For inferring a treatment effect from the difference between a treated and untreated group on a quantitative outcome measured before and after treatment, current methods are analysis of covariance ...(ANCOVA) of the outcome with the baseline as covariate, and analysis of variance (ANOVA) of change from baseline. This article compares both methods on power and bias, for randomized and nonrandomized studies.
The methods are compared by writing both as a regression model and as a repeated measures model, and are applied to a nonrandomized study of preventing depression.
In randomized studies both methods are unbiased, but ANCOVA has more power. If treatment assignment is based on the baseline, only ANCOVA is unbiased. In nonrandomized studies with preexisting groups differing at baseline, the two methods cannot both be unbiased, and may contradict each other. In the study of depression, ANCOVA suggests absence, but ANOVA of change suggests presence, of a treatment effect. The methods differ because ANCOVA assumes absence of a baseline difference.
In randomized studies and studies with treatment assignment depending on the baseline, ANCOVA must be used. In nonrandomized studies of preexisting groups, ANOVA of change seems less biased than ANCOVA, but two control groups and two baseline measurements are recommended.
Summary 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).
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.
•We conducted a randomised controlled trial of the Expand Your Horizon programme.•The programme teaches participants to focus on the functionality of their body.•The programme was administered to ...women with a negative body image.•Compared to control participants, programme participants experienced improvement in body image.•Programme participants also experienced decreased levels of self-objectification.
This study tested Expand Your Horizon, a programme designed to improve body image by training women to focus on the functionality of their body using structured writing assignments. Eighty-one women (Mage=22.77) with a negative body image were randomised to the Expand Your Horizon programme or to an active control programme. Appearance satisfaction, functionality satisfaction, body appreciation, and self-objectification were measured at pretest, posttest, and one-week follow-up. Following the intervention, participants in the Expand Your Horizon programme experienced greater appearance satisfaction, functionality satisfaction, and body appreciation, and lower levels of self-objectification, compared to participants in the control programme. Partial eta-squared effect sizes were of small to medium magnitude. This study is the first to show that focusing on body functionality can improve body image and reduce self-objectification in women with a negative body image. These findings provide support for addressing body functionality in programmes designed to improve body image.
Abstract 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.
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.
In a cluster randomized trial clusters of persons, for instance, schools or health centers, are assigned to treatments, and all persons in the same cluster get the same treatment. Although less ...powerful than individual randomization, cluster randomization is a good alternative if individual randomization is impossible or leads to severe treatment contamination (carry-over). Focusing on cluster randomized trials with a pretest and post-test of a quantitative outcome, this paper shows the equivalence of four methods of analysis: a three-level mixed (multilevel) regression for repeated measures with as levels cluster, person, and time, and allowing for unstructured between-cluster and within-cluster covariance matrices; a two-level mixed regression with as levels cluster and person, using change from baseline as outcome; a two-level mixed regression with as levels cluster and time, using cluster means as data; a one-level analysis of cluster means of change from baseline. Subsequently, similar equivalences are shown between a constrained mixed model and methods using the pretest as covariate. All methods are also compared on a cluster randomized trial on mental health in children. From these equivalences follows a simple method to calculate the sample size for a cluster randomized trial with baseline measurement, which is demonstrated step-by-step.
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.
•Finite mixture models have great flexibility in modelling complex life course data.•Assists in grouping individuals with similar development over time.•Guidance for key model selection ...considerations and estimation in 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.
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).
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