*[Under review]*

Also called the *Cochrane’s Review Manager*, it is a freely available software, initially built to ease the planning of protocols, the writing of reviews, and the storing of information, within the Cochrane organization. Reviews could be systematic or non-systematic. In case the researcher wishes to include a quantitative analysis in its systematic review such as a meta-analysis , *RevMan 5 *could readily do the job and could additionally produce some interesting graphical outputs of the data entered.

In this short tutorial about this point and click software, *RevMan*, we intend to plot the *Risk of bias summary* and…

*[Under Review]*

Data consisting of forty eight(48) comparative studies of Rosiglitazone against another treatment treated as control, are provided on the risk of Myocardial Infarctions (MI), and on Cardiovascular death (Death), from cardiovascular causes(Tian et al. 2009). We load them here, then copy them in a txt.file and export them in an excel.file(but save the file as a csv.file, preferable for R manipulation) in which we add headers: study ID, name of the study, n.TRT, MI.TRT, Death.TRT, n.CTRL, MI.CTRL, Death.CTRL.

The first 6 and the last 6 observations are:

We use the function *metabin()* from the *library “meta”* to fit…

*[Under Review]*

We consider seven(7) treatment single arms on the hypertensive health condition monitored by the systolic blood pressure. Blood pressure is a regularly preferred biomarker(*e.g. body temperature is a well-known biomarker for fever*) to study efficacy of potential anti-hypertensive agents aimed at reducing the risk of complications such as stroke. It is less expensive and more feasible. Blood pressure readings are always given in pairs, reported in a fraction: on the numerator *the systolic blood pressure and on the *denominator *the diastolic blood pressure(e.g. *132/88 mmHg*). *Both blood pressures are correlated and they both influence the risk factors of…

*[under review]*

If you wish to figure out the distribution (and so its corresponding parameters, e.g normal distribution has mean and variance as parameters) your univariate data may best fit into, *“fitdistrplus”* helps to generate estimate of these parameters corresponding to a specified distribution. It fits univariate distributions to non-censored data by maximum likelihood (mle), moment matching (mme), quantile matching (qme) or maximizing goodness-of-fit estimation (mge). The latter is also known as minimizing distance estimation. Generic methods are print, plot, summary, quantile, logLik, vcov and coef.

1. fit a gamma distribution by maximum likelihood estimation

Install the package “fitdistrplus”. Use…

Following what we did here, we apply one of the recommendations about using a boosted logistic regression, implemented in the generalized boosted modeling (gbm) package in R [7]. The goal, is to get better propensity scores for a fairer balance of pretreatment covariate distributions across the two trials: the AC population is considered the control population, and the BC population is considered the treated population, just as previously set.

We are interested in, what would be the anchored A vs B effect, if:

The AC population + BC population were all part of the BC trial vs the AC population…

*[Under review]*

*[Under review]*

Clinical trials often compare distinct treatments with little in common. In this short note, we are interested in comparing two treatments A and B, from two independent trials (the AC trial and the BC trial), that have a single treatment C in common and, patients characteristics of the same nature (e.g. age, gender, race, comorbidities, etc). Such characteristics, are considered to be recorded before treatment assignment. …

*[Under review]*

Let’s say, the mean number of children given birth by Cameroonian women aged between 40–49, has been estimated to 5.66 in the past. We collect samples from a new recent population data to check whether the new population mean number of children given birth by Cameroonian women aged between 40–49, is statistically significantly different from 5.66. We mean by “population” here, “women aged between 40–49”. So based on this example, we claim the following “Null hypothesis/Statement”:

until a new evidence finds “him” “guilty”! to “reject” “him”!

We are doing a simulation study, so we can design the new…

This is a CORRECTED version of the following post on ECMI-

European Consortium for Mathematics in Industry

*[Under review]*

It is quite unrealistic to decide on, the *benefits and harms of an intervention* or, the validity of a *clinical hypothesis*, based on a single study alone. Results, often vary from one study to the next. ** Meta-analytic** approaches have been conceptualized gradually for decades now, to synthesize the body of evidence gathered through a systematic search process. They typically generate estimates of the comparative effects of all included interventions, along with their range of uncertainty. They are invaluable tools to…

Teaching/Research Assistant in Mathematics/(Bayesian) Statistics, Writer