Objective: The positive effect of antidiabetic medication on cognitive decline has been given some support by, among others, Secnik et al (2021) and Secnik et al (2022). However, as they are observational studies, it is not clear whether the effect is causal.

Research design and methods: Using the Swedish Dementia Registry and supplementary Swedish registers/databases, we identified 1,873 patients (4,732 observations) with diagnosis of diabetes and Alzheimer’s disease or mixed-pathology dementia who were followed-up at least once after dementia diagnosis. The association of use of metformin with Mini-Mental State Examination scores in patients with diabetes and dementia was studied in two ways. 1) The difference between the last and the first score for each patient was compared with treatment (use of metformin) and subjected to a new sensitivity analysis. 2) The difference between scores for each patient at the points in time when there was a change in use of metformin (either start of use, or discontinuation of use) was studied.

Results: There is an association between cognitive decline and use of metformin. However, any conclusion of a causal relationship is tenuous. 

Conclusion: The present study offers no basis for causal conclusions, but given the association, further examination of cognitive effects of metformin is warranted.

Objective: The positive effect of antidiabetic medication on cognitive decline has been given some support by, among others, Secnik et al (2021) and Secnik et al (2022). However, as they are observational studies, it is not clear whether the effect is causal.

Research design and methods: Using the Swedish Dementia Registry and supplementary Swedish registers/databases, we identified 1,873 patients (4,732 observations) with diagnosis of diabetes and Alzheimer’s disease or mixed-pathology dementia who were followed-up at least once after dementia diagnosis. The association of use of metformin with Mini-Mental State Examination scores in patients with diabetes and dementia was studied in two ways. 1) The difference between the last and the first score for each patient was compared with treatment (use of metformin) and subjected to a new sensitivity analysis. 2) The difference between scores for each patient at the points in time when there was a change in use of metformin (either start of use, or discontinuation of use) was studied.

Results: There is an association between cognitive decline and use of metformin. However, any conclusion of a causal relationship is tenuous. 

Conclusion: The present study offers no basis for causal conclusions, but given the association, further examination of cognitive effects of metformin is warranted.

When teaching we usually not only demonstrate/discuss how a certain method works, but, not less important, why it works. In contrast, the Wald confidence interval for a binomial p constitutes an excellent example of a case where we might be interested in why a method does not work. It has been in use for many years and, sadly enough, it is still to be found in many textbooks in mathematical statistics/statistics. The reasons for not using this interval are plentiful and this fact gives us a good opportunity to discuss all of its deficiencies and draw conclusions which are of more general interest. We will mostly use already known results and bring them together in a manner appropriate to the teaching situation. The main purpose of this article is to show how to stimulate students to take a more critical view of simplifications and approximations. We primarily aim for master’s students who previously have been confronted with the Wilson (score) interval, but parts of the presentation may as well be suitable for bachelor’s students. 

The problem of constructing a reasonably simple yet wellbehaved confidence interval for a binomial parameter p is old but still fascinating and surprisingly complex. During the last century, many alternatives to the poorly behaved standard Wald interval have been suggested. It seems though that the Wald interval is still much in use in spite of many efforts over the years through publications to point out its deficiencies. This paper constitutes yet another attempt to provide an alternative and it builds on a special case of a general technique for adjusted intervals primarily based on Wald type statistics. The main idea is to construct an approximate pivot with uncorrelated, or nearly uncorrelated, components. The resulting AN (Andersson–Nerman) interval, as well as a modification thereof, is compared with the well-renowned Wilson and AC (Agresti–Coull) intervals and the subsequent discussion will in itself hopefully shed some new light on this seemingly elementary interval estimation situation. Generally, an alternative to the Wald interval is to be judged not only by performance, its expression should also indicate why we will obtain a better behaved interval. It is argued that the well-behaved AN interval meets this requirement.

We consider the problem of deciding on sampling strategy, in particular sampling design. We propose a risk measure, whose minimizing value guides the choice. The method makes use of a superpopulation model and takes into account uncertainty about its parameters through a prior distribution. The method is illustrated with a real dataset, yielding satisfactory results. As a baseline, we use the strategy that couples probability proportional-to-size sampling with the difference estimator, as it is known to be optimal when the superpopulation model is fully known. We show that, even under moderate misspecifications of the model, this strategy is not robust and can be outperformed by some alternatives.

The Poisson distribution is here used to illustrate Bayesian inference concepts with the ultimate goal to construct credible intervals for a mean. The evaluation of the resulting intervals is in terms of mismatched priors and posteriors. The discussion is in the form of an imaginary dialog between a teacher and a student, who have met earlier, discussing and evaluating the Wald and score confidence intervals, as well as confidence intervals based on transformation and bootstrap techniques. From the perspective of the student the learning process is akin to a real research situation. The student is supposed to have studied mathematical statistics for at least two semesters.

Rising nonresponse rates in social surveys make the issue of nonresponse bias contentious. There are conflicting messages about the importance of high response rates and the hazards of low rates. Some articles (e.g. Groves and Peytcheva, 2008) suggest that the response rate is in general not a good predictor of survey quality. Equally, it is well known that nonresponse may induce bias and increase data collection costs.  We go back in the history of the literature of nonresponse and suggest a possible reason to the notion that even a rather small nonresponse rate makes the quality of a survey debatable. We also explore the relationship between nonresponse rate and bias, assuming non-ignorable nonresponse and focusing on estimates of totals or means. We show that there is a ‘safe area’ enclosed by the response rate on the one hand and the correlation between the response propensity and the study variable on the other hand; in this area, 1) the response rate does not greatly affect the nonresponse bias and 2) the nonresponse bias is small.

High nonresponse is a very common problem in sample surveys today. In statistical terms we are worried about increased bias and variance of estimators for population quantities such as totals or means. Different methods have been suggested in order to compensate for this phenomenon. We can roughly divide them into imputation and calibration and it is the latter approach we will focus on here. A wide spectrum of possibilities is included in the class of calibration estimators. We explore linear calibration, where we suggest using a nonresponse version of the design-based optimal regression estimator. Comparisons are made between this estimator and a GREG type estimator. Distance measures play a very important part in the construction of calibration estimators. We show that an estimator of the average response propensity (probability) can be included in the “optimal” distance measure under nonresponse, which will help to reduce the bias of the resulting estimator. To illustrate empirically the theoretically derived results for the suggested estimators, a simulation study has been carried out. The population is called KYBOK and consists of clerical municipalities in Sweden, where the variables include financial as well as size measurements. The results are encouraging for the “optimal” estimator in combination with the estimated average response propensity, where the bias was reduced for most of the Poisson sampling cases in the study.

The Poisson distribution is here used to illustrate Bayesian inference concepts with the ultimate goal to construct credible intervals for a mean. The evaluation of the resulting intervals is in terms of potential negative effects of mismatched priors and posteriors. The discussion is in the form of an imaginary dialogue between a teacher and a student, who have met earlier, discussing and evaluating the Wald and score confidence intervals, as well as confidence intervals based on transformation and bootstrap techniques. From the perspective of the student the learning process is akin to a real research situation. By this time the student  is supposed to have studied mathematical statistics for at least two semesters.

Tack vare tidigare forskning vet vi förhållandevis mycket om hur det är att vara medlem eller frivillig i ideella organisationer i Sverige. Men hur är det att vara anställd i det svenska civilsamhället? Med denna studie vill vi bidra till kunskapen om anställdas arbetsvillkor och drivkrafter i ideella organisationer i Sverige. Områden av intresse inkluderar till exempel vad som kännetecknar organisationer med anställda i det svenska civilsamhället, vilka de anställda är och vad de har för befattningar och villkor samt hur de anställda ser på för- och nackdelar med att arbeta i ideell sektor i jämförelse med privat och offentlig sektor.

High nonresponse is a very common problem in sample surveys today. In statistical terms we are worried about increased bias and variance of estimators for population quantities such as totals or means. Different methods have been suggested in order to compensate for this phenomenon. We can roughly divide them into imputation and calibration and it is the latter approach we will focus on here. A wide spectrum of possibilities is included in the class of calibration estimators. We explore linear calibration, where we suggest using a nonresponse version of the design-based optimal regression estimator. Comparisons are made between this estimator and a GREG type estimator. Distance measures play a very important part in the construction of calibration estimators. We show that an estimator of the average response propensity (probability) can be included in the "optimal" distance measure under nonresponse, which will help reducing the bias of the resulting estimator.  To illustrate empirically the theoretically derived results for the suggested estimators, a simulation study has been carried out. The population is called KYBOK and consists of clerical municipalities in Sweden, where the variables include financial as well as size measurements. The  results are encouraging for the "optimal" estimator in combination with the estimated average response propensity, where the bias was highly reduced for the Poisson sampling cases in the study. 

Many investigations have shown that artemisinin-based combination therapies are effective in the treatment of uncomplicated malaria and that they do not increase parasite resistance to treatment as much as treatment with single substance. We study the relation between some biological covariates and the time to first recurrence of malaria for children treated for malaria in a clinical trial. One group received artesunate plus sulfadoxine-pyrimethamine and the other only sulfadoxine-pyrimethamine. We consider the event malaria-free for the first 42 (and 84) days. We use logistic regression models for the analyses. The main results show that the probability of no recurrence is higher if the parasite density in the blood is high. The results are inconclusive for other explanatory biological variables. The infecting parasites having genes that indicate resistance, gave different results at the two different treatment centres. There was no appreciable difference in the effects of treatment over the two follow-up periods and these treatments do not have any effect on the probability of a recurrence.

There are expressions for nonresponse bias, all of which require population quantities. In one expression for nonresponse bias, due to Bethlehem (1988, 2009), the bias is approximately equal to a function of the population covariance between the study variable and the response propensity (probability) and the population mean of the propensities. The covariance is hard to estimate (due to nonresponse). To empirically examine the covariance and the nonresponse bias, we have done two studies where the sample values of survey variables are known and the response propensities are estimated.The first study is a mail survey of a population of residents in the city of Solna in Sweden,20-74 years of age. The questionnaire consists of items on marital status and income; we have obtained the true values of those from the Swedish Tax Agency. We also know birth country, the type of area of residents, specific age and gender of each sampled individual.The second study is a web survey at Stockholm University, the population is faculty employees at the department of psychology. This survey is a census and the variables that we regard as our study variables are income from university and total income. The true values of income from university are given by the HR-department and total income from the Tax Agency.

The Poisson distribution is here used to illustrate transformation and bootstrap techniques in order to construct a confidence interval for a mean. A comparison is made between the derived intervals and the Wald  and score confidence intervals. The discussion takes place in a classroom, where the teacher and the students have previously discussed and evaluated the Wald and score confidence intervals. While step by step  interactively getting acquainted  with new techniques,  the students will learn about the effects of e.g. bias and asymmetry and ways of dealing with such phenomena. The primary purpose of this teacher-student communication is therefore not to find the  best possible interval estimator for this particular case, but rather to provide a study displaying a teacher and her/his students interacting with each other in an efficient and rewarding way. The teacher has a strategy of encouraging the students to take initiatives. This is accomplished by providing the necessary background of the problem and some underlying theory after which the students are confronted with questions and problem solving. From this the learning process starts. The teacher has to be flexible according to how the students react.  The students are supposed to have studied mathematical statistics for at least two semesters. 

Rising nonresponse rates in social surveys makes the issue of nonresponse bias highly contentious. Nonresponse may induce bias and increase data collection costs. We study the relationship between response rate and bias, assuming non-ignorable nonresponse and focusing on estimates of totals or means. We show that there is a ‘safe area’ enclosed by the response rate on the one hand and the correlation between the response propensity and the study variable on the other hand; in this area, 1) the response rate does not greatly affect the nonresponse bias and 2) the nonresponse bias is small.

This paper explores the different ways in which auxiliary information can be put to use in calibrated weighting adjustment under survey nonresponse.  Information is often present at two levels, the population level and the sample level. The many options available in executing the calibration derive from several factors: One is the order in which the two sources of information enters into calibration, a choice of a bottom-up as opposed to a top-down approach. Another is whether the calibration should be carried out sequentially in two steps, or in one single step with the combined information. A third question is whether one can simplify the procedure, at no major loss of accuracy, by transcribing individual population auxiliary data from the register to the sample units only. We make a systematic list of the possibilities arising for calibration adjustment in this setting. An empirical study concludes the paper.