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.

Testing treatment effects in repeated measures designs: Trimmed means and bootstrapping

2000 ◽  
Vol 53 (2) ◽  
pp. 175-191 ◽  
Author(s):  
H. J. Keselman ◽  
Rhonda K. Kowalchuk ◽  
James Algina ◽  
Lisa M. Lix ◽  
Rand R. Wilcox
Keyword(s):  
Repeated Measures ◽  
Treatment Effects ◽  
Repeated Measures Designs ◽  
Trimmed Means

Repeated measures experiments in forestry: focus on analysis of response curves

1991 ◽  
Vol 21 (7) ◽  
pp. 957-965 ◽  
Author(s):  
M. P. Meredith ◽  
S. V. Stehman
Keyword(s):  
Repeated Measures ◽  
Sugar Maple ◽  
Treatment Effects ◽  
Multivariate Analyses ◽  
Primary Objective ◽  
Response Curves ◽  
Period Analysis ◽  
Time Period ◽  
Repeated Measures Designs ◽  
Over Time

Treatment effects over time are frequently investigated using repeated measures designs, but analyses of these experiments frequently fail to address a primary objective of collecting data over time, namely description of the response curve. The analysis advocated in this paper utilizes the intrinsic continuity of the repeated measures factor by focusing on response curves. Treatments are compared by analyzing estimated coefficients of response curves proposed by the investigator. This approach provides more information on treatment effects than analyses that compare treatments separately at each time period. Analysis of estimated coefficients is easier to interpret than multivariate analyses of variance and does not require often biologically implausible assumptions of split-plot analyses currently in vogue. An example describing effects of aluminum on sugar maple (Acersaccharum Marsh.) seedling growth illustrates the method.

scholarly journals Ranking procedures for repeated measures designs with missing data: Estimation, testing and asymptotic theory

2021 ◽  
pp. 096228022110463
Author(s):  
Kerstin Rubarth ◽  
Markus Pauly ◽  
Frank Konietschke
Keyword(s):  
Missing Data ◽  
Repeated Measures ◽  
Binary Data ◽  
Treatment Effects ◽  
Missing Values ◽  
Small Sample ◽  
Type I ◽  
Test Procedures ◽  
Data Set ◽  
Repeated Measures Designs

We develop purely nonparametric methods for the analysis of repeated measures designs with missing values. Hypotheses are formulated in terms of purely nonparametric treatment effects. In particular, data can have different shapes even under the null hypothesis and therefore, a solution to the nonparametric Behrens-Fisher problem in repeated measures designs will be presented. Moreover, global testing and multiple contrast test procedures as well as simultaneous confidence intervals for the treatment effects of interest will be developed. All methods can be applied for the analysis of metric, discrete, ordinal, and even binary data in a unified way. Extensive simulation studies indicate a satisfactory control of the nominal type-I error rate, even for small sample sizes and a high amount of missing data (up to 30%). We apply the newly developed methodology to a real data set, demonstrating its application and interpretation.

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.

scholarly journals Post-exercise pulsatility index indicates treatment effects in peripheral arterial occlusive disease (PAOD)

2021 ◽  
Author(s):  
Andrej Udelnow ◽  
Maria Hawemann ◽  
Ivo Buschmann ◽  
Frank Meyer ◽  
Zuhir Halloul
Keyword(s):  
Repeated Measures ◽  
Pulsatility Index ◽  
Treatment Effects ◽  
Resistance Index ◽  
Peak Systolic Velocity ◽  
Arterial Tree ◽  
Post Exercise ◽  
Diastolic Velocity ◽  
Before And After ◽  
Healthy Persons

Summary Background Hypothesis: Post-exercise measurements better discriminate PAOD-patients from healthy persons and they more sensitively detect hemodynamic improvements after treatment procedures than resting measurements. Methods A total of 19 healthy volunteers and 23 consecutive PAOD-patients underwent measurements of peak systolic velocity (PSV), end-diastolic velocity (EDV), minimal diastolic velocity (MDV), time-averaged maximum velocities (TAMAX), resistance index (RI) and pulsatility index (PI) before and after a standard exercise test (at 1, 2, 3, 4 and 5 min) before and after treatment (incl. epidemiological data, PAOD risk factors and comorbidities). Results In resting values, healthy persons and PAOD-patients did not differ significantly in any of the hemodynamic parameters. PSV increased after treatment in PAOD-patients by 5 cm/s (paired t‑test, p: 0.025); however, when the amplitude of autoregulatory changes related to the resting values were calculated, PAOD-patients showed clearly less hemodynamic changes after exercise than healthy persons (p: 0.04; 0.002; <0.001 for PSV, TAMAX and PI, resp.). The time course after exercise was compared by repeated measures of ANOVA. Healthy persons differed significantly in PI, RI and PSV from PAOD patients before and after treatment (p<0.001 each). The PAOD-patients revealed a significantly improved PI after treatment (p: 0.042). The only factor contributing significantly to PI independently from grouping was direct arterial vascularization as compared to discontinuous effects by an obstructed arterial tree. Conclusion Healthy persons cannot be well differentiated from PAOD-patients solely by hemodynamics at rest but by characteristic changes after standard exercise. Treatment effects are reflected by higher PI-values after exercise.

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