ds.meanSdGp {dsBaseClient} | R Documentation |

This function calculates the mean and SD of a continuous variable for each class of a single factor.

ds.meanSdGp(x = NULL, y = NULL, type = "both", do.checks = FALSE, datasources = NULL)

`x` |
This must be named as a character string (e.g. "AGE"). It must denote a continuous variable of class numeric. |

`y` |
This must be named as a character string (e.g. "sex"). It must denote a categorical variable of class factor. |

`type` |
This must be specified as a character string ("combine", "split" or "both"). If "combine" the results for each group are reported combined over all studies. If "split" the table of means by group (for example) has a separate column for each study. If "both" the tables each have an additional column reporting the sum across all studies in each group in addition to the columns (produced by "split") for each study alone. This parameter defaults to "both" unless "combine" or "split" are specified. |

`do.checks` |
This parameter defaults to FALSE. It determines whether administrative checks are undertaken to ensure that the input objects are defined in all studies, and that the variables are of equivalent class in each study. By defaulting the checks to FALSE, we save time, and if you hit a problem that you cannot understand you can reset do.checks to TRUE and make sure that the input objects are correctly defined in all studies. |

`datasources` |
a list of opal object(s) obtained after login in to opal servers; these
objects hold also the data assign to R, as |

This function calculates the mean, standard deviation (SD), N (number of observations) and the standard error of the mean (SEM) of a continuous variable broken down into subgroups defined by a single factor. It also reports the total number of observations (=Ntotal), the total number of valid observations, i.e. non-missing observations = observations where neither the continuous variable nor the factor is misssing, (Nvalid) and the total number of missing observations (Nmissing). Nvalid=Ntotal-Nmissing and all three quantities represent the sum across all groups and all studies. If any one subgroup consists of between 1 and "nfilter" observations, the function simply reports that fact and suggests that you use a different grouping variable. As in other functions such as ds.table1D, the value of nfilter can be chosen by the data custodian when each Opal server is originally set up. By default it is set to 5. There are IMPORTANT DIFFERENCES between ds.meanSdGp compared to the function ds.meanByClass. (A) ds.meanSdGp does not actually subset the data it simply calculates the required statistics and reports them. This means you cannot use this function if you wish to physically break the data into subsets. On the other hand, it makes the function very much faster than ds.meanByClass if you do not need to create physical subsets. (B) ds.meanByClass allows you to specify up to three categorising factors, but ds.meanSdGp only allows one. However, this is not a serious problem. If you have two factors (e.g. sex with two levels [0 and 1] and BMI.categorical with three levels [1,2,3]) you simply need to create a new factor that combines the two together in a way that gives each combination of levels a different value in the new factor. So, in the example given, the calculation newfactor=(3*sex)+BMI gives you six values: sex=0, BMI=1, newfactor=1; sex=0, BMI=2, newfactor=2; sex=0, BMI=3, newfactor=3; sex=1, BMI=1, newfactor=4; ds.assign command and you then use newfactor as the single categorising factor. (C) At present, ds.meanByClass calculates the sample size in each group to mean the TOTAL sample size (i.e. it includes all observations in each group regardless whether or not they include missing values for the continuous variable or the factor). The calculation of sample size in each group by ds.meanSdGp always reports the number of observations that are non-missing both for the continuous variable and the factor. This makes sense - in the case of ds.meanByClass, the total size of the physical subsets was important, but when it comes down only to ds.meanSdGp which undertakes analysis without physical subsetting, it is only the observations with non-missing values in both variables that contribute to the calculation of means and SDs within each group and so it is logical to consider those counts as primary. The only reference ds.meanSdGp makes to missing counts is in the reporting of Ntotal and Nmissing overall (ie not broken down by group). For the future, we plan to extend ds.meanByClass to report both total and non-missing counts in subgroups.

If type = "combine" the function returns a list consisting of a four tables denoting: mean by group; standard deviation (SD) by group; number of non-missing observations (Nvalid) by group; and standard error of the mean (SEM) by group. All of these are COMBINED ACROSS STUDIES. For information, SEM = SD/sqrt(Nvalid). These are all returned in list format with names: Mean_gp, StDev_gp, Nvalid_gp and SEM_gp. If you need to use them in their original class (e.g. matrix), you need to use the conventional R function unlist() to convert them back to their original form. The output list also includes: Total_Nvalid (the total number of valid [non-missing] observations across all groups in all studies; Total_Nmissing (the total number of observations with either or both x and y missing); and Total_Ntotal (the total number of observations [with data missing or not]). If type ="split", the mean, SD, Nvalid and SEM are reported by group and by study. The first four elements of the returned output list are therefore: Mean_gp_study; StDev_gp_study; Nvalid_gp_study; and SEM_gp_study. If there are three studies and we are breaking things down by five groups in each study, each of the first four list elements consists of a table with five rows (one for each group) and three columns (one for each study). The returned output also includes Total_Nvalid, Total_Nmissing and Total_Ntotal as before. If type = "both" the output is precisely the same as with type="split" and each of its components has the same name, but each table (e.g. Mean_gp_study) will now have an extra column on the right had side (so a fourth column in the example above) which contains the appropriate combined value in each group across all studies together. In other words, the four columns replicate the results obtained when type = "combine". CRUCIALLY, IF ONE OR MORE OF THE GROUPS IN ANY OF THE STUDIES CONTAINS BETWEEN 1 and nfilter OBSERVATIONS, the returned output list will ONLY include the warning: [1] "At least 1 cell count is 1-nfilter, please regroup".

Burton PR

ds.subsetByClass to subset by the classes of factor vector(s).

ds.subset to subset by complete cases (i.e. removing missing values), threshold, columns and rows.

## Not run: # #load that contains the login details # data(logindata) # #Example 1: Calculate the mean, SD, Nvalid and SEM of the continuous variable AGE.60 (age in # #years centralised at 60), broken down by TID.f (a six level factor relating to survival time) # #and report the pooled results combined across studies. # ds.meanSdGp("AGE.60","TID.f","combine") # #Example 2: Calculate the mean, SD, Nvalid and SEM of the continuous variable AGE.60 (age in # #years centralised at 60), broken down by TID.f (a six level factor relating to survival time) # #and report both study-specific results and the pooled results combined across studies. Do the # #checks for consistency of variables in all studies. Save the returned output to msg.b. # msg.b <- ds.meanSdGp("AGE.60", "SEXF", "both", do.checks=TRUE) # msg.b # PRODUCES THIS OUTPUT # $Mean_gp_study # study1 study2 COMBINE # SEXF_1 -4.099893 -5.199134 -4.568966 # SEXF_2 -2.384477 -3.057421 -2.690300 # # $StDev_gp_study # study1 study2 COMBINE # SEXF_1 13.67313 14.52537 14.04313 # SEXF_2 14.87182 14.64741 14.77026 # # $Nvalid_gp_study # study1 study2 COMBINE # SEXF_1 931 693 1624 # SEXF_2 1108 923 2031 # # $SEM_gp_study # study1 study2 COMBINE # SEXF_1 0.4481188 0.5517731 0.3484744 # SEXF_2 0.4467804 0.4821253 0.3277427 # # $Total_Nvalid # [1] 3655 # # $Total_Nmissing # [1] 45 # # $Total_Ntotal # [1] 3700 # # Example 3: # Calculate the mean, SD, Nvalid and SEM of the continuous variable SBP (systolic BP), broken # down by CVA (1 = had a stroke, 0 = no stroke) report the study-specific results only. The # output shows that there are inadequate numbers of stroke cases to carry out this particular # analysis: at least one cell contains between 1 and nfilter (the chosen value of the disclosure # filter - typically 4) observations. Given that the CVA grouping is as simple as it can be, it # is impossible to regroup in this setting. The only option would be to have chosen a different # level for the nfilter. If it is 0, there is no limitation on cell counts: but whether or not # cell counts in the range, say, 1-4 are to be viewed as providing a significant risk is # something that should be decided before the analysis starts. In reality, for many biomedical # studies, particularly when data users have signed a data access agreement explicitly stating # they will not try to identify individuals or infer their characteristics, many researchers may # choose to turn the disclosure filter off (nfilter=0). But for particularly sensitive data, or # data obtained from official governmental sources, e.g. census data, there may simply be no # option but to pick a filter of say 4. # ds.meanSdGp("SBP", "CVA", "split") # PRODUCES THIS OUTPUT # [1] "At least 1 cell count is 1-nfilter, please regroup" # # clear the Datashield R sessions and logout #datashield.logout(opals) ## End(Not run)

[Package *dsBaseClient* version 5.0.0 ]