Technos College, Japan

Poster created by students in the 2018 IWU Freeman Asia Internship Program

On the Formation of Weighting Adjustment Cells for Unit Nonresponse

A method is proposed for weighting adjustments for unit nonresponse based on a crossclassification by the estimated propensity to respond and by the predicted mean of a survey outcome. Simulations to assess the performance of the method are described

Weighting Adjustments for Unit Nonresponse with Multiple Outcome Variables

Weighting is a common form of unit nonresponse adjustment in sample surveys where entire questionnaires are missing due to noncontact or refusal to participate. Weights are inversely proportional to the probability of selection and response. A common approach computes the response weight adjustment cells based on covariate information. When the number of cells thus created is too large, a coarsening method such as response propensity stratification can be applied to reduce the number of adjustment cells. Simulations in Vartivarian and Little (2002) indicate improved efficiency and robustness of weighting adjustments based on the joint classification of the sample by two key potential stratifiers: the response propensity and the predictive mean, both defined in Section 2. Predictive mean stratification has the disadvantage that it leads to a different set of weights for each key outcome. However, potential gains in efficiency and robustness make it desirable to use a joint classification. Here, we consider the efficiency and robustness of weights that jointly classify on the response propensity and predictive mean, but the base the predictive mean dimension on a single canonical outcome variable

Does Weighting for Nonresponse Increase the Variance of Survey Means?

Nonresponse weighting is a common method for handling unit nonresponse in surveys. A widespread view is that the weighting method is aimed at reducing nonresponse bias, at the expense of an increase in variance. Hence, the efficacy of weighting adjustments becomes a bias-variance trade-off. This note suggests that this view is an oversimplification -- nonresponse weighting can in fact lead to a reduction in variance as well as bias. A covariate for a weighting adjustment must have two characteristics to reduce nonresponse bias - it needs to be related to the probability of response, and it needs to be related to the survey outcome. If the latter is true, then weighting can reduce, not increase, sampling variance. A detailed analysis of bias and variance is provided in the setting of weighting for an estimate of a survey mean based on adjustment cells. The analysis suggests that the most important feature of variables for inclusion in weighting adjustments is that they are predictive of survey outcomes; prediction of the propensity to respond is a secondary, though useful, goal. Empirical estimates of root mean squared error for assessing when weighting is effective are proposed and evaluated in a simulation study

Experimental modulation of capsule size in Cryptococcus neoformans

Experimental modulation of capsule size is an important technique for the study of the virulence of the encapsulated pathogen Cryptococcus neoformans. In this paper, we summarize the techniques available for experimental modulation of capsule size in this yeast and describe improved methods to induce capsule size changes. The response of the yeast to the various stimuli is highly dependent on the cryptococcal strain. A high CO(2) atmosphere and a low iron concentration have been used classically to increase capsule size. Unfortunately, these stimuli are not reliable for inducing capsular enlargement in all strains. Recently we have identified new and simpler conditions for inducing capsule enlargement that consistently elicited this effect. Specifically, we noted that mammalian serum or diluted Sabouraud broth in MOPS buffer pH 7.3 efficiently induced capsule growth. Media that slowed the growth rate of the yeast correlated with an increase in capsule size. Finally, we summarize the most commonly used media that induce capsule growth in C. neoformans

Acral necrosis by Stenotrophomonas maltophilia

Keywords:necrosis;skin and soft tissue infection;Stenotrophomonas maltophilia Abstract Background Stenotrophomonas maltophilia (SM) has been considered a nosocomial pathogen. Nevertheless, community acquired infection may occur more frequently than usually recognized. Case We describe distal necrosis of the fingers by SM in a farmer, contracted in the community and successfully treated with a combination of cotrimoxazole and ciprofloxacin. The patient was diagnosed with chronic lymphocytic leukaemia 6 months later. Conclusions This unusual presentation shows that infection with SM should be included in the differential diagnosis of the skin and soft tissue infection, even in apparently healthy patients

Antimicrobial Susceptibility of Stenotrophomonas maltophilia Isolates from a Korean Tertiary Care Hospital

We determined the antimicrobial susceptibility of 90 clinical isolates of Stenotrophomonas maltophilia collected in 2009 at a tertiary care hospital in Korea. Trimethoprim-sulfamethoxazole, minocycline, and levofloxacin were active against most of the isolates tested. Moxifloxacin and tigecycline were also active and hold promise as therapeutic options for S. maltophilia infections

Methods for Population-Adjusted Indirect Comparisons in Health Technology Appraisal

Standard methods for indirect comparisons and network meta-analysis are based on aggregate data, with the key assumption that there is no difference between the trials in the distribution of effect-modifying variables. Methods which relax this assumption are becoming increasingly common for submissions to reimbursement agencies such as NICE. These use individual patient data from a subset of trials to form population-adjusted indirect comparisons between treatments, in a specific target population. Recently proposed population adjustment methods include the Matching-Adjusted Indirect Comparison (MAIC) and the Simulated Treatment Comparison (STC). Despite increasing popularity, MAIC and STC remain largely untested. Furthermore, there is a lack of clarity about exactly how and when they should be applied in practice, and even whether the results are relevant to the decision problem. There is therefore a real and present risk that the assumptions being made in one submission to a reimbursement agency are fundamentally different to – or even incompatible with – the assumptions being made in another for the same indication. We describe the assumptions required for population-adjusted indirect comparisons, and demonstrate how these may be used to generate comparisons in any given target population. We distinguish between anchored and unanchored comparisons according to whether a common comparator arm is used or not. Unanchored comparisons make much stronger assumptions which are widely regarded as infeasible. We provide recommendations on how and when population adjustment methods should be used, and the supporting analyses that are required, in order to provide statistically valid, clinically meaningful, transparent and consistent results for the purposes of health technology appraisal. Simulation studies are needed to examine the properties of population adjustment methods and their robustness to breakdown of assumptions