multicenter intervention studies

This article reviews three traditional methods for the analysis of multicenter trials with persons nested within clusters, i.e., centers, namely naïve regression (persons as units of analysis), fixed effects regression, and the use of summary measures (clusters as units of analysis), and compares these methods with multilevel regression. The comparison is made for continuous (quantitative) outcomes, and is based on the estimator of the treatment effect and its standard error, because these usually are of main interest in intervention studies. When the results of the experiment have to be valid for some larger population of centers, the centers in the intervention study have to present a random sample from this population and multilevel regression may be used. It is shown that the treatment effect and especially its standard error, are generally incorrectly estimated by the traditional methods, which should, therefore, not in general be used as an alternative to multilevel regression.

Long-term retention of a theatrical script by repertory actors: the role of context

Four actors were requested to perform Sartre's No Exit after a retention interval of more than 5 months. Their recall of the play was studied either with the spatial and visual contextual cues normally available during a performance or without such cues. Total recall was still considerable, equalling 85%. The number of paraphrases of, and inferences on, the original text was however quite large (32%), suggesting that the actors had learned their lines according to their meaning rather than as a result of rote memorisation. The context manipulation showed that actors' recall is facilitated by the availability of spatial-visual information, but only to a limited extent. The relative importance of textual and contextual cues in the long-term retention of a play is discussed.