Features that make rds an effective data collection

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Features that make RDS an effective data collection method in hard to reach populations also complicate the analysis of RDS data. Authors should describe methods they used to adjust for the sample design in their analysis, both when making estimates and when quantifying the uncertainty in those estimates. 34 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 81 82
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Several different estimators exist for estimating the prevalence of a specific trait (e.g., HIV prevalence) from RDS data 44-50 . There are also a number of different methods for producing confidence intervals around these estimates . Evaluations of these methods have been equivocal and the best estimator may depend on specific features of a study 55 . At this time there is no consensus that one estimator should be universally used. As such, we recommend authors clearly describe the statistical methods used, including those to adjust for sample design, both when making estimates and when quantifying the uncertainty in those estimates. If multiple estimators were used to make estimates, each estimate should be presented. Many approaches have also been used to perform multivariate analysis using RDS data . Authors should clearly state which approach was used and why. If multiple approaches were used, results from each approach should be presented. Other decisions that should be reported include any ‘trimming’ of ‘outliers’, ‘data smoothing’, or imputation used, either based on an explicit choice by the researcher or an implicit choice embedded in the software used. Investigators should pre-determine analyses at least for the primary study objectives in a study protocol. Often additional analyses are needed, either instead of, or as well as, those originally envisaged, and these may sometimes be motivated by the data. Authors should report whether particular analyses were motivated by data inspection. Even though the distinction between pre- specified and exploratory analyses may sometimes be blurred, authors should clarify reasons for particular analyses. In general, there is no one correct statistical analysis but, rather, several possibilities that may address the same question, but make different assumptions. If groups being compared are not similar with regard to some characteristics, adjustment should be made for possible confounding variables by stratification or by multivariable regression. 63 Analysts should fully describe specific procedures for variable selection and not only present results from the final model. If model comparisons are made to narrow down a list of potential confounders for inclusion in a final model, this process should be described. It is helpful to tell readers if one or two covariates are 35 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 83 84
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responsible for a great deal of the apparent confounding in a data analysis. Other statistical analyses such as imputation procedures, data transformation, and calculations of attributable risks should also be described. Nonstandard or novel approaches should be referenced. As a
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  • Spring '13
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