scholarly journals Statistical analysis of two arm randomized pre-post designs with one post-treatment measurement

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Fei Wan
Keyword(s):  
Treatment Effect ◽  
Repeated Measures ◽  
Informed Choice ◽  
Clinical Effectiveness ◽  
Change Score ◽  
Post Treatment ◽  
Analysis Of Covariance ◽  
Alternative Methods ◽  
Main Effect ◽  
Comparable Performance

Abstract Background Randomized pre-post designs, with outcomes measured at baseline and after treatment, have been commonly used to compare the clinical effectiveness of two competing treatments. There are vast, but often conflicting, amount of information in current literature about the best analytic methods for pre-post designs. It is challenging for applied researchers to make an informed choice. Methods We discuss six methods commonly used in literature: one way analysis of variance (“ANOVA”), analysis of covariance main effect and interaction models on the post-treatment score (“ANCOVAI” and “ANCOVAII”), ANOVA on the change score between the baseline and post-treatment scores (“ANOVA-Change”), repeated measures (“RM”) and constrained repeated measures (“cRM”) models on the baseline and post-treatment scores as joint outcomes. We review a number of study endpoints in randomized pre-post designs and identify the mean difference in the post-treatment score as the common treatment effect that all six methods target. We delineate the underlying differences and connections between these competing methods in homogeneous and heterogeneous study populations. Results ANCOVA and cRM outperform other alternative methods because their treatment effect estimators have the smallest variances. cRM has comparable performance to ANCOVAI in the homogeneous scenario and to ANCOVAII in the heterogeneous scenario. In spite of that, ANCOVA has several advantages over cRM: i) the baseline score is adjusted as covariate because it is not an outcome by definition; ii) it is very convenient to incorporate other baseline variables and easy to handle complex heteroscedasticity patterns in a linear regression framework. Conclusions ANCOVA is a simple and the most efficient approach for analyzing pre-post randomized designs.

Analyzing pre-post randomized studies with one post-randomization score using repeated measures and ANCOVA models

2018 ◽  
Vol 28 (10-11) ◽  
pp. 2952-2974 ◽  
Author(s):  
Fei Wan
Keyword(s):  
Least Squares ◽  
Treatment Effect ◽  
Repeated Measures ◽  
Interaction Model ◽  
Ordinary Least Squares ◽  
Analysis Of Covariance ◽  
Baseline Score ◽  
Sample Mean ◽  
Heterogeneity Of Treatment Effect ◽  
Randomized Studies

The analysis of covariance (ANCOVA) or repeated measures (RM) models are often used to compare the treatment effect between different arms in pre-post randomized studies. ANCOVA adjusts the baseline score as a covariate in regression models. RM treats both the baseline and post-randomization scores as outcome variables. We aim to establish the underlying connections between ANCOVA and a constrained RM (“cRM”). We start with the interrelated concepts in a pre-post randomized designs: homogeneous vs. heterogeneous study populations, the marginal vs. the conditional treatment effect, and homogeneity vs. heterogeneity of treatment effect. We then demonstrate the asymptotic equivalence between the ANCOVA and cRM estimators for the marginal treatment effect and discuss the conditions under which ANCOVA needs to include a baseline score by treatment interaction term. In particular, an ANCOVA interaction model with a mean centered baseline score can assess both the marginal treatment effect and the heterogeneity in the conditional treatment effect. However, the ordinary least squares (OLS)-based inference is not valid for unconditional inference because this interaction model typically has heteroskedastic errors, and ordinary least squares treats the sample mean of the baseline score as a known parameter. We propose a bootstrap and a heteroskedasticity consistent variance estimator for heteroskedastic ANCOVA. Our simulation studies demonstrate that the proposed methods provide valid inferences for testing both the marginal treatment effect and the heterogeneity of treatment effect using an ANCOVA interaction model. We used an acupuncture headache trial to elucidate the proposed approaches.

scholarly journals Size of hippocampal pyramidal neurons in schizophrenia

2003 ◽  
Vol 183 (5) ◽  
pp. 414-417 ◽  
Author(s):  
J. R. Highley ◽  
M. A. Walker ◽  
B. McDonald ◽  
T. J. Crow ◽  
M. M. Esiri
Keyword(s):  
Repeated Measures ◽  
Pyramidal Neurons ◽  
Analysis Of Covariance ◽  
Hippocampal Pyramidal Neurons ◽  
Hippocampal Cell ◽  
Repeated Measures Analysis ◽  
Main Effect ◽  
Neuron Size ◽  
And Gender ◽  
Meta Analyses

BackgroundMeta-analyses of hippocampal size have indicated that this structure is smaller in schizophrenia. This could reflect a reduction in the size of constituent neurons or a reduced number of neurons.AimsTo measure the size of hippocampal pyramidal neurons in the brains of people with and without schizophrenia.MethodPyramidal neuron size in hippocampal subfields was estimated stereologically from sections taken at 5 mm intervals throughout the whole length of right and left hippocampi from the brains of 13 people with schizophrenia and 16 controls. Results were assessed using repeated-measures analysis of covariance looking for a main effect of diagnosis and gender, and interactions of these with side.ResultsWe were unable to detect significant differences related to diagnosis, gender or side for any hippocampal subfield for this series of cases.ConclusionsFor this series of brains, hippocampal cell size is unchanged in schizophrenia.

scholarly journals Beyond total treatment effects in randomised controlled trials: Baseline measurement of intermediate outcomes needed to reduce confounding in mediation investigations

2018 ◽  
Vol 15 (3) ◽  
pp. 247-256 ◽  
Author(s):  
Sabine Landau ◽  
Richard Emsley ◽  
Graham Dunn
Keyword(s):  
Clinical Outcomes ◽  
Treatment Effect ◽  
Average Treatment Effect ◽  
Post Treatment ◽  
Analysis Of Covariance ◽  
Change Scores ◽  
Intermediate Variable ◽  
Common Causes ◽  
Total Treatment ◽  
Baseline Values

Background: Random allocation avoids confounding bias when estimating the average treatment effect. For continuous outcomes measured at post-treatment as well as prior to randomisation (baseline), analyses based on (A) post-treatment outcome alone, (B) change scores over the treatment phase or (C) conditioning on baseline values (analysis of covariance) provide unbiased estimators of the average treatment effect. The decision to include baseline values of the clinical outcome in the analysis is based on precision arguments, with analysis of covariance known to be most precise. Investigators increasingly carry out explanatory analyses to decompose total treatment effects into components that are mediated by an intermediate continuous outcome and a non-mediated part. Traditional mediation analysis might be performed based on (A) post-treatment values of the intermediate and clinical outcomes alone, (B) respective change scores or (C) conditioning on baseline measures of both intermediate and clinical outcomes. Methods: Using causal diagrams and Monte Carlo simulation, we investigated the performance of the three competing mediation approaches. We considered a data generating model that included three possible confounding processes involving baseline variables: The first two processes modelled baseline measures of the clinical variable or the intermediate variable as common causes of post-treatment measures of these two variables. The third process allowed the two baseline variables themselves to be correlated due to past common causes. We compared the analysis models implied by the competing mediation approaches with this data generating model to hypothesise likely biases in estimators, and tested these in a simulation study. We applied the methods to a randomised trial of pragmatic rehabilitation in patients with chronic fatigue syndrome, which examined the role of limiting activities as a mediator. Results: Estimates of causal mediation effects derived by approach (A) will be biased if one of the three processes involving baseline measures of intermediate or clinical outcomes is operating. Necessary assumptions for the change score approach (B) to provide unbiased estimates under either process include the independence of baseline measures and change scores of the intermediate variable. Finally, estimates provided by the analysis of covariance approach (C) were found to be unbiased under all the three processes considered here. When applied to the example, there was evidence of mediation under all methods but the estimate of the indirect effect depended on the approach used with the proportion mediated varying from 57% to 86%. Conclusion: Trialists planning mediation analyses should measure baseline values of putative mediators as well as of continuous clinical outcomes. An analysis of covariance approach is recommended to avoid potential biases due to confounding processes involving baseline measures of intermediate or clinical outcomes, and not simply for increased precision.

scholarly journals Is conventionally calculated anchor-based minimum clinically important difference value catches the real clinical increment? Determining the situations that make the answer “no” by a simulation study

2019 ◽  
Vol 1 (1) ◽  
pp. 522-528
Author(s):  
Selcen Yüksel ◽  
Pervin Demir ◽  
Afra Alkan
Keyword(s):  
Sample Size ◽  
Simulation Study ◽  
Roc Analysis ◽  
Minimum Clinically Important Difference ◽  
Change Score ◽  
Post Treatment ◽  
Alternative Methods ◽  
Response Patterns ◽  
The Real ◽  
Clinically Important Difference

Abstract The aim of this study was to examine the accuracy of conventionally used method-optimal cutoff of Receiver Operating Characteristic (ROC) curve- to determine the minimum clinically important difference (MCID), which is the estimator of responsiveness for scales, by a simulation study. The baseline person parameters were firstly generated and, by using these values, two gold standard groups were constructed as “improved” and “non-improved” after the treatment. Five point-likert response patterns were obtained for 20 items in each group, representing pre- and post-treatment responses of individuals. The mean change score between post treatment and baseline scores for the improved group was considered as real MCID (MCIDR), after baseline and post-treatment total scores were calculated from response patterns. The cut-off for change score specified by ROC analysis, which best discriminates between improved group and not improved group, MCIDROC, was compared to MCIDR. The scenarios of simulation were consisted of sample size and distribution of total scores for improved group. The data were generated for each of 40 scenarios with 1000 MCMC repeats. It was observed that the MCIDR and MCIDROC were not so affected by sample size. However, MCIDROC overestimated the MCIDR values in all scenarios. Briefly, the cut-off points obtained by ROC analysis found to be greater than the real MCID values. Therefore, alternative methods are required to calculate MCID.

The More, the Merrier? Roles of Device-Student Ratio in Collaborative Inquiries and Its Interactions With External Scripts and Task Complexity

2021 ◽  
pp. 073563312110107
Author(s):  
Cixiao Wang ◽  
Huixiao Le
Keyword(s):  
Conceptual Understanding ◽  
Repeated Measures ◽  
Task Complexity ◽  
Mainland China ◽  
Fifth Grade ◽  
Learning Context ◽  
Simple Task ◽  
Fifth Grade Students ◽  
Main Effect ◽  
The Relationship

In collaborative learning, the intuition “the more device, the merrier” is somehow widely acknowledged, but little research has investigated the relationship between device-student ratio and the learning outcome. This study aims to investigate not only the main effect of different device-student ratio, also to identify the moderators in the learning context including task complexity, external script availability and students’ familiarity to the collaboration settings. A three-round quasi-experiment was conducted in a primary school in mainland China, 130 fifth-grade students from four classes participated. Group worksheet including conceptual understanding and problem-solving tasks were used to collect participants’ inquiry performance. Repeated measures ANOVA was employed in data analysis. Findings indicate that 1:m device-student ratio could be beneficial, and external scripts, and prior collaboration experience could moderate such effect. The different effect of 1:m device-student ratio to 1:1 is only significant in the situation when students are faced with relatively simple task, and the effect size is larger when external script is present. When the task is more complicated, such effect of device-student ratio would only emerge after a period of collaboration. This finding challenged the intuition that one-to-one device-student ratio could be better. Related discussions and recommendations to teaching were made.

Increasing Precision without Altering Treatment Effects: Repeated Measures Designs in Survey Experiments

2021 ◽  
pp. 1-18
Author(s):  
SCOTT CLIFFORD ◽  
GEOFFREY SHEAGLEY ◽  
SPENCER PISTON
Keyword(s):  
Treatment Effect ◽  
Repeated Measures ◽  
Treatment Effects ◽  
Repeated Measurements ◽  
Experimental Designs ◽  
Treatment Effect Size ◽  
Standard Design ◽  
Survey Experiments ◽  
Repeated Measures Designs ◽  
Alternative Designs

The use of survey experiments has surged in political science. The most common design is the between-subjects design in which the outcome is only measured posttreatment. This design relies heavily on recruiting a large number of subjects to precisely estimate treatment effects. Alternative designs that involve repeated measurements of the dependent variable promise greater precision, but they are rarely used out of fears that these designs will yield different results than a standard design (e.g., due to consistency pressures). Across six studies, we assess this conventional wisdom by testing experimental designs against each other. Contrary to common fears, repeated measures designs tend to yield the same results as more common designs while substantially increasing precision. These designs also offer new insights into treatment effect size and heterogeneity. We conclude by encouraging researchers to adopt repeated measures designs and providing guidelines for when and how to use them.

scholarly journals Estimating and reporting treatment effects in clinical trials for weight management: using estimands to interpret effects of intercurrent events and missing data

2021 ◽  
Author(s):  
Sean Wharton ◽  
Arne Astrup ◽  
Lars Endahl ◽  
Michael E. J. Lean ◽  
Altynai Satylganova ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Missing Data ◽  
Weight Management ◽  
Treatment Effect ◽  
Repeated Measures ◽  
Treatment Effects ◽  
Randomized Clinical Trials ◽  
Weight Losses ◽  
The Difference ◽  
Treatment Of Obesity

AbstractIn the approval process for new weight management therapies, regulators typically require estimates of effect size. Usually, as with other drug evaluations, the placebo-adjusted treatment effect (i.e., the difference between weight losses with pharmacotherapy and placebo, when given as an adjunct to lifestyle intervention) is provided from data in randomized clinical trials (RCTs). At first glance, this may seem appropriate and straightforward. However, weight loss is not a simple direct drug effect, but is also mediated by other factors such as changes in diet and physical activity. Interpreting observed differences between treatment arms in weight management RCTs can be challenging; intercurrent events that occur after treatment initiation may affect the interpretation of results at the end of treatment. Utilizing estimands helps to address these uncertainties and improve transparency in clinical trial reporting by better matching the treatment-effect estimates to the scientific and/or clinical questions of interest. Estimands aim to provide an indication of trial outcomes that might be expected in the same patients under different conditions. This article reviews how intercurrent events during weight management trials can influence placebo-adjusted treatment effects, depending on how they are accounted for and how missing data are handled. The most appropriate method for statistical analysis is also discussed, including assessment of the last observation carried forward approach, and more recent methods, such as multiple imputation and mixed models for repeated measures. The use of each of these approaches, and that of estimands, is discussed in the context of the SCALE phase 3a and 3b RCTs evaluating the effect of liraglutide 3.0 mg for the treatment of obesity.

Effectiveness of transdiagnostic seminars to support patients with common mental disorders: a multi-service practice research network study

2021 ◽  
pp. 1-13
Author(s):  
Chris Gaskell ◽  
Ryan Askey-Jones ◽  
Martin Groom ◽  
Jaime Delgadillo
Keyword(s):  
Treatment Effect ◽  
High Intensity ◽  
Post Treatment ◽  
Waiting List ◽  
Effect Sizes ◽  
Research Network ◽  
Randomised Control Trial ◽  
Practice Research ◽  
Matched Sample ◽  
Post Treatment Effect

Abstract Background: This was a multi-site evaluation of psycho-educational transdiagnostic seminars (TDS) as a pre-treatment intervention to enhance the effectiveness and utilisation of high-intensity cognitive behavioural therapy (CBT). Aims: To evaluate the effectiveness of TDS combined with high-intensity CBT (TDS+CBT) versus a matched sample receiving CBT only. Second, to determine the consistency of results across participating services which employed CBT+TDS. Finally, to determine the acceptability of TDS across patients with different psychological disorders. Method: 106 patients across three services voluntarily attended TDS while on a waiting list for CBT (TDS+CBT). Individual and pooled service pre–post treatment effect sizes were calculated using measures of depression, anxiety and functional impairment. Effectiveness and completion rates for TDS+CBT were compared with a propensity score matched sample from an archival dataset of cases who received high-intensity CBT only. Results: Pre–post treatment effect sizes for TDS+CBT were comparable to the matched sample. Recovery rates were greater for the group receiving TDS; however, this was not statistically significant. Greater improvements were observed during the waiting-list period for patients who had received TDS for depression (d = 0.49 compared with d = 0.07) and anxiety (d = 0.36 compared with d = 0.04). Conclusions: Overall, this new evidence found a trend for TDS improving symptoms while awaiting CBT across three separate IAPT services. The effectiveness of TDS now warrants further exploration through an appropriately sized randomised control trial.

scholarly journals No Self Without Salience: Affective and Self-relevance Ratings of 552 Emotionally Valenced and Neutral Dutch Words

2021 ◽  
Author(s):  
Lora I. Dimitrova ◽  
Eline M. Vissia ◽  
Hanneke Geugies ◽  
Hedwig Hofstetter ◽  
Sima Chalavi ◽  
...  
Keyword(s):  
Repeated Measures ◽  
Emotional Valence ◽  
Age And Gender ◽  
Gender And Age ◽  
Main Effect ◽  
Emotional Salience ◽  
And Gender ◽  
The Relationship

AbstractIt is unknown how self-relevance is dependent on emotional salience. Emotional salience encompasses an individual's degree of attraction or aversion to emotionally-valenced information. The current study investigated the interconnection between self and salience through the evaluation of emotional valence and self-relevance. 56 native Dutch participants completed a questionnaire assessing valence, intensity, and self-relevance of 552 Dutch nouns and verbs. One-way repeated-measures ANCOVA investigated the relationship between valence and self, age and gender. Repeated-measures ANCOVA also tested the relationship between valence and self with intensity ratings and effects of gender and age. Results showed a significant main effect of valence for self-relevant words. Intensity analyses showed a main effect of valence but not of self-relevance. There were no significant effects of gender and age. The most important finding presents that self-relevance is dependent on valence. These findings concerning the relationship between self and salience opens avenues to study an individual's self-definition.

An outcomes study of eTherapy for dual diagnosis using Breaking Free Online

2014 ◽  
Vol 7 (2) ◽  
pp. 52-62 ◽  
Author(s):  
Sarah Elison ◽  
Jonathan Ward ◽  
Glyn Davies ◽  
Nicky Lidbetter ◽  
Daniel Hulme ◽  
...  
Keyword(s):  
Mental Health ◽  
Dual Diagnosis ◽  
Substance Dependence ◽  
Clinical Effectiveness ◽  
Outcome Data ◽  
Post Treatment ◽  
Service Users ◽  
Original Cohort ◽  
Content Type ◽  
Self Help

Purpose – In recent years there has been a proliferation of computer-based psychotherapeutic interventions for common mental health difficulties. Building on this, a small number of such interventions have now been developed to address substance dependence, one of which is Breaking Free Online (BFO). A new “eTherapy” self-help service, which was set up by the UK mental health charity Self-Help Services, has provided access to BFO to service users presenting with comorbid mental health and substance misuse difficulties. The purpose of this paper is to evaluate a range of clinical outcomes in the first cohort of service users accessing this dual diagnosis service. Design/methodology/approach – A number of standardised psychometric assessments were conducted with service users at baseline and post-treatment at discharge from the service. Outcome data were available for 47 service users out of an original cohort of 74. Findings – Statistically significant improvements were found in terms of measures of social functioning, depression, anxiety, alcohol and drug use and social anxiety. Clinically relevant gains were also identified, with fewer service users reaching threshold scores for depression and anxiety at post-treatment compared to baseline. Effect sizes also indicated that the identified improvements across the psychometric measures were robust and significant. Research limitations/implications – These findings provide further support for the clinical effectiveness of BFO, and also provide evidence that an eTherapy self-help service may be appropriate for some individuals presenting with dual diagnosis. Further research is underway with larger and alternative clinical populations to examine the effectiveness of BFO and also this novel eTherapy self-help approach. Originality/value – This paper has provided initial data to support effectiveness of a novel eTherapy service for dual diagnosis.

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