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Analyzing Proportion Scores as Outcomes for Prevention Trials: a Statistical Primer.

ABSTRACT: In prevention trials, outcomes of interest frequently include data that are best quantified as proportion scores. In some cases, however, proportion scores may violate the statistical assumptions underlying common analytic methods. In this paper, we provide guidelines for analyzing frequency and proportion data as primary outcomes. We describe standard methods including generalized linear regression models to compare mean proportion scores and examine tools for testing normality and other assumptions for each model. Recommendations are made for instances when the assumptions are not met, including transformations for proportion scores that are non-normal. We also discuss more sophisticated analytical tools to model change in proportion scores over time. The guidelines provide ready-to-use analytical strategies for frequency and proportion data that are commonly encountered in prevention science.

SUBMITTER: Chen K

PROVIDER: S-EPMC5860877 | biostudies-literature | 2017 Apr

REPOSITORIES: biostudies-literature

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Analyzing Proportion Scores as Outcomes for Prevention Trials: a Statistical Primer.

Chen Kehui K Cheng Yu Y Berkout Olga O Lindhiem Oliver O

Prevention science : the official journal of the Society for Prevention Research 20170401 3

In prevention trials, outcomes of interest frequently include data that are best quantified as proportion scores. In some cases, however, proportion scores may violate the statistical assumptions underlying common analytic methods. In this paper, we provide guidelines for analyzing frequency and proportion data as primary outcomes. We describe standard methods including generalized linear regression models to compare mean proportion scores and examine tools for testing normality and other assump ...[more]

PMID: 26960687

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Project description:BACKGROUND:Patient Reported Outcomes (PRO) are gaining more and more importance in the context of clinical trials. The assessment of PRO is frequently performed by questionnaires where the multiple items of a questionnaire are usually pooled within summarizing scores. These scores are used as variables to measure subjective aspects of treatments and diseases. In clinical research, the calculation of these scores is mostly kept very simple, e.g. by a simple summation of item values. In the medical literature, there is hardly any guidance for performing a refinements of questionnaires and for deducing adequate scores. In contrast, in psychometric literature, there are plenty of more sophisticated methods, which overcome typical assumptions made in traditional (sum) scores, however to the prize of more complicated algorithms, which might be difficult to communicate. When faced with the practical task to refine an existing questionnaire, there exist a clear gap of guidance for applied medical researchers. By this article we try to fill this important gap between psychometric theory and medical application by illustrating our methodological choices on the example of a clinical PRO questionnaire. METHODS:Based on our experiences with the refinement of the BCTOS, a PRO questionnaire to assess aesthetic and function after breast conserving therapy in breast cancer patients, we present the following general steps that we performed by refining the BCTOS questionnaire and its scores: 1. Refinement of the length of the questionnaire and the (item-factor) structure. 2. Selection of the factor score estimation method. 3. Validation of the refined questionnaire and scores with respect to validity, reliability and structure based on a validation cohort. RESULTS:Our step-step-step procedure helped us to shorten the current form of the BCTOS and to redefine the factor structure. By this, the compliance of patients can be increased and the interpretation of the results becomes more coherent. CONCLUSIONS:We present a step-by-step procedure to refine an existing medical questionnaire along with its scores illustrated and discussed by the refinement of the BCTOS. TRIAL REGISTRATION:Due to the character of the study (no intervention study), no registration was performed.

Project description:IntroductionRandomised controlled trials (RCTs) aim to assess the effect of one (or more) unproven health interventions relative to other reference interventions. RCTs sometimes use an ordinal outcome, which is an endpoint that comprises of multiple, monotonically ordered categories that are not necessarily separated by a quantifiable distance. Ordinal outcomes are appealing in clinical settings as specific disease states can represent meaningful categories that may be of clinical importance to researchers. Ordinal outcomes can also retain information and increase statistical power compared to dichotomised outcomes and can allow multiple clinical outcomes to be comprised in a single endpoint. Target parameters for ordinal outcomes in RCTs may vary depending on the nature of the research question, the modelling assumptions and the expertise of the data analyst. The aim of this scoping review is to systematically describe the use of ordinal outcomes in contemporary RCTs. Specifically, we aim to: [Formula: see text] Identify which target parameters are of interest in trials that use an ordinal outcome, and whether these parameters are explicitly defined. [Formula: see text] Describe how ordinal outcomes are analysed in RCTs to estimate a treatment effect. [Formula: see text] Describe whether RCTs that use an ordinal outcome adequately report key methodological aspects specific to the analysis of the ordinal outcome. Results from this review will outline the current state of practice of the use of ordinal outcomes in RCTs. Ways to improve the analysis and reporting of ordinal outcomes in RCTs will be discussed.Methods and analysisWe will review RCTs that are published in the top four medical journals (British Medical Journal, New England Journal of Medicine, The Lancet and the Journal of the American Medical Association) between 1 January 2012 and 31 July 2022 that use an ordinal outcome as either a primary or a secondary outcome. The review will identify articles through a PubMed-specific search strategy. Our review will adhere to guidelines for scoping reviews as described in the PRISMA-ScR checklist. The study characteristics and details of the study design and analysis, including the target parameter(s) and statistical methods used to analyse the ordinal outcome, will be extracted from eligible studies. The screening, review and data extraction will be conducted using Covidence, a web-based tool for managing systematic reviews. The data will be summarised using descriptive statistics.

Dataset Information

Analyzing Proportion Scores as Outcomes for Prevention Trials: a Statistical Primer.

Publications

Analyzing Proportion Scores as Outcomes for Prevention Trials: a Statistical Primer.

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