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MT_Average_data_frame.def @ 19929

Last change on this file since 19929 was 19929, checked in by Paul Leo, 3 months ago
Tmp commit, only use in test.... Testing whether or not using T distribution a) works b) agrees with MT VS calculation of confidence intervals in birth calculations when count is between 20 and 100
File size: 11.9 KB

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1	# Generic average .def file
2	#Uses the IBIS-Q data_frame for cross1 and cross2
3	# Uses %spvar1% in place of name of continuous variable
4	# spvar1 is set in Module.xml file as a configuration parameter value
5	# !! Also be sure to have data_where param to not include missing values (99, 999) in average
6	#MT_ = Montana cell suppression version
7	#Applies MT cell suppression rules
8	#
9	f type special
10	#########################################
11	--------BoNdArY--------
12	1 script
13	OPTIONS MPRINT MLOGIC SYMBOLGEN NONUMBER NODATE PAGESIZE=4000 LINESIZE=200;
14	OPTION SPOOL;
15
16	************************ 1. TMP ****************************;
17	* The dataset 'tmp' is the numerator dataset that has been read ;
18	* in already by ibis-q. Any filters have already been applied. ;
19	* The proc summary counts births by cross1 and cross2. ;
20	* The variable "x" must be in the dataset, it is set equal to 1.;
21	****************************************************************;
22	proc summary data=tmp;
23	var %spvar1%;
24	class %cross1%
25	?cross2? %cross2%
26	;
27	*format mean 5.2 count 9.0 sum 9.0 stderr 6.2;
28	output out=tmp mean=mean n=count sum=sum stderr=stderr;
29	run;
30	proc sort data=tmp; by %cross1%
31	?cross2? %cross2%
32	; run;
33	proc print data=tmp noobs;
34	title1 '---------------------------------';
35	title2 '1. TMP - numerator dataset';
36	run;
37
38	********************* 2. data_frame ************************;
39	* df_%cross1%%cross2% is a dataset created by IBIS-Q. ;
40	* It consists of %cross1% and %cross2% (if the user specified ;
41	* %cross2%) and a variable named "count" that is set to "0". ;
42	* The results of the proc summary must be merged with the ;
43	* df_%cross1%%cross2% dataset. ;
44	****************************************************************;
45	data frame;
46	set df_%cross1%%cross2%;
47	run;
48	proc sort data=frame; by %cross1%
49	?cross2? %cross2%
50	; run;
51	proc print data=frame noobs;
52	title2 '2. Data Frame';
53	run;
54
55	data new;
56	length mean count sum stderr 8; *set length so that values don't get truncated;
57	merge frame tmp; *must list frame dataset first, then tmp;
58	by %cross1%
59	?cross2? %cross2%
60	;
61	run;
62	proc print data=new noobs;
63	title2 '2. NEW, after frame merged with tmp';
64	run;
65	data tmp;
66	set new;
67	drop _TYPE_ _FREQ_;
68
69	*Only keep the grand total;
70	?cross2? if %cross1% = . and %cross2% ^= . then delete;
71	?cross2? if %cross1% ^= . and %cross2% = . then delete;
72
73	run;
74
75	proc print data=tmp noobs;
76	title2 '2. TMP';
77	run;
78
79	************* 3. Flag variable and popcross macro ***********;
80	* The flag variable checks the cross variables for presence of ;
81	* variables that are found in the population dataset. IBIS-q ;
82	* created popcross vars based on info from the .CFG file. ;
83	* The popcross macro will merge the numerator and denominator ;
84	* data, matching up the appropriate values of the cross vars. ;
85	*****************************************************************;
86	* IBIS does not need the population dataset to produce an avg. ;
87	* The following code, through the end of the macro, is used to ;
88	* compute popcount for the data stability indicator and to check ;
89	* the numerator and denominator counts for the cell suppression. ;
90	*****************************************************************;
91	%let flag=0;
92	?popcross1? %let flag=1;
93	?popcross2? %let flag=1;
94	?popcross1? ?popcross2? %let flag=2;
95
96	%macro popcross;
97
98	%if &flag=0 %then %do;
99	proc summary data=poptmp;
100	var popcount;
101	output out=pop sum=popcount;
102	run;
103	proc sql;
104	create table rate as
105	select tmp., pop.
106	from tmp, pop
107	quit;
108	%end;
109
110	%if &flag=1 %then %do;
111	proc summary data=poptmp;
112	var popcount;
113	class %popcross1% %popcross2%;
114	output out=pop sum=popcount;
115	run;
116	proc sql;
117	create table rate as
118	select tmp., pop.
119	from tmp, pop
120	where
121	?popcross1? tmp.%cross1%=pop.%popcross1%;
122	?popcross2? tmp.%cross2%=pop.%popcross2%;
123	quit;
124	%end;
125
126	%if &flag=2 %then %do;
127	proc summary data=poptmp;
128	var popcount;
129	class %popcross1% %popcross2% ;
130	output out=pop sum=popcount;
131	run;
132	proc sql;
133	create table rate as
134	select tmp., pop.
135	from tmp, pop
136	where tmp.%cross1%=pop.%popcross1% and
137	tmp.%cross2%=pop.%popcross2%;
138	quit;
139	%end;
140	%mend;
141	%popcross;
142	proc print data=rate noobs; title2 '3. merged tmp and pop prior to cell suppression';
143
144	******************** 4. tmp, again **************************;
145	* Create the output variables for the IBIS xml/map file. ;
146	* The following uses T distribution, for count, but when count ;
147	* is above 30, it is really using normal distribution ;
148	*****************************************************************;
149	data tmp;
150	set rate;
151
152	*****************************************************************;
153	* Student's t distribution. df=count-1. The "Z" value is really ;
154	* the critical value of "t", two tailed, with an alpha of 0.025. ;
155	*****************************************************************;
156	if count=1 then Z= .;
157	else if count=2 then Z= 12.706;
158	else if count=3 then Z= 4.303;
159	else if count=4 then Z= 3.182;
160	else if count=5 then Z= 2.776;
161	else if count=6 then Z= 2.571;
162	else if count=7 then Z= 2.447;
163	else if count=8 then Z= 2.365;
164	else if count=9 then Z= 2.306;
165	else if count=10 then Z= 2.262;
166	else if count=11 then Z= 2.228;
167	else if count=12 then Z= 2.201;
168	else if count=13 then Z= 2.179;
169	else if count=14 then Z= 2.160;
170	else if count=15 then Z= 2.145;
171	else if count=16 then Z= 2.131;
172	else if count=17 then Z= 2.120;
173	else if count=18 then Z= 2.110;
174	else if count=19 then Z= 2.101;
175	else if count=20 then Z= 2.093;
176	else if count=21 then Z= 2.086;
177	else if count=22 then Z= 2.074;
178	else if count=23 then Z= 2.069;
179	else if count=24 then Z= 2.064;
180	else if count=25 then Z= 2.060;
181	else if count=26 then Z= 2.056;
182	else if count=27 then Z= 2.052;
183	else if count=28 then Z= 2.048;
184	else if count=29 then Z= 2.045;
185	else if count=30 then Z= 2.042;
186	else if count>30 then Z= 1.96;
187
188	t1=mean-(Z*stderr);
189	if (t1<0) then t1=0;
190	if count in (0 1) then t1=.;
191	LL=put(t1, 8.2);
192	UL=put((mean+(z*stderr)), 8.2);
193	LL=compress(LL);
194	UL=compress(UL);
195	n=count; *ibis-q needs a count variable named 'n';
196
197	proc print data=tmp; title2 '4. tmp again - t1=mean*stderr, LL,UL,n before suppression';
198	run;
199	run;
200
201	******************** 5. tmp, again Redflag and cell suppression ***********;
202	* Create the output variables for the IBIS xml/map file. ;
203	* Add Redflag ;
204	* Redflag is used to describe why valuses have been suppressed ;
205	* Redflag values created here may be converted to images or special ;
206	* characters in IBIS-View application XSLTfiles, for instance: ;
207	* (xslt\html\query\module\result\ResultPage.xslt, ...Values.xslt) ;
208	* ;
209	* And Apply Montana specific Small numbers rules as follows ;
210	* (Comments include rulse for Vital Statistics Counts and Rates( ;
211	* 1. If denominator (popcount > 300) ;
212	* 1a. If count <5 ;
213	* Suppress cells ;
214	* 1b. Suppress complimentarty cells if you can arrive at the ;
215	* number in a cell less than 5 by subtraction ;
216	* Note: I think we just suppress everything in case #1a and 1b ;
217	* 2. If count > 5 and < 20 ;
218	* Suprss rates, but compute counts ;
219	* This would include rates like deaths per 100,000, low birth ;
220	* weight as % of live births, or ratios like maternal mortality ;
221	* per 100 live births. ;
222	* if 20<=count<=100 (events between 20 and 100) ;
223	* (according to Cody this does not apply for avgs?? ;
224	* 2. If denominater (popcount < 300) ;
225	* 2a. If count > 20 ;
226	* Report count and rates with confidence intervals ;
227	* 2b. If count > 20 ;
228	* Suppress rates and counts ;
229	* ZW-s program uses ".A" to identify cells for suppression. ;
230	* I have co-opted his method so I can use the MT logic for cell ;
231	* suppression instead of the standard IBIS logic. And I need to use ;
232	* ZWs program because it will suppress the table marginals that can ;
233	* be used to calculate the suppressed cells. If this code is used, ;
234	* the .def file should have the MT_ prefix. Needs suppressed_variables ;
235	* code at the end of this file to work. ;
236	*******************************************************************************;
237
238	************** ASK LOIS ABOUT STDERR COMPUTATIONS *************************;
239
240	data tmp;
241	set tmp;
242	n=count; *ibis-q needs a count variable named 'n';
243
244
245	if popcount>=300 then do;
246
247	* Suppress cells if absolute count <5 ;
248
249	if 1<=count<5 then do;
250	mean=.A;
251	LL=.A;
252	UL=.A;
253	redflag=put('Suppressed', $15.);
254	n=.A;
255	end;
256
257	* do not compute rates based on fewer than 20 events, but provide counts;
258
259	if 5<=count<20 then do;
260	mean=.A;
261	LL=.A;
262	UL=.A;
263	redflag=put('Suppressed Avg', $16.);
264	end;
265
266	if 20<=count<=100 then do;
267	* for events between 20 and 100, use T distribution to calculate confidence intervals;
268	* note LL and UL are calculated in step 4 above.
269
270	redflag=put('-', $15.);
271
272	end;
273
274	* and normal approximation for more than 100 events. ;
275	* which is also computed in step 4 above
276
277	if count>100 then do;
278
279	redflag=put('-', $15.);
280	end;
281	end;
282
283	if popcount<300 then do;
284
285	* suppress if popcount <300 and count < 20 events. ;
286
287	if 1<=count<20 then do;
288	mean=.A;
289	LL=.A;
290	UL=.A;
291	redflag=put('Suppressed', $15.);
292	n=.A;
293	end;
294
295
296	* report rate and n if popcount <300 and count >= 20 events. ;
297
298	if 20<=count<=100 then do;
299	* for events between 20 and 100, use T distribution to calculate confidence intervals;
300	* note LL and UL are calculated in step 4 above.
301
302	redflag=put('-', $15.);
303
304	end;
305
306	* and normal approximation for more than 100 events. ;
307	* which is also computed in step 4 above
308
309	if count>100 then do;
310
311	redflag=put('-', $15.);
312	end;
313	end;
314
315	if count=0 then do;
316	mean=.A;
317	LL=.A;
318	UL=.A;
319	end;
320
321	if count<0 then do;
322	mean=0;
323	mean=.A;
324	LL=.A;
325	UL=.A;
326	redflag=put('No Variance', $15.); *no variance;
327	end;
328
329	proc print data=tmp; title2 '4. tmp again - output variables for the IBIS xml/map and Redflag and cell suppression';
330	run;
331
332	--------BoNdArY--------
333	# definition for output file
334	f out_variable average
335	f xml_out_map_file XMLAverageDenomLCLUCL.map
336	--------BoNdArY--------
337	f out_detail lbl_not_used__see_xml_out_map_file
338	mean 15.2
339	n 15.2
340	LL 15.2
341	UL 15.2
342	redflag 16.0
343	--------BoNdArY--------
344
345	**************** 6. SUPPRESSED VARIABLES ***********************;
346	* ZW-s CGI program must be told how many variables it will need to ;
347	* suppress and which ones they are. NOTE: If the SAS code, above, is;
348	* commented out, these lines can be left in the .def file without ;
349	* causing any problems. They will only be used if the SAS code, ;
350	* above is active, OR if the small_num and small_pop parameters ;
351	* are active in the .CFG file, and with non-zero values. ;
352	* Sometimes ZW-s program does not turn .A into **, perhaps because ;
353	* I've turned off suppression, but it seems more than that - the ;
354	* workaround for this is to put ** above, but not add them to ;
355	* suppressed variable lists below ;
356	********************************************************************;
357	--------BoNdArY--------
358	1 suppressed_variables 4
359	mean
360	n
361	LL
362	UL
363	--------BoNdArY--------

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